• Radiation Oncology Journal
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• v.17 no.2
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• pp.91-99
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• 1999

Propose : This study was performed to find out the prognostic factors affecting local control, survival and disease free survival rate in nasopharyngeal carcinomas treated with chemotherapy and radiation therapy. Materials and Methods : We analysed 47 patients of nasopharyngeal carcinomas, histologically confirmed and treated at Chonnam University Hospital between July 1986 and June 1996, retrospectively. Range of patients' age were from 16 to 80 years (median; 52 years). Thirty three (70$\%$) patients was male. Histological types were composed of 3 (6$\%$) keratinizing, 30 (64$\%$) nonkeratinizing squamous cell carcinoma and 13 (28$\%$) undifferentiated carcinoma. Histoiogicai type was not known in 1 patient (2$\%$). We restaged according to the staging system of 1997 American Joint Committee on Cancer Forty seven patients were recorded as follows: 71: 11 (23$\%$), T2a; 6 (13$\%$), T2b; 9 (19$\%$), 73; 7 (15$\%$), 74: 14 (30$\%$), and NO; 7 (15$\%$), Nl: 14 (30$\%$), N2; 21 (45%), N3: 5 (10%). Clinical staging was grouped as follows: Stage 1; 2 (4$\%$), IIA: 2 (4$\%$), IIB; 10 (21$\%$), III; 14 (30$\%$), IVA; 14 (30$\%$) and IVB; 5 (11$\%$). Radiation therapy was done using 6 MV and 10 MV X- ray of linear accelerator. Electron beam was used for the Iymph nodes of posterior neck after 4500 cGy. The range of total radiation dose delivered to the primary tumor was from 6120 to 7920 cGy (median; 7020 cGy). Neoadjuvant chemotherapy was performed with cisplatin +5-fluorouracil (25 patients) or cisplatin+pepleomycin (17 patients) with one to three cycles. Five patients did not received chemotherapy. Local control rate, survival and disease free suwival rate were calculated by Kaplan-Meier method. Generalized Wilcoxon test was used to evaluate the difference of survival rates between groups. multivariate analysis using Cox proportional hazard model was done for finding prognostic factors. Results: Local control rate was 81$\%$ in 5 year. Five year survival rate was 60$\%$ (median survival; 100 months). We included age, sex, cranial nerve deflicit, histologic type, stage group, chemotherapy, elapsed days between chemotherapy and radiotherapy, total radiation dose, period of radiotherapy as potential prognostic factors in multivariate analysis. As a result, cranial none deficit (P=0.004) had statistical significance in local control rate. Stage group and total radiation dose were significant prognostic factors in survival (P=0.000, P=0.012), and in disease free survival rates (P=0.003, P=0.008), respectively. Common complications were xerostomia, tooth and ear problems. Hypothyroidism was developed in 2 patients. Conclusion : In our study, cranial none deficit was a significant prognostic factor in local control rate, and stage group and total radiation dose were significant factors in both survival and disease free survival of nasopharyngeal carcinoma. We have concluded that chemotherapy and radiotherapy used in our patients were effective without any serious complication.

• Journal of Korean Society of Forest Science
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• v.33 no.1
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• pp.33-58
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• 1977

