Variant Of Problem Research Articles

Drug-induced diarrhea: a diverse problem in clinical medicine

Currently, the problem of diarrheal syndrome against the background of therapy with non-antimicrobial drugs, with a frequency of occurrence of up to 24%, has become particularly relevant. This is due to an increase in the number of people taking drug therapy for a long time, an increase in the population of older people, an increase in comorbidity, polypragmasia and hypersensitivity of this group of patients to medications. The mechanism of drug-induced diarrhea is complex and insufficiently studied. Many drugs are able to affect various pathogenetic links, causing disorders of the intestinal microbiome, motor evacuation dysfunction, layering on existing intestinal diseases with the development of functional disorders, catarrhal and erosive ulcerative lesions, which makes it difficult to diagnose and choose an adequate therapeutic tactic. The situation is often complicated by the impossibility of canceling the therapy of the underlying disease or changing the treatment regimen, which can lead to serious consequences for a patient with diarrhea syndrome. Thus, the issues of early diagnosis, differential diagnosis and approaches to the treatment of drug-induced diarrhea are becoming increasingly important in clinical practice.

Structural and functional analysis of Buchanan’s constitutional contract

Buchanan attempted to seek new theoretical construction through the contract method by introducing elements such as ‘individual’, ‘veil of uncertainty’, ‘public choice’, ‘decision costs’, and ‘unanimous agreement principle’. He not only transformed the contract theory model but also interpreted the ideas of consensus politics in an acceptable manner. Specifically, ‘individuals’ are entities of choice and behavior, and the process of choice allows individuals to express different goals and interests. The ‘veil of uncertainty’ prompts individuals to think about issues from a fair point of view when interests cannot be identified and future prospects are unpredictable and to make choices that are beneficial for both themselves and the group. ‘Public choice’ can solve the problem of justice diversity because it does not exclude different or conflicting motivations and goals. ‘Decision costs’ and ‘external costs’ both limit the way collective decisions are made and help identify the normal consequences of constitutional choices. The ‘unanimous agreement principle’ gives everyone equal status, allowing each person the right to pursue their own goals and values and preventing others from abusing their power. The use of the aforementioned theoretical elements not only helps people discuss constitutional choices at a general level but also helps to present the true face of the collective decision-making process, thereby establishing an empirical theory about the actual logic of individual actions in the political system.

Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions

The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox’s partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes.

Meta-learning to address diverse Earth observation problems across resolutions

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth observation problems across different resolutions. METEOR is an adaptive deep meta-learning model with several modifications that allow it to ingest images with a variable number of spectral channels and to predict a varying number of classes per downstream task. It uses knowledge mined from land cover information worldwide to adapt to new unseen target problems with few training examples. METEOR outperforms competing self-supervised approaches on five downstream tasks, showing its relevance to addressing novel and impactful geospatial problems with only a handful of labels.

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Revisiting the establishment of the legal system in Dagestan in 1920–30s

The Relevance of the research topic. Contradictory and diverse problems of the national-state structure in a federal state, involving consideration of national characteristics and cultural experience of the peoples included in it, make it relevant to study the historical experience of the formation of the Soviet legal system in Dagestan in 1920–30s.Problem statement. With the establishment of the Soviet power, the Dagestanis were offered a new model of social development. The transformation of the political, economic and social structure was accompanied by changes in the system of values, ideological attitudes, beliefs and norms that the majority of society had to share. Since the «outdated» value-normative system, regulating family, social, economic, and political relations, was focused on maintaining the existing development model, the approval of the new paradigm was difficult and contradictory.The Research objectives. To highlight the process of approval of the Soviet legal system in Dagestan in 1920-30s using reliable documentary material and accumulated historiographical experience.The Research methods. To solve the tasks set, the following methods were used: objectivism, historicism, historical-systemic and historical-typological.The Results and key conclusions. The legitimization of the Soviet legal system in the first years after the establishment of Soviet power required the preservation of the traditional elements of law for the Dagestanis – Muslim (Sharia) and customary (Adat) law. Since the late 1920s, the time of the «forced» construction of socialism in the country, the traditional legal structures in the Republic have been liquidated. Under the control of party bodies by the time of the adoption of the Constitution of 1937, the legal system of Dagestan acquires all-Russian structural and functional features. The establishment of a new legal system and a socialist sense of justice was promoted by the policy of indigenization (korenization) and promotion, which in Dagestan had its own specific features, due to the multinational nature of the republic.

The Assemblage and Dismantling of Access Barriers in Administrative Bureaucracies: Constructing the Problem of Diversity in the German Welfare State.

