Research Problem Research Articles

Sex differences in human performance.

Sex as a biological variable is an underappreciated aspect of biomedical research, with its importance emerging in more recent years. This review assesses the current understanding of sex differences in human physical performance. Males outperform females in many physical capacities because they are faster, stronger and more powerful, particularly after male puberty. This review highlights key sex differences in physiological and anatomical systems (generally conferred via sex steroids and puberty) that contribute to these sex differences in human physical performance. Specifically, we address the effects of the primary sex steroids that affect human physical development, discuss insight gained from an observational study of 'real-world data' and elite athletes, and highlight the key physiological mechanisms that contribute to sex differences in several aspects of physical performance. Physiological mechanisms discussed include those for the varying magnitude of the sex differences in performance involving: (1) absolute muscular strength and power; (2) fatigability of limb muscles as a measure of relative performance; and (3) maximal aerobic power and endurance. The profound sex-based differences in human performance involving strength, power, speed and endurance, and that are largely attributable to the direct and indirect effects of sex-steroid hormones, sex chromosomes and epigenetics, provide a scientific rationale and framework for policy decisions on sex-based categories in sports during puberty and adulthood. Finally, we highlight the sex bias and problem in human performance research of insufficient studies and information on females across many areas of biology and physiology, creating knowledge gaps and opportunities for high-impact studies.

MENTORING IN THE HEALTHCARE SYSTEM: PROBLEMS AND PROSPECTS

ANNOTATION Rationale. During the period of rapid development of science and medical technologies, the professional and working environment is faced with the urgent task of finding mechanisms and tools for increasing the level of professional competencies and practical skills of medical specialists in the process of professional socialization. The sociological study conducted by the authors was focused on identifying factors of successful/unsuccessful professional socialization of young medical specialists, one of which is mentoring. Purpose of the study. Determining the role of the mentoring institution as a factor influencing the success/failure of professional socialization of medical specialists. Materials and methods. A survey was conducted among clinical residents of the Far Eastern State Medical University. A total of 184 respondents were interviewed using a questionnaire using a Google form. To solve the research problems, methods of analysis, comparison and generalization of the obtained data, analysis of regulatory documents, survey results and secondary data from sociological research were used. At the level of data processing and work with source material, descriptive statistics methods provided by SPSS and EXCEL software were used. Results. In the course of a sociological study conducted by the authors, the most pressing problems of introducing and implementing mentoring practices in the healthcare system were identified. The role of the mentoring institution as a socializing factor influencing the success/failure of professional socialization of medical specialists is also determined. The study formulates tasks for further research into the process of professional socialization of medical specialists from the point of view of identifying factors of a positive and destructive nature, and the role of socialization institutions in this process. Conclusion. Problems in the implementation of mentoring practices in healthcare related to the low mentoring activity of practicing medical specialists have been identified. A special need for the implementation of mentoring practices was identified at the stage of postgraduate training (clinical residency) by 83% of respondents and at the stage of starting independent professional work by 61% of respondents. Respondents expressed a positive attitude towards the introduction of mentoring in healthcare: 90% of respondents indicated that having a mentor when obtaining a medical specialty is mandatory.

Drop-out rates in animal-assisted psychotherapy - Results of a quantitative meta-analysis.