A bark comprises about 10 to 20 percents of a typical log by volume, and is generally considered as an unwanted residue rather than a potentially valuable resourses. As the world has been confronted with decreasing forest resources, natural resources pressure dictate that a bark should be a raw material instead of a waste. The utilization of the largely wasted bark of genus Pinus, Quercus, and Populus grown in Korea can be enhanced by learning its physical and mechanical properties. However, the study of tree bark grown in Korea have never been undertaken. In the present paper, an investigative study is carried out on the bark of three genus, eleven species representing not only the major bark trees but major species currently grown in Korea. For each species 20 trees were selected, at Suweon and Kwang-neung areas, on the same basis of the diameter class at the proper harvesting age. One $200cm^2$ segment of bark was obtained from each tree at brest height. Physical properties of bark studied are: bark density, moisture content of green bark (inner-, outer-, and total-bark), fiber saturation point, hysteresis loop, shrinkage, water absorption, specific heat, heat of wetting, thermal conductivity, thermal diffusivity, heat of combustion, and differential thermal analysis. The mechanical properties are studied on bending and compression strength (radial, longitudinal, and tangential). The results may be summarized as follows: 1. The oven-dry specific gravities differ between wood and bark, further more even for a given bark sample, the difference is obersved between inner and outer bark. 2. The oven-dry specific gravity of bark is higher than that of wood. This fact is attributed to the anatomical structure whose characters are manifested by higher content of sieve fiber and sclereids. 3. Except Pinus koraiensis, the oven-dry specific gravity of inner bark is higher than that of outer bark, which results from higher shrinkage of inner bark. 4. The moisture content of bark increases with direct proportion to the composition ratio of sieve components and decreases with higher percent of sclerenchyma and periderm tissues. 5. The possibility of determining fiber saturation point is suggested by the measuring the heat of wetting. With the proposed method, the fiber saturation point of Pinus densiflora lies between 26 and 28%, that of Quercus accutissima ranges from 24 to 28%. These results need be further examined by other methods. 6. Contrary to the behavior of wood, the bark shrinkage is the highest in radial direction and the lowest in longitudinal direction. Quercus serrata and Q. variabilis do not fall in this category. 7. Bark shows the same specific heat as wood, but the heat of wetting of bark is higher than that of wood. In heat conductivity, bark is lower than wood. From the measures of oven-dry specific gravity (${\rho}d$) and moisture fraction specific gravity (${\rho}m$) is devised the following regression equation upon which heat conductivity can be calculated. The calculated heat conductivity of bark is between $0.8{\times}10^{-4}$ and $1.6{\times}10^{-4}cal/cm-sec-deg$. $$K=4.631+11.408{\rho}d+7.628{\rho}m$$ 8. The bark heat diffusivity varies from $8.03{\times}10^{-4}$ to $4.46{\times}10^{-4}cm^2/sec$. From differential thermal analysis, wood shows a higher thermogram than bark under ignition point, but the tendency is reversed above ignition point. 9. The modulus of rupture for static bending strength of bark is proportional to the density of bark which in turn gives the following regression equation. M=243.78X-12.02 The compressive strength of bark is the highest in radial direction, contrary to the behavior of wood, and the compressive strength of longitudinal direction follows the tangential one in decreasing order.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.10 no.2
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• pp.97-114
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• 1977

