Research Journal - BUITEMS

BUITEMS Quality & Excellence in Education

J. App. Em. Sc Vol 2. Issue 1, July, 2011

ISSN 1814-070X

BUITEMS JOURNAL OF APPLIED & EMERGING SCIENCES

Quality & Excellence in Education

Balochistan University of Information Technology Engineering and Management Sciences


J. App. Em. Sc

ISSN 1814-070X

Vol 2. Issue 1, July, 2011

JOURNAL OF APPLIED AND EMERGING SCIENCES Balochistan University of Information Technology Engineering and Management Sciences, Quetta

Patron Engr. Ahmed Farooq Bazai

Editors M.A.K Malghani, PhD Ghulam Hussain Jaffar, PhD Jamil Ahmad, PhD

Co-Editors Muhammad Nawaz, PhD

Editorial Committee: Francescha Damiola, PhD (France); T. Suzuki, PhD (Japan); Simon Maddocks, PhD (Australia); Richard Mougwe, PhD (France); Guntram Borck, PhD (Germany); Khushnooda Ramzan, PhD (Saudi Arabia); Sonia Garritano, PhD (Italy); Ali Muhammad Warya, PhD (Pakistan); Rehan Sadiq Shaikh, PhD (Pakistan); Masroor Ellahi Babar, PhD (Pakistan); Muhammad Younis, PhD (Pakistan); Farhat Abbas, PhD (Pakistan); Abdul Wali, PhD (Pakistan); Zafar Iqbal Randhawa, PhD (Pakistan); Ehsanullah Kakar, PhD; A.H.S Bukhari, PhD (Pakistan); Abid Hussain Rizvi, PhD (Pakistan) ; Muhammad Azam Kakar, PhD (Pakistan); Barkat Ali, MS (Pakistan) ; Muhammad. Saeed, PhD (Pakistan) ; Nazir Ahmed, PhD (Pakistan);

Acknowledgement: Financial Support of BUITEMS through the forum of Directorate of Research is gratefully acknowledged.


INSTRUCTIONS TO AUTHORS Authors are requested to send the manuscripts typed in MS WORD and on floppy or CD-ROM,only those articles will be considered in which the work reported is original and the results are solely contributed to this journal. Screening of the articles will be done by the referees. In case they do not consider an articles suitable for publishing, they can reject the article or send it back to the author for revision. The comments of referees are considered as the final decision.

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References References should be cited in the text by author and year, not by number. If there are more than two authors, reference should be made to the first author followed by et al in the text. References at the end of the paper should be listed alphabetically by authors’ names, followed by initials, year of publication, full title of the paper, name of the journal volume number, initial and final page numbers. References to books should include: name(s) of author(s), initials, year of publication, title of the book, edition if not the first, initials and name(s) of editor(s) if any, preceded by ed(s), place of publication, publisher, and pages referred to.

Examples Reference pattern_Journal Marazita ML, Ploughman LM, Rawlings B, Remington E, Arnos KS, and Nance WE. (1993) Genetic Epidemiological studies of early onset deafness in U.S school-age population. Am J Med Genet 46:486-491. Reference pattern_book Karathwohl DR. (1988) How to prepare a research proposal. Guidelines for funding and dissertations in the social and behavioural sciences. 3rd Ed. New York, Syracuse University Press. OR Blumenfeld H (2001) Neuroanatomy through Clinical Cases. Yale University School of Medicine, New York. Reference pattern_conference Asmussen LE, Hicks DW, Leonard RA, Knisel WG, and Perkins HF. “Potential Pesticide Contamination in Groundwater Recharge Areas: A model Simulation”. Proceedings of the Georgia Water Resource Conference, University of Georgia. 161-164, Georgia, May, 1989. Authors are requested to prepare the manuscript carefully before submitting it for publication so as to minimize the corrections. Proofs of yours articles will not be provided. lf there is any Editorial revision, it must thus be made while your article is still in manuscript.


CONTENTS , Maqsood Ahmed Khan, Ehsanullah Kakar, Dost Muhammad Baloch Salah Ud Din Azad GLEAMS COMPUTER MODEL PESTICIDE PREDICTION IN TWO SOILS

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Muhammad Nadeem and Adul Hussain Shah Bukhari NOTATIONS FOR FACILITATING SOFTWARE SECURITY DESIGN

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Maqsood Ahmed Khan, Ehsanullah Kakar, Dost Mohammad Baloch, Salah Ud Din Azad CALIBRATION OF TIME DOMAIN REFLECTOMETRY (TDR) SOIL MOISTURE POINT PROBE FOR TWO SOILS

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Ansaruddin Syed AN INTERESTING IDENTITY OF TWO INTEGRALS APPEARING IN REPRESENTATION THEORY

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Rubbia Majeed, Ehsanullah Kakar, Maqsood Ahmed Khan OPTIMIZING THE HC RECOVERY ALONG WITH CO2 STORAGE

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Faisal A. K. Kakar, Murad Khalid, Faizan A. Suri ENHANCED OUTDOOR-TO-INDOOR COVERAGE ESTIMATION IN MICROCELLS

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Samia Parveen, Agha Mohammad Raza, Maria Nasrullah ANTIBACTERIAL ACTIVITY OF DIFFERENT SPICES AGAINST S.AUREUS, E.COLI AND KLEBSELLA

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Salah Ud Din Azad, Maqsood Ahmed Khan, Ehsanullah Kakar, Dost Mohammad Baloch IMPACT OF WATER SCARCITY ON ECONOMY AND WETLAND HABITAT WITH SPECIAL REFERENCE TO BALOCHISTAN

66

Naqibullah, Agha Mohammad Raza, Dost Mohammad Baloch, Mohammad Saeed, Arif Awan, Farhat Abbas, Wadood Kakar EFFECT OF DIFFERENT PARAMETERS ON OPTIMUM PRODUCTION OF MICROBIAL ALPHA AMYLASE PRODUCTION FROM BACILLUS SUBTILLUS

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Muhammad Nadeem and Abdul Hussain Shah Bukhari INFORMATION TECHNOLOGY MANAGEMENT IN PAKISTANI ORGANIZATIONS: CURRENT STATE AND IMPROVEMENT OPPORTUNITIES

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Mohammad Shah Khan and Ahmed Shah Khan CURRENT TRENDS IN HR (HUMAN RESOURCES): WHAT EMPLOYEES EXPECT FROM• TODAY’S•POLICY-MAKERS?

84

Abdul Wahid Baloch, Muhammad Gohram Khan Malghani , Mohammad Shafee Khan ISOLATION AND BIOCHEMICALCHARACTERIZATION OF SALMONELLA & E.COLI FROM BOVINE MILK COLLECTED FROM SALE SHOPS, GOVERNAMENTAL & PRIVATE DAIRY FARMS AT QUETTA PAKISTAN

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M. NAWAZ Goldbach Primes Associated with 2n

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GHULAM HUSSAIN JAFFAR STUDY ON GROWTH POTENTIAL OF SIPLI AND THALLI BREEDS OF SHEEP ON DIFFERENT RATIONS

101

Mohammed Gohram Khan Malghani,Dr Abdul Majeed Cheema,Maqsood Ahmad Khan,Dr Mudassar. BIOACCUMULATION DETECTION & COMPARISON OF HEAVY METALS IN FRESH WATER VEGETABLES WITH WASTE WATER VEGETABLES OF QUETTA CITY.

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GLEAMS COMPUTER MODEL PESTICIDE PREDICTION IN TWO SOILS

GLEAMS COMPUTER MODEL PESTICIDE PREDICTION IN TWO SOILS Maqsood Ahmed Khan1, Ehsanullah Kakar2, Dost Muhammad Baloch3, Salah Ud Din Azad1 1

Department of Environmental Management & Policy, 2Department of Civil Engineering, 3 Department of Biotechnology & Informatics, Balochistan University of Information Technology, Engineering & Management Sciences, Quetta

Abstract Recent concerns about the presence of pesticide residues in surface and groundwater have resulted in a need for computer model simulation to assess the impacts of agrichemicals on potential surface and groundwater contamination. GLEAMS (Groundwater Loading Effects of Agricultural Management Systems), is a mathematical model developed for fieldsize areas to evaluate the effects of agricultural management systems on the movement of agrichemicals. It can be used as a tool to evaluate the effects of different tillage systems on pesticide, and nutrient losses. Agriculture is being increasingly criticized for the deterioration of surface and subsurface water resources all over the world. In the Balochistan province of Pakistan, almost all the urban and rural population depend on groundwater resources for drinking water, irrigation, and the water for livestock use. The objective of this study was to simulate pesticide (Metolachlor) losses from conventional tillage and no-till systems using the GLEAMS pesticide submodel. The pesticide submodel simulated results showed that the runoff losses of Metolachlor from convectional tillage were 94%, and by sediment, the losses were 92% higher than that for the no-till system. However, the percolation losses of Metolachlor were 61%, and the total Metolachlor losses were 39% higher from no-till system as compared to conventional tillage system. The total Metolachlor losses were higher for no-till system because 91 to 99% of the annual total no-till losses were by percolation as compared to 41 to 90% for conventional tillage.

