Miracle-dz/UniFAR-FTUs huggingface dataset:
On Assignment to Classes in Latent Class Logit Models

Random parameter logit models address unobserved preference heterogeneity in discrete choice analysis. The latent class logit model assumes a discrete heterogeneity distribution, by combining a conditional logit model of economic choices with a multinomial logit (MNL) for stochastic assignment to classes. Whereas point estimation of latent class logit models is widely applied in practice, stochastic assignment of individuals to classes needs further analysis. In this paper we analyze the statistical behavior of six competing class assignment strategies, namely: maximum prior MNL probabilities, class drawn from prior MNL probabilities, maximum posterior assignment, drawn posterior assignment, conditional individual-specific estimates, and conditional individual estimates combined with the Krinsky–Robb method to account for uncertainty. Using both a Monte Carlo study and two empirical case studies, we show that assigning individuals to classes based on maximum MNL probabilities behaves better than randomly drawn classes in market share predictions. However, randomly drawn classes have higher accuracy in predicted class shares. Finally, class assignment based on individual-level conditional estimates that account for the sampling distribution of the assignment parameters shows superior behavior for a larger number of choice occasions per individual.
Grothendieck quantaloids for allegories of enriched categories

For any small involutive quantaloid Q we define, in terms of symmetric quantaloid-enriched categories, an involutive quantaloid Rel(Q) of Q-sheaves and relations, and a category Sh(Q) of Q-sheaves and functions; the latter is equivalent to the category of symmetric maps in the former. We prove that Rel(Q) is the category of relations in a topos if and only if Q is a modular, locally localic and weakly semi-simple quantaloid; in this case we call Q a Grothendieck quantaloid. It follows that Sh(Q) is a Grothendieck topos whenever Q is a Grothendieck quantaloid. Any locale L is a Grothendieck quantale, and Sh(L) is the topos of sheaves on L. Any small quantaloid of closed cribles is a Grothendieck quantaloid, and if Q is the quantaloid of closed cribles in a Grothendieck site (C,J) then Sh(Q) is equivalent to the topos Sh(C,J). Any inverse quantal frame is a Grothendieck quantale, and if O(G) is the inverse quantal frame naturally associated with an \'etale groupoid G then Sh(O(G)) is the classifying topos of G.
Effect of cowpea aphid on the biosynthetic pathway of salicylic acid in Glycine max cv. Namdan at reproductive growth stages

Cowpea aphid (Aphis craccivora Koch) infestation accumulated phytohormone salicylic acid (SA) in leaves of soybean (Glycine max cv. Namdan) as these soybean plants were at stages R1 (beginning to bloom) and R3 (beginning pod development). Activity of major enzymes involving biosynthesis of SA, such as phenylalanine ammonia-lyase (PAL), benzoic acid 2-hydroxylase (BA2H), was also enhanced under aphid effect. Changes in activity of these enzymes was closely correlated with the content of SA in the tissues. The enhancement of the SA biosynthetic pathway may reduce the effects of A. craccivora on G. max cv. Namdan plants at reproductive growth stages.
Proposed Service and Marketing Communication Initiatives to Strengthen Blibli Positioning in E-Commerce Industry

Since the beginning of the pandemic, the trend of online shopping in Indonesia has been increasing as people are reluctant to shop outside their homes. This triggered the growth of the e-commerce industry, which then resulted in high competition in the industry. Each company provides a wide selection of products and competitive prices to increase sales. Blibli, as one of the leading e-commerce companies in Indonesia under Djarum Group, believes that there needs to be other initiatives beyond price and product variety to strengthen the company’s position. Blibli is committed to providing convenience and security in shopping by providing maximum service. By using primary and secondary data, the author analyses conditions of competitors, customer segmentation that prioritizes services, and how to effectively communicate the services that Blibli has. Primary data is obtained through questionnaires distributed to active users of e-commerce. Through the research conducted, it can be concluded that Blibli’s main competitors (Tokopedia and Shopee) currently do not use services as a differentiator. The author also proposes customer segmentation, namely the millennial generation, who are aware of the importance of service in online shopping. Blibli’s “positioning” proposed in this research is basically in accordance with Blibli’s current “positioning,” but Blibli needs to update its marketing communication strategy so that customers understand Blibli’s advantages. The proposal submitted by the author is expected to help Blibli strengthen its position in the industry as a company that always prioritizes customer satisfaction through excellent services.
Sistema de Votação Eletrônica baseado em Assinatura Cega e BigchainDB

