• Korean Journal of Soil Science and Fertilizer
• /
• v.7 no.2
• /
• pp.71-97
• /
• 1974

Many studies on the changes of the materials in the water-logged paddy soil have been reported, but there will be several problems to apply them on the field soil. The main differences between the method of soil packed in beaker or column tube to that of natural field furrow slice are with or without of the rice root and the effect of water percolation. On the other hand, the mechanism of the water percolation on the changes of material in the natural field furrow slice are gradually understood. The purpose of this experiment is to know the effect of the rice cultivation on the chemical and physical changes of material in the water-logged paddy soil. Obtained results are as follows. 1. The physical and chemical changes on the water-logged paddy soil in the non-planted control-plot were nearly the same as the beaker or column tube experiment, while in the planted plot, slightly altered patterns were observed. 2. The relation between the number of tillers and total cation, $Ca^{{+}{+}}$, $Mg^{{+}{+}}$, Fe and Mn in the leachate showed very high significance. T hisresult showed that the leaching of those cation was promoted by growing of the rice r- of the rice root. 3. On the other hand, the concentration of the potassium, silica and phosphorus in leachates was gradually decreased and that of $NH_4$-N could not detect after the stage of active tillering. These facts revealed that such components were absorbed by rice plant. 4. The highly significant correlation between the number of tillers and the concentration of the total cation, $Ca^{{+}{+}}$, $Mg^{{+}{+}}$, $Fe^{{+}{+}}$, Fe and Mn in the percolated water was observed except that of $Mg^{{+}{+}}$. It was also showed that the rice root promoted the leaching of those cation. 5. The very high significance in the correlation between $HCO_3{^-}$ and the number of tillers indicated that the higher activity of the rice root was, the more $HCO_3{^-}$ concentration in the leachate was increased. 6. The relationship between the $HCO_3{^-}$ and the total cation, $Ca^{{+}{+}}$, $Mg^{{+}{+}}$, $Fe^{{+}{+}}$, Fe and Mn was appeared very highly significant. $HCO_3{^-}$, the metabolite of the rice root, promoted the leaching of $Ca^{{+}{+}}$, $Mg^{{+}{+}}$, $Fe^{{+}{+}}$ and Mn. This fact might be a result that these cations were leached as the form of bicarbonate. 7. The iron in the leachate was the form of $Fe^{{+}{+}}$ and the correlation between $Fe^{{+}{+}}$ and $HCO_3{^-}$ was very highly significant. This result indicated that it seemed to be ferrous bicarbonate when it is leached out. 8. In the rhizosphere, ferrous iron was decreased gradually and the concentration of glucose was as high as 2 to 3 times in comparison with the other parts of the soil. These facts were the same as the previous reports in which rhizosphere was oxidized by the oxigen excreted from the root, and was enriched by the organic matter which was also excreted from the root and accumulated residues of the root. 9. ${\beta}$-Glucosidase and phosphatase activity in the rhizosphere was higher than that of the other parts of the soil. This facts might be attributed to the vigorous activity of microorganism in the rhizosphere where glucose concentration was high. 10. The pH in the leachate of the planted plot was lower than that of control, and the Eh on the planted soil was elevated in the last stage.

• Tuberculosis and Respiratory Diseases
• /
• v.43 no.6
• /
• pp.945-953
• /
• 1996