Key Words: GLEAMS, Simulation, Computer Model, Prediction, Runoff, Hydrology

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INTRODUCTION GLEAMS (Ground Loading Effects of Agricultural Management Systems) is a mathematical model developed for fieldsize areas to evaluate the effects of agricultural management systems on the movement of agricultural chemicals within and through the plant root zone. GLEAMS is a management oriented based model, and should not be used as an absolute predictor of pesticide losses. It can be used as a tool to evaluate the effects of different tillage systems on pesticide losses through runoff, erosion (sediment), and percolation. GLEAMS output includes pesticide losses in runoff, sediments and in percolate. Output frequency can be by daily (storm-by-storm), monthly, yearly, or a combination of these [1]. On a daily basis, the pesticide component simulates up to 10 pesticides. GLEAMS can simulate the pesticide in runoff, sediment, and percolate; and the redistribution of on a daily basis, the pesticide component simulates up to 10 pesticides. GLEAMS can simulate the pesticide in runoff, sediment, and percolate; and the redistribution of pesticide in the root zone. Daily, monthly or annual outputs can be generated for periods of up to 50 year [2].

Soil organic matter content is soil, climate, and management dependent. Organic matter is not sensitive in hydrology except as it affects water retention. Organic matter content is sensitive in erosion since it affects soil particle aggregation and sediment transport as well as the sediment enrichment ratio. It is sensitive in pesticide adsorption extending from sediment yield and pesticide transport. Organic matter is also important in mineralization and denitrification.

Soil physical conditions are affected by site preparation techniques. Soil compaction may occur as a result of conventional and some other tillage systems. The soil properties affected by compaction are bulk density, porosity, and field capacity. These important properties are regarded as sensitive parameters in the water balance computations in GLEAMS. They affect runoff, percolation, evaporation, and transpiration. These four components are sensitive in determining the fate of pesticide and nutrient, additionally runoff is a sensitive input in erosion (sediment yield), which in turn is sensitive to chemical transport [3].

Surface residue is crop residue on the soil surface when simulation begins. Surface residue affects soil temperature, and nitrogen and phosphorus mineralization. The parameter is not sensitive in long-term simulation, but may be very sensitive in short-term simulation of a low-input production system [3].

Porosity may be another sensitive parameter in the water balance computation of GLEAMS that is affected by tillage. Its effects are opposite to those of bulk density [3]. Manning’s•“n”•for•overland•flow•profile•is•a measure of the resistance to flow. Different tillage systems have different impacts on surface roughness. Soil cover and roughness slow overland flow and reduces its sediment transport capacity. The higher the•“n”•value•the•greater•the•resistance•and lower the flow velocity. Flow velocity and sediment transport are inversely related to Manning’s•“n”•[3].

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Identification of the Problem

losses in surface runoff from the farms are found to be excessive, no demands can be made to change the farm management practices unless it can be shown that, current practices make a comparatively important contribution to the water pollution problem. Therefore, the need for development of sound site-specific chemical management schemes which will minimize the transport of agrichemicals away from its place of application necessitates that, different farm management system be compared for minimum losses of agrichemicals. Intensive regulations of agricultural chemicals use may be effective in preventing surface and groundwater contamination. However, over strict regulations could needlessly limit the use of effective agrichemicals thereby resulting in an increase in production costs and a possible reduction in product quality and yield. Therefore, recommendations and regulations on surface and groundwater contamination must be based on sound technology and an awareness of all costs, benefits, and risks.

The presence of pesticide residues in surface and groundwater is cause for increasing pubic concern for non-point source pollution from agricultural lands. Agriculture is being increasingly criticized for the deterioration of surface and subsurface water resources of countries all over the world. Pesticides have a tremendous economics importance in helping to provide sufficient supplies of food and fiber to a very rapidly growing world population at a reasonable cost. As a result of intensive cropping, agricultural chemicals use has become an integral part of most agricultural production systems. Roughly 3, 30,000 tons of nitrogen fertilizer and 10 million tons of nitrogen through manures, crop residues, rainfall, and biological fixation are applied to agricultural crops yearly [4]. The primary purpose of using pesticide is to control harmful insects, and to increase production. They pose no environmental hazard as long as they are not transported from their original place of application, but these chemicals may move and accumulate at harmful concentration in a sink such as a lake or ground water. Runoff water flowing towards streams and lakes may carry sediments, nutrients, and pesticides in harmful quantities that can be potential danger to aquatic life.

Objectives The objectives of this study were to study the sensitivity of the GLEAMS model to conventional and no-tillage systems simulating pesticide (Metolachlor) losses, and to recommend a tillage system based on least pesticide losses.

Rationale Simulation models are essential tools in designing and developing water management systems to satisfy both environmental and agricultural goals. For economic reasons, continued use of pesticide and fertilizers is expected for the foreseeable• future• in• worlds’• agriculture. Recent concerns about surface and ground water contamination by agricultural chemicals have resulted in a need for mathematical models to assess the impacts of agricultural management practices on potential surface and ground water loadings of agrichemicals [4]. If agrichemicals

RELATED RESEARCH Studies on the GLEAMS models sensitivity to different tillage systems, and research related to tillage systems on runoff, soil, pesticide, and nutrient losses have provided a better understanding of the effects of some tillage practices on soil physical, chemical, and biological properties. These properties directly or indirectly affect surface runoff, soil, pesticide, and nutrient losses.

GLEAMS Studies [1] studied the chemical transport in a representative agricultural management

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Field Plot Studies [5] studied the movement of bromide in a Flanagan silt loam managed under five different tillage systems. They found that bromide movement in the soil involved an interaction between tillage systems and rainfall intensity. Bromide movement in the soil was not significantly different for the selected tillage treatments under the medium and low rainfall intensities. Under the high rainfall intensity, bromide movement in the soil managed under longterm continuous no-till system was greater than that which occurred in the other tillage systems. [6] studied pesticide runoff losses from small plots subjected to simulated rainfall. Their study determined that the reduction in pesticide losses did not occur for the herbicides, which are transported primarily with runoff water.

system near coastal plains, Georgia. The experimental plots were 0.8 hectare in size; the soil was loamy sand, and the crop was corn. The plots were treated with Atrazine, Alachlor, Carbora, and a Bromide tracer. Comparisons of observed and GLEAMS model simulated concentrations were found to be reasonable. On the basis of limited comparisons with actual data the GLEAMS model appeared to give reasonable predictions. The controlled-release high mobility pesticide applications study using the GLEAMS model, were simulated using a 50-year climatic record at Tifton, Georgia on two representative soils that occur in groundwater recharge areas of the coastal plains. They concluded that application of the GLEAMS model comparing controllersrelease formulations of pesticides with conventional controlled release formulations might provide potential benefits in reducing pesticide movement to groundwater. [2] reported that GLEAMS simulated mass of Fenamiphos, Fenamiphos Sulfoxide, and Fenamiphos Sulphone in the root zone compared favorably with field data. Simulated and observed concentrations with depth in the soil at selected dates also corresponded.

MATERIALS AND METHODS A long-term (10-year) tillage study was conducted by establishing experimental plots (each 0.0037 ha) of an average slope of 5%, to determine the effects of different tillage systems on pesticide losses. The topsoil within the plots was loam and subsoil was silt loam. Two tillage systems, conventional and no-till were used. Some of the soil physical and chemical properties used as GLEAMS input parameters of both upper (0-15 cm) and lower soil layers (1560 cm) were determined by taking soil samples•and•analyzed•in•the•Department’s Soil Testing Laboratories, while, some were taken from unpublished local field data. The Hydrometer Method [7] was used for particle size analysis, Bran and Lubbe Technicon Auto Analyzer based on Colorimetric Method was used for labile phosphorus measurement, Mehlick-III Extracting Procedure, Brinkmann D.C.800 Colorimeter for total phosphorus determination, and Flash Combustion Method (CARLO ERRA Nitrogen Analuzer 1500) was used for total nitrogen determination. Simulations with the GLEAMS model (PC Version 2.10) were conducted for the 10-year period for pesticide losses.

[4] studied the potential pesticide contamination in groundwater recharge areas in the Georgia coastal plains using the GLEAMS model. Soil data were mapped and grouped according to their textural characteristics that showed that clayed soils covered 50% of the total study area. The GLEAMS model was applied to generate 50-year simulations of the transport and degradation of three classes of pesticides. A simulation was made for the pesticides in each of three soils (sand, loam and clay). The model results indicated that the predicted mass loss of pesticides ranged from 12.2% in the sandy soils to less than 0.0001% for the pesticide simulated in clayey soils. They concluded that soil characteristics and agricultural management have a profound effect on the quality of groundwater in aquifer recharge areas.

