Diverse Information Research Articles

A Unified Framework Based on Graph Consensus Term for Multiview Learning.

In recent years, multiview learning technologies have attracted a surge of interest in the machine learning domain. However, when facing complex and diverse applications, most multiview learning methods mainly focus on specific fields rather than provide a scalable and robust proposal for different tasks. Moreover, most conventional methods used in these tasks are based on single view, which cannot be readily extended into the multiview scenario. Therefore, how to provide an efficient and scalable multiview framework is very necessary yet full of challenges. Inspired by the fact that most of the existing single view algorithms are graph-based ones to learn the complex structures within given data, this article aims at leveraging most existing graph embedding works into one formula via introducing the graph consensus term and proposes a unified and scalable multiview learning framework, termed graph consensus multiview framework (GCMF). GCMF attempts to make full advantage of graph-based works and rich information in the multiview data at the same time. On one hand, the proposed method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods; on the other hand, learned graphs can be flexibly chosen to construct the graph consensus term, which can more stably explore the correlations among multiple views. To this end, GCMF can simultaneously take the diversity and complementary information among different views into consideration. To further facilitate related research, we provide an implementation of the multiview extension for locality linear embedding (LLE), named GCMF-LLE, which can be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.

Comparison of Genetic Diversity and Core Locus Information of Abies yuanbaoshanensis Natural Population and Progeny

Comparison of Genetic Diversity and Core Locus Information of Abies yuanbaoshanensis Natural Population and Progeny

Mobile App Use among Persons with Fibromyalgia: A Cross-sectional Survey

Persons with fibromyalgia experience a diverse set of symptoms. Recommendations for management generally focus on multidisciplinary approaches involving multiple modalities. Mobile apps can be an essential component for self-management, yet little is known about how persons with fibromyalgia use mobile apps for health-related purposes. A cross-sectional survey (N = 663) was conducted to understand the real-world use of apps among persons with fibromyalgia. The survey included 2 main foci: 1) eHealth literacy and use of information sources, and 2) mobile app use patterns and preferences for health-related purposes, including the types of apps used and usage characteristics of apps currently in use, as well as those that had been discontinued. Respondents’ average eHealth literacy as measured by eHealth Literacy Scale (eHEALS) was 31.4 (SD = 7.1), and they utilized diverse information sources. Approximately two-thirds of the sample used mobile apps; the remaining one-third did not. Diverse health management needs were represented in the apps reported, including scheduling/time management, notetaking, fitness, and wellness. Compared to apps that had been discontinued, participants rated apps that they still used higher in terms of ease of use and used them more frequently. Reasons for discontinuing app use included issues with privacy, the effort required, lack of interest, and lack of perceived quality. Other reasons for app nonuse were lack of awareness and how-to knowledge, indicating that disseminating information about apps and addressing other barriers, such as providing user support, are critical to increasing uptake. These study findings can inform both app design and dissemination. PerspectiveThis article presents how persons with fibromyalgia use mobile apps to manage their health. The findings could inform the development of digital interventions or programs for this population.

Assessment of Bangsamoro Library and Archives’ Online Library Services: Implications to Users’ Information Needs Satisfaction

As technological advancements continue to reshape the landscape of information access, libraries have transitioned to digital platforms to provide a wide array of resources and services. This study aimed to assess the quality of the online library services of Bangsamoro Library and Archives in relation to the satisfaction of the users’ information needs. A modified questionnaire, which was content-validated, and pilot-tested to establish its internal consistency and reliability was distributed to one hundred (100) actual users of the library. Descriptive statistics was used to organize the data on the participants’ assessment of the quality of the online services and users’ information needs satisfaction and regression analysis to determine the significant influence of service quality on the online services on their information needs satisfaction. Findings reveal that participants assessed the quality of online library services considering Affect of Service and Information Control as very good, while participants’ information needs in terms of educational needs and information needs were rated as generally satisfied, indicating that the library has diverse information resources available and can provide online library services needed by the participants for their informational needs. Moreover, the quality of online library services assessment significantly influenced the participants' information needs satisfaction. It is concluded that the public library understudy can cater to the information needs of the community it is serving and services and information resources in the library are being accessed by the users. This study highlights the need for the library personnel to strengthen the area on the Information Control dimension, and the subscription of electronic databases, audiovisual materials, and updated library resources.

Designing Child Nutrition Interventions to Engage Fathers: Qualitative Analysis of Interviews and Co-Design Workshops.