A urna eletrônica contribuiu para sufrágios mais ágeis e eliminou muitas das fraudes conhecidas. Por outro lado, também fez crescer a desconfiança do eleitorado com o processo, uma vez que o eleitor não é mais capaz de observar o que acontece com o seu voto. A auditoria publica do voto é um assunto muito debatido, pois os meios disponíveis para auditá-lo comprometem o anonimato da escolha do eleitor, considerado um requisito obrigatório na democracia brasileira. Este trabalho apresenta um modelo de sistema eletrônico de votação baseado em Blockchain e Assinaturas Cegas para permitir a auditagem e garantir o anonimato da escolha do eleitor durante o processo.
Polynomial filtering for stochastic systems with Markovian switching coefficients

In this paper the state estimation problem for discrete-time Markovian switching systems affected by additive noise (not necessarily Gaussian) is solved following a polynomial approach. The key point for the derivation of the optimal polynomial filter is the possibility to represent the Markov switching systems as bilinear systems (linear drift, multiplicative noise) by means of a suitable state augmentation. By construction, the optimal polynomial filter of a given degree /spl nu/ provides the minimum error variance among all polynomial output transformations of the same degree. Obviously, for /spl nu/ > 1 better performances are obtained with respect to linear filters. Simulation results are reported as a validation of the theory.
Steady self-similar inviscid flow

We consider solutions of the 2-d compressible Euler equations that are steady and self-similar. They arise naturally at interaction points in genuinely multi-dimensional flow. We characterize the possible solutions in the class of flows L^\infty-close to a constant supersonic background. As a special case we prove that solutions of 1-d Riemann problems are unique in the class of small L^\infty functions. We also show that solutions of the backward-in-time Riemann problem are necessarily BV.
Antibacterial and Antifungal Properties of a Novel Antimicrobial Peptide GK-19 and Its Application in Skin and Soft Tissue Infections Induced by MRSA or Candida albicans

The increasing resistance of human pathogens promotes the development of novel antimicrobial agents. Due to the physical bactericidal mechanism of membrane disruption, antimicrobial peptides are considered as potential therapeutic candidates without inducing microbial resistance. Scorpion venom-derived peptide, Androctonus amoreuxi Antimicrobial Peptide 1 (AamAP1), has been proved to have broad-spectrum antimicrobial properties. However, AamAP1 can induce hemolysis and shows strong toxicity against mammalian cells. Herein, the antimicrobial activity and mechanism of a novel synthetic antimicrobial peptide, GK-19, derived from AamAP1 and its derivatives, was evaluated. Five bacteria and three fungi were used to evaluate the antimicrobial effects of GK-19 in vitro. Scalded mice models combined with skin and soft tissue infections (SSTIs) were used to evaluate its applicability. The results indicated that GK-19 could not only inhibit Gram-positive and Gram-negative bacterial growth, but also kill fungi by disrupting the microbial cell membrane. Meanwhile, GK-19 showed negligible toxicity to mammalian cells, low hemolytic activity and high stability in plasma. Furthermore, in scalded mice models combined with SSTIs induced by either Methicillin-Resistant Staphylococcus aureus (MRSA) or Candida albicans, GK-19 showed significant antimicrobial and healing effects. Overall, it was demonstrated that GK-19 might be a promising drug candidate in the battle against drug-resistant bacterial and fungal infections.
Water Use for Seepage-Irrigated Pepper with Plastic Mulch in Florida

