Search results for: ordered logistic regression

Authors: Thi Thi Zin, Pyke Tin, Ikuo Kobayashi, Yoichiro Horii

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Today dairy farm experts and farmers have well recognized the importance of dairy cow Body Condition Score (BCS) since these scores can be used to optimize milk production, managing feeding system and as an indicator for abnormality in health even can be utilized to manage for having healthy calving times and process. In tradition, BCS measures are done by animal experts or trained technicians based on visual observations focusing on pin bones, pin, thurl and hook area, tail heads shapes, hook angles and short and long ribs. Since the traditional technique is very manual and subjective, the results can lead to different scores as well as not cost effective. Thus this paper proposes an algebraic geometric imaging approach for an automatic dairy cow BCS system. The proposed system consists of three functional modules. In the first module, significant landmarks or anatomical points from the cow image region are automatically extracted by using image processing techniques. To be specific, there are 23 anatomical points in the regions of ribs, hook bones, pin bone, thurl and tail head. These points are extracted by using block region based vertical and horizontal histogram methods. According to animal experts, the body condition scores depend mainly on the shape structure these regions. Therefore the second module will investigate some algebraic and geometric properties of the extracted anatomical points. Specifically, the second order polynomial regression is employed to a subset of anatomical points to produce the regression coefficients which are to be utilized as a part of feature vector in scoring process. In addition, the angles at thurl, pin, tail head and hook bone area are computed to extend the feature vector. Finally, in the third module, the extracted feature vectors are trained by using Markov Classification process to assign BCS for individual cows. Then the assigned BCS are revised by using multiple regression method to produce the final BCS score for dairy cows. In order to confirm the validity of proposed method, a monitoring video camera is set up at the milk rotary parlor to take top view images of cows. The proposed method extracts the key anatomical points and the corresponding feature vectors for each individual cows. Then the multiple regression calculator and Markov Chain Classification process are utilized to produce the estimated body condition score for each cow. The experimental results tested on 100 dairy cows from self-collected dataset and public bench mark dataset show very promising with accuracy of 98%.

Keywords: algebraic geometric imaging approach, body condition score, Markov classification, polynomial regression

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Authors: Jui-Chen Huang

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This research used artificial intelligence method to explore elderly’s opinions on the reuse of telecare, its effect on their service quality, satisfaction and the relationship between customer perceived value and intention to reuse. This study conducted a questionnaire survey on the elderly. A total of 124 valid copies of a questionnaire were obtained. It adopted Backpropagation Network (BPN) to propose an effective and feasible analysis method, which is different from the traditional method. Two third of the total samples (82 samples) were taken as the training data, and the one third of the samples (42 samples) were taken as the testing data. The training and testing data RMSE (root mean square error) are 0.022 and 0.009 in the BPN, respectively. As shown, the errors are acceptable. On the other hand, the training and testing data RMSE are 0.100 and 0.099 in the regression model, respectively. In addition, the results showed the service quality has the greatest effects on the intention to reuse, followed by the satisfaction, and perceived value. This result of the Backpropagation Network method is better than the regression analysis. This result can be used as a reference for future research.

Keywords: artificial intelligence, backpropagation network (BPN), elderly, reuse, telecare

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: O. Crisan, A. D. Crisan, I. Mercioniu, R. Nicula, F. Vasiliu

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FePt-based systems are currently under scrutiny for their possible use as future materials for perpendicular magnetic recording. Another possible application is in the field of permanent magnets without rare-earths, magnets that are capable to operate at higher temperatures than the classic Nd-Fe-B magnets. Within this work, FeCoPt alloys prepared by rapid solidification from the melt are structurally and magnetically characterized. Extended transmission electron microscopy analysis shows the high degree of L10 ordering. X-ray diffraction is used to characterize the phase structure and to obtain the structural parameters of interest for L10 ordering. Co-existence of hard CoFePt and CoPt L10 phases with the soft fcc FePt phase is obtained within a refined microstructure made of alternatively disposed grains of around 5 to 20 nm in size. Magnetic measurements show increased remanence close to the parent L10 FePt phase and not so high coercivity due to the significant presence of the soft magnetic constituent phase. A Curie temperature of about 820K is reported for the FeCoPt alloy.