The article extends the literature on the construction of "diversity management" by personnel managers in corporate America. Such research has highlighted that Human Resource (HR) specialists draw heavily on social-scientific thinking in implementing various remedies against discrimination. However, it has paid less attention to how such esoteric views of reality, comprising such "things" as "structural barriers" impeding occupational advancement and "diversity sensitivity," have been successfully established as a self-evident reality in the workplace. In order to more thoroughly investigate how the world of diversity management is established outside the circle of academic specialists, the article employs perspectives from science and technology studies on the ways in which sociotechnical assemblages, i.e., networks of human actors and material devices, enact scientific ontologies. It applies such perspectives to a German case of diversity management, a program of "intercultural opening" that seeks to make bureaucracies of the welfare state more accessible to immigrants. The article delineates the specific ontology behind this version of diversity management, rooted in sociological perspectives on social mobility, and explores the various techniques and instruments through which officers of intercultural opening establish this ontology as a visible reality in municipal administrations.

Reconsidering the Problem of Common-Method Variance in Organizational Communication Research

This manuscript details the different attributes associated with the problem of common-method variance. First, upon defining validity, we review the two primary ways by which scholars attempt to control for common-method variance, and in doing so discuss their merits. Second, we provide two alternative explanations that may also account for the appearance of disparate correlations, neither of which have to do with common-method variance. Finally, we offer a set of parsimonious solutions for the problem of common-method variance, namely CFA without correlated residuals or modeled method factors. Overall, the purpose of this manuscript is to provide guidance for organizational communication scholars when dealing with this problem.

First-Year Undergraduate Students’ Ways of Thinking in Combinatorics

The fundamental counting principle and counting problems, jointly called combinatorics in this study, commence in the last year of secondary school and proceeds in undergraduate studies and beyond. The South African secondary school situation reflect the same worldwide scenario where counting principles is regarded as difficult and poorly performed in the final national examinations. The purpose of this study was to explore undergraduate students’ mental constructions in solving counting problems against the backdrop of Grade 12 fundamental counting principles. The Action-Process-Object-Schema theory was used to describe undergraduate students’ thinking ways in combinatorics. A single case of a first-year class of 182 students was considered in this study, whereby they all wrote a task on combinatorics and a seven were further interviewed. The findings revealed that students were skilled at solving problems involving the counting principles, which was mainly step-by-step application of the formulae. This conception is at action level according to APOS theory, but the goal of teaching is to guide students to attain the object mental conception. Object conception allows for solving of diverse real-world counting problems and promotes mathematical thinking skills.

Narwhal Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm

Recently, metaheuristic algorithms have become a very interesting research field due to their ability to address complex and diverse problems. This paper presents a novel metaheuristic called Narwhal Optimizer (NO) inspired by narwhals behaviors. The NO algorithm mimics the hunting mechanism of narwhals. The narwhals are marine mammals known for their sophisticated communication based on clicks sound to locate their prey. The algorithm is based on three main steps: signal emission, signal propagation, and position updating of the narwhals. The hunting process, which is based on signal emission and propagation, is formulated as an optimization algorithm. The strategies observed in narwhal pods are emulated to enhance exploration and exploitation in the search space. The NO algorithm is benchmarked on 13 well-known functions, including unimodal, multimodal, and fixed-dimension multimodal functions. The experimental results showed that NO provides satisfactory and reasonable solutions in terms of avoiding local minima and achieving global optimality.

Multi-Criteria Decision-Making and Machine Learning Techniques: A Multidisciplinary Analysis of the World Military Scenario

Military development is increasingly related to strategic independence and technological advantages, representing an important factor in countries’ scientific, technological, and economic development. In this context, the present work proposes a methodology for evaluating the world’s military power based on the integration between Multi-Criteria Decision-Making (MCDM) and Machine Learning. To this end, data from the military, economic, and strategic spheres of 140 countries were analyzed and evaluated considering 12 quantitative variables. Considering the large volume of data to be analyzed and establishing a ranking of countries, the Principal Component Analysis (PCA), an unsupervised Machine Learning technique, was used. PCA allowed the analysis of 140 countries, identifying three factors representing the collective behavior of the 12 original variables, providing a world ranking of military power. The results were compared with specialized rankings on the subject and proved robust and reliable. As a contribution to the academy and society, the authors highlighted that this work fills the gap regarding the application of PCA in military problems to classify the observations, emphasized that the proposed methodology can be replicated in the most diverse problems and serve as input in other MCDM and data science techniques. In addition, the multidisciplinary of PCA allows the technique to support the decision-making process in the most diverse areas of knowledge that involve data analysis in tactical, operational, and strategic problems.