Bell pepper (Capsicum annuum) is an important crop for Florida and accounts for a significant fraction of its agricultural water use. Most of it is produced in south Florida, where the water table is shallow. Seepage irrigation is one of the most common systems used in south Florida for growing plastic-mulched vegetable crops, including bell pepper. Compared with an open field production, covering the soil with impermeable plastic reduces soil evaporation and increases transpiration. This 3-page fact sheet summarizes the results from a crop water use study for the seepage-irrigated pepper in south Florida. Written by Sanjay Shukla, Niroj Shrestha, and Fouad H. Jaber, and published by the UF Department of Agricultural and Biological Engineering, August 2014.
GME-Dialogue-NET: Gated Multi-modal Sentiment Analysis Model Based on Fusion Mechanism

: In comparison with the single mode, the utilization of multi-mode information of text, video and audio could lead to more accurate sentiment analysis. GME-Dialogue-NET, a gated multi-modal sentiment analysis model, is raised for the multi-modal emotion prediction and sentiment analysis. The model judges whether the audio or video modal is the noise through GME (Gated Multi-modal Embedding, GME) and then accepts or refuses the modal information based on the judgement. The model uses the Attention Mechanism of context vector to allocate more attention to the context with greater relevance to the current sentence. GME-Dialogue-NET divides participants of the dialogue into speaker and listener to better capture the dependence between emotion and state. It raises that the fusion mechanism CPA (Circulant-Pairwise Attention, CPA) could pay effective attention with different degrees on different modals to attain more helpful emotional and sentimental representation and thus make emotion prediction and sentiment analysis. Compared with the current model, both the weighted accuracy and the F1 score of emotion prediction were improved, especially for the three emotions of sadness, anger and excitement. In the sentiment regression task, the comparison between GME-Dialogue-NET with current advanced model Multilogue-Net shows that MAE (Mean absolute error, MAE) of GME-Dialogue-NET reduces by 0.1 percentage and the Pearson Correlation Coefficient (R) of GME-Dialogue-NET rises by 0.11 percentage.
Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance

Visual Abstract The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact detection of trained professionals who usually meticulously inspect and manually annotate EEG signals. However, validation of these methods is hindered by the lack of a gold standard as data are mostly private and data annotation is time consuming and error prone. In the effort to circumvent these issues, we propose an iterative learning model to speed up and reduce errors of manual annotation of EEG. We use a convolutional neural network (CNN) to train on expert-annotated eyes-open and eyes-closed resting-state EEG data from typically developing children (n = 30) and children with neurodevelopmental disorders (n = 141). To overcome the circular reasoning of aiming to develop a new algorithm and benchmarking to a manually-annotated gold standard, we instead aim to improve the gold standard by revising the portion of the data that was incorrectly learned by the network. When blindly presented with the selected signals for re-assessment (23% of the data), the two independent expert-annotators changed the annotation in 25% of the cases. Subsequently, the network was trained on the expert-revised gold standard, which resulted in improved separation between artifacts and nonartifacts as well as an increase in balanced accuracy from 74% to 80% and precision from 59% to 76%. These results show that CNNs are promising to enhance manual annotation of EEG artifacts and can be improved further with better gold-standard data.
Run-to-Run Process Adjustment Using Categorical Observations

When quality characteristics cannot be measured on a continuous scale due to either inherent or outside constraints, qualitative observations can be collected alternatively. Under this situation, most conventional run-to-run (R2R) process-control algorithms that are developed based on quantitative measurements cannot be implemented. In this paper, we develop a run-to-run control scheme that uses qualitative information for process adjustments. A two-phase modeling and adjustment strategy is introduced and demonstrated by a real example from a deep reactive ion etching (DRIE) process: model building and parameter estimation is performed in Phase I, and a latent-model control law, a categorical R2R controller, is developed for process regulation in Phase II. Simulation results show that the proposed algorithm exhibits competitive control performance, less adjustment effort, and a larger stability region than the conventional exponentially weighted moving average (EWMA) controller.
Based on Improved Artificial Neural Network Sewage Monitoring Alarm System Method