Keywords: melt-spinning, FeCoPt alloys, high-resolution electron microscopy (HREM), ordered L10 structure

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Wei-Song Hung

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We utilized pressure, vacuum, and evaporation-assisted self-assembly techniques through which graphene oxide (GO) was deposited on modified polyacrylonitrile (mPAN). The fabricated composite GO/mPAN membranes were applied to dehydrate 1-butanol mixtures by pervaporation. Varying driving forces in the self-assembly techniques induced different GO assembly layer microstructures. XRD results indicated that the GO layer d-spacing varied from 8.3 Å to 11.5 Å. The self-assembly technique with evaporation resulted in a heterogeneous GO layer with loop structures; this layer was shown to be hydrophobic, in contrast to the hydrophilic layer formed from the other two techniques. From the pressure-assisted technique, the composite membrane exhibited exceptional pervaporation performance at 30 C: concentration of water at the permeate side = 99.6 wt% and permeation flux = 2.54 kg m-2 h-1. Moreover, the membrane sustained its operating stability at a high temperature of 70 C: a high water concentration of 99.5 wt% was maintained, and a permeation flux as high as 4.34 kg m-2 h-1 was attained. This excellent separation performance stemmed from the dense, highly ordered laminate structure of GO.

Keywords: graphene oxide, self-assembly, alcohol dehydration, polyacrylonitrile (mPAN)

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Authors: Ikpechukwu Njoku

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The study examined appraisal of shipping trade influence on the economic growth in Nigeria from 1981-2016 by the use of secondary data collected from the Central Bank of Nigeria. The main objectives are to examine the trend of shipping trade in Nigeria as well as determine the influence of economic growth on gross domestic product (GDP). The study employed both descriptive and influential tools. The study adopted cointegration regression method for the analysis of each of the variables (shipping trade, external reserves and external debts). The results show that there is a statistically significant relationship between GDP and external reserves with p-value 0.0190. Also the result revealed that there is a statistically significant relationship between GDP and shipping trade with p-value 0.000. However, shipping trade and external reserves contributed positively at 1% and 5% level of significance respectively while external debts impacted negatively to GDP at 5% level of significance with a long run variance of cointegration regression. Therefore, the study suggests that government should do all it can to curtail foreign dominance and repatriation of profit for a more sustainable economy as well as upgrade port facilities, prevent unnecessary delays and encourage exportable goods for maximum deployment of ships.

Keywords: external debts, external reserve, GDP, shipping trade

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Authors: Kaycee Bills

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This study will determine if students with disabilities have higher student loan debt payments than other student populations. The hypothesis was that students with disabilities would have significantly higher student loan debt payments than other students due to the length of time they spend in school. Using the Bachelorette and Beyond Study Wave 2015/017 dataset, quantitative methods were employed. These data analysis methods included linear regression and a correlation matrix. Due to the exploratory nature of the study, the significance levels for the overall model and each variable were set at .05. The correlation matrix demonstrated that students with certain types of disabilities are more likely to fall under higher student loan payment brackets than students without disabilities. These results also varied among the different types of disabilities. The result of the overall linear regression model was statistically significant (p = .04). Despite the overall model being statistically significant, the majority of the significance values for the different types of disabilities were null. However, several other variables had statistically significant results, such as veterans, people of minority races, and people who attended private schools. Implications for how this impacts the economy, capitalism, and financial wellbeing of various students are discussed.

Keywords: disability, student loan debt, higher education, social work

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Authors: Asrat Agalu Abejew

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Background: Antimicrobial resistance (AMR) is a silent tsunami and one of the top global threats to health care and public health. It is one of the common agendas globally and in Ethiopia. Emerging AMR will be a double burden to Ethiopia, which is facing a series of problems from infectious disease morbidity and mortality. In Ethiopia, although there are attempts to document AMR in healthcare institutions, comprehensive and all-inclusive analysis is still lacking. Thus, this study is aimed to determine trends in AMR from 2016-2021. Methods: A retrospective analysis of secondary data recorded in the Amhara Public Health Institute (APHI) from 2016 to 2021 G.C was conducted. Blood, Urine, Stool, Swabs, Discharge, body effusions, and other Microbiological specimens were collected from each study participants, and Bacteria identification and Resistance tests were done using the standard microbiologic procedure. Data was extracted from excel in August 2022, Trends in AMR were analyzed, and the results were described. In addition, the chi-square (X2) test and binary logistic regression were used, and a P. value < 0.05 was used to determine a significant association. Results: During 6 years period, there were 25143 culture and susceptibility tests. Overall, 265 (46.2%) bacteria were resistant to 2-4 antibiotics, 253 (44.2%) to 5-7 antibiotics, and 56 (9.7%) to >=8 antibiotics. The gram-negative bacteria were 166 (43.9%), 155 (41.5%), and 55 (14.6%) resistant to 2-4, 5-7, and ≥8 antibiotics, respectively, whereas 99(50.8%), 96(49.2% and 1 (0.5%) of gram-positive bacteria were resistant to 2-4, 5-7 and ≥8 antibiotics respectively. K. pneumonia 3783 (15.67%) and E. coli 3199 (13.25%) were the most commonly isolated bacteria, and the overall prevalence of AMR was 2605 (59.9%), where K. pneumonia 743 (80.24%), E. cloacae 196 (74.81%), A. baumannii 213 (66.56%) being the most common resistant bacteria for antibiotics tested. Except for a slight decline during 2020 (6469 (25.4%)), the overall trend of AMR is rising from year to year, with a peak in 2019 (8480 (33.7%)) and in 2021 (7508 (29.9%). If left un-intervened, the trend in AMR will increase by 78% of variation from the study period, as explained by the differences in years (R2=0.7799). Ampicillin, Augmentin, ciprofloxacin, cotrimoxazole, tetracycline, and Tobramycin were almost resistant to common bacteria they were tested. Conclusion: AMR is linearly increasing during the last 6 years. If left as it is without appropriate intervention after 15 years (2030 E.C), AMR will increase by 338.7%. A growing number of multi-drug resistant bacteria is an alarm to awake policymakers and those who do have the concern to intervene before it is too late. This calls for a periodic, integrated, and continuous system to determine the prevalence of AMR in commonly used antibiotics.