Background : Many acute and chronic lung diseases including pneumoconiosis are characterized by the presence of increased numbers of activated macrophages. These macrophages generate several inflammatory cell chemoattractants, by which neutrophil migrate from vascular compartment to the alveolar space. Recruited neutrophils secrete toxic oxygen radicals or proteolytic enzymes and induce inflammatory response. Continuing inflammatory response results in alteration of the pulmonary structure and irreversible fibrosis. Recently, a polypeptide with specific neutrophil chemotactic activity, interleukin-8(IL-8), has been cloned and isolated from a number of cells including : monocytes, macrophages and fibroblasts. IL-1 and/or TNF-${\alpha}$ preceded for the synthesis of IL-8, and we already observed high level of IL-1 and TNF-${\alpha}$ in the pneumoconioses. So we hypothesized that IL-8 may be a central role in the pathogenesis of pneumoconiosis. In order to evaluate the clinical utility of IL-8 as a biomarker in the early diagnosis of pneumoconiosis, we investigated the increase of IL-8 in the pneumoconiotic patient and the correlation between IL-8 level and progression of pneumoconiosis. Method : We measured IL-8 in the serum of 48 patients with pneumoconiosis and 16 persons without dust exposure history as a control group. Pneumoconiotic cases were divided into 3 groups according to ILO Classification : suspicious group(n=16), small opacity group(n=16) and large opacity group(n=16). IL-8 was measured by a sandwich enzytne immunoassay technique. All data were expressed as the $mean{\pm}standard$ deviation. Results: 1) The mean value of age was higher in the small opacity and large opacity group than comparison group, but smoking history was even. Duration of dust exposure was not different among 3 pneumoconiosis groups. 2) IL-8 level was $70.50{\pm}53.63pg/m{\ell}$ in the suspicious group, $107.50{\pm}45.88pg/m{\ell}$ in the small opacity group, $132.50{\pm}73.47pg/m{\ell}$ in the large opacity group and $17.85{\pm}33.85pg/m{\ell}$ in the comparison group. IL-8 concentration in all pneumoconiosis group was significant higher than that in the comparison group(p<0.001). 3) IL-8 level tended to increase with the progression of pneumoconiosis. Multiple comparison test using Anova/Scheffe analysis showed a significant difference between suspicious group and large opacity group(p<0.05). 4) The level of IL-8 was correlated with the progression of pneumoconiosis(r=0.4199, p<0.05). Conclusion : IL-8 is thought to be a good biomarker for the early diagnosis of pneumoconiosis.

• The Korean Journal of Nuclear Medicine
• /
• v.34 no.5
• /
• pp.418-425
• /
• 2000

Purpose: There is no established formula for estimating renal depths in Korean. As a result, we undertook this study to develop a new formula, and to apply this formula in the calculation of glomerular filtration rate (GFR). Materials and Methods: We measured the renal depth (RD) on the abdominal CT obtained in 300 adults (M:F: 167:133, mean age 50.9 years) without known renal diseases. The RDs measured by CT were compared with the estimated RDs based on the Tonnesen and Taylor equations. New formulas were derived from the measured RDs in 200 out of 300 patients based on several variables such as sex, age, weight, and height by multiple regression analysis. The RDs estimated from the new formulas were compared with the measured RDs in the remaining 100 patients as a control. In 48 patients who underwent Tc-99m DTPA renal scintigraphy, GFR was measured with three equations (new formula, Tonnesen and Taylor equations), respectively, and compared with each other. Results: The mean values of the RDs measured from CT were 6.9 cm for right kidney of the men (MRK), 6.7 cm for left kidney of the men (MLK), 6.7 cm for right kidney of the women (WRK), and 6.6 cm for left kidney of the women (WLK). The RDs estimated from Tonnesen equation were shorter than the ones measured from CT significantly. The newly derived formulas were 12.813 (weight/height)+0.002 (age)+ 2.264 for MRK, 15.344 (weight/height)+0.011 (age)+0.557 for MLK, 12.936 (weight/height)+ 0.014 (age)+1.462 for WRK and 13.488 (weight/height)+0.019 (age)+0.762 for WLK. The correlation coefficients of the RD measured from CT and estimated from the new formula were 0.529 in MRK, 0.729 in MLK, 0.601 in WRK, and 0.724 in WLK, respectively. The GFRs from the new formula were significantly higher than those from the Tonnesen equation significantly, which was the most similar to normal GFR values. Conclusion: We generated new formulas for estimating RD in Korean from the data by CT. By adopting these formulas, we expect that GFR can be measured by the Gates method accurately in Korean.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.1-23
• /
• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Tuberculosis and Respiratory Diseases
• /
• v.45 no.6
• /
• pp.1265-1276
• /
• 1998

