Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating a mixed-profile MDCEV: case of daily activity type and duration

Abstract Multiple Discrete Continuous Extreme Value (MDCEV) has become popular in the past years. Yet, the model suffers from an ‘empirical identification’ issue that is mainly due to inter-relations between two of its parameters, α and γ. This paper presents a hybrid optimization paradigm (named HELPME) to address this issue in a basic MDCEV formulation and take full advantage of the model by estimating a ‘mixed-profile. ’ HELPME benefits from a coarse-to-fine search strategy, in which a customized Electromagnetism-like meta-heuristic precedes a gradient-based approach. The Atlanta Regional Travel Survey (2011) is used to empirically analyze performance of HELPME as well as significance of the accuracy gap between the mixed-profile, and α and γ profiles. As part of the results, it is observed that in-sample fit is significantly improved, percentage error of out-of-sample prediction is reduced up to 97% in a 90% confidence level, and bias of out-of-sample predictions are reduced up to 67%.
Smith ScholarWorks Smith ScholarWorks A Comparison of Stress, Symptoms, Physical Activity, and A Comparison of Stress, Symptoms, Physical Activity, and Adiposity Among Women at Midlife Before and During the Adiposity Among Women at Midlife Before and During the Pandemic Pandemic

Background: The COVID-19 pandemic presented challenges that disproportionately impacted women. Household roles typically performed by women (such as resource acquisition and caretaking) became more difficult due to financial strain, fear of infection, and limited childcare options among other concerns. This research draws from an ongoing study of hot flashes and brown adipose tissue to examine the health-related effects of the COVID-19 pandemic among 162 women aged 45–55 living in western Massachusetts. Methods: We compared women who participated in the study pre- and early pandemic with women who participated mid-pandemic and later-pandemic (when vaccines became widely available). We collected self-reported symptom frequencies (e.g., aches/stiffness in joints, irritability), and assessments of stress, depression, and physical activity through questionnaires as well as measures of adiposity (BMI and percent body fat). Additionally, we asked open-ended questions about how the pandemic influenced women’s health and experience of menopause. Comparisons across pre-/early, mid-, and later pandemic categories were carried out using ANOVA and Chi-square analyses as appropriate. The Levene test for homogeneity of variances was examined prior to each ANOVA. Open-ended questions were analyzed for yes/no responses and general themes. Results: Contrary to our hypothesis that women would suffer negative health-related consequences during the COVID-19 pandemic, we found no significant differences in women’s health-related measures or physical activity across the pandemic. However, our analysis of open-ended responses revealed a bi-modal distribution of answers that sheds light on our unexpected findings. While some women reported higher levels of stress and anxiety and lower levels of physical activity, other women reported benefitting from the remote life that the pandemic imposed and described having more time to spend on physical activity or in quality time with their families. Conclusions: In this cross-sectional comparison of women during the pre-/early, mid-, and later-pandemic, we found no significant differences across means in multiple health-related variables. However, open-ended questions revealed that while some women suffered health-related effects during the pandemic, others experienced conditions that improved their health and well-being. The differential results of this study highlight a need for more nuanced and intersectional research on risk, vulnerabilities, and coping among mid-life women.
Dependent function analytic hierarchy process model for energy efficiency virtual benchmark and its applications in ethylene equipments

The existed evaluation of energy efficiency of ethylene equipments does not consider the different raw materials,process technology and equipment sizes,and ignores the relative factors of energy efficiency index,so it cannot analyse the saving energy choice among energy consumption factors.The paper puts forward the energy efficiency analysis method for ethylene equipments according to technologies,scales and data distribution of energy consumption including fuel,power,steam and water inner ethylene process boundary.Furthermore,energy efficiency virtual benchmarking method is proposed based on dependent function analytic hierarchy process model(DFAHP),with which it can manipulate multi-factors and energy efficiency index together.It outweighs the traditional evaluation method such as the mean method and optimal index method.The applications of an ethylene equipment,even the whole ethylene industry for energy efficiency analysis are tested.The validity of proposed method is verified,which is more helpful to find the saving energy chance and quantified energy targets.
Exploring theNutrient Release Potentialof Organic MaterialsasIntegrated SoilFertility Management Components Using SAFERNAC

The aim of this study was to establish the nutrient release potential of different organic materials and assess thei r role in integrated soil fertility management for coffee using the new coffee yield model SAFERNAC. It involved an incubation experiment conducted at TaCRI Lyamungu Screenhouse for 180 days between April and September 2011. Cattle manure, coffee leaves, p ulp and husks,Albizzialeaves and four green manure plants � Mucuna pruriens,Lupinus albus,Canavaliaensiformisand Crotalaria ochroleucawere
Dynamic Route Guidance Algorithms for Robust Roadway Networks