Keywords: AMR, trend, pattern, MDR

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Authors: Chaluntorn Vichasilp, Sutee Wangtueai

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This study aims to develop a new method to detect active compounds in Thai herbs (1-deoxynojirimycin (DNJ) in mulberry leave, anthocyanin in Mao and curcumin in turmeric) using near infrared spectroscopy (NIRs). NIRs is non-destructive technique that rapid, non-chemical involved and low-cost determination. By NIRs and chemometrics technique, it was found that the DNJ prediction equation conducted with partial least square regression with cross-validation had low accuracy R2 (0.42) and SEP (31.87 mg/100g). On the other hand, the anthocyanin prediction equation showed moderate good results (R2 and SEP of 0.78 and 0.51 mg/g) with Multiplication scattering correction at wavelength of 2000-2200 nm. The high absorption could be observed at wavelength of 2047 nm and this model could be used as screening level. For curcumin prediction, the good result was obtained when applied original spectra with smoothing technique. The wavelength of 1400-2500 nm was created regression model with R2 (0.68) and SEP (0.17 mg/g). This model had high NIRs absorption at a wavelength of 1476, 1665, 1986 and 2395 nm, respectively. NIRs showed prospective technique for detection of some active compounds in Thai herbs.

Keywords: anthocyanin, curcumin, 1-deoxynojirimycin (DNJ), near infrared spectroscopy (NIRs)

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Authors: Samul Sahile Kebede

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Back ground: -Immune hemolytic anemia commonly affects human immune deficiency, infected individuals. Among anemic HIV patients in Africa, the burden of IHA due to autoantibody was ranged from 2.34 to 3.06 due to the drug was 43.4%. IHA due to autoimmune is potentially a fatal complication of HIV, which accompanies the greatest percent from acquired hemolytic anemia. Objective: -The main aim of this study was to determine the magnitude and associated factors of immune hemolytic anemia among human immuno deficiency virus infected adults at the university of Gondar comprehensive specialized hospital north west Ethiopia from March to April 2021. Methods: - An institution-based cross-sectional study was conducted on 358 human immunodeficiency virus-infected adults selected by systematic random sampling at the University of Gondar comprehensive specialized hospital from March to April 2021. Data for socio-demography, dietary and clinical data were collected by structured pretested questionnaire. Five ml of venous blood was drawn from each participant and analyzed by Unicel DHX 800 hematology analyzer, blood film examination, and antihuman globulin test were performed to the diagnosis of immune hemolytic anemia. Data was entered into Epidata version 4.6 and analyzed by STATA version 14. Descriptive statistics were computed and firth penalized logistic regression was used to identify predictors. P value less than 0.005 interpreted as significant. Result; - The overall prevalence of immune hemolytic anemia was 2.8 % (10 of 358 participants). Of these, 5 were males, and 7 were in the 31 to 50 year age group. Among individuals with immune hemolytic anemia, 40 % mild and 60 % moderate anemia. The factors that showed association were family history of anemia (AOR 8.30 at 95% CI 1.56, 44.12), not eating meat (AOR 7.39 at 95% CI 1.25, 45.0), and high viral load 6.94 at 95% CI (1.13, 42.6). Conclusion and recommendation; Immune hemolytic anemia is less frequent condition in human immunodeficiency virus infected adults, and moderate anemia was common in this population. The prevalence was increased with a high viral load, a family history of anemia, and not eating meat. In these patients, early detection and treatment of immune hemolytic anemia is necessary.