Background : Nitric oxide(NO) is an endogenously produced free radical that plays an important role in regulating vascular tone, inhibition of platelet aggregation and white blood cell adhesion to endothelial cells, and host defense against infection. The highly reactive nature of NO with oxygen radicals suggests that it may either promote or reduce oxidant-induced cell injury in several biological pathways. Oxidant injury and interactions between pulmonary vascular endothelium and leukocytes are important in the pathogenesis of acute lung injury, including acute respiratory distress syndrome(ARDS). In ARDS, therapeutic administration of NO is a clinical condition providing exogenous NO in oxidant-induced endothelial injury. The role of exogenous NO from NO donor or the suppression of endogenous NO production was evaluated in oxidant-induced endothelial injury. Method : The oxidant injury in cultured rat lung microvascular endothelial cells(RLMVC) was induced by hydrogen peroxide generated from glucose oxidase(GO). Cell injury was evaluated by $^{51}$chromium($^{51}Cr$) release technique. NO donor, such as S-nitroso-N-acetylpenicillamine(SNAP) or sodium nitroprusside(SNP), was added to the endothelial cells as a source of exogenous NO. Endogenous production of NO was suppressed with N-monomethyl-L-arginine(L-NMMA) which is an NO synthase inhibitor. L-NMMA was also used in increased endogenous NO production induced by combined stimulation with interferon-$\gamma$(INF-$\gamma$), tumor necrosis factor-$\alpha$(TNF-$\alpha$), and lipopolysaccharide(LPS). NO generation from NO donor or from the endothelial cells was evaluated by measuring nitrite concentration. Result : $^{51}Cr$ release was $8.7{\pm}0.5%$ in GO 5 mU/ml, $14.4{\pm}2.9%$ in GO 10 mU/ml, $32.3{\pm}2.9%$ in GO 15 mU/ml, $55.5{\pm}0.3%$ in GO 20 mU/ml and $67.8{\pm}0.9%$ in GO 30 mU/ml ; it was significantly increased in GO 15 mU/ml or higher concentrations when compared with $9.6{\pm}0.7%$ in control(p < 0.05; n=6). L-NMMA(0.5 mM) did not affect the $^{51}Cr$ release by GO. Nitrite concentration was increased to $3.9{\pm}0.3\;{\mu}M$ in culture media of RLMVC treated with INF-$\gamma$ (500 U/ml), TNF-$\alpha$(150 U/ml) and LPS($1\;{\mu}g/ml$) for 24 hours ; it was significantly suppressed by the addition of L-NMMA. The presence of L-NMMA did not affect $^{51}Cr$ release induced by GO in RLMVC pretreated with INF-$\gamma$, TNF-$\alpha$ and LPS. The increase of $^{51}Cr$ release with GO(20 mU/ml) was prevented completely by adding 100 ${\mu}M$ SNAP. But the add of SNP, potassium ferrocyanate or potassium ferricyanate did not protect the oxidant injury. Nitrite accumulation was $23{\pm}1.0\;{\mu}M$ from 100 ${\mu}M$ SNAP at 4 hours in phenol red free Hanks' balanced salt solution. But nitrite was not detectable from SNP upto 1 mM The presence of SNAP did not affect the time dependent generation of hydrogen peroxide by GO in phenol red free Hanks' balanced salt solution. Conclusion : Hydrogen peroxide generated by GO causes oxidant injury in RLMVC. Exogenous NO from NO donor prevents oxidant injury, and the protective effect may be related to the ability to release NO. These results suggest that the exogenous NO may be protective on oxidant injury to the endothelium.

