Selection Issues Research Articles

Template-assisted fabrication of polyether ether ketone hollow fiber membrane for highly efficient separation of binary dye mixtures

Poly(ether ether ketone) (PEEK) is among the most reliable membrane materials in membrane-based separation processes due to its excellent properties especially under harsh environment. Successfully implementing the controllable construction of the pore structure into the fabrication process of PEEK hollow fiber membranes (HFMs) can make a significant contribution to addressing the issue of membrane selectivity and permeability. Herein, we developed a flexible route for manufacturing PEEK HFMs by melt-spinning technology combined with the template-assisted crystallization process. This route effectively reduced the amount of organic solvent in the membrane preparation process, and the crystallization induced strategy remarkably improved the connectivity of membrane pore structure and fixed pore structure of PEEK HFMs. Furthermore, we conducted a comprehensive investigation into the separation mechanism and performance stability of the prepared membranes in binary dye mixtures. The results unveiled that the suitable pore structure of the PEEK HFMs that maintained a high salt permeability with a high dye retention, synergized with strong negative charge contributed to excellent performance in dye/salts or dye/dye binary mixtures separations. A long-term stability of PEEK HFMs for treatment of the simulated textile wastewater with rejection coefficient of above 99% to dyes like congo red (CR) and a low NaCl rejection (<10%) indicated that PEEK HFMs have a bright prospect in dye/salt separation for water purification and resource reclamation.

Responding to his Critics: A New Account of Origen's Peri Archon

Abstract: Why did Origen write Peri Archon ? The standard view, often tinged with triumphant hues, envisions him as a "man of the church," embarking upon a bold research agenda while laying waste to his heterodox opponents. But I believe this picture misses the mark. It was really Origen—and not these heretics—who was in the dock. And it was those whom he regarded as his fellow Christians in Alexandria, including his bishop, who made up the prosecution. In this essay I argue that Peri Archon was Origen's response to these in-house critics, who increasingly regarded his views on select issues as heretical, or at least, less than orthodox. This essay has two parts. In the first, I examine Origen's self-portrait in the preface of Peri Archon , demonstrating how he shrewdly maneuvered his project into orthodox waters. In the second, I explain why he adopted this tactic. I spotlight five issues from his earlier writings where he challenged positions adopted by most Christians in his day, including prominent church leaders. These controversial stances surface again in Peri Archon but often in a diplomatic form and always in an orthodox frame. Peri Archon was Origen's apologia to fellow Christians.

A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication.

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets.

Time varying effects in survival analysis: a novel data-driven method for drift identification and variable selection

In this paper we address the problem of survival models when high-dimensional panel data are available. We discuss two related issues: The first one concerns the issue of variable selection and the second one deals with the stability over time of such a selection, since presence of time dimension in survival data requires explicit treatment of evolving socio-economic context. We show how graphical models can serve two purposes. First they serve as the input for a first algorithm to to assess the temporal stability of the data: Secondly, allow the deployment of a second algorithm which partially automates the process of variable selection, while retaining the option to incorporate domain expertise in the process of empirical model-building. To put our proposed methodology to the test, we utilize a dataset comprising Italian firms funded in 2009 and we study the survival of these entities over the period of 10 years. In addition to revealing significant volatility in the set of variables explaining firm exit over the years, our novel methodology enables us to offer a more nuanced perspective than the conventional one regarding the critical roles played by traditional variables such as industrial sector, geographical location, and innovativeness in firm survival.

Advancements in Brain Research: The In Vivo/In Vitro Electrochemical Detection of Neurochemicals.

Neurochemicals, crucial for nervous system function, influence vital bodily processes and their fluctuations are linked to neurodegenerative diseases and mental health conditions. Monitoring these compounds is pivotal, yet the intricate nature of the central nervous system poses challenges. Researchers have devised methods, notably electrochemical sensing with micro-nanoscale electrodes, offering high-resolution monitoring despite low concentrations and rapid changes. Implantable sensors enable precise detection in brain tissues with minimal damage, while microdialysis-coupled platforms allow in vivo sampling and subsequent in vitro analysis, addressing the selectivity issues seen in other methods. While lacking temporal resolution, techniques like HPLC and CE complement electrochemical sensing's selectivity, particularly for structurally similar neurochemicals. This review covers essential neurochemicals and explores miniaturized electrochemical sensors for brain analysis, emphasizing microdialysis integration. It discusses the pros and cons of these techniques, forecasting electrochemical sensing's future in neuroscience research. Overall, this comprehensive review outlines the evolution, strengths, and potential applications of electrochemical sensing in the study of neurochemicals, offering insights into future advancements in the field.

MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

КОНСТРУКТИВНЫЕ СООТНОШЕНИЯ В ЭЛЕКТРИЧЕСКИХ МАШИНАХ МЕХАТРОННЫХ СИСТЕМ С СОСРЕДОТОЧЕННЫМИ ОБМОТКАМИ СТАТОРА И ЗУБЧАТЫМ БЕЗОБМОТОЧНЫМ РОТОРОМ

The issues of substantiation and selection of basic design relationships of induction machines with single-tooth windings, which can be part of newly developed mechatronic systems, are considered. &#x0D; The relationships substantiated in this work can be applied in the development of new mechatronic systems, especially when the task of developing mechatronic modules for specified dimensions is often encountered in practice, which requires the development of both motors, new angle and speed sensors. In addition, this task becomes more urgent when setting harsh operating conditions, such as low and high temperatures, shocks, vibrations, and operation in submersible servos filled with oil. All this makes it difficult or impossible to use a number of other non-electromechanical sensors, such as optical, discrete Hall sensors, which further narrows the range of purchased products for embedding in the specified dimensions of the module. The considered ratios allows to implement sensors not only for different product dimensions, but also to take into account the purpose, accuracy requirements, and design meters with different electrical reduction, with different number of phases. An additional advantage of developing sensors according to these ratios is manufacturability, simplicity and speed of development and the ability to quickly launch into production. &#x0D; In addition, the derived ratios make it possible to design an angle sensors, speed sensors, as well as electric motors and generators, which may be in demand in a number of special cases, for example, if it is necessary to minimize the moment of inertia of the motor rotor and to build high-speed machines.

Credit rating, repayment willingness and farmer credit default

This study, grounded in survey data collected from farmers in three northeastern provinces of China-specifically Heilongjiang, Jilin, and Liaoning in 2019-investigates the relationships among credit ratings, repayment willingness, and credit default among farmers. To address the dual sample selection issue stemming from both credit demand and credit accessibility, a three-stage Prohibit model is employed. The research empirically examines the impact of both traditional credit ratings and digital credit ratings on farmer credit default while delving into the interplay between the two. Furthermore, a mediation effect model is utilized to explore how credit ratings influence credit default through their effect on farmers' willingness to repay. The main findings as follows: Firstly, both traditional and digital credit ratings serve to deter farmers' credit defaults. Specifically, as a farmer attains a high-credit status in the traditional sense or achieves a higher digital credit score, the likelihood of credit default diminishes. Secondly, the inhibitory impact of both traditional and digital credit ratings on farmers' credit defaults is mutually reinforcing and complementary. Lastly, the mechanistic analysis reveals that both traditional and digital credit ratings effectively mitigate credit defaults by enhancing farmers' repayment willingness. In this regard, traditional credit ratings operate through the reputation and punishment mechanisms, while the supervision mechanism is not implicated. On the other hand, digital credit ratings operate through the reputation, punishment, and supervision mechanisms. Consequently, a comprehensive approach to reducing rural financial risks involves bolstering the cultivation of a robust reputation atmosphere and credit system within rural financial markets, along with the systematic enhancement of the efficacy of credit rating supervision mechanisms.

Green Solvents for Extraction of Natural Food Colorants from Plants: Selectivity and Stability Issues.