Animal-assisted psychotherapy is an emerging field with great potential and growing popularity. However, empirical research on its effectiveness is insufficient, and consistent evidence about patients' commitment is missing. The present meta-analysis addresses this gap by systematically comparing drop-out rates in animal-assisted psychotherapy and by relating the resulting across study drop-out rate to across study drop-out rates reported in meta-analyses on conventional psychotherapy. Fifty-seven studies published until August 2022 were identified as eligible for meta-analytic comparison, that is, they conducted animal-assisted psychotherapy on at least one group of psychiatric patients and reported drop-out rates. Potential moderating influences of the type of animal and patients' disorder were considered, as well as multiple other demographic and study design variables. The across study drop-out rate in animal-assisted psychotherapy was 11.2%. This was significantly lower than the across meta-analyses drop-out rate of conventional psychotherapy (d = -.45, p = .0005). Although effects of moderator variables could not be evaluated statistically due to too small and heterogeneous data sets, descriptive results suggest influences of the type of animal and patient disorder. However, study quality ratings identified serious shortcomings regarding proper research design, most critically concerning the report of effect size measures, the use of standardized intervention plans and Open Science practices. Drop-out constitutes a major problem of psychotherapeutic research and practice. By proposing that the inclusion of an animal in the psychotherapeutic setting can enhance patients' commitment and by outlining challenges and opportunity of animal-assisted psychotherapy, this meta-analysis offers a starting point for future research in this evolving field.

Modifikasi Produksi dan Biaya Bahan Bakar Minyak pada Kapal Ciremai

Some of the main research problems are the lack of fuel efficiency use, difficulties in loading and unloading process, the refueling process tardiness and the ships route’s delay of the determined and planned routes that could affect the Ciremai Ship’s On Time Performance (OTP). The research aims to determine and analyze the effect of production and fuel oil cost modifications based on the income of Ciremai Ship and its implications for its financial performance after “Three in One” modification. This research used Descriptive statistical analysis method and hypothesis testing using SmartPLS-3. The research sample was taken from the entire routes of Ciremai Ship over the last four years of 48 months. The secondary research data was taken from the financial reports related to production modifications, fuel oil costs, the income and financial performance of the ship. Research findings show that the production modifications have a direct influence on financial performance while fuel costs and income do not have a direct influence on it. The production modifications do not have a direct effect on income instead fuel costs have a direct effect on income and production modifications do not have an indirect effect on financial performance through income.

The Effects of a Digital Marketing Orientation on Business Performance

Customer relationship management (CRM) has become increasingly important as a result of the pressure organizations are under to remain competitive. CRM has been and is widely promoted as essential to a company’s ability to survive. According to this study, CRM is more than just a computer program or software package. We believe that for organizations to use CRM effectively, it must be viewed from a strategic point of view. Therefore, this study focuses on the consequences of digital marketing on business performance, specifically, the consequences of customer relationship orientation and the use of CRM technologies as a support for analysis, data integration, and access on business performance. This study addresses a contemporary and relevant research problem that has national and international relevance. The research is based on quantitative methods to test hypotheses. Data were gathered from 73 organizations. The findings show the relationships between CRM and customer satisfaction, market effectiveness, and profitability. Customer relationship orientation positively affects customer satisfaction, market effectiveness, and market profitability. CRM technologies significantly improve business performance. This research contributes to the existing knowledge by shedding light on the complex relationships among CRM, customer relationship orientation, market effectiveness, market profitability, and customer satisfaction. Based on the research results, we provide practical recommendations for managers and decision makers.

Towards sustainable urbanism: The arcology and organic compact urban formation as the approach of sustainable urban design- A comparative study

Arcology is a new vision of urban design for densely populated settlements. It is referred to as the urban formation aimed at achieving the highest level of utilization in the least area of land to reduce human impact on the environment. Moreover, the integration of this formation with the environment will work to reduce energy consumption, and achieve an economy that is characterized by adaptation and self-sufficiency, leading towards sustainable urbanism. The current study deals with this new concept in sustainable urban design, in an attempt to investigate its impact on traditional urban formation and to make a comparison between them. The lack of a clear conception of the term ‘arcology’ and comparing it with the organic compact urban formation as a significant concepts of sustainable urban design, constitutes the main research problem. The current study aims to uncover this concept to extract the 'arcology' parameters and compare them with the organic compact urban formation, as an approach that can be adopted in sustainable urban design to reduce the human impact on the environment. To get to the aforementioned goal, it's required to figure out the theoretical framework and apply it onto two selected study areas: Arcosanti and Bab al-Sheikh (an area within the historical centre of Baghdad) to test its validity using the comparative analysis and space syntax methodology. The research concluded that the organic fabric corresponds to the 'arcology' parameters and indicators, highlighting the importance of its employment in urban planning and design to reduce the negative impact of the urban structure on the urban ecosystem.

DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance.

Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

Justifiably, while big data is the primary interest of research and public discourse, it is essential to acknowledge that small data remains prevalent. The same technological and societal forces that generate big datasets also produce a more significant number of small datasets. Contrary to the notion that more data is inherently superior, real-world constraints such as budget limitations and increased analytical complexity present critical challenges. Quality versus quantity trade-offs necessitate strategic decision-making, where small data often leads to quicker, more accurate, and cost-effective insights. Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix and Google (MAANG) can easily lead AI research due to their access to vast datasets, creating a barrier for small and mid-sized enterprises that lack similar access. This article addresses this imbalance by exploring DL techniques optimized for small datasets, offering a comprehensive review of historic and state-of-the-art DL models developed specifically for small datasets. This study aims to highlight the feasibility and benefits of these approaches, promoting a more inclusive and equitable AI landscape. Through a PRISMA-based literature search, 175+ relevant articles are identified and subsequently analysed based on various attributes, such as publisher, country, utilization of small dataset technique, dataset size, and performance. This article also delves into current DL models and highlights open research problems, offering recommendations for future investigations. Additionally, the article highlights the importance of developing DL models that effectively utilize small datasets, particularly in domains where data acquisition is difficult and expensive.

Analysis of IFC interoperability data schema for project representation

Collaborative work processes from the perspective of Building Information Modeling demand technical and organizational information requirements from managers. In response, data schema Industry Foundation Classes (IFC) has been making significant entities and domain inclusions with a semantic level capable of supporting OpenBIM interoperability throughout the project lifecycle. This paper applies Design Science Research methodology to explore how the IFC should be specified so that BIM actors can meet the representation context of a project translating data through Inheritance Diagrams, which offer auxiliary contributions. The Protocol developed to address the research problem revealed that some information requirements had not been met, as the universe of optional data in the schema is broad. Therefore, an artifact is created to fully meet the demand and is validated by different software. This reveals that the insertion of data can be performed to meet the desired interoperability and stands a valuable tool for BIM actors.

Optimising orthodox style learning in physical education through a variational training model

Background: The research problem is to improve our understanding of how different training approaches can improve bullet-shot results. Research Objectives: This study aims to investigate the impact of different training models on the learning outcomes of orthodox-style shot put. Methods: The research method employed was an experiment with a one-group pre-test and post-test design to enhance student learning outcomes using a varied training model. The subjects consisted of 32 students from a high school in Palembang. The study was conducted over 30 days, with four weekly meetings. During each meeting, 2-3 training models were presented, resulting in a total of 10 variation training models, namely Passing Techniques in Football Baseball, Techniques that Hold the Repulsion, Hanging the Ball, Resisting Pull, Clap Hand, Reject Target, Throwing Darts, Shoot in Target, and Load Rejection One Shoot. Data analysis used SPSS version 23 to calculate the significant value of the pretest and posttest results. Finding/Results: The study found that using varied training models resulted in significantly better learning outcomes in orthodox-style bullet throwing. Conclusion: The study concluded that varied training models have a considerable positive influence on learning outcomes in the orthodox style of instruction. This suggests that the findings contribute to a deeper understanding of effective training methods in sports education and have practical implications for athletic training programs. We could conduct longitudinal studies to investigate the long-term impact of diverse training on skill retention and entry into competitive athletics.

Linking Teaching and Research in Conservation of the Built Environment: Case Studies from Courses of the University of Cyprus Graduate Programme on Conservation

ABSTRACT Teaching and research are both equally essential to postgraduate studies in architectural conservation. The interaction between them is also crucial. Various perspectives exist on the connection between teaching and research, including research-led, research-oriented, and research-based teaching. Thus, differences arise based on whether the focus is on research content or on research processes and problems, whether students are treated as audiences or as participants, and whether teaching is teacher- or student-centred. This article presents case studies (courses) that link teaching and research from a new interdisciplinary postgraduate programme at the University of Cyprus on architectural conservation. It also discusses this issue within the international context of teaching and research in architectural conservation.