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Most of the initial parameters were based on the physical and climatic conditions of the areas (plots) and the values recommended in the GLEAMS user’s• manual• [3].• Parameter• values• used in the pesticide submodel are defined in Tables 1-3. Daily rainfall and mean monthly minimum and maximum temperature data for Greensboro, North Carolina were obtained from the National Climatic Data Center Asheville, North Carolina, and mean monthly solar radiation data from the GLEAMS climatic data base for Reidsville, North Carolina (about 20km north of the experimental plots), were used. Metolachlor pesticide has been simulated using the pesticide submodel of GLEAMS for monthly and annual summary output. Input parameter values for Metolachlor characteristics were used from the GLEAMS•user’s•manual. Table 1: GLEAMS input parameter values PARAMETER DEFINITION DAREA Total Drainage area of the field (ha) BST Fraction of Plant available water in the soil when simulation begins (cc/cc) CONA Soil Evaporation Parameter CHS Hydraulic slope of the field (m/m) WLW Ratio of field length to field width RD Effective rooting depth (cm) ISOIL Code for soil horizons in the root zone NOSOHZ Number of Soil Horizons in the Root Zone BOTHOR Depth to bottom of each soil horizon BR15 Wilting point of each soil horizon (cm/cm) OM Organic matter content of each soil horizon (percent) CLAY Clay percent in each soil horizon SILT

Silt percent in each soil horizon

TEMPX

Mean monthly maximum temperature for each 0 month ( C) Mean monthly minimum temperature for each 0 month ( C) Mean monthly solar radiation for each month 2 (MJ/cm )

TEMPN RAD

Table 2: Parameter values used in Pesticide Sub-model PARAMETER DEFINITION H2OSOL Metolachlor water solubility (mg/L)

VALUE 0.0037 0.40 4.5 0.50 2.75 60 2 2 15, 60 0.11,0.22 1.00, 0.50 19.00,32.15 36.96, 45.17 --------

VALUE 530

HAFLIF

Foliar residue half-life (days)

5

KOC

Partitioning coefficient

200

FOLRES

0.00

COFUP

Concentration of Metolachlor residue on the foliage when simulation begins (ppm) Fraction of Metolachlor on the foliage available for washoff by rainfall. Coefficient of Metolachlor uptake by plant

1

SOLLIF

Soil half-life (days)

90

APRATE

Rate of application of active ingredient (Kg/ha)

2.53

WSHRFC

6

0.60

SOURCE Field Local Data Assumed GLEAMS manual [10] Field Local Data Field Local Data Field Local Data Field Local Data Field Local Data Field Local Data GLEAMS Manual [10] Assumed Field Local Data (Hydrometer Method) Field Local Data (Hydrometer Method) National Climatic Data Center Asheville, NC National Climatic Data Center Asheville, NC GLEAMS data base

SOURCE Table P1, GLEAMS manual [10] Table P1, GLEAMS Manual [10] Table P1, GLEAMS manual [10] Assumed Table P1, GLEAMS manual [10] Table P1, LGEAMS Manual [10] Table P1, GLEAMS manual [10] USDA Extension Service Office, Greensboro, NC



Table 3: Tillage Updateable Parameter Values ( CT – Conventional Tillage, NT - No-till) PARADEFINITION TILLAGE METER CT NT RC Effective saturated conductivity of the soil horizon immediately 0.11 3.29 below the root zone (cm/hr) CN2 SCS curve number for moisture condition II 85 78 POR Porosity of each soil horizon (cc/cc) 0.50, 0.49 0.50, 0.45 FC Field capacity of each soil horizon (cm/cm) 0.22, 0.44 0.21, 0.24 9.50, 0.11, 16.0, 3.29 SATK Saturated conductivity of each soil horizon (cm/hr). These SATK values were adjustments from the local SATK values that were too high to be entered as GLEAMS input. CFACT Soil loss ratio for overland profile segment Varies NFACT Manning’s•“n”•for•overland•flow•profile segment Varies 118 1066 RESDW Crop residue on the ground surface when simulation begins (Kg/ha)

RESULTS AND DISCUSSION Simulated pesticide (Metolachlor) losses to surface runoff, sediment, and percolation from 1981-90, are given in Table 4. Runoff losses of Metolachlor (Fig. 1) by conventional tillage (CT) were from 88% to 99% more than from No-Till (NT) system. The overall 10-year annual averaged runoff losses of Metolachlor from conventional tillage were 35% of total losses as compared to 1% from No-Till. The sediment losses of Metolachlor (Fig. 2) were 54 to 99% more in conventional tillage than from no-till system. No-till sediment losses of Metolachlor were 0 to 0.50% of the total metolachlor losses, and conventional tillage sediment losses of metolachlor were 0.1 to 4% of the total metolachlor losses. This showed that sediment losses of pesticide from conventional tillage were 88 to 100% higher than no-till. Table 4: GLEAMS Predicted Metolachlor (pesticide) losses from 1981-90. (g/ha) - gram per hectare, CT- conventional tillage, NT - no-till. Year 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 Average

Runoff Losses (g/ha) CT NT 23.9 1.90 132.6 16.0 35.8 0.65 33.0 1.00 14.4 0.38 18.0 0.64 45.3 3.13 43.2 1.67 73.3 0.16 11.1 0.53 43.0 2.60

Sediment Losses (g/ha) CT NT 0.07 0.03 6.51 1.00 1.74 0.09 1.72 0.08 0.72 0.03 1.06 0.04 2.18 0.22 2.66 0.13 3.44 0.01 0.70 0.03 2.08 0.17

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Percolation Losses (g/ha) CT NT 60.6 162.0 95.2 168.6 72.0 167.7 140.2 354.7 63.45 158.5 21.39 104.9 101.4 277.6 24.81 98.09 75.40 246.9 111.7 240.3 76.61 197.9

Total Losses (g/ha) CT NT 84.5 163.9 234.0 185.0 110.0 168.0 175.0 356.0 78.6 158.9 40.4 105.6 149.0 281.0 70.6 99.9 152.0 247.0 123.0 240.0 121.0 200.0

BUITEMS Quality & Excellence in Education GLEAMS COMPUTER MODEL PESTICIDE PREDICTION IN TWO SOILS

140

120 CT

NT

Runoff Pesticide Losses (g/ha)

100

80

60

40

20

0 1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

Average

Year

Fig 1: GLEAMS predicted metolachlor (pesticide) losses by runoff for conventional tillage (CT) and no-till (NT) from 1980-90.

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6 CT

NT

Pesticide Sediment (Erosion) Losses (g/ha)

5

4

3

2

1

0 1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

Average

Year

Fig 2: GLEAMS predicted sediment (erosion) losses of metolachlor (pesticide) for conventional tillage (CT) and no-till (NT) from 1981-90.

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Pesticide Percolation Losses (g/ha)

400 350 CT

300

NT

250 200 150 100 50 0 1981

1982

1983

1984

1985

Year

1986

1987

1988

1989

1990

Average

Fig 3: GLEAMS predicted percolation metolachlor losses for conventional tillage (CT) and no-till (TN) systems from 1981- 90.

Total Pesticide Losses (g/ha)

400 350 CT

300

NT

250 200 150 100 50 0 1981

1982

1983

1984

1985

Year

1986

1987

1988

1989

1990

Average

Fig 4: GLEAMS predicted total metolachlor (pesticide) losses for conventional tillage (CT) and no-till (NT) systems from 1981-90.

subsurface losses were more than that of surface losses.

Metolachlor losses through percolation (Fig. 3) were 43 to 79% (2 to 5 times) more from no-till systems as compared to conventional tillage systems. From no-till system the metolachlor losses by percolation were 91 to 99% of total losses. The 10-year average percolation losses of metolachlor from no-till system were 61% more than that from conventional tillage. The total metolachlor losses (Fig. 4) from no-till systems were 1 to 2 times more than that from conventional tillage system. This difference is mainly due to high water solubility, low adsorption characteristics of metolachlor and more percolation losses from no-till system. Since the no-till system reduces runoff and increases infiltration and percolation therefore, in case of no-till systems, the

SUMMARY Experimental plots (each 0.0037 ha) of average slope of 5% were established to study the pesticide losses from conventional and no-till systems using GLEAMS computer model. GLEAMS parameter values and other input data were obtained from the experimental plots, CREAMS users guide, and the GLEAMS users manual. The pesticide submodel simulated results showed that the runoff losses of Metolachlor from convectional tillage were 94%, and by sediment, the losses were 92% higher than that for the no-till system. However, the

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percolation losses of Metolachlor were 61%, and the total Metolachlor losses were 39% higher from no-till system as compared to conventional tillage system. The total Metolachlor losses were higher for no-till system because 91 to 99% of the annual total no-till losses were by percolation as compared to 41 to 90% for conventional tillage.

CONCLUSIONS It• can• be• concluded• from• this• study’s simulated results that the GLEAMS model is sensitive to conventional tillage and no-till system, and that the no-till system may be good for reduced runoff and sediment losses of pesticide but it is no good for percolation and total losses of pesticide. It can further be concluded from these simulated results that no-till is not effective in reducing the losses of highly soluble chemicals. Since there is no or minimum compaction of soil as a result of no-till, that is why the water losses by percolation is higher as compared to runoff.

RECOMMENDATIONS No-till may not be a very effective system in reducing the losses of highly soluble agrichemicals and the GLEAMS model can be used as an effective tool for evaluating different tillage systems for pesticide losses by runoff, sediment and percolation.

ACKNOWLEDGEMENT The authors are thankful to the faculty and staff of the Department of Civil Engineering, BUITEMS, Quetta for their cooperation.