Consumers associate the color of food with its freshness and quality. More and more attention is being paid to natural colorants that bring additional health benefits to humans. Such natural substances are the carotenoids (yellow to orange), the anthocyanins (red to blue), and the betalains (red and yellow), which are very sensitive to exposure to light, air, high temperatures, and chemicals. Stability and diversity in terms of color can be optimized by using environmentally friendly and selective extraction processes that provide a balance between efficacy, safety, and stability of the resulting extracts. Green solvents like water, supercritical fluids, natural deep eutectic solvents, and ionic liquids are the most proper green solvents when combined with different extraction techniques like maceration, supercritical extraction, and ultrasound-assisted or microwave-assisted extraction. The choice of the right extracting agent is crucial for the selectivity of the extraction method and the stability of the prepared colorant. The present work reviews the green solvents used for the extraction of natural food colorants from plants and focuses on the issues related to the selectivity and stability of the products extracted.

Integration of traditional and telematics data for efficient insurance claims prediction

AbstractWhile driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features.To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance with multiple sources of data. It is shown that the proposed framework can efficiently integrate traditional data and telematics data and can also deal with possible favorable selection issues related to telematics data availability. Our findings are supported by a simulation study and empirical analysis in a synthetic telematics dataset.

Optimization for Calibration of Survey Weights under a Large Number of Conflicting Constraints

In the analysis of survey data, sampling weights are needed for consistent estimation of the population; however, weights are typically modified through a process termed “calibration” to increase their efficiency and stability by ensuring weighted sums of auxiliary variables match a collection of controls. It is often the case that no single set of weights can be found that simultaneously incorporates all of these controls. Together they induce a large number of constraints and restrictions that don’t produce a feasible solution space. We present an optimization framework and an accompanying fast computational methodology to address this issue of constraint achievement or selection within a restricted space that will produce a stabilized set of calibrated weights. Our approach comes closest to the simultaneous achievement of a large number of conflicting constraints, while providing diagnostics about which constraints may not be exactly met. Our motivating example is the post-stratification for the National Survey on Drug Use and Health. We also make connections to covariate balancing approaches for observational studies. Computations were performed in R and code is provided in the supplementary material.

Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for Support Vector Classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.

Holographic nanodiamond–polymer composite grating with unprecedented slow-neutron refractive index modulation amplitude

We demonstrate exceptionally high slow-neutron diffraction efficiency (≈ 70% of transmitted intensity diffracted to the first order) from a holographic nanodiamond–polymer composite grating of only tens of micrometers thickness at the average neutron wavelength of 5.3 nm. By meticulous choice of materials for extreme refractive index modulation in a thin grating structure, we overcome typical wavelength and angular selectivity issues usually encountered when pursuing high diffraction efficiencies with a thick grating. This achievement paves the way for the implementation of flux-efficient diffractive elements, well-suited for polychromatic beams, and weakly collimated slow-neutron beams.

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.

Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined nature of the mediator selection issue. Despite recent developments, the existing methods are inadequate for addressing the complex relationships introduced by confounders. To tackle these challenges, we propose a novel approach called DP2LM (Deep neural network-based Penalized Partially Linear Mediation). This approach incorporates deep neural network techniques to account for nonlinear effects in confounders and utilizes the penalized partially linear model to accommodate high dimensionality. Unlike most existing works that concentrate on mediator selection, our method prioritizes estimation and inference on mediation effects. Specifically, we develop test procedures for testing the direct and indirect mediation effects. Theoretical analysis shows that the tests maintain the Type-I error rate. In simulation studies, DP2LM demonstrates its superior performance as a modeling tool for complex data, outperforming existing approaches in a wide range of settings and providing reliable estimation and inference in scenarios involving a considerable number of mediators. Further, we apply DP2LM to investigate the mediation effect of DNA methylation on cortisol stress reactivity in individuals who experienced childhood trauma, uncovering new insights through a comprehensive analysis.

Recent Advances in Hydrogenation of CO2 to CO with Heterogeneous Catalysts Through the RWGS Reaction.