• Korean Journal of Weed Science
• /
• v.15 no.4
• /
• pp.254-261
• /
• 1995

The nationwide weed survey was conducted in lowland rice fields over whole country of Korea in 1992 in order to determine a change of weed community and to identify a major dominant weed species and/or problem weed. Based on morphological characteristics of weeds, population ratio of broad leaf weed was 42.6%, grasses weed-9.0%, sedges-33.4% and others were 15.0%. Annual weed was 33.4% while perennial weed was 66.6% in terms of life cycle of weeds. Meanwhile, there was different weed occurrence as affected by planting method of the rice plant. In hand transplanted paddy fields predominant weed species was Sagittaria trifolia L., Monochoria vaginalis Presl., and Aneilema japonica Kunth. In machine transplanted rice fields of infant and young rice seedling Eleocharis kuroguwai Ohwi. and S. trifolia L. were more predominant. There was high occurrence of M. vaginalis, Echinochloa crus-galli L., and Leesia japonica Makino in water seeding while E. crus-galli and Cyperus serotinus Rottb. were predominant weed species in dry seeded rice. Monoculture of the rice plant would cause to high occurrence of E. kuroguwai, S. trifolia, M. vaginalis, E. crus-galli, and Sagittaria pygmaea Miq and there was higher population of S. trifolia, S. pygmaea, M. vaginalis, E crus-galli, and E. kuroguwai in double cropping system based on rice culture. In particular, there was high different weed occurrence under different transplanting times. E. kuroguwai, S. trifolia, S. pygmaea, M. vaginalis, and C. serotinus were higher population at the transplanting of 25 May and S. trifolia, E crus-galli, C. serotinus, and M. vaginalis at 10 June and S. pygmaea, E. kuroguwai, M. vaginalis, S. trifolia, and E. crusgalli at 25 June in Korea, respectively. Autumn tillage in terms of tillage time would cause more predominant weed species such as S. trifolia, E. kuroguwai, M. vaginalis, and S. pygmaea while spring tillage was higher population of E. kuroguwai, S. trifolia, E. crusgalli, M. vaginalis, and S. pygmaea. In plain area of paddy field there was higher occurrence of E. kuroguwai, S. trifolia, M. vaginalis, E. crus-galli, and S. pygmaea and in mid-mountainous area S. trifolia, E. kuroguwai, M. vaginalis, E. crus-galli, and Ludwigia prostrate Roxb. while in mountainous area S. trifolia, M. vaginalis, Potamogeton distinctus Ben., E. kuroguwai, and E. crus-galli were. In 1992 the most ten predominant weed species at the rice field of Korea based on summed dominant ratio(SDR) were E. kuroguwai > S. trifolia > E. crus-galli > M. vaginalis > S. pygmaea > C. serotinus > L. prostrate > P. distinctus > A. japonica > Scirpus juncoides Roxb.

• Tuberculosis and Respiratory Diseases
• /
• v.52 no.1
• /
• pp.24-36
• /
• 2002

Background: Bronchial reactivity is known to be a component of airway hyperresponsiveness, a cardinal feature of asthma, with bronchial sensitivity, and is increments in response to induced doses of bronchoconstrictors as manifested by the steepest slope of the dose-response curve. However, there is some controversy regarding methods of measuring bronchial reactivity and clinical impact of such measurements. The purpose of this study was to evaluate the clinical significance and assess the clinical use by analyzing the relationship of the bronchial sensitivity, the clinical severity and the changes in pulmonary function with bronchial reactivity. Method: A total of 116 subjects underwent a methacholine bronchial provocation test. They were divided into 3 groups : mild intermittent, mild persistent, moderate and cough asthma. Severe patients were excluded. Methacholine PC20 was determined from the log dose-response curve and PC40 was determined by one more dose inhalation after PC20. The steepest slope of log dose-response curve, connecting PC20 with PC40, was used to calculate the bronchial reactivity. Body plethysmography and a single breath for the DLCO were done in 43 subjects before and after methacholine test. Results: The average bronchial reactivity was 38.0 in the mild intermittent group, 49.8 in the mild persistent group, 61.0 in the moderate group, and 41.1 in the cough asthma group. There was a weak negative correlation between PC20 and bronchial reactivity. A heightened bronchial reactivity tends to produce an increased clinical severity in patients with a similar bronchial sensitivity and basal spirometric pulmonary function. There were significant correlations between the bronchial reactivity and the initial pulmonary function before the methacholine test in the order of sGaw, Raw, $FEV_1$/FVC, MMFR. There were no correlations between the bronchial sensitivity and the % change in the pulmonary function parameters after the methacholine test. However, there were significant correlations between the bronchial reactivity and the PEF, $FEV_1$, DLCO. Conclusion: There was weak significant negative correlation between the bronchial reactivity and the bronchial sensitivity, and the bronchial reactivity closely reflected the severity of the asthma. Accordingly, measuring both the bronchial sensitivity and the bronchial reactivity can be of assistance in assessing of the ongoing disease severity and in monitoring the effect of therapy.

• Proceedings of the KOR-KST Conference
• /
• 1995.02a
• /
• pp.101-113
• /
• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.