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BUITEMS Quality & Excellence in Education NOTATIONS FOR FACILITATING SOFTWARE SECURITY DESIGN

REFERENCES · Asmussen L.• E.,• D.• W.• Hicks,• R.• A.• Leonard,• W.G.• Knisel,• and• H.• F.• Perkins.• “Potential Pesticide•Contamination•in•Groundwater•Recharge•Areas:•A•model•Simulation”.• Proceedings•of the Georgia Water Resource Conference, University of Georgia. 161-164, Georgia, May, 1989. · Barisas, S.•G.,•J.•L.•Baker,•H.•P.•Johnson,•J.•Laflen.• “Effect•of•Tillage•Systems•on•Runoff•Loses of•Nutrients,•A•Rainfall•Simulation•Study”.• TRANSACTIONS•of•the•ASAE.•893-897. 1978. · Baker, J.• L,• J.• M.• Laflen,• H.• P.• Johson.• “Effects• of• Tillage• Systems on Runoff Losses of Pesticides:•A•Rainfall•Simulated•Study”.• TRANSACTIONS•of•the•ASAE:•886-892. 1978. · Bales, J.•and•R.•P.•Betson.• “The•Curve•Number•as•a•Hydrologic•Index”.• In•Proc.•Rainfall-Runoff relationship, ed. V. P. Singh. Littlelon, Co: Water Resources Publ. 1982. · Bicki, T. JL, Lei Guo. “Tillage•and•Simulated•Rainfall•Intensity•Effects•on•Bromide•Movement•in an Agriudoll”. Soil Science Society of America Journal (55); 794799. 1991. · Day, P.•R.,•“Particle•Fraction•and•Particle•Size•Analysis. In•methods•of•Soil•Analysis”.• (C.•A. Black et al., eds). Vol. I. pp. 545-567. American Society of Agronomy Madison, Wisconsin 1965. · Hankins, R.•H.• “Runoff•Curve•Numbers•from•Partial•Area•Watershed”.• J.•Irr.•And•Drain.•Dix. ASCE 105: 375-389. 1979. · Knisel, W.G.,•“Procedure•to•Estimate•Effect•of•Conservation•Tillage•on•Rescuing•Direct•Runoff Using•the•SCS•Curve•Numbers”.•CREAMS•User’s•Guide.•420-425. 1980. · Knisel, W.G.,• “GLEAMS:• Groundwater• Loading• Effects• of• Agricultural• Management• Systems. University of Georgia, Coastal Plain Experiment Station, Biological & Agricultural Engineering Department,•Publication•No.•5.• GLEAMS•User’s•Guide.•1993. · Kanwar, R.•S.,•J.•L.•Baker,•D.•G.•Baker.•“Tillage•and•Phosphorus•Split•N-Fertilization Effects on Subsurface Drainage•Water•Quality•and•Crop•Yields”.• TRANSACTIONS•of•the•ASAE•31•(2): 453-461. 1988. · Laflen, L. M., M. A. Tabatabai. “Nitrogen•and•Phosphorus•losses•from•Corn-soybeans rotations as•affected•by•Tillage•practices”.• TRANSACTIONS•of•the•ASAE:•58 – 63. 1984. · Leonard, R.•A.,•and•W.•G.•Knisel.• “Groundwater•Loadings•by•Controlled-release Pesticides: A GLEAMS•Simulation”.• TRANSACTIOS• of•the•ASAE•32•(6)•:•1915-1922. 1989. · Leonard, R.•A.,•W.•G.•Knisel,•F.•M.•Davis•and•A.•W.•Johnson.• “Validating•GLEAMS•with Field Data• for• Fenamiphos• and• its• Metabolites”.• Journal• of• Irrigation• and• Drainage• Engineering. 116(1): 24 – 34. 1990. · Leonard, R.•A.,•C.•C.•Trumman,•W.•G.•Knisel,•and•F.•M.•Davis.• “Pesticide•Runoff•Simulations: Long-Term Annual Means Vs. Event Extremes”.• Weed•Technology•(6):•725 -730. 1992. · McDowell, L.• L.,• K.• C.• McGrogor.• “Nitrogen• and• Phosphorus• Losses• in• Runoff• from• No-till Soybeans”.• TRANSACTION•of•the•ASAE•643 – 648. 1980. · Mostaghimi, Said,• J.• M.• Flagg,• T.• A.• Dillaha,• V.• O.• Shanholtx.• “Phosphorus Losses from Croplands•As•Affected•by•Tillage•System•and•Fertilizer•Application•Method”.• Water•Resources Bullentin 24 (4): 735 -742. 1988a. · Mostaghimi, Saied,•T.•A.,•Dillaha,•V.•O.•Shanholtz.• “Influence•of•tillage•Systems•and•residue Levels on Runoff,•Sediment,•and•Phosphorus•Losses”.• TRANSACTIONS•of•the•ASAE•31•91): 128 – 132. 1988b. · Rawls, W.•J.,•C.•A.•Onstad•and•H.•H.•Richardson.• “GLEAMS•user’s•guide”.•405 – 419. 1993. · Rawls, W.•J.,•and•H.•H.•Richardson.• “Runoff•Curve•Numbers•for•Conservation Tillage”.• J.•Soil and Water Cons. 38 (6): 494 – 496. 1983. · Yoo, K.• H.,• J.• T.• Touchton• and• R.• H.• Walker.• “Runoff,• Sediment,• and• Nutrient• Losses• from Various• tillage• Systems• of• Cotton”.• Elsevier• Science• Publications• B.• V.,• Amsterdam (Netherlands), Soil and Tillage Research 12: 13-24. 1988. · Yoo, K.• H.,• J.• T.• Yoon,• and• L.• M.• Sorleau.• “Runoff• Curve• Numbers• Deternimed• by• Three Methods•under•Conventional•and•Conservation•Tillage”.• TRANSACTION•of•the•ASAE•36•91): 57-63. 1993. · Yoo, K. H., and E. W. Rochester. “Variation•of•Runoff•Characteristics•under•conservation•Tillage Systems”.• TRANSACTIONS•of•the•ASAE•3295):•1625-1630. 1989.

11


NOTATIONS FOR FACILITATING SOFTWARE SECURITY DESIGN Muhammad Nadeem1* and Adul Hussain Shah Bukhari1 1

Faculty of Information and Communication Technology, Balochistan University of Information Technology, Engineering & Management Sciences, Quetta *Corresponding author e-mail: rabbea_09@yahoo [email protected]

Abstract The conventional software designing tools do not address the software security design, the security considerations are taken care of independently and there is no de facto unified mechanism•to•design•software’s•functional•requirements•along•with•the•security•requirements,•it allows the applications more vulnerable to the security threats, this is especially true in clientserver / web based systems. In this research designing notations are being proposed that can be integrated with the existing designing tools to address software security design. The notations have less abstraction in order to design security requirements more clearly and effectively. Security is not a feature that can be added to software or "bolted on" after other software features are codified, nor can it be "patched in" after attacks have occurred in the field. Instead, security must be built in from the very beginning (requirements specification) and included in every subsequent System Development Life Cycle phase. ________________________________________________________________________________ Keywords:

Software Design, Security, Notations, Reliability, UML

INTRODUCTION The existing software designing tools, both classical and object oriented, have notations / symbols that are capable to design the functionality of the system but lack the notations to design the security of the system, therefore its strongly needed that there must be notations that can be used with the existing tools that cover both the functional and security design of the software, this will result in generation of artifacts representing both functional and security requirements.

The Motivational Factors

to how much extent do they address the software security requirements? The second part proposes some notations that can be used with the existing software designing & modeling tools to address software security design along with the rest of the design. Some examples have also been presented that use the proposed notations.

Importance of Security The security needs are evident at every stage for an enterprise, the application security lies at the core of the security paradigm as depicted by figure-1.

The software is the essential part of Information Technology infrastructure for an enterprise; hence the enterprise security involves the security of the software being used, the authors strongly support the view that the security requirements should be reflected in the artifacts created for software functional requirements at the analysis or design stage.

How this Research is organized The first part of this research investigates the de facto designing and modeling tools being used by the software designers; and explores

12

Figure 1 – Security Architecture


This• research• discusses• the• application’s security to be achieved by proper design of software applications.

development practices to be adopted; they comprise dynamic, extensible and interoperable collections of services, software components and information shared by various entities performing transactional tasks. In defining a general-purpose policy-based security framework, security policies for the confidentiality, integrity and availability of services and information need to be considered. In [3], the policies for authentication, access control, security management, identity administration and accountability have been proposed; and the implementation mechanisms and componentbased generic security services for webenabled applications are also discussed.

LITERATURE REVIEW The Contribution of Umlsec UMLSec has been proposed as an extension of UML, it addresses four security concepts namely• “Fair• Exchange”,• “Confidentiality• or Secrecy”,• “Secure• Information• Flow”• and “Secure• Communication• Link”• to• facilitate• the security aware software designing [1]. The concepts presented in UMLSec are very good initiative to secure systems development however the authors have found that abstraction level of UMLSec is very high, which leads to problems to depict the security requirements in the software design artifacts.

Security for Mobile Application Designing Designing applications for mobile devices needs special attention as there is involvement of public medium (i.e. wireless), possibility of loss or theft due to small size, extensive mobility, and power consumption issues etc. we typically need to address the following security concerns [4]:

Integration of MAC and UML Artifacts Researchers have been focusing on the notion that security must be give key importance in the design of software applications at all stages of the lifecycle for accurate and precise policy definition, authorization, authentication, enforcement, and assurance. UML is one of the dominant players in software design used for specifying, visualizing, constructing and documenting artifacts of software. UML provides alternate diagrams to get different perspectives for different stakeholders, e.g.: use case diagrams for the interaction of users with system components, class diagrams for the static classes and relationships among them, and sequence diagrams for the dynamic behavior of instances of the class diagram.