With the continuous increase in CO2 emissions, primarily from the combustion of coal and oil, the ecosystem faces a significant threat. Therefore, as an effective method to minimize the issue, the Reverse Water Gas Shift (RWGS) reaction which converts CO2 towards CO attracts much attention, is an environmentally-friendly method to mitigate climate change and lessen dependence on fossil fuels. Nevertheless, the inherent thermodynamic stability and kinetic inertness of CO2 is a big challenge under mild conditions. In addition, it remains another fundamental challenge in RWGS reaction owing to CO selectivity issue caused by CO2 further hydrogenation towards CH4 . Up till now, a series of catalysis systems have been developed for CO2 reduction reaction to produce CO. Herein, the research progress of the well-performed heterogeneous catalysts for the RWGS reaction were summarized, including the catalyst design, catalytic performance and reaction mechanism. This review will provide insights into efficient utilization of CO2 and promote the development of RWGS reaction.

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

With rapid social and economic development, the process of population aging has increased the demand for community elderly healthcare service (CEHS) in China. However, the traditional government-oriented service supply cannot meet the various needs of CEHS, and it is critical to select a suitable supplier of CEHS to provide high-quality green services in the community. Therefore, this study focuses on the issue of green supplier selection of CEHS, explores an improved transformation method for processing multi-type data, and proposes an integrated method of multi-attribute group decision-making (MAGDM) which innovatively applies the degree of overall deviation measure (ODM) to determine expert weight. Finally, the effectiveness and accuracy of the new method are verified by experimental analysis. The results show that H2 is the top choice in the green supplier selection of CEHS, followed by H1, H4, H8, H5, H6, H3, H7, H12, H11, H9, and H10. In addition, the authors apply the traditional ED method to calculate expert weights and compare the results of ODM and ED. It is a fact that the improved ODM method should be more efficient and accurate than the traditional ED method.

Актуальные вопросы диагностики и выбора эффективной антибактериальной терапии острого бронхита и внебольничной пневмонии у детей

Введение. Наиболее частыми формами инфекций нижних дыхательных путей в практике врача являются острый бронхит и внебольничная пневмония. Врачу приходится принимать решение о тактике ведения таких пациентов с учетом клинической картины и результатов обследования. Внебольничная пневмония является распространенным и потенциально угрожающим жизни заболеванием органов дыхания. Острота проблемы состоит в том, что в периоды эпидемий гриппа или других респираторных вирусных инфекций активируется и Streptococcus рneumoniae, что приводит к росту заболеваемости внебольничной пневмонией. В последние годы этиология пневмонии значительно расширилась, и среди ее причин важное место, помимо бактерий, стали занимать пневмотропные вирусы (новый коронавирус SARS-CoV-2, MERS, вирусы гриппа А, в т. ч. пандемический H1N1, птичьего гриппа, бокавирусы, метапневмовирусы и др.). Ассоциация вирусов и бактерий утяжеляет течение болезни. Рассмотрены подходы к диагностике и терапии бронхита и внебольничной пневмонии. Лечебные мероприятия больным с острым бронхитом включают раннее начало терапии. Антибактериальная терапия острого бронхита назначается при доказанном бактериальном генезе заболевания, гипертермии более 3 дней, выраженных симптомах интоксикации и др. Общепринятым методом этиологической расшифровки пневмонии является микробиологическое исследование мокроты, а также исследование крови на предмет бактериемии. Промежуточное место занимает метод бактериоскопии; окраска мокроты по Граму позволяет врачу ориентироваться в выборе эмпирической антибактериальной терапии. В последние годы используются метод полимеразной цепной реакции и исследование пневмококкового антигена в моче; иммунохроматографически определяется полисахарид С пневмококка. Критерием качества оказания помощи пациентам с пневмонией является начало стартовой терапии антибактериальными препаратами. Результаты. В статье оценена клиническая эффективность лечения, включающего прием амоксициллина, результаты позволяют рекомендовать амоксициллин к использованию в амбулаторной практике. Background. The most common forms of lower respiratory tract infections in a doctor's practice are acute bronchitis and community-acquired pneumonia. The doctor has to decide on the management tactics of such patients, taking into account the clinical picture and examination results. Community-acquired pneumonia is a common and potentially life-threatening respiratory disease. The severity of the problem is that during periods of influenza epidemics or other respiratory viral infections Streptococcus pneumoniae is also activated, which leads to an increase in the incidence of community-acquired pneumonia. In recent years, the etiology of pneumonia has expanded significantly, and among the causes of community-acquired pneumonia, in addition to bacteria, pneumotropic viruses (new coronavirus SARS-CoV-2, MERS, influenza A viruses, including pandemic H1N1, avian influenza, bocaviruses) began to occupy an important place, metapneumoviruses, etc.). The association of viruses and bacteria aggravates the course of the disease. Approaches to the diagnosis and treatment of bronchitis and community-acquired pneumonia are considered. Therapeutic measures for patients with acute bronchitis include early initiation of therapy. Antibacterial therapy for acute bronchitis is prescribed for proven bacterial origin of the disease, hyperthermia for more than 3 days, severe symptoms of intoxication, etc. The generally accepted method of etiological deciphering of pneumonia is a microbiological examination of sputum, as well as a blood test for bacteremia. The bacterioscopy method occupies an intermediate place; Gram staining of sputum allows the doctor to guide the choice of empirical antibacterial therapy. In recent years, the polymerase chain reaction method and the study of pneumococcal antigen in urine have been used; Polysaccharide C of pneumococcus is determined immunochromatographically. The criterion for the quality of care for patients with pneumonia is the initiation of initial therapy with antibacterial drugs. Results. The article evaluates the clinical effectiveness of treatment including amoxicillin was assessed; the results allow us to recommend amoxicillin for use in outpatient practice.