Fourier feature network for 3D vessel reconstruction from biplane angiograms

3D reconstruction of biplane cerebral angiograms remains a challenging, unsolved research problem due to the loss of depth information and the unknown pixelwise correlation between input images. The occlusions arising from only two views complicate the reconstruction of fine vessel details and the simultaneous addressing of inherent missing information. In this paper, we take an incremental step toward solving this problem by reconstructing the corresponding 2D slice of the cerebral angiogram using biplane 1D image data. We developed a coordinate-based neural network that encodes the 1D image data along with a deterministic Fourier feature mapping from a given input point, resulting in a slice reconstruction that is more spatially accurate. Using only one 1D row of biplane image data, our Fourier feature network reconstructed the corresponding volume slices with a peak signal-to-noise ratio (PSNR) of 26.32 ± 0.36, a structural similarity index measure (SSIM) of 61.38 ± 1.79, a mean squared error (MSE) of 0.0023 ± 0.0002, and a mean absolute error (MAE) of 0.0364 ± 0.0029. Our research has implications for future work aimed at improving backprojection-based reconstruction by first examining individual slices from 1D information as a prerequisite.

Transforming higher education in Tanzania: unleashing the true potential of AI as a transformative learning tool

PurposeThis study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.Design/methodology/approachA mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.FindingsIn this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.Research limitations/implicationsThe data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.Originality/valueA unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.

A new generation of nanobody research tools using improved mass spectrometry-based discovery methods

Single domain antibodies (“nanobodies”) derived from the variable region of camelid heavy-chain only antibody variants have proven to be widely useful tools for research, as well as therapeutic and diagnostic applications. In addition to traditional display techniques, methods to generate nanobodies using direct detection by mass spectrometry and DNA sequencing have been highly effective. However, certain technical challenges have limited widespread application. We have optimized a new pipeline for this approach that greatly improves screening sensitivity, depth of antibody coverage, antigen compatibility, and overall hit rate and affinity. We have applied this improved methodology to generate significantly higher affinity nanobody repertoires against widely used targets in biological research – i.e., GFP, tdTomato, GST, and mouse, rabbit, and goat IgG. We have characterized these reagents in affinity isolations and tissue immunofluorescence microscopy, identifying those that are optimal for these particularly demanding applications, and engineering dimeric constructs for ultra-high affinity. This study thus provides new nanobody tools directly applicable to a wide variety of research problems, and improved techniques enabling future nanobody development against diverse targets.

Current insights into environmental acetochlor toxicity and remediation strategies.

Acetochlor is a selective pre-emergent herbicide that is widely used to control annual grass and broadleaf weeds. However, due to its stable chemical structure, only a small portion of acetochlor exerts herbicidal activity in agricultural applications, while most of the excess remains on the surfaces of plants or enters ecosystems, such as soil and water bodies, causing harm to the environment and human health. In recent years, researchers have become increasingly focused on the repair of acetochlor residues. Compared with traditional physical and chemical remediation methods, microorganisms are the most effective way to remediate chemical pesticide pollution, such as acetochlor, because of their rich species, wide distribution, and diverse metabolic pathways. To date, researchers have isolated and identified many high-efficiency acetochlor-degrading strains, such as Pseudomonas oleovorans, Klebsiella variicola, Bacillus subtilus, Rhodococcus, and Methylobacillus, among others. The microbial degradation pathways of acetochlor include dechlorination, hydroxylation, N-dealkylation, C-dealkylation, and dehydrogenation. In addition, the microbial enzymes, including hydrolase (ChlH), debutoxylase (Dbo), and monooxygenase (MeaXY), responsible for acetochlor biodegradation are also being investigated. In this paper, we review the migration law of acetochlor in the environment, its toxicity to nontarget organisms, and the main metabolic methods. Moreover, we summarize the latest progress in the research on the microbial catabolism of acetochlor, including the efficient degradation of microbial resources, biodegradation metabolic pathways, and key enzymes for acetochlor degradation. At the end of the article, we highlight the existing problems in the current research on acetochlor biodegradation, provide new ideas for the remediation of acetochlor pollution in the environment, and propose future research directions.