Dynamic Route Guidance Algorithms for Robust Roadway Networks Agop Koulakezian Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2016 This Thesis focuses on developing robust dynamic route guidance algorithms to reduce traffic congestion in roadway networks. While recurring traffic congestion is normally the focus in planning and investment decisions, almost half of traffic congestion is caused by non-recurring traffic disturbances, primarily caused by incidents, vehicle breakdowns, extreme weather events, etc. In order to reduce traffic congestion, we focus on the problem of understanding the effect of traffic disturbances and reducing their impact on roadway networks. We introduce a systematic framework for defining the context of robustness based on the severity, frequency and predictability of traffic disturbances and for developing a robust design for roadway networks based on network design goals. We also present methods to speed up traffic assignment algorithms through compiler optimizations and parallelism, to efficiently measure the effect of traffic disturbances, and enable real-time ITS applications including dynamic route guidance systems. Next, we introduce a hybrid metric for measuring robustness in a roadway network by extending the shortest-path betweenness metric from network science, and augmenting it with links weights based on dynamic traffic flow metrics. Finally, we implement a robust dynamic traffic assignment algorithm for roadway networks based on this metric and test it on a large-scale calibrated network model for the Greater Toronto Area. Performance results show that the robust traffic assignment algorithm reduces vehicle travel times compared to existing traffic assignment algorithms, with and without the presence of disturbances in the form of traffic incidents. This makes a strong case for traffic planners and operators to use robust dynamic route guidance systems within actual implementations of real-time ITS strategies to help proactively alleviate traffic congestion due to disturbances.
Rödel H.G. ASPECTS OF SOCIAL BEHAVIOR AND REPRODUCTION IN THE WILD RABBIT IMPLICATIONS FOR RABBIT

Further knowledge on aspects of social behavior in the wild rabbit (Oryctolagus cuniculus) including the link to reproduction could possibly point to new ways how to improve housing and breeding conditions in rabbit farming. In our long-term study on a 2-hectare field enclosure population of wild rabbits, we explored group-level and individual-level differences in agonistic behavior of females and their potential associations to reproductive traits including offspring survival. The frequency of agonistic behavior in which females were involved increased with increasing group size, and was lower in groups with a more heterogeneous age-structure. At the individual level, reproducing females were involved in more agonistic interactions when groupmates gave birth and thus built their burrows and nests at around the same time, and higher-ranking mothers were particularly aggressive when other females approached close to their nursery burrows. Associations between females’ social environment and reproduction were evident as the numbers of litters and offspring per female were lower at higher female densities, high-ranking females produced more offspring and had a lower offspring mortality than low-ranking ones, and cases of infanticide were lower in more stable groups which we quantified by the more heterogeneous age structure of the females’ rank hierarchy in such groups. Furthermore, perinatal offspring mortality was increased in females with a delayed burrow and nest building activity, i.e. which dug their nursery burrow and built their nest only during the last 24 h pre-partum, possibly driven by the more unfavorable social environment experienced by such females. Most importantly, our studies highlight the importance of the presence of litter siblings in improving an individual’s social environment, which resulted in an earlier onset of breeding in such females. Higher levels of positive social interactions with litter siblings were also associated with lower stress hormone (corticosterone) levels and with a better health status in terms of lower loads with an intestinal nematode. These findings on ameliorating effects of litter sibling presence in growing rabbits as well as in reproducing females may be a promising starting point worth further exploration in the context of group housing of domestic rabbits.
Access Comparison of New Multilevel Inverter Topology with Conventional Topologies Used for Induction Heating

aws.hashim@mu.edu. iq Abstract In this article, a comparison of innovative multilevel inverter topology with standard topologies has been conducted. The proposed single phase five level inverter topology has been used for induction heating system. This suggested design generates five voltage levels with a fewer number of power switches. This reduction in number of switches decreases the switching losses and the number of driving circuits and reduce the complexity of control circuit. It also reduces the cost and size for the filter used. Analysis and comparison has been done among the conventional topologies (neutral clamped and cascade H-bridge multilevel inverters) with the proposed inverter topology. The analysis includes the total harmonic distortion THD, efficiency and overall performance of the inverter systems. The simulation and analysis have been done using MATLAB/ SIMULINK. The results show good performance for the proposed topology in comparison with the conventional
Laboratory Screening of Cowpea ( Vigna unguiculata ) Genotypes against Pulse Beetle, Callosobruchus maculatus