Table S# 1 2 3 4 5 6 7

The security concerns discussed in Table-1 depict the need of secure system design; these factors are generally true for all embedded systems; the factors identified were a source of motivation for the researchers to work out on the notations that can be used for the design of secure systems.

However,• UML’s• support• for• the• definition• of security requirements for these diagrams and their constituent elements (e.g., actors, systems, use cases, classes, instances, include/ extend/ generalize relationships, methods, data, etc.) is lacking. Efforts have already been made e.g., in [2] security issue by incorporating Mandatory Access Control (MAC) into use case, class, and sequence diagrams, providing support for the definition of clearances and classifications for relevant UML elements have been addressed.

RESEARCH METHODOLOGY THE PROPOSED NOTATIONS

This section contains the detailed description of the notations for designing software security. The notations are especially useful when designing security related client-server / web based software applications.

Policy Based Security Frameworks Web-based applications are one important candidates that need

1 – Security concerns of mobile applications Security Concern User identification Secure storage Secure data communication Secure network access Secure content Secure software execution environment Temper resistant implementation

of the secure

13


the PSDT is an integer value that starts from “1”• in• a• given• design• artifact• and• is incremented by one, and assigned to the next notations• used.• The• “Security Descriptor Table”• holds• the• collection• of• key-value pairs that describe details about the security requirement. More rows can be added at the end of the table to hold the optional or custom “Key-Value”•pairs,•see•table-3.

CREATING NOTATIONS The figure 3 gives a general structure of the notations used in this research.

PSDT

The PSDT is an integer value that represents Pointer to Security

S

Table 3 – Security Descriptor Table for Encryption Security Descriptor S.# KEY VALUE 1 PSDT An Integer value 2 Source Describes source 3 Destination Describes destination Defines Protocol to be 4 Protocol / Algorithm used Defines server port to be 5 Server port used Defines client port to be 6 Client port used (Description of custom 7 (custom parameter) parameter)

The symbol that represents some security related

notations Each symbol used to depict some security scenario has a security descriptor table associated with it; which is a collection of keyvalue pairs, the general structure of the “Security•Descriptor•Table•has•been•shown•in table-2.

Figure 2: Guidelines for designing security

Table 2 – General Structure of Security Descriptor Security Descriptor KEY VALUE S# 1 PSDT An Integer value 2 Attribute Value 3 Attribute Value (Description of custom 4 (custom parameter) parameter)

CLIENT COMPUTER AUTHENTICATION Client Figure 4: Client Computer Authentication

Each symbol used to depict some security scenario has a security descriptor table associated with it, which is a collection of keyvalue pairs. The security descriptor table has some mandatory attributes having their respective values, in order to provide flexibility the designer may add additional key-value pairs in the security descriptor table.

The figure 5 represents the notation proposed for the Client Computer Authentication; the associated• “Security• Descriptor• Table”• is shown in the table-4.

Table 4 – Security Descriptor Table for Client Authentication Security Descriptor S.# KEY VALUE 1 PSDT An Integer value 2 Client Describes client machine 3 Destination Describes destination machine Describes the authentication mechanism to be used, e.g., IP 4 Mechanism based authentication, Certificate based authentication etc. (custom (Description of custom 5 parameter) parameter)

THE SYMBOLS USED TO MODEL NEW NOTATIONS ENCRYPTION Source

Destination PSDT

Destination PSDT

Figure 3 - Encryption

The figure 4 depicts the encryption process, A security descriptor i.e., tabular structure, is associated with each symbol; it is pointed to by the• “security• notation”• using• PSDT• which stands for Pointer to Security Descriptor Table,

14


CONTENT VERIFICATION USING HASH

SERVER COMPUTER AUTHENTICATION Client

Source

Server

PSDT

PSDT

Destinatio n

Figure 7: Content verification using hash value

Figure 5: Server Computer Authentication

The figure 8 represents the notation proposed for “Content verification using hash value”; associated• “Security• Descriptor• Table”• is shown in table-7.

The figure 6 represents the notations proposed for Server computer authentication; the associated• “Security• Descriptor• Table”• is shown in table-5.

Table 7 – Security Descriptor Table for Hashing Security Descriptor S# KEY VALUE

Table 5 – Security Descriptor Table for Server Authentication Security Descriptor S# KEY VALUE 1 PSDT An Integer value 2 Client Describes client machine Describes the server 3 Server machine Describes the authentication mechanism to be used, e.g., 4 Mechanism IP based authentication, Certificate based authentication etc. (Description of custom 5 (custom parameter) parameter)

1

PSDT

An Integer value

2

Source

Describes source machine

3

Destination

4

Algorithm / Mechanism

5

(optional / custom parameter)

Describes destination machine Defines Algorithm / Mechanism to be used e.g., MD5 Hash etc (Description of optional parameter)

AUTHENTICATION User

Resource PSDT

SESSION MAINTAINED COMMUNICATION

Figure 8: Authentication

The figure 9 represents the notation proposed for “Authentication”; the associated “Security Descriptor•Table”•is•shown•in•table-8.

Destinati

Source PSDT

Figure 6: Session maintained communication

The figure 7 represents notation proposed for “Session Maintained Communication”; associated• “Security• Descriptor• Table”• is shown in table-6.

Table 8 – Security Descriptor Table for Authentication Security Descriptor S# KEY VALUE

Table 6 – Security Descriptor Table for Sessions Security Descriptor S.# KEY VALUE

1

PSDT

2

User

3

Resource

1

PSDT

An Integer value

2

Source

Describes source

3

Destination

Describes destination

4

Service / Mechanism

4

Protocol / Mechanism

Defines Protocol / Mechanism to be used e.g., HTTP etc

5

(custom parameter)

5

Session Expiry time

n – time units

6

(custom parameter)

(Description of custom parameter)

15

An Integer value User description who needs to be authenticated for certain resource Describes resource being accessed e.g., a file, service or device etc. Defines authentication service or mechanism to be used, e.g., password based authentication, biometric technique etc. (Description of custom parameter)


USER VERIFICATION USING SENSORY

4

Mechanism

5

(custom parameter)

EXPERIENCE

User

Resource

PSDT

ANONYMOUS ACCESS

Figure 9: Sensory experience

The figure 10 represents the notation proposed• for• “User• verification• using• sensory experience”; the associated “Security Descriptor Table”•is•shown•in•table-9.

User PSDT

2

3

4

5

PSDT

Resource

Mechanism

(custom parameter)

Resource

The figure 12 represents the notation proposed for “Anonymous Access”; the associated• “Security• Descriptor• Table”• is

shown in the table-11.

An Integer value

User

.

Figure 11: Anonymous access

Table 9 – Security Descriptor Table for Sensory Experience Security Descriptor S# KEY VALUE 1

Defines mechanism to be used for the authorization, e.g., Role Based Access Control (RBAC) etc. (Description of optional parameter)

Table 11: Access

User description who needs to be authenticated for certain resource Describes resource being accessed e.g., a file, service or device etc. Defines mechanism to be used, e.g., user is asked to recognize a pattern containing a particular phrase / letters, or he may be required to recognize an audio or visual situation. (Description of custom parameter)

S#

Security Descriptor Table for Anonymous

KEY

Security Descriptor VALUE

1

PSDT

2

User

3

Resource

4

Mechanism

5

(custom parameter)

An Integer value User description who anonymously accesses certain resource Describes resource being accessed e.g., a file, operation, service or device etc. Defines mechanism to be used for anonymous access. (Description of custom parameter)

AUTHORIZATION User PSDT

A

RESULTS AND DISCUSSION

Resource

USING NOTATIONS WITH EXISTING TOOLS

The proposed notations were used with the existing design and modeling tools, some of the examples are discussed in this section.

Figure 10: Authorization

The figure 11 represents the notation proposed for the “Authorization”; the associated “Security• Descriptor• Table”• is shown in the table-10.

USING NOTATIONS WITH UML DIAGRAMS In this example a user enters the Login ID and password which is encrypted and sent to the server for the verification, on repeated login failures the user is asked to recognize a pattern in order to ensure that it is not a machine trying to guess a password. This problem is solved using UML activity diagrams with integration of proposed notations.

Table 10 – Security Descriptor Table for Authorization Security Descriptor S# KEY VALUE 1

PSDT

2

User

3

Resource

An Integer value User description who needs to be authorized for certain resource Describes resource being accessed e.g., a file, operation, service or device etc.