Методологический анализ особенностей личности, отражающих характер ее реагирования на несправедливость

The article addresses the research problem concerning the study of personality traits essential for understanding an individual’s reactions to injustice within the psychological field. Methodological analysis is conducted to systematize and substantiate scientific principles, psychological theories, and concepts relevant to understanding how personality traits influence responses to injustice. Aligning with principles of systematicity and determinism from various theoretical perspectives, the sensitivity of individuals to justice, their belief in it, personality traits, and aggressive behaviors are examined. Through this analysis, these individual characteristics are justified as reflecting a person-centered approach to their relationship with injustice. However, they are not considered rigid or closed; rather, their variability is underscored, influenced by diverse subjective characteristics.

Health and skill-related physical fitness tests: An assessment in determining the students’ physical fitness level

The primary objective of this study is to comprehensively evaluate and implement health and skill-related physical fitness tests as pre-assessment tools to assess students' physical fitness levels in educational settings. This study employed a descriptive evaluation technique to identify research problems. There are eight faculties in the Visayas State University, the sampling was open to all students who wanted to participate in the study and 273 respondents were obtained. To establish a standard, people with disabilities, immunocompromised and with a history of cardiovascular diseases were exempted from participating in physical fitness tests, as they have a different way of catching up with their health status. The study findings contribute to understanding the relationships between body composition and various facets of physical fitness. The insights gained from physical fitness testing serve as crucial reference points for designing effective and personalized physical activity programs. The negative correlation between BMI, muscular endurance and balance implies that people with higher BMI may benefit from specific exercises to improve these areas. In contrast, the positive correlation with agility suggests that agility-focused activities help those with higher BMI. The findings underscore the need for specific interventions essential to improve overall fitness and well-being, emphasizing the importance of addressing specific strengths and weaknesses through nuanced fitness programs tailored to individual profiles. Keywords: health, ability, physical condition, evaluation, fitness level.

Teologi Trauma: Trauma Pada Anak Dampak Dari Perceraian Orangtua

Divorce is rarely seen as a way out of various household problems. Divorce is not the right solution to resolve the conflict; of course, a divorce can hurt others, especially the child; the divorce of parents can cause trauma to the child. In fact, the wounds that a child may have suffered will probably continue to carry it to adulthood; The impact on each child may vary, depending on the child's age at the time of the parents' divorce, the circumstances of the divorce, and the personality of the child. When a divorce occurs within a family, the child is the primary victim who is most affected by the negative impact of the divorce; The purpose of this writing is to explain that divorce is a phenomenon of social change taking place in today's society; divorce results in damage to relationships between family members that do not support the growth and development of the child; divorces cause family dysfunction; divorcing creates an emotional distance between the child and the parent; and divorce can also result in trauma to the child. The author uses a qualitative method to analyze this research problem using interviews and literature.

Relevansi Pemikiran John C. Maxwell Untuk Kepemimpinan Kristen Yang Efektif Masa Kini

Leadership helps organizations, institutions, or institutions to achieve their goals optimally and effectively. Without the leadership function being implemented or applied consistently, an organization will not run as expected, including Christian leadership (churches or Christian service institutions). This research seeks to emphasize the importance of implementing leadership in Christian organizations and therefore this research aims to describe John C. Maxwell's thoughts on leadership for effective Christian leadership. The research method used is descriptive, namely describing thoughts that contribute to answering existing research problems. It was found that Maxwell's leadership model, especially the characteristics of effective leadership, has contributed to effective Christian leadership today.

The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS.

We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.