Sewage discharge has become a key issue affecting the quality of the water environment, and how to effectively monitor and manage sewage discharge behavior has become a key factor to avoid water pollution and improve water quality. However, the current domestic sewage discharge monitoring system is not perfect, resulting in the lack of effective monitoring of enterprise sewage discharge by regulatory authorities, which provides an opportunity for enterprises to steal discharge. In the background of sewage treatment plant, the comprehensive design of sewage monitoring and alarm system is carried out based on the idea of physical information fusion. The design adopts a four-layer information physical architecture, which is divided into four parts: perception communication, fusion processing, push, and execution. In the fusion treatment part, the neural network intelligent algorithm is used to predict the dissolved oxygen, and the oxygen delivery is adjusted according to the predicted value to achieve accurate aeration and optimize the effluent quality. The push and execution parts adopt multiparameter monitoring to realize the smooth operation of equipment and ensure the system security. A new optimal control strategy of dissolved oxygen based on neural network is proposed. Through a large number of experiments and historical data, the intake index and dissolved oxygen value of the aeration tank under the condition of optimal outlet water are obtained as samples. According to the sample training, the BP neural network optimized by particle swarm optimization algorithm is adopted to achieve accurate prediction of dissolved oxygen under different inlet water conditions. The smooth operation of sewage treatment equipment is accomplished by the lower machine and the upper machine. In sewage treatment, each process section collects the equipment status in strict accordance with the order of sewage monitoring facilities. Then the communication network between the upper computer and the lower computer and the sensor is designed. The lower machine adopts PLC as the core, programming PLC through STEP7, and uses PID algorithm to control dissolved oxygen. The PC is developed in C language, so as to realize user login, real-time data display, over-limit fault alarm, report query, user management, etc. The PC integrates MATLAB neural network on the platform to predict dissolved oxygen through mixed programming quantity. The sewage alarm system based on improved artificial neural network is sensitive and has excellent performance. It provides a new idea for intelligent sewage detection and real-time monitoring.
Analysis of the Fatty Acid Profile of the Tissues of Hunted Mallard Ducks (Anas platyrhynchos L.) from Poland

Simple Summary The contemporary human diet contains many times more saturated fatty acids than that of our Palaeolithic ancestors, and the ratio of n-6 to n-3 polyunsaturated fatty acids (PUFAs) is nearly 20 times higher. Lifestyle changes and an unsuitable diet have led to the rapid development of civilization diseases and sudden deaths. This is clearly evident to contemporary consumers, who look for foods produced with respect for the natural environment, which also have an original flavor and health-promoting properties, such as game meat. Abstract The aim of the study was to analyse the fatty acid profile of selected tissues of mallard ducks (Anas platyrhynchos L.), in relation to where they were obtained and their sex, with regard to the human diet. The study was carried out on material obtained from mallard ducks from two study areas: the Siedlce hunting district and the Leszno hunting district. The research material was the breast and leg muscles of 28 mallards. The samples were frozen and stored at −20 °C. The fatty acid profiles in the biological samples were determined by selected ion recording (SIR). The results showed significantly (p < 0.05) lower average levels of saturated fatty acids (SFAs) and monounsaturated fatty acids (MUFAs) and significantly higher (p < 0.05) average levels of polyunsaturated fatty acids (PUFAs), n-6 PUFAs, and n-3 PUFAs in the breast muscles of the mallards obtained in the Siedlce hunting district. This in conjunction with the higher (p < 0.05) hypocholesterolaemic/hypercholesterolaemic index (h/H) in the leg muscles and lower (p < 0.05) atherogenic and thrombogenic indices (AI and TI) in the leg and breast muscles of mallards in the Siedlce hunting district indicate the higher health-promoting value of the meat of ducks from this region. The average n-6/n-3 PUFA ratio in the breast muscles was significantly (p < 0.05) higher in mallards obtained in the Leszno hunting district. Males of the species had a significantly (p < 0.05) higher average n-6/n-3 PUFA ratio in the breast muscles than females. The PUFA/SFA ratio was significantly (p < 0.05) higher in the leg muscles of the female mallards than in the males.
No association between fertility desire and HIV infections among men and women: Findings from community-based studies before and after implementation of an early antiretroviral therapy (ART) initiation program in the rural district of North-western Tanzania