Keywords: anemia, hemolytic, immune, auto immune, HIV/AIDS

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Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

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Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval

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Authors: Fulya Zaralı, Harun Resit Yazgan

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Logistics centers represent areas that all national and international logistics and activities related to logistics can be implemented by the various businesses. Logistics centers have a key importance in joining the transport stream and the transport system operations. Therefore, it is important where these centers are positioned to be effective and efficient and to show the expected performance of the centers. In this study, the location selection problem to position the logistics center is discussed. Alternative centers are evaluated according certain criteria. The most appropriate center is identified using the axiomatic design method.

Keywords: axiomatic design, logistic center, facility location, information systems

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Authors: Ahmed Bolaji Alarape, Oluwatobi Damilola Aba

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The growth pattern of Oreochromis niloticus and Sarotherodon galilaeus in Epe Lagoon Lagos State was investigated. One hundred (100) samples of each species were collected from fishermen at the landing site. They were transported to the Fisheries Laboratory of National Institute of Oceanography for identification, sexing morphometric measurement. The results showed that 58.0% and 56.0 % of the O.niloticus and S.galilaeus were female respectively while 42.0% and 44.0% were male respectively. The length-weight relationship of O.niloticus showed a strong regression coefficient (r = 0.944) (p<0.05) for the combined sex, (r =0.901) (p<0.05) for female and (r=0.985) (p<.05) for male with b-value of 2.5, 3.1 and 2.8 respectively. The S.galilaeus also showed a regression coefficient of r=0.970; p<0.05 for the combined sex, r=0.953; p<0.05 for the female and r= 0.979; p<0.05 for the male with b-value of 3.4, 3.1 and 3.6 respectively. O.niloticus showed an isometric growth pattern both in male and female. The condition factor in O.niloticus are 1.93 and 1.95 for male and female respectively while that of S.galilaeus is 1.95 for both sexes. Positive allometric was observed in both species except the male O.niloticus that showed negative allometric growth pattern. From the results of this study, the growth pattern of the two species indicated a good healthy environment.

Keywords: Epe Lagoon, length-weight relationship, Oreochromis niloticus, Sarotherodon galilaeus

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Authors: Ryo Shikimoto, Hidehito Niimura, Hisashi Kida, Kota Suzuki, Yukiko Miyasaka, Masaru Mimura

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Introduction: In Japan, the most aged country in the world, it is important to explore predictive factors of cognitive function among the elderly. Coping behavior relieves chronic stress and improves lifestyle, and consequently may reduce the risk of cognitive impairment. One of the most widely investigated frameworks evaluated in previous studies is approach-oriented and avoidance-oriented coping strategies. The purpose of this study is to investigate the relationship between cognitive function and coping strategies among elderly residents in urban areas of Japan. Method: This is a part of the cross-sectional Arakawa geriatric cohort study for 1,099 residents (aged 65 to 86 years; mean [SD] = 72.9 [5.2]). Participants were assessed for cognitive function using the Mini-Mental State Examination (MMSE) and diagnosed by psychiatrists in face-to-face interviews. They were then investigated for their each coping behaviors and coping strategies (approach- and avoidance-oriented coping) using stress and coping inventory. A multiple regression analysis was used to investigate the relationship between MMSE score and each coping strategy. Results: Of the 1,099 patients, the mean MMSE score of the study participants was 27.2 (SD = 2.7), and the numbers of the diagnosis of normal, mild cognitive impairment (MCI), and dementia were 815 (74.2%), 248 (22.6%), and 14 (1.3%), respectively. Approach-oriented coping score was significantly associated with MMSE score (B [partial regression coefficient] = 0.12, 95% confidence interval = 0.05 to 0.19) after adjusting for confounding factors including age, sex, and education. Avoidance-oriented coping did not show a significant association with MMSE score (B [partial regression coefficient] = -0.02, 95% confidence interval = -0.09 to 0.06). Conclusion: Approach-oriented coping was clearly associated with neurocognitive function in the Japanese population. A future longitudinal trial is warranted to investigate the protective effects of coping behavior on cognitive function.