Research studies aimed at screening cowpea genotypes against Callosobruchus maculatus commonly known as a bruchid, under the no-choice infestation condition, were conducted at the Department of Plant Science, University of Zambia in Lusaka, Zambia during 2021 on 110 cowpea genotypes. The cowpea genotypes evaluated included 3 parental lines, 97 mutant lines, and 10 pure lines in order to discern the tolerant cowpea genotype against Callosobruchus maculatus that would aid the reduction of storage losses encountered in cowpea production. Parameters measured included. , number of eggs laid per 100 seeds, adult emergence per cent, developmental period, growth index, per cent infestation, weight loss per cent, and frass produced. Significant differences were observed among the genotypes for all the parameters considered in the investigation. The most tolerant genotype BBBT1-11 across all parameters investigated, recorded 13.01 no. of eggs/100 seed compared to MS1-8-2-6-9-1 with 128.67 eggs/100 seeds. The adult emergence of BBBT1-11 was at 12.72% compared to the highest LTBT1-5 at 88.11%. BBBT1-11 also recorded the lowest growth index of 0.42 and frass produced of 0.23g compared to the highest growth index record of 4.19 in BB10-4-2-3-3 and the highest frass produced of 5.41g recorded in MS1-8-2-6-9-1. BBBT1-11 could be further assessed for the possible presence of bio chemical and genetic parameters, that could explain the basis of the observed differences among the genotypes in their reaction to Callosobruchus maculatus . Besides, BBBT1-11 can also be incorporated in the breeding program with the target of breeding cowpea varieties that are tolerant to bruchid infestation. Furthermore, BBBT1-11 may be released so as to address the storage losses experienced at postharvest in cowpea production.
IMMUNOLOGICAL GENES SELECTED FOR ENVIRONMENTAL VARIANCE COULD CONTROL THE ANIMAL RESILIENCE

Environmental variance of traits (V E) has recently been related with resilience. Thus, a greater knowledge of the genetic background of V E could help to understand better the animal resilie nce. A successful selection experiment in rabbits for a hi gh and a low VE of litter size (LS) allowed to identify differences in resilience between animals. The line with a low VE of LS seemed to cope better with the environmental disturbances than the line with a hig h VE of LS. The aim of this study was to identify genomic regions modified by selection of V E and that could affect the animal resilience. For t hat, genotypes from 91 does of base population, and 142 of the line with a high VE of LS and 134 of the line with a low VE of LS at generation 11 were used to identify signa tures of selection. 93 genotypes at generation 13 were used to validate the results. Th e signatures of selection were identified using thr ee complementary analysis: runs of homozygosity (ROH), variations of linkage disequilibrium (VarLD) and fixation index (FST). A whole-genome sequencing (WGS) analysis was per formed on 54 animals at generation 10 to highlight the genes with functi onal mutations. We identified 311 candidate genes with relevant functional mutation in their transcri ption unit. 107 of them had functions related to th e stress response, reproduction and embryo developmen t, carbohydrate and lipid metabolism, and/or immune system. Functional mutations fixed in one o f the rabbit lines and absent in the other were identified in the genes C3orf20, GRN, EPCAM, ENSOCUG00000017494, ENSOCUG00000024926, ENSOCUG00000026560, MYLK, HECA and NMNAT3. The biological pathways of candidate genes explain the differences found between the rabbit li nes in immune response biomarkers (plasma cortisol, leukocytes, and acute-phase protein level s), in plasma concentrations of cholesterol and triglycerides, mortality and resilience. Also, thes e results could explain the correlated response of the VE of LS with embryo implantation, embryo survival an d LS. However, the real implications of these genes for VE and animal resilience must still be unravelled thr ough their functional analysis.
R and Its Applications on the Ecological Research

The increase of research activities in recent years has generated a lot of data to be analysed. Research-related communities need a powerful software to perform their analyses. And, it can be a problem, particularly for those who live in developing countries, where their financial capability is low to buy the proprietary programs. R may provide the solution to this obstacle. Since it is an open source software, which can be installed on major operating systems. In addition, it is highly maintained by R Core Team, which ensures that the program and its packages work well on across platforms. The increase usage of R, especially in universities is not only a proof that the program can be relied on, but it is also a guarantee that the software will continue developing. R and its capability for ecological research activities particularly will be described on this short note
Personalized Frequency Modulated Transcranial Electrical Stimulation for Associative Memory Enhancement