Table 12 – Security Descriptor Table for Figure 13

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Security Descriptor KEY VALUE

S#

Input user ID & PSDT: 1

Perform rest of the

Figure 12: The Enhanced Activity Diagram

Following are the Security Descriptor tables (table 12 & 13) associated with figure 13. Table 13 – Security Descriptor Table for Figure 13 Security Descriptor KEY VALUE S# 1 PSDT 1 User’s•machine

3

Destination

Authentication Server

4

Protocol / Algorithm

Public Key Encryption

2

User

System user

3

Resource

Account

4

Mechanism

Recognize a fuzzy word being displayed in a bitmap image

Currently this research has focused the security concerns related to web applications / client server computing, but security encompasses several other areas as well the common examples may be the security of code being executed in a machine, security of passwords and other credentials stored somewhere etc. also need to be modeled and the design notations are not yet proposed. The proposed security techniques shall be evaluated by carrying out a project by two different groups first using the proposed security techniques for designing the application and the second group using the conventional methods for the same, the software designed using security aware designing tools should be less vulnerable to the threats.

PSDT: 2

Source

2

FUTURE WORK / OPEN ISSUES

NO

2

PSDT

research contributes by proposing notations to facilitate software security design.

Failed login attempts > 3 YES

1

The software developers / designers feel handicapped unless they have proper CASE tools to facilitate them and make the software engineering process more and more efficient. Developing the CASE (Computer Aided Software Engineering) tools to facilitate software designers has got vital importance; it is being left for the future work.

CONCLUSIONS With the passage of time the importance of security is increasing and this growth shall be exponential in the future. The security of software applications in general and web based / client server applications in particular lie at the center of the security focus. Security designing tools and frameworks will become very common in the next few years, therefore as a theoretical work to support such future frameworks and CASE tools there is need of extensive research in security design. This

There are still some security concepts that may be too abstract to be represented as a notation that may be used to model software security, therefore when ever designing notations for any security concept / issue care must be taken on the fact weather it can represent the concept precisely or not. Designing confusing notations may lead to ambiguity to the designing process.

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BUITEMS Quality & Excellence in Education CALIBRATION OF TIME DOMAIN REFLECTOMETRY (TDR) SOIL MOISTURE POINT PROBE FOR TWO SOILS

ACKNOWLEDGMENT The central and critical intellectual acknowledgement belongs to my supervisor Dr. A. H. S. Bukhari for his guidelines and continuous support throughout this work both in technical and research paper writing areas. REFERENCES · Jan• Jurjens,• “UMLSec:• Extending• UML• for• Secure• Systems• Development”,• Software• & Systems Engineering, Department of Informatics, Munich University of Technology, Germany, 2002. · Thuong Doan and Steven Demurjian,•“MAC•and•UML•for•Secure•Software•Design”,•FMSE’•04, October 29, 2004, Washington, DC, USA, copyright ACM 1-58113-971-3/04/0010. · Marian Ventuneac, Tom Coffey, Ioan Salomie, "A policy-based security framework for webenabled applications", [email protected], University of Limerick, Department of Electronic and Computer Engineering, Proceedings of the 1st international symposium on Information and communication technologies, 2003 · Anand Raghunathan, Srivaths Ravi, Sunil Hattangady, and Jean-Jacques Quisquater, "Securing Mobile Appliances: New Challenges for the System Designer"; NEC Laboratories America, Princeton, NJ, USA; Proceedings of the “Design, Automation and Test in Europe (DATE)”•Conference•and•Exhibition,•©•2003•IEEE

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CALIBRATION OF TIME DOMAIN REFLECTOMETRY (TDR) SOIL MOISTURE POINT PROBE FOR TWO SOILS Maqsood Ahmed Khan1, Ehsanullah Kakar2, Dost Mohammad Baloch3, Salah Ud Din Azad1 1

3

Department of Environmental Management & Policy, 2Department of Civil Engineering, Department of Biotechnology & Informatics, Balochistan University of Information Technology, Engineering & Management Sciences Quetta

Abstract Maintaining adequate soil moisture in the root zone is crucial in achieving good plant growth. Accurate measurement of soil moisture is essential to keep the right level of soil moisture. Many studies have reported the successful application of Time Domain Reflectometry (TDR) for soil moisture measurement. This study was initiated to obtain calibration curves for soil water content determinations by TDR for two soil types. Measurements were taken in the laboratory for a silt loam and a sandy loam soils, using TDR Soil Moisture Measurement Instrument, Moisture PointTM Model MP-917, and Moisture Point Probe type-K. TDR probe calibration was performed for two soil types contained in wooden boxes (100 cm x 100 cm x 80 cm). The calibration was accomplished by comparing the volumetric moisture content (qTDR) and time delay (tTDR) response of TDR probe to that of the gravimetric volumetric moisture content (qgrav). The TDR measurements were taken, in triplicates, at four depths (0-15 cm, 15-30 cm, 30-45 cm, and 45-60 cm) for 38 days after wetting the soil. Soil samples for the gravimetric moisture content measurements were collected from the same locations from where TDR readings were taken. The study has demonstrated that the TDR technique is a reliable alternative method for measuring soil moisture content. The moisture content measurements obtained with TDR were comparable to that of the gravimetric method and showed a good relationship to gravimetric determinations (r2=0.85 for silt loam and 0.89 for sandy loam). KEY WORDS: Calibration, Time Domain Reflectometry (TDR), Soil Moisture, Root zone, Time Delay

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INTRODUCTION As early as 1939, geologists and others recognized a relationship between the dielectric properties of soil, rock and other materials, and their moisture content. However, they lacked the instrumentation necessary to make full use of it.

consultants the power and flexibility to measure and log water relationships of soils and other materials by fast, accurate, easy to use TDR methods. The convenient full featured push button ease of use, direct reading of actual Time Delay and Volumetric Moisture Content, is made possible by this model.

Time Domain Reflectometry, commonly known as TDR, largely developed as the result of World War II radar research, offered a method to define these dielectric relationships. With the advent of commercial TDR research oscilloscopes in the early 1960's, it became feasible to test this new technology. Today, TDR technology is the cutting edge methodology for many diverse applications including the determination of basic soil water. The practical interest stems from the fact that dry soil has a dielectric constant range of 2 to 4 compared to values of 78 to 81 for water. Therefore, the dielectric properties provide an excellent measure of the water content of soil (Selig and Mansukhani, 1976). Topp et al. (1980) placed different type of soils and soil like materials around coaxial transmission lines with 5 cm spacing and 100 or 30 cm length and found that the dielectric constant was only affected by water content.

TDR Moisture Point has been engineered to meet current and future needs, and has the capability to accept new software and hardware offered by Soil moisture. TDR eliminates the need for using nuclear based instrumentation and the associated radiation, health and safety hazards. It eliminates site specific calibration and the requirement for costly, specialized licensed personnel associated with neutron probes. It also provides auto-logging capabilities not practical with nuclear techniques. Dasberg and Dalton (1985), found that the water content measurements obtained with TDR showed a good relationship to gravimetric determinations and were also comparable to neutron probe measurements. Topp and Davis (1985), compared the water content measurements obtained with TDR and gravimetric methods, and it showed that generally both were the same values.

Many studies have reported that application of TDR to soil measurement has been successful. It has become an acceptable method for nondestructive estimation of soil water content. TDR converts the travel time of a high frequency, electromagnetic pulse into volumetric water content. In practice it generates a fastrise pulse and sends it at the speed of light down a transmission line consisting of two parallel Waveguides (probe) that are inserted or buried in the soil. The velocity of propagation of the high frequency, broad band 3GHz wave in soil is determined primarily by the water content. The wave is reflected from the open ends of the Waveguides (probe) and returns along the original path. By microprocessor, the travel time of the wave is used to directly calculate the dielectric constant of the soil. The actual time delay and correlated volumetric water content are also digitally displayed on screen.

Although application of TDR has been successful in many reported studies, question still arises with regard to the versatility of the method when used among different textured soils. The purpose of the study was to compare the soil moisture content measurements carried out by TDR and gravimetric method for two different soil types and to obtain calibration curves for soil water content determinations by TDR.

MATERIALS AND METHODS Measurements were taken for a silt loam and a sandy loam soils, using TDR Soil Moisture Measurement Instrument, Moisture PointTM Model MP-917, and Moisture Point Probe typeK.

Moisture Point uses the latest technology of instrumentation specifically designed to give research scientists, commercial growers, and

20


Calibration was performed for the soils in the wooden box (100 cm x 100 cm x 80 cm) for probe to be used for data collection (Figure 1). The composition of soils used in the study in given in table 1. The calibration was accomplished by comparing the TDR readings (both Vol. MC and Time Delay) to those of the Gravimetric Volumetric Water Content (Tables 2 & 3). For this purpose, three soil samples from each depth were collected from the box immediately after the TDR readings at the exact location at four depths (0-15 cm, 15-30 cm, 30-45 cm, and 45-60 cm) at different intervals for 38 days after wetting of soil. All determinations were made in triplicates and the average values were used. Along with taking the TDR readings (MC and Time Delay), the volumetric water content was calculated by weighing wet and an oven dried samples for each soil depth. Volumetric water content (gravimetric) versus Volumetric water content (TDR) and Time Delay data was ready to be used to fit an appropriate regression equation.

RESULTS AND DISCUSSION The average volumetric water contents measured with TDR, and obtained by gravimetrically from actual soil samples along with the TDR Time Delay readings for silt loam and sandy loam are given in Tables 2 and 3 respectively. Figures 2 and 3 show the relationships between gravimetrically determined volumetric water contents and TDR measurements; the gravimetric water content Vs TDR Time Delay; and TDR water content Vs Time Delay measurements for silt loam and sandy loam soils.