Keywords: approach-oriented coping, cognitive impairment, coping behavior, dementia

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Authors: Jayanta Bora, Sukhvinder Kaur, Ashok Agarwal, Sangeeta Kaul

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Background: It is important for targeted interventions to consider vulnerabilities of female sex workers (FSWs) such as poverty, work-related mobility and literacy for effective human immunodeficiency virus (HIV) prevention. This paper examines the association between vulnerability and behavioural outcomes among FSWs in Thane district, Maharashtra under USAID PHFI-PIPPSE project. Methods: Data were used from the Behavioural Tracking Survey, a cross-sectional behavioural study conducted in 2015 with 503 FSWs randomly selected from 12 TI-NGOs which were functioning and providing services to FSWs in Thane district prior to April 2014 in Thane district of Maharashtra. We have created the “vulnerability index”, a composite index of literacy, factors of dependence (alternative livelihood options, current debt), and aspects of sex work (mobility and duration in sex work) as a dependent variable. The key independent measures used were program exposure to intervention, service uptake, self-confidence, and self-identity. Bi-variate and multivariate logistic regressions were used to examine the study objectives. Results: A higher proportion of FSWs who were in the age-group 18–25 years from brothel/street /home/ lodge-based were categorized as highly vulnerable to HIV risk as compared to bar-based sex worker (74.1% versus 59.8%, P,0.002); regression analysis highlighted lower odds of vulnerability among FSWs who were aware of services and visited NGO clinic for medical check-up and counselling for STI [AOR= 0.092, 95% CI 0.018-0.460; P,0.004], However, lower odds of vulnerability on confident in supporting fellow sex worker in crisis [AOR= 0.601, 95% CI 0.476-0.758; P, 0.000] and were able to turn away clients when they refused to use a condom during sex [AOR= 0.524, 95% CI 0.342-0.802; P, 0.003]. Conclusion: The results highlight that FSWs associated with TIs and getting services are less vulnerable and highly empowered. As a result of behavioural change communication and other services provided by TIs, FSWs were able to successfully negotiate about condom use with their clients and manage solidarity in the crisis situation for fellow FSWs. Therefore, it is evident from study paper that TI prevention programs may transform the lives of masses considerably and may open a window of opportunity to infuse the information and awareness about HIV risk.

Keywords: female sex worker, HIV prevention, HIV service uptake, vulnerability

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Al Romano, Jeryl C. Hije, Mechaela Marie O. Tabiolo

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The decline in water quality is a major factor in aquatic disease outbreaks and can lead to significant mortality among aquatic organisms. Understanding the relationship between dissolved oxygen (DO) and water temperature is crucial, as these variables directly impact the health, behavior, and survival of fish populations. This study investigated how DO levels, water temperature, and atmospheric temperature interact in Sampaloc Lake to assess the risk of fish mortality. By employing a combination of linear regression models and machine learning techniques, researchers developed predictive models to forecast DO concentrations at various depths. The results indicate that while DO levels generally decrease with depth, the predicted concentrations are sufficient to support the survival of common fish species in Sampaloc Lake during March, April, and May 2025.

Keywords: aquaculture, dissolved oxygen, water temperature, regression analysis, machine learning, fish mass mortality, early warning system

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Zheng-Yan Ruan, Chien-Hao Wang, Hong-Jen Lin, Chien-Peng Huang, Ying-Hao Chen, En-Cheng Yang, Chwan-Lu Tseng, Joe-Air Jiang

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Bees play a vital role in pollination. This paper focuses on the weighing process of honey. Honey is usually stored at the comb in a hive. Bee farmers brush bees away from the comb and then collect honey, and the collected honey is weighed afterward. However, such a process brings strong negative influences on bees and even leads to the death of bees. This paper therefore presents an Internet of Things-based weight monitoring system which uses weight sensors to measure the weight of honey and simplifies the whole weighing procedure. To verify the system, the weight measured by the system is compared to the weight of standard weights used for calibration by employing a linear regression model. The R² of the regression model is 0.9788, which suggests that the weighing system is highly reliable and is able to be applied to obtain actual weight of honey. In the future, the weight data of honey can be used to find the relationship between honey production and different ecological parameters, such as bees’ foraging behavior and weather conditions. It is expected that the findings can serve as critical information for honey production improvement.

Keywords: internet of things, weight, honey, bee

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Authors: Sireesha Tamada, Debraj Bhattacharjee, Pranab K. Dan, Prabha Bhola

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The most important component in an automotive transmission system is the gearbox which controls the speed of the vehicle. In an automatic transmission, the right positioning of actuators ensures efficient transmission mechanism embodiment, wherein the challenge lies in formulating the number of actuators associated with modelling a gearbox. Data with respect to actuation and gear shifting sequence has been retrieved from the available literature, including patent documents, and has been used in this proposed heuristics based methodology for modelling actuation sequence in a gear box. This paper presents a methodological approach in designing a gearbox for the purpose of obtaining an optimal shifting sequence. The computational model considers factors namely, the number of stages and gear teeth as input parameters since these two are the determinants of the gear ratios in an epicyclic gear train. The proposed transmission schematic or stick diagram aids in developing the gearbox layout design. The number of iterations and development time required to design a gearbox layout is reduced by using this approach.