Forecasting GDP growth: The economic impact of COVID‐19 pandemic

AbstractThe primary goal of this study is to effectively measure the impact of a severe random shock, such as the COVID‐19 pandemic on aggregate economic activity in Greece, seven other euro area economies, three Scandinavian countries, and the United States. The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (GDP) growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. Our analysis confirms that the outbreak of the pandemic had a profound effect on the economies under study, and reveals that different predictor variables are able to explain different quantiles of the underlying real GDP growth distribution for analyzed countries, suggesting that the quantile modeling approach improves the ability to adequately explain real GDP series compared with the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables.

Data-Driven Decision Support for Equipment Selection and Maintenance Issues for Buildings

Equipment costs play a critical role in decision making during design and construction, which requires up-to-date information and data. The design of this study incorporates the inputs from the literature review on the influencing factors of equipment costs and major targeted equipment types to enhance decision support for equipment selection, project construction, and maintenance issues. Two traditional cost estimation methods and five machine-learning methods were compared in this study to identify significant attributes related to the predictions of the costs and residual values of each targeted equipment type. The novelty of this study is that the developed method improves prediction accuracy by establishing a comprehensive and well-structured database framework. A comparison of this method with the existing prediction models reveals that the results and the accuracy of multiple regression analysis are improved in the range of (3% to 33.97%) with the use of a modified decision-tree model combined with support vector machines. The major contribution of this research is the design, implementation, and validation of a machine-learning-based modified decision tree with a support vector machine model for improved accuracy and decision support in construction management. Future research should consider the relationship between geographical variations and value changes.

Marriages of love and convenience: The French dating market and the revolution of romantic love (19th-20th century)

ABSTRACT Did love conquer marriage between the 19th and 20th centuries in France? Does a personal choice imply a free choice? To date, no study has had access to sources that are both numerous and sufficiently stable over the vast period of the 19th and 20th centuries to allow a comprehensive examination of the simultaneous emergence of the love marriage norm and its impact on spouse selection. This paper sets out specifically to explore the issues of spouse selection and the eco-intimate changes in marriages, highlighting the evolution of the love marriage norm, using the archives of matrimonial agencies and advertisements from the 19th and 20th centuries. While the writings of the time debated the merits of marrying for money versus marrying for love, the dating market was serving a bourgeois clientele in search of homogamy. The study of agency operations shows how these marriages first unite two families and two heritages from the same milieu. This good match is seen as a necessary condition for mutual affection. When, in the 20th century, matrimonial ads more systematically enabled individuals to search for their spouse themselves, without the help of their family, these economic stakes did not disappear, but underwent a significant evolution. They are now put on the same level as moral and physical criteria. While homogamy was still preferred, it was no longer considered the only condition for a happy union.