Soil Type

Clay (%)

Silt (%)

Sand (%)

Bulk Density 3 (g/cm )

Field Capacity (%)

Silt Loam

21

63

16

1.24

49

Sandy Loam

10.5 31.5 58 1.35 Table 1. Composition and properties of soils used in the study.

21

25


POWER ON

MODE

OFF

DISPLAY

MEASURE

PROBE CABLE

MOISTURE POINTTM

TDR 100 cm

5 cm 15 cm 80 cm

15 cm 15 cm 15 cm 10 cm 5 cm 100 cm EXPERIMENT BOX

Figure 1. Experimental setup for TDR Moisture Point Calibration.

The calculated regression equations and coefficients of determination (r2) are also included in the plots and are as below:

and for sandy loam soil,

q grav = 1.9241q TDR - 18.658 r 2 = 0.89 (4) q grav = 31.308t TDR - 63.523 r 2 = 0.86

for silt loam soil,

q TDR = 16.379t TDR - 23.615 r 2 = 0.99

2

q grav = 1.2006q TDR - 1.8981 r = 0.85 (1)

(5) (6)

These data show satisfactory close correlation between TDR and gravimetrically determined water content measurements. The lower correlation coefficient between qgrav and qTDR

q grav = 23.02t TDR - 39.125 r 2 = 0.85 (2) q TDR = 19.103t TDR - 30.755 r 2 = 0.99 (3)

22


may be due to spatial variability in the horizontal and vertical planes containing the sampling and measuring volumes. In addition to this the human error can also be a factor. Days After Wetting

Gravimetric Vol. M.C. (%) (M.C.%*Bulk Density)

1 2 3 5 6 8 9 10 15 16 17 18 19 23 25 26 28 32 38

51.82 50.78 48.96 45.88 48.23 45.83 47.98 47.90 46.19 43.13 43.32 40.91 39.85 39.48 39.68 34.80 35.51 34.40 34.55

Moisture Point Probe Readings M.C. (%)

Time Delay (nano-sec)

41.45 41.62 44.58 40.45 40.50 41.52 43.23 42.03 37.90 37.61 36.45 34.98 32.56 33.87 34.40 34.49 33.18 30.53 31.00

3.80 3.82 3.94 3.73 3.71 3.78 3.87 3.79 3.59 3.56 3.52 3.45 3.32 3.38 3.39 3.43 3.36 3.21 3.23

Table 2. Average Moisture Contents and Time Delay for Silt Loam.

Days After Wetting

Gravimetric Vol. M.C. (%) (M.C.%*Bulk Density)

0 2 3 4 5 6 7 8 9 10 12

36.90 25.91 24.69 22.42 22.04 22.56 21.31 20.87 19.61 19.79 18.17

Moisture Point Probe Readings MC Time Delay (%) (nano-sec) 28.44 21.61 23.41 21.31 22.08 20.55 20.90 19.45 20.63 20.87 19.57

3.19 2.73 2.84 2.74 2.79 2.70 2.72 2.64 2.69 2.74 2.66

Table 3. Average Moisture Contents and Time Delay for sandy loam.

23


Figure 2. Comparison between TDR and gravimetric data for silt loam soil.

Overall Avg. Grav. MC & Moisture point MC

Grav. MC (%)

58 53 48 43

y = 1.2006x - 1.8981 R² = 0.8492

38 33 28 30

32

34

36

38

40

42

44

46

Moisture Point, MC (%)

Overall average Grav. MC & Time Delay

Grav. MC (%)

55 50 45 40

y = 23.02x - 39.125 R² = 0.8522

35 30 3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4.0


Moisture Point MC (%)

Overall Avg. Moisture Point MC & Time Delay 50.00 45.00 40.00 35.00

y = 19.103x - 30.755 R² = 0.9962

30.00 25.00 3.10

3.20

3.30

3.40

3.50

3.60


24

3.70

3.80

3.90

4.00


Grav. MC and TDR MC for sandy loam Soil

Grav. MC (%)

40 35 30

y = 1.9241x - 18.658 R² = 0.8892

25 20 15 19

20

21

22

23

24

25

26

27

28

29

TDR MC (%)

Figure 3. Comparison between TDR and gravimetric data for sandy loam soil.

Grav. MC and Time Delay

Grav. MC (%)

40 35 30

y = 31.308x - 63.523 R² = 0.8643

25 20 15 2.60

2.70

2.80

2.90

3.00

3.10

3.20


TDR MC and Time Delay for sandy loam soil

TDR MC (%)

29 27 25

y = 16.379x - 23.615 R² = 0.985

23 21 19 17 2.60

2.70

2.80

2.90


25

3.00

3.10

3.20


CONCLUSIONS The calibration study has demonstrated that the TDR technique for the measurement of soil water content is very close to gravimetric method. Therefore it can be used to estimate the volumetric water content in soils used in research studies.

REFERENCES · · ·

· · · · · ·

·

·

·

· ·

· ·

·

· ·

Amato, M., and J.T. Ritchie. 1995. Small spatial scale soil water content measurement with time-domain reflectometry. Soil Sci. Soc. Am. J., 59(2):325-329 Baker, J.M., and Lascano. 1989. The spatial sensitivity of time domain reflectometry. Soil Sci. 147: 378-384. Chieng, S.T. and G.A. Hughes-Games. 1995. Effects of Subirrigation and Controlled Drainage on Crop Yield, Water Table Fluctuation and Soil Properties. Subirrigation and Controlled Drainage. Lewis Publishers: 231-246. Dalton, F.N. and M.Th. van Genuchten. 1986. Time domain Reflectometry method for measuring soil water content and salinity. Geoderma 38:237-250. Dasberg, S. and F.N. Dalton. 1985. Time Domain Reflectometry Field Measurements of Soil Water Content and Electrical Conductivity. Soil Sci. Soc. J. 49:293-297. Dasberg, S. and J.W. Hopmans. 1992. Time Domain Reflectometry Calibration for Uniformly and Nonuniformly Wetted Sandy and Clayey Soils. Soil Sci. Soc. J. 56:1341-1345. De-Silva, F.F., R. Wallach, A. Polak, and Y. Chen. 1998. Measuring water content of soil substitutes with time-domain reflectometry (TDR). J. Am. Soc. Hortic. Sci. 123(4): 734-737. Dirksen, C. and S. Dasberg. 1993. Improved Calibration of Time Domain Reflectometry Soil Water Content Measurements. Soil Sci. Soc. J. 57:660-667. Jacobsen, O.H., and P. Schjonning. 1993. A laboratory calibration of time domain reflectometry for soil water measurement including effects of bulk density and texture. J-Hydrol., 151(2/4): 147-157. Lee, J., R. Horton, K. Noborio, and D. B. Jaynes. 2001. Characterization of preferential flow in undisturbed, structured soil columns using a vertical TDR probe. J-contam-hydrol. Amsterdam : Elsevier Science B.V., 51(3/4) : 131-144. Miyamoto, T., R. Kobayashi, T. Annaka, and J. Chikushi. 2001. Applicability of multiple length TDR probes to measure water distributions in an Andisol under different tillage systems in Japan. Soil-tillage-res. Amsterdam, The Netherlands : Elsevier Science B.V. 60 (1/2): 91-99. Nadler, A., S. Dasberg and I. Lapid. 1991. Time Domain Reflectometry Measurements of Water Content and Electrical Conductivity of Layered Soil Columns. Soil Sci. Soc. J. 55:938943. Nielsen, D.C., H.J. Lagae, and R.L. Anderson. 1995. Time domain reflectometry measurements of surface soil water content. Soil Sci. Soc. Am J., 59(1): 103-105. Nissen, H.H., P. Moldrup, L.W. de Jonge, and O.H. Jacobsen. 1999. Time domain reflectometry coil probe measurements of water content during fingered flow. Soil Sci. Soc. Am. J. 63: 493-500. Noborio, K. 2001. Measurement of soil water content and electrical conductivity by time domain reflectometry. A review. Elsevier Comput-electron-agric Amsterdam. 31(3): 213-237. Salas, R., D.R. Bouldin, and E. Molina. 1996. Calibration of the time-domain reflectrometer and determination of the volumetric water content of the soil profile in an ultisol of Costa Rica. Commun-soil-sci-plant-anal. Monticello, N.Y.: Marcel Dekker Inc. 27(9/10): 2433-2442. Sun, Z. J., G.D. Young, R.A. McFarlane, and B.M. Chambers. 2000. The effect of soil electrical conductivity on moisture determination using time-domain reflectometry in sandy soil. Can-J-soil-sci. Ottawa : Agricultural Institute of Canada. 80 (1): 13-22. Topp, G.C. and J.L. Davis. 1985. Measurement of Soil Water Content using Time Domain Reflectometry (TDR): A Field Evaluation. Soil Sci. Soc. J. 49:19-24. Young, M.H., J.B. Fleming, P.J. Wierenga, and A.W. Warrick. 1997. Rapid laboratory calibration of time domain reflectometry using upward infiltration. Soil Sci. Soc. Am. J., 61(3): 707-712.