Keywords: automatic transmission, gear-shifting, multi-stage planetary gearbox, rank ordered clustering

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Authors: Pratima Thapa

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Background: It is very critical to understand that awareness of family planning and proper utilization of contraceptives is an important indicator for reducing maternal and neonatal mortality and morbidity. It also plays an important role in promoting reproductive health of the women in an underdeveloped country like ours. Objective: To assess knowledge, attitude and practices of contraception among married women of reproductive age group in selected wards of Dharan Sub-Metropolitan City. Materials and methods: A cross-sectional descriptive study was conducted among 209 married women of reproductive age. Simple random sampling was used to select the wards, population proportionate sampling for selecting the sample numbers from each wards and purposive sampling for selecting each sample. Semi-structured questionnaire was used to collect data. Descriptive and inferential statistics were used to interpret the data considering p-value 0.05. Results: The mean ± SD age of the respondents was 30.01 ± 8.12 years. Majority 92.3% had ever heard of contraception. Popular known method was Inj. Depo (92.7%). Mass media (85.8%) was the major source of information. Mean percentage score of knowledge was 45.23%.less than half (45%) had adequate knowledge. Majority 90.4% had positive attitude. Only 64.6% were using contraceptives currently. Misbeliefs and fear of side effects were the main reason for not using contraceptives. Education, occupation, and total income of the family was associated with knowledge regarding contraceptives. Results for Binary Logistic Regression showed significant correlates of attitude with distance to the nearest health facility (OR=7.97, p<0.01), education (OR=0.24, p<0.05) and age group (0.03, p<0.01). Regarding practice, likelihood of being current user of contraceptives increased significantly by being literate (OR=5.97, p<0.01), having nuclear family (OR=4.96, p<0.01), living in less than 30 minute walk distance from nearest health facility (OR=3.34, p<0.05), women’s participation in decision making regarding household and fertility choices (OR=5.23, p<0.01) and husband’s support on using contraceptives (OR=9.05, p<0.01). Significant and positive correlation between knowledge-attitude, knowledge-practice and attitude-practice were observed. Conclusion: Results of the study indicates that there is need to increase awareness programs in order to intensify the knowledge and practices of contraception. The positive correlation indorses that better knowledge can lead to positive attitude and hence good practice. Further, projects aiming to increase better counselling about contraceptives, its side effects and the positive effects that outweighs the negative aspects should be enrolled appropriately.

Keywords: attitude, contraceptives, knowledge, practice

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Authors: Azeem Ur Rehman, Asma Tayyaba

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This article deals with the promotional effects of CeO2 on PtPd/CeO2-OMC electro catalysts. The synthesized catalysts are characterized using different physico chemical techniques and evaluated in a formic acid oxidation fuel cell. N2 adsorption/desorption analysis shows that CeO2 modification increases the surface area of OMC from 1005 m2/g to 1119 m2/g. SEM, XRD and TEM analysis reveal that the presence of CeO2 enhances the active metal(s) dispersion on the CeO2-OMC surface. The average particle size of the dispersed metal decreases with the increase of Pt/Pd ratio on CeO2-OMC support. Cyclic voltametry measurement of Pd/CeO2-OMC gives 12 % higher anodic current activity with 83 mV negative shift of the peak potential as compared to unmodified Pd/OMC. In bimetallic catalysts, the addition of Pt improves the activity and stability of the catalysts significantly. Among the bimetallic samples, Pd3Pt1/CeO2-OMC displays superior current density (74.6 mA/cm2), which is 28.3 times higher than that of Pt/CeO2-OMC. It also shows higher stability in extended period of runs with least indication of CO poisoning effects.

Keywords: CeO2, ordered mesoporous carbon (OMC), electro catalyst, formic acid fuel cell

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Authors: B. E. Narkhede, S. K. Mahajan, B. T. Patil, R. D. Raut

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Information quality is a major performance measure for an Enterprise Resource Planning (ERP) system of any firm. This study identifies various critical success factors of information quality. The effect of various critical success factors like project management, reengineering efforts and interdepartmental communications on information quality is analyzed using a multiple regression model. Here quantitative data are collected from respondents from various firms through structured questionnaire for assessment of the information quality, project management, reengineering efforts and interdepartmental communications. The validity and reliability of the data are ensured using techniques like factor analysis, computing of Cronbach’s alpha. This study gives relative importance of each of the critical success factors. The findings suggest that among the various factors influencing information quality careful reengineering efforts are the most influencing factor. This paper gives clear insight to managers and practitioners regarding the relative importance of critical success factors influencing information quality so that they can formulate a strategy at the beginning of ERP system implementation.