(De)constructing the "Otherness”

The problem of social and cultural diversity has been a classic issue in the humanities and the social sciences throughout the entire human history. Starting with the 1970s academics were interested in a specific feature of this inter-cultural problem, namely how Western societies have understood and interpreted oriental societies through the period of imperial expansion. Even nowadays, some of the most complicated academic dialogues (and not only) are centered around the theories of post-colonialism and nationalism. The ‘subjects’ of these debates are mainly the Middle Eastern states and the predominant variable is the region’s main religion, Islam. The Orient signifies a system of representations framed by political forces that brought the Orient into Western learning, Western consciousness, and Western empires. The Orient exists for the West, and is constructed by and in relation to the West. The critics of Orientalism consider this theory to be a manner of regularized (or Orientalized) writing, vision, and study, dominated by imperatives, perspectives, and ideological biases ostensibly suited to the Orient. This article proposes a brief introspection into the creation of alterity in Western literature and a review of the perspective offered by the best-known critic of this paradigm, Edward Said.

The status and challenging perspectives of 3D-printed micro-batteries.

In the era of the Internet of Things and wearable electronics, 3D-printed micro-batteries with miniaturization, aesthetic diversity and high aspect ratio, have emerged as a recent innovation that solves the problems of limited design diversity, poor flexibility and low mass loading of materials associated with traditional power sources restricted by the slurry-casting method. Thus, a comprehensive understanding of the rational design of 3D-printed materials, inks, methods, configurations and systems is critical to optimize the electrochemical performance of customizable 3D-printed micro-batteries. In this review, we offer a key overview and systematic discussion on 3D-printed micro-batteries, emphasizing the close relationship between printable materials and printing technology, as well as the reasonable design of inks. Initially, we compare the distinct characteristics of various printing technologies, and subsequently emphatically expound the printable components of micro-batteries and general approaches to prepare printable inks. After that, we focus on the outstanding role played by 3D printing design in the device architecture, battery configuration, performance improvement, and system integration. Finally, the future challenges and perspectives concerning high-performance 3D-printed micro-batteries are adequately highlighted and discussed. This comprehensive discussion aims at providing a blueprint for the design and construction of next-generation 3D-printed micro-batteries.

Multiplex Functional Characterization of Protein Variant Libraries in Mammalian Cells with Single-Copy Genomic Integration and High-Throughput DNA Sequencing.

Sequencing-based, massively parallel genetic assays have enabled simultaneous characterization of the genotype-phenotype relationships for libraries encoding thousands of unique protein variants. Since plasmid transfection and lentiviral transduction have characteristics that limit multiplexing with pooled libraries, we developed a mammalian synthetic biology platform that harnesses the Bxb1 bacteriophage DNA recombinase to insert single promoterless plasmids encoding a transgene of interest into a pre-engineered "landing pad" site within the cell genome. The transgene is expressed behind a genomically integrated promoter, ensuring only one transgene is expressed per cell, preserving a strict genotype-phenotype link. Upon selecting cells based on a desired phenotype, the transgene can be sequenced to ascribe each variant a phenotypic score. We describe how to create and utilize landing pad cells for large-scale, library-based genetic experiments. Using the provided examples, the experimental template can be adapted to explore protein variants in diverse biological problems within mammalian cells.

In vitro biological studies and computational prediction-based analyses of pyrazolo[1,5-a]pyrimidine derivatives.

There is a need for new pharmaceutical discoveries from bioactive nitrogenous derivatives due to the emergence of scourges, numerous pandemics, and diverse health problems. In this context, pyrazolo[1,5-a]pyrimidine derivatives 12a and 12b were synthesized and screened to evaluate their biological potentials in vitro as antioxidants, anti-diabetics, anti-Alzheimer's, anti-arthritics, and anti-cancer agents. Additionally, the computational pharmacokinetic and toxicity properties of the two pyrazolo[1,5-a]pyrimidines 12a and 12b were calculated and analyzed. The preliminary studies and results of this work represent the initial steps toward more advanced studies and define the bioactive chemical structure of pyrazolo[1,5-a]pyrimidine derivatives with the goal of exploring new drugs to address numerous health problems.

Editorial: Coordination between financial reporting quality, investors’ utility function, social legitimacy, digitalization, and judicial independence.