Background: Fertility is associated with the desire to have children. The impacts of HIV and antiretroviral therapy (ART) on fertility are well known, but their impacts on the desire for children are less well known in Tanzania. We used data from two studies carried out at different periods of ART coverage in rural Tanzania to explore the relationship between HIV infection and fertility desires in men and women. Methods: We conducted secondary data analysis of the two community-based studies conducted in 2012 and 2017 in the Magu Health and Demographic system site, in Tanzania. Information on fertility desires, HIV status, and social–economic and demographic variables were analyzed. Fertility desire was defined as whether or not the participant wanted to bear a child in the next two years. The main analysis used log-binomial regression to assess the association between fertility desire and HIV infection. Results: In the 2012 study, 43% (95% CI 40.7-45.3) of men and 33.3% (95% CI 31.8 - 35.0) women wanted another child in the next two years. In 2017 the percentage rose to 55.7% (95% CI 53.6 - 57.8) in men and 41.5% (95% CI 39.8 - 43.1) in women. Although fertility desire in men and women were higher in HIV uninfected compared to HIV infected, age-adjusted analysis did not show a statistical significance difference in both studies (2012: PR=1.02, 95%CI 0.835 - 1.174, p<0.915 and 2017: PR = 0.90 95%CI 0.743 - 1.084 p= 0.262). Discussion: One-third of women and forty percent of men desired for fertility in 2012, while forty percent of women and nearly half of men desired for fertility in 2017. The data showed fertility desire, in 2012 and 2017 were not related to HIV infection in both periods of ART coverage.
Inhibitory Control in Male and Female Adolescents with Autism Spectrum Disorder (ASD)

ABSTRACT The present study examined potential sex- and age-related differences in inhibitory control in adolescents with and without ASD. A computerized flanker visual filtering task and a go/no-go task were used to assess the ability to resist interference from visual distractors (RIVD) and prepotent response inhibition, respectively. Overall, the ASD and non-ASD groups performed comparably on both tasks and no sex-related differences or interactions (group-by-sex) were apparent. Consistent with past research, however, we did observe a significant age-related improvement in RIVD performance among the ASD group (but not the non-ASD group).
The Effect of Gender Diversity in The Boardroom and Company Growth on Environmental, Social, and Governance Disclosure (ESGD)

Research aims: This study aims to obtain empirical evidence regarding gender diversity in the boardroom on ESGD (environmental, social, and governance disclosures). In this case, an entity should not only be profit-oriented but rather carry out a form of corporate social responsibility so that the company's sustainability is considered a form of decision-making.Design/Methodology/Approach: The sample was companies listed on the Indonesia Stock Exchange for 2010-2018. The data obtained were based on the annual and sustainability reports. This study used a quantitative approach with multiple linear analyses, with the help of the Software STATA 15 program as hypothesis testing.Research findings: The results uncovered that gender diversity in the boardroom had a significant and positive effect on environmental, social, and governance disclosures, and company growth strengthened their relationship.Theoretical contribution/Originality: This study attempts to see how the company's growth in building CSR in companies with board diversity. In addition, the results of this study are a discussion of and in line with the theory of legitimacy, upper echelons, and stakeholders.Practitioner/Policy implication: The practical contribution of this study is that the placement of people with gender diversity on the board is crucial. When the company recruits a woman to its board, its ESGD level will change. Research limitation/Implication: This study was limited to the value of overall disclosure, so it is hoped that future researchers can categorize each environmental, social, and governance indicator more broadly.