Keywords: Enterprise Resource Planning (ERP), information systems (IS), multiple regression, information quality

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Authors: Quangyen Tran, YeZhuang Tian, Chengfeng Li

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Psychological contract violations may lead to damaging an organization through losing its potential employees; it is a very significant concept in understanding the employment relationships. The authors selected contents of psychological contract violation scale based on the nine areas of violation most relevant to managerial samples (High pay, training, job security, career development, pay based on performance, promotion, feedback, expertise and quality of co-workers and support with personal problems), using regression analysis, the degree of psychological contract violations was measured by an adaptation of a multiplicative scale with Cronbach’s alpha as a measure of reliability. Through the regression analysis, psychological contract violations was found have a positive impact on employees’ job satisfaction, the frequency of psychological contract violations was more intense among male employees particularly in terms of training, job security and pay based on performance. Job dissatisfaction will lead to a lowering of employee commitment in the job, enterprises in Hanoi, Vietnam should therefore offer lucrative jobs in terms of salary and other emoluments to their employees.

Keywords: psychological contract, psychological contract violation, job satisfaction, subordinate employees, employers’ obligation

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Authors: Redrisse Djoumessi Fobasso, Baidyanath Sahu, Andre M. Strydom

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The R₂TX₃ (R = rare-earth, T = transition, and X = s and p block element) series of compounds are interesting owing to their fascinating structural and magnetic properties. In this present work, we have studied the magnetic and physical properties of the new Gd₂AgSi₃ polycrystalline compound. The sample was synthesized by the arc-melting method and confirmed to crystallize in the tetragonal α-ThSi₂-type crystal structure with space group I4/amd. Dc– and ac–magnetic susceptibility, specific heat, electrical resistivity, and magnetoresistance measurements were performed on the new compound. The structure provides a unique position in the unit cell for the magnetic trivalent Gd ion. Two magnetic phase transitions were consistently found in dc- and ac-magnetic susceptibility, heat capacity, and electrical resistivity at temperatures Tₙ₁ = 11 K and Tₙ₂ = 20 K, which is an indication of the complex magnetic behavior in this compound. The compound is found to be metamagnetic over a range of temperatures below and above Tₙ₁. From field-dependent electrical resistivity, it is confirmed that the compound shows unusual negative magnetoresistance in the antiferromagnetically ordered region. These results contribute to a better understanding of this class of materials.

Keywords: complex magnetic behavior, metamagnetic, negative magnetoresistance, two magnetic phase transitions

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Authors: Runglaksamee Rodkam

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The purpose of this research was to study the influence of accountants’ potential performance on their working process, a case study of Government Savings Banks in the northeast of Thailand. The independent variables included accounting knowledge, accounting skill, accounting value, accounting ethics, and accounting attitude, while the dependent variable included the success of the working process. A total of 155 accountants working for Government Savings Banks were selected by random sampling. A questionnaire was used as a tool for collecting data. Descriptive statistics in this research included percentage, mean, and multiple regression analyses. The findings revealed that the majority of accountants were female with an age between 35-40 years old. Most of the respondents had an undergraduate degree with ten years of experience. Moreover, the factors of accounting knowledge, accounting skill, accounting a value and accounting ethics and accounting attitude were rated at a high level. The findings from regression analysis of observation data revealed a causal relationship in that the observation data could explain at least 51 percent of the success in the accountants’ working process.

Keywords: influence, potential performance, success, working process

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Authors: Gwendolyn Royal-Smith