The current issue is comprised of scholarly articles falling in the domain of a wide range of research themes, for example, financial market performance, investment strategies, corporate governance characteristics, financial reporting quality, corporate social responsibility (CSR), corruption, and judicial independence in the context of foreign direct investment (FDI). All the articles published in the current issue have highlighted interesting, diverse, and thought-provoking research problems.

An Improved Particle Swarm Optimization Method for Nonlinear Optimization

Nonlinear optimization is becoming more challenging in information sciences and various industrial applications, but nonlinear problems solved by the classical particle swarm‐based methods are usually characterized by low efficiency, accuracy, and convergence speed in specific issues. To solve these problems and enhance the nonlinear optimization performance, an improved metaheuristic particle swarm optimization (PSO) model is proposed here. First, the optimization principles and model of the new method are introduced, and algorithms of the improved PSO are presented by updating the displacement and velocity of the moving particle according to Euler–Maruyama (EM) principle rather than traditional standard normal distribution. Then, the influence of the model parameters, input dimensions, and different nonlinear problems on the PSO optimization characterizations are studied by Pareto set solving and optimization performance comparison. The analysis regarding diverse nonlinear problems and optimization methods manifests that the improved method is capable of solving various nonlinear problems especially for multiobjective models, while the robustness and reliability can always keep consistent regardless of the change of model parameters. Finally, the performance evaluation is exhibited by the case study of nonlinear parameter optimization, 3 groups of CEC benchmark problems, and rank‐sum test for 6 comparable optimization algorithms, which all verify its effectiveness and reliability, as well as the significance and great application promise. The results show that the new proposed PSO method has the fastest convergence speed and least iteration numbers in searching for the global best solution of 9 nonlinear problems among 8 different optimization models indicated by the p values smaller than 0.05. Additionally, the main conclusions showing the calculation efficiency, stability, robustness, and great application promise of the proposed method are summarized, and future work is discussed.

Multipath Branch Model‐Aided Differential Evolution Algorithm Based on Regional Error

This paper introduces a general approach to multiobjective optimization design. In surrogate model‐aided algorithms, the prediction accuracy of the surrogate model plays a key point in determining the optimization results. In order to overcome the defect of a single model in the adaptive antenna optimization problem and improving the efficiency and accuracy of the model, a method called multipath branching model‐aided differential evolution algorithm analysis (MMDEA‐RE) is proposed. The key innovations include: A novel combination model integrating Gaussian process, multilayer perceptron, and radial basis function. Compared with the traditional single model, the combination model can deal with complex and diverse optimization problems more effectively and has stronger robustness and versatility. A method of regional error analysis near the prediction point is proposed, which can select a suitable model with high prediction accuracy after a few iterations. The effectiveness and superiority of MMDEA‐RE are demonstrated by the optimization of two linear array antennas and one 5G antenna. The contribution of this study is to improve the accuracy and generality of surrogate models in antenna design. This proposed method has practical implications and potential applications for improving antenna optimization techniques.

Outcomes From a Physician Health Center

This retrospective cohort study describes the population of physicians seeking medical evaluation in a dedicated physician health center and identifies factors associated with needing practice restrictions. Participants had an initial evaluation between January 1, 2016, and December 31, 2022. We report personal and professional demographics and types of medical conditions in this cohort. An ordinal logistic regression analysis was used to identify factors associated with occupational outcomes. Physicians in a wide variety of specialties from 34 different states presented for evaluation of diverse medical problems. More than half of the participants presented with occupational concerns. The presence of a neurologic or psychiatric illness were the only factors associated with temporary or permanent restrictions. Physicians with medical conditions impacting their ability to practice have a professional obligation to obtain a thorough, objective medical evaluation. Such evaluations should support and protect patients, employers, and the physicians themselves.

Aberrantly High FBXO31 Impairs Oocyte Quality in Premature Ovarian Insufficiency.

Premature ovarian insufficiency (POI), which is defined as loss of ovarian function that occurs before the age of 40, causes menstrual disturbances, infertility, and diverse health problems in females. Despite the limited understanding of the molecular basis underlying POI pathology, we had previously demonstrated that the cooperation of miR-106a and FBXO31 plays a pivotal role in diminished ovarian reserve (DOR), with FBXO31 serving as a putative target of miR-106a. In this study, we found that FBXO31 is aberrantly expressed in granulosa cells of POI patients, leading to accumulated reactive oxygen species (ROS) and cell apoptosis via the p53/ROS pathway. Furthermore, our results demonstrated that high levels of FBXO31 in mouse ovaries impair oocyte quality. Our study revealed that FBXO31 may serve as a novel indicator and play a significant role in the etiology of POI.