Associative memory (AM) is the ability to remember the relationship between previously unrelated items. AM is significantly affected by normal aging and neurodegenerative conditions, thus there is a growing interest in applying non-invasive brain stimulation (NIBS) techniques for AM enhancement. A growing body of studies identifies posterior parietal cortex (PPC) as the most promising cortical target for both transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES) to modulate a cortico-hippocampal network that underlines AM. In that sense, theta frequency oscillatory tES protocols, targeted towards the hallmark oscillatory activity within the cortico-hippocampal network, are increasingly coming to prominence. To increase precision and effectiveness, the need for EEG guided individualization of the tES protocols is proposed. Here, we present the study protocol in which two types of personalized oscillatory tES–transcranial alternating current stimulation (tACS) and oscillatory transcranial direct current stimulation (otDCS), both frequency-modulated to the individual theta-band frequency (ITF), are compared to the non-oscillatory transcranial direct current stimulation (tDCS) and to the sham stimulation. The study has cross-over design with four tES conditions (tACS, otDCS, tDCS, sham), and the comprehensive set of neurophysiological (resting state EEG and AM-evoked EEG) and behavioral outcomes, including AM tasks (short-term associative memory, face–word, face–object, object-location), as well as measures of other cognitive functions (cognitive control, verbal fluency, and working memory).
Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning

This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.
The Application of Active Control Technology to Air to Air Missiles

The active control technology(ACT) which had been used widely in the field of plane design is introduced to overall design of air to air missiles, and the judge criterion of stability for static instability missile body is brought forward. Afterward the question of arti-ficial stability for static instability missile body is debated emphatically.
The Psychometric Properties and Cutoff Score of the Child and Adolescent Mindfulness Measure (CAMM) in Chinese Primary School Students

To date, the Child and Adolescent Mindfulness Measure (CAMM) has been translated into several languages, including Chinese. This study aimed to explore the reliability and validity of the Chinese version of the CAMM and to identify the appropriate cutoff score among Chinese primary school students. A total of 1283 participants (52.2% males; 11.52 ± 0.78 years of age) completed a series of questionnaires to evaluate their mental health, including mindfulness, subjective well-being, positive youth development (PYD), depression, and anxiety. Item analysis, Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), criterion-related validity analysis, Receiver Operating Characteristic (ROC) analysis, and reliability analysis were performed. The results show that the Chinese version of the CAMM had acceptable item–scale correlation (r = 0.405–0.775, p < 0.001) and was the best fit for the two-factor ESEM model (χ2 = 168.251, p < 0.001, df = 26, TLI = 0.910, CFI = 0.948, RMSEA = 0.065, SRMR = 0.033) among Chinese primary school students. Additionally, the total score of the Chinese version of the CAMM was significantly associated with subjective well-being and PYD (r = 0.287–0.381, p < 0.001), and negatively associated with depression, and anxiety (r = −0.612–−0.542, p < 0.001). Moreover, a cutoff score of 22 or higher revealed a significant predictive power for all the included criteria. Finally, the Chinese version of the CAMM had good internal consistency (Cronbach’s α = 0.826, McDonald’s ω = 0.826). Altogether, the Chinese version of the CAMM had satisfactory psychometric properties, and it can be applied to Chinese children.
Mental and Body Health: The Association between Psychological Factors, Overweight, and Blood Pressure in Young Adults

Comorbidity between cardiometabolic risk factors and major mental health disorders is a public health concern. The close interconnection between the mental and physical aspects of health precludes considering each condition separately. Accordingly, this study sought to explore the interrelationships between psychological factors, overweight, and blood pressure in young adults. One hundred and forty-five young adults participated in the study and were classified according to two independent characteristics: weight condition (normal weight, overweight) and blood pressure (low blood pressure, high blood pressure). Anxiety, depression, and emotional dysregulation were assessed. The results confirmed certain associations, highlighting how cardiometabolic risk factors, such as blood pressure and body mass index, were associated in different ways with mental health, although an interaction between the variables was not reported. In particular, a relationship between body mass index and depression and between anxiety and blood pressure was detected.
Predicting the Risk of Future Multiple Suicide Attempt among First-Time Suicide Attempters: Implications for Suicide Prevention Policy

Suicide is listed in the top ten causes of death in Taiwan. Previous studies have pointed out that psychiatric patients having suicide attempts in their history are more likely to attempt suicide again than non-psychiatric patients. Therefore, how to predict the future multiple suicide attempts of psychiatric patients is an important issue of public health. Different from previous studies, we collect the psychiatric patients who have a suicide diagnosis in the National Health Insurance Research Database (NHIRD) as the study cohort. Study variables include psychiatric patients’ characteristics, medical behavior characteristics, physician characteristics, and hospital characteristics. Three machine learning techniques, including decision tree (DT), support vector machine (SVM), and artificial neural network (ANN), are used to develop models for predicting the risk of future multiple suicide attempts. The Adaboost technique is further used to improve prediction performance in model development. The experimental results show that Adaboost+DT performs the best in predicting the behavior of multiple suicide attempts among psychiatric patients. The findings of this study can help clinical staffs to early identify high-risk patients and improve the effectiveness of suicide prevention.