26

BUITEMS Quality & Excellence in Education An Interesting Identity of Two Integrals Appearing in Representation Theory

AN INTERESTING IDENTITY OF TWO INTEGRALS APPEARING IN REPRESENTATION THEORY Ansaruddin Syed Department of Mathematics, Faculty of Arts and Basic Sciences, Balochistan University of Information technology, Engineering and Management Sciences (BUITEMS) Quetta Corresponding e-mail: [email protected]

Abstract A certain UIR matrix element of SO(2, 1), when evaluated by two different methods, leads to two different integral expressions for it. The identity of these two expressions is established by using appropriate changes of variables of integration. Keywords:

Matrix Elements, Integrals, Change of variables.

irreducible representation matrix element [1], when evaluated by two completely different methods, leads to two different integral expressions for it viz.

INTRODUCTION In the representation theory on non-compact rotation group SO(2, 1), a certain unitary

Ás t /t ( p / , p, z )

and

{Ás tt / ( p, p / ,-z )}´ , z < 0 ,

Where i) for z > 0 , [2],

Ás t /t ( p / , p, z ) =

1 ef - tanhz / 2 ip [ ò df exp( -ip / f ) | chz - chfshz | -1/ 2+ir | | 4p |f|f0 (z ) ¥ / -1/ 2+ir | ò df exp(-ip f ) | chz + chfshz )

+ tt /

-¥

ii)

ef - tanh z / 2 ip | | 1 - ef tanh z / 2

ef + tanhz / 2 ip | ] 1 + ef tanhz / 2

for z < 0 , [2] ,

Ás t /t ( p / , p ,z ) ¥

=

1 ef - tanh z / 2 ir [ ò df exp( -ip /f ) | chz - chfshz |-1/ 2+ir | | 4p -¥ 1 - ef tanhz / 2 / -1/ 2+ir | ò df exp(-ip f ) | chz + chfshz |

+t

|f | >f0 ( -z )

+ tt

/

ef + tanh z / 2 ip | 1 + ef tanh z / 2

/ -1/ 2+ir | ò df exp(-ip f ) | chz + chfshz |

|f | 0 is defined by

tanh

f0 (z / 2) 2

= e -z ,

v) t, t/ take the values +, - , vi) p, p / and z are real numbers. The purpose of the present paper is to directly establish the identity of these two expressions by carrying out appropriate changes of variables of integration appearing in them.

PROOF OF THE IDENTITY From (i) above, we will have, for z < 0 ,

{F

=

s / ´ / ( p, p ,-z )} tt f 1 -1 / 2-ir e + tanh z / 2 -ip | [ ò df exp(ipf ) | chz + chfshz | | 4p |f |f 0 (-z )

/

¥

tt ò df exp(ipf ) | chz - chfshz |

-1/ 2-ir

|

-¥

|

ef + tanh z / 2 f

1 + e tanh z / 2

e f - tanh z / 2 f

1 - e tanh z / 2

| -ip

| -ip ]

(1 - t 2 )1/ 2+ir f (-z ) (e f - t)(1 - tef ) -1 / 2-ir ef - t -ip [ ò df exp(ipf ) | | | | f f 4p e 1 - te -f ( - z ) 0

=

/

0

-f 0 (-z )

+

t

ò

df exp(ipf ) |

( ef - t )(1 - tef ) ef

-¥

f

¥

+

t

ò

df exp(ipf ) |

(e - t )(1 - te ) ef

f0 (-z )

+

/

¥

tt ò df exp(ipf ) |

f

(e f + t)(1 + tef ) ef

-¥

|

-1/ 2- ir

| -1 / 2-ir |

|

-1/ 2-i r

|

|

ef - t 1 - tef

f

e -t

-ip /

1 - te f

ef + t 1 + tef

where t = - tanh z /2 > 0 , and we have used the easily verifiable facts that

28

| -ip

/

| -ip ]

/


e -f

chz + chfsh z =

1- t

chz - chfsh z =

2

e -f 1- t2

(e f - t )(1 - te f )

,

(e f + t )(1 + te f )

.

Calling e j as x , we will have dx =

ef df Þ df =

dx x

f < f 0 (-z ) Þ ef < ef

0

,

( -z )

=-

1 1 1 = Þx< , tanh z / 2 t t

1 t

f > f 0 (-z ) Þ x > , f > -f0 (-z ) Þ ef > e -f f < -f 0 (-z ) Þ x < t,

0

(-z )

= t Þ x > t,

f > -¥ Þ ef > 0 Þ x > 0,

f < ¥ Þ ef < ¥ Þ x < ¥, so that the above expression will become

(1 - t 2 )1/ 2+ir ´ 4p 1/ t

[ò t

dx ip ( x - t )(1 - tx) -1/ 2-ir x - t -ip x ( ) ( ) x x 1 - tx t

/

dx ip (t - x)(1 - tx) -1 / 2-ir t - x -ip x ( ) ( ) x 1 x tx 0

+tò

¥

/

dx ip ( x - t)(tx - 1) -1 / 2-ir x - t ip x ( ) ( ) x tx - 1 1/ t x

+

t ò

+

tt / ò

¥ dx 0

x

x ip (

/

( x + t )(1 + tx) -1 / 2-ir x + t ip ( ) ] ) 1 + tx x /

29

.


We now make a change of variable of integration from x to x / , where i) in the first integral, with t < x < 1/t , we put

x/ =

Þx=

x-t 1 - tx

x/ + t

,

1 + tx /

1–tx =

dx /

2

dx = (1 - t ) ii)

x/ =

t - x/

,

1 - tx /

,

x=

1 ® x/ ® ¥ t

,

1- t2

(1 - tx / ) 2

,

1 - tx /

x=0

,

® x/ = t

t-x x/ 2 = (1 - t ) x t - x/ ,

x=t

® x/

,

=0,

in the third integral, with 1/t < x < ¥ , we put

x-t tx - 1

dx = -

tx - 1 =

,

1- t2 /

(tx - 1)

x/ =

® x/ = 0

x=t

1–tx=

1- t2

x/ - t Þx= tx - 1

iv)

,

,

t-x 1 - tx

dx = -

x/ =

1 + tx /

,

1 + tx /

x-t x/ 2 = (1 - t ) / x x +t

in the second integral, with 0 < x < t , we put

Þx=

iii)

1- t2

2

dx /

,

1- t2 tx / - 1 x = 1/t

,

t-x x/ 2 = (1 - t ) / x x -t

® x/ ® ¥

,

in the fourth integral, with 0 < x < ¥ , we put

x+t 1 + tx

30

,

x ® ¥ ® x / = 1/ t ,


x/ - t

Þx=

1- t2

dx =

1 + tx =

,

1 - tx /

dx /

/ 2

,

1- t2 1 - tx /

x+t x/ 2 = (1 - t ) / x x -t

,

® x/ = t

x=0

,

x ® ¥ ® x / = 1/ t

,

.

(1 - tx )

Then the above expression will then transform to

s / ´ / ( p, p ,-z )} tt

{F

¥

ò dx

[

1-t 2

/

0

/

t ò dx . t

¥

+

1/ t

tt ò dx t

=

[

ò

(tx / - 1) 2

1 - t2

/

x

0 t

+tò

x

¥

+t

/

ò 1/ t

/ -ip /

(x )

/

(

x

/

.

1- t2

x / + t 1 + tx

x/

2

((1 - t )

.

-1/ 2-ir / -ip x ) ( ) /

1- t2

t - x / 1 - tx

x/ - t

) /

ip-1

x/

2

(x / + t)(1 + tx / )

|

((1 - t )

.

x / - t 1 - tx

/

x/ + t

| -1/ 2+ir |

1 + tx

(t - x / )(1 - tx / )

|

/

) -1/ 2-ir ( x / ) -ip /

1- t2

´

/ -ip /

(x )

ip-1

x/

/

x/ 1 - t 2 -1/ 2-ir / -ip ip-1 2 ) ((1 t ) . ) (x ) / / / tx - 1 x - t tx - 1

x

dx /

((1 - t )

´

x/ - t

x

dx /

0

(

(1 - tx / ) 2 1 - tx

(x / ) -ip |

/

2

t - x/

1- t2

(1 - t 2 )1/ 2-ir 4p ¥ dx /

ip-1

( ) (1 - tx / ) 2 1 - tx /

t ò dx / . /

x/ + t

1- t2

1/ t

+

=

( ) (1 + tx / ) 2 1 + tx /

0

+

(1 - t 2 )1/ 2+ir 4p

/

|

-1 / 2+ir

t - x/ 1 - tx

(x / - t)(tx / - 1) x

|

/

|

-1/ 2+ir

| ip

/

|

/

|ip

x/ - t /

tx - 1

31

| ip

/

/

-1/ 2-ir / ip ) ( ) ] x /


+ tt

/

1/ t

ò t

=

dx / x

/

/ -ip /

(x )

|

( x / - t )(1 - tx / ) x

/

|

-1/ 2+ir

|

x/ - t 1 - tx

/

|ip ]

f 1 ¥ / s e - tanh z / 2 ip [ ò df exp(-ip f ) | chz - chfshz | | | 4p -¥ 1 - ef tanh z / 2

+t

s

/

df exp(-ip f ) | chz + chfsh z | |

ò |f |>f0 ( -z )

+ tt

/

s

/

ò df exp(-ip f ) | chz + chfshz | | |f |