The fresh water prawn, Macrobrachium rosenbergi(de Man) is a very common species in Indopacific region, which inhaits both fresh and brackish water in low land area, most of rivers and especially aboundant in the lower reaches which are influenced by sea water. It is one of the largest and commercial species of genus Macrobrachium, which is commonly larger than $18\~21cm$ in body length, from the basis of eye-stalked to the distal of telson. As a part of the researches in order to investigate the possibilities on transplantation and propagation of this species, this work dealt with the problems on the effects of chlorinities upon zoeal larvae and post-larvae 1). metamorphosis rate and optimum chlorinity for metamorphosis to post-larve, 2). tolerance and comparative survival rate on various chlorinties, from fresh water to sea water $(19.38\%_{\circ}\;Cl)$, which reared for six days upon each stage of zoeal larvae, 3). accomodation rate on chlonities which reared for twelve days after transmigration into variant chlorinities of the range from $3.68\%_{\circ}$ Cl to $1.53\%_{\circ}$ Cl in the way of rearing of the range from $3.82\%_{\circ}$ Cl to $11.05\%_{\circ}$ upon each stage of zoea, 4). tolerance on both of fresh and sea water upon zoeal larva and post-larva under the condition of $28^{\circ}C{\pm}1$ in temperature and feeding on Artenia salina nauplii, 5). relationship between various chlorinities and grwth of post-larvae under the condition of $28^{\circ}C$ in tmperature and feeding on meat of clam. Thus these investigations were performed in order to grope for a comfortable method on seedmass production. Up to the present, the study on the effects of chlorinity upon earlier zoeal larvae and post-larvae of Macrobrachium species has been scarcely performed by workers with the exception of Lewis(1961) and Ling (1962,, 1967), even so their works were not so detailed. On the other hand, larvae of several species of this genus were reared at the water which mixed sea water so as to carry out complete metamorphosis to post-larva by workers in order to investigate on earlier 1 arval and earlier post-larval development, such as Macrobrachium lamerrei (Rajyalakshmi, 1961), M. rosenbergi and M. nipponense (Uno and Kwoa, 1969; Kwon and Uno, 1969), M. acanthurs (Choudhury, 1970; Dobkin, 1971), M. carcinus(Choudhury, 1970), M. formosense(Shokita, 1970), M. olfersii (Duggei et al., 1975), M. novaehallandiae (Greenwood et al., 1976), M. japonicum (Kwon, 1974) and M. lar (Shokita, personal communication), and there fore it is regarded that chlorinity is, generally, one of absolute factors to rear zoeal larvae of brackish species of Macrobrachium genus. Synthetic results on this work is summarized as the follwings: 1) Zoeal larvae required different chlorinities to grow according to each stage, and generally, it is regarded that optimum range of living and growing is from $7.63\%_{\circ}Cl\to\;7.63\%_{\circ}Cl$, and while differences of metamorphsis rate, from first zoea to post-larva, is rarely found in this range, and however it occurs apparently in both of situation at $7.63\%_{\circ}Cl$ below and $16.63\%_{\circ}Cl$ above and moreover, metamorphosis rate is delayed somewhat in case of lower chlorinity as compared with high chlorinity in these situations. 2) Accomodation in each chlorinity on the range, from fresh water to sea water, is different according to larval stages and while the best of it is, generally, on the range from $14.24\%_{\circ}Cl$ to $8.28\%_{\circ}Cl$ and favorite chlorinity of zoea have a tendency to remove from high chlorinity to lower chlorinity in order to advance larval age throughout all zoeal stages, setting a conversional stage for eighta zoea stage. 3) Optimum chlorinity of living and growth upon postlarvae is on the range of $4.25\%_{\circ}Cl$ below, and in proportion as approach to fresh water, growth rate is increased. 4) Post-large are able to live better in fresh water in comparison with zoeal larvae, which are only able to live within fifteen hours, and by contraries, post-larvae are merely able to live for one day as compared with ?미 larvar, which are able to live for six days more in sea water $19.38\%_{\circ}Cl\;above$. 5) Also, in case of transmigration into higher and lower chlorinities in the way of rearing in the initial chlorinities $ 3.82\%_{\circ}Cl,\;7.14%_{\circ}Cl\;and\;11.05%_{\circ}Cl$, accoodation rate is a follow: accomodation capacity in ease of removing into higher chlorinities from lower chlorinities is increased in proportion as earlier stages, setting a conversional stage for eighth zoea stage, and by contraries, in case of advanced stages from eighth zoea it is incraesed in proportion as approach to post-larva stage in the case of transmigration into lower chlorinity from higher chlorinity. On the other hand, it is interesting that in case of reciprocal transmigration between two different chlorinitiess, each survival rate is different, and in this case, also, its accomodation in each zoea stage has a tendency to vary according to larval stages as described above, setting a conversional stage for eighth zoea stage. 6) It is likely that expension of radish pigments on body surface is directly proportional to chlorinity during the period of zoea rearing, and therefore it seems like all body surfacts of zoea larvae be radish coloured in case of higher chlorinity. 7) By the differences that each zoeal larvae, postlarvae, juvaniles and adult prawn are required different chlorinity for inhabiting in each, it is regarded that this species migrats from up steam to near the estuary of the river which the prawns inhabits commonly in natural field for spawning and growth migration. 8) It had better maintainning chlorinities according to zoeal stage for a comfortable method on seed-mass production that earlier larva stages than eighth zoea are maintained on the range from $8\%_{\circ}Cl\;to\;12\%_{\circ}Cl$ to rear, and later larva stages than eighth zoea, by contraries, are gradually regula ted-to love chlorininity of the range from $7\%_{\circ}Cl\;to\;4\%_{\circ}Cl$ according to advance for post-larva stage.

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