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Cocaine ranks as one of the world’s most addictive and commonly abused stimulant drugs. Recent evidence indicates that the abuse of cocaine has risen so quickly among females that this group now accounts for about 40 percent of all users in the United States. Neuropsychological studies have demonstrated that specific neural activation patterns carry higher risks for neurocognitive and executive function in cocaine addicted females thereby increasing their vulnerability for poorer treatment outcomes and more frequent post-treatment relapse when compared to males. This study examined secondary data with a convenience sample of 164 cocaine addicted male and females to assess neurocognitive and executive function. The principal objective of this study was to assess whether individual performance on the Stroop Word Color Task is predictive of treatment success by gender. A second objective of the study evaluated whether individual performance employing neurocognitive measures including the Stroop Word-Color task, the Rey Auditory Verbal Learning Test (RALVT), the Iowa Gambling Task, the Wisconsin Card Sorting Task (WISCT), the total score from the Barratte Impulsiveness Scale (Version 11) (BIS-11) and the total score from the Frontal Systems Behavioral Scale (FrSBE) test demonstrated differences in neurocognitive and executive function performance by gender. Logistic regression models were employed utilizing a covariate adjusted model application. Initial analyses of the Stroop Word color tasks indicated significant differences in the performance of males and females, with females experiencing more challenges in derived interference reaction time and associate recall ability. In early testing including the Rey Auditory Verbal Learning Test (RALVT), the number of advantageous vs disadvantageous cards from the Iowa Gambling Task, the number of perseverance errors from the Wisconsin Card Sorting Task (WISCT), the total score from the Barratte Impulsiveness Scale (Version 11) (BIS-11) and the total score from the Frontal Systems Behavioral Scale, results were mixed with women scoring lower in multiple indicators in both neurocognitive and executive function.

Keywords: cocaine addiction, gender, neuropsychology, neurocognitive, executive function

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Authors: Osman Adiguzel

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Shape memory alloys take place in a class of smart materials by exhibiting a peculiar property called the shape memory effect. This property is characterized by the recoverability of two certain shapes of material at different temperatures. These materials are often called smart materials due to their functionality and their capacity of responding to changes in the environment. Shape memory materials are used as shape memory devices in many interdisciplinary fields such as medicine, bioengineering, metallurgy, building industry and many engineering fields. The shape memory effect is performed thermally by heating and cooling after first cooling and stressing treatments, and this behavior is called thermoelasticity. This effect is based on martensitic transformations characterized by changes in the crystal structure of the material. The shape memory effect is the result of successive thermally and stress-induced martensitic transformations. Shape memory alloys exhibit thermoelasticity and superelasticity by means of deformation in the low-temperature product phase and high-temperature parent phase region, respectively. Superelasticity is performed by stressing and releasing the material in the parent phase region. Loading and unloading paths are different in the stress-strain diagram, and the cycling loop reveals energy dissipation. The strain energy is stored after releasing, and these alloys are mainly used as deformation absorbent materials in control of civil structures subjected to seismic events, due to the absorbance of strain energy during any disaster or earthquake. Thermal-induced martensitic transformation occurs thermally on cooling, along with lattice twinning with cooperative movements of atoms by means of lattice invariant shears, and ordered parent phase structures turn into twinned martensite structures, and twinned structures turn into the detwinned structures by means of stress-induced martensitic transformation by stressing the material in the martensitic condition. Thermal induced transformation occurs with the cooperative movements of atoms in two opposite directions, <110 > -type directions on the {110} - type planes of austenite matrix which is the basal plane of martensite. Copper-based alloys exhibit this property in the metastable β-phase region, which has bcc-based structures at high-temperature parent phase field. Lattice invariant shear and twinning is not uniform in copper-based ternary alloys and gives rise to the formation of complex layered structures, depending on the stacking sequences on the close-packed planes of the ordered parent phase lattice. In the present contribution, x-ray diffraction and transmission electron microscopy (TEM) studies were carried out on two copper-based CuAlMn and CuZnAl alloys. X-ray diffraction profiles and electron diffraction patterns reveal that both alloys exhibit superlattice reflections inherited from the parent phase due to the displacive character of martensitic transformation. X-ray diffractograms taken in a long time interval show that diffraction angles and intensities of diffraction peaks change with the aging duration at room temperature. In particular, some of the successive peak pairs providing a special relation between Miller indices come close to each other. This result refers to the rearrangement of atoms in a diffusive manner.

Keywords: shape memory effect, martensitic transformation, reversibility, superelasticity, twinning, detwinning

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Authors: Alexandru George Vaduva, Adriana Vlad, Bogdan Badea

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In this research, we conduct a Monte Carlo analysis on a two-dimensional χ2 test, which is used to determine the minimum distance required for independent sampling in the context of chaotic signals. We investigate the impact of transforming initial data sets from any probability distribution to new signals with a uniform distribution using the Spearman rank correlation on the χ2 test. This transformation removes the randomness of the data pairs, and as a result, the observed distribution of χ2 test values differs from the expected distribution. We propose a solution to this problem and evaluate it using another chaotic signal.

Keywords: chaotic signals, logistic map, Pearson’s test, Chi Square test, bivariate distribution, statistical independence

Procedia PDF Downloads 99