Fathers play a pivotal role in parenting and child feeding, but they remain underrepresented in intervention studies, especially those focused on disadvantaged populations. A better understanding of fathers' experiences and needs regarding support access and child nutrition information in the context of disadvantage can inform future interventions engaging fathers. This study aims to explore fathers' experiences; perceived enablers; and barriers to accessing support and information related to parenting, child feeding, and nutrition and to co-design principles for tailoring child nutrition interventions to engage fathers. Australian fathers of children aged 6 months to 5 years with lived experience of disadvantage participated in semistructured interviews and co-design workshops, primarily conducted via videoconference. Creative analogies were used to guide the ideation process in the workshops. A total of 25 interviews and 3 workshops (n=10 participants) were conducted, with data analyzed using reflexive thematic analysis and the Capability, Opportunity, and Motivation-Behavior model. The interview data illuminated factors influencing fathers' initiation in seeking support for parenting, child feeding, and nutrition, including their experiences. It highlighted fathers' diverse information needs and the importance of an inclusive environment and encouragement. Enablers and barriers in accessing support related to parenting and child nutrition were identified at the individual (eg, personal goals and resource constraints), interpersonal (family support and false beliefs about men's caregiving role), organizational (inadequate fathering support), and systemic levels (father-inclusive practice and policy). Digital data collection methods enabled Australia-wide participation, overcoming work and capacity barriers. Videoconferencing technology was effectively used to engage fathers creatively. Key principles for engaging fathers were co-designed from the workshop data. Interventions and resources need to be father specific, child centered, and culturally appropriate; promote empowerment and collaboration; and provide actionable and accessible strategies on the what and how of child feeding. Fathers preferred multiformat implementation, which harnesses technology-based design (eg, websites and mobile apps) and gamification. It should be tailored to the child's age and targeted at fathers using comprehensive promotion strategies. Fathers faced barriers to accessing support and information related to parenting and feeding that may not adequately address their needs. Future interventions could integrate the co-designed principles to engage fathers effectively. These findings have implications for health service delivery and policy development, promoting father-inclusive practice.

DeepMCGCN: Multi-channel Deep Graph Neural Networks

Graph neural networks (GNNs) have shown powerful capabilities in modeling and representing graph structural data across various graph learning tasks as an emerging deep learning approach. However, most existing GNNs focus on single-relational graphs and fail to fully utilize the rich and diverse relational information present in real-world graph data. In addition, deeper GNNs tend to suffer from overfitting and oversmoothing issues, leading to degraded model performance. To deeply excavate the multi-relational features in graph data and strengthen the modeling and representation abilities of GNNs, this paper proposes a multi-channel deep graph convolutional neural network method called DeepMCGCN. It constructs multiple relational subgraphs and adopts multiple GCN channels to learn the characteristics of different relational subgraphs separately. Cross-channel connections are utilized to obtain interactions between different relational subgraphs, which can learn node embeddings richer and more discriminative than single-channel GNNs. Meanwhile, it alleviates overfitting issues of deep models by optimizing convolution functions and adding residual connections between and within channels. The DeepMCGCN method is evaluated on three real-world datasets, and the experimental results show that its node classification performance outperforms that of single-channel GCN and other benchmark models, which improves the modeling and representation capabilities of the model.

Career Development Programs at Yonsei University College of Medicine

The career choices of medical students are significant for both individual students and society, which relies on a robust public healthcare system. Medical schools should provide a conducive environment and diverse information to enable students to make mature career decisions. Yonsei University College of Medicine conducts extracurricular programs for students’ career development, including the Career Choice Expo, Career Path Survey, Special Lecture on Career Development, and a Visible Radio Show focused on career counseling. Additionally, the intracurricular activities offered by the college include career advising to students through faculty advisors in learning communities based on students’ reflective writing about career-related activities. Medical students, in the process of forming their career decisions, compare what they have learned in the medical school curriculum with information acquired through extracurricular activities, taking into consideration their individual characteristics. Through longitudinal discussions with faculty advisors in learning communities, medical students not only gain recognition for the validity of their exploratory activities but also develop a sense of self-efficacy in making career decisions. The career education program at Yonsei University College of Medicine aligns with recent perspectives emphasizing the integration of career counseling for medical students into the curriculum in order to increase effectiveness.

RL-CoPref: a reinforcement learning-based coordinated prefetching controller for multiple prefetchers

Modern processors employ data prefetchers to alleviate the impact of long memory access latency. However, current prefetchers are designed for specific memory access patterns, which perform poorly on mixed applications with multiple memory access patterns. To address these issues, RL-CoPref, a reinforcement learning (RL)-based coordinated prefetching controller for multiple prefetchers, is proposed in this paper. RL-CoPref takes diverse program context information as the input, learns to maximize cumulative rewards, and evaluates prefetch quality based on prefetch hits/misses and memory bandwidth utilization. It can dynamically adjust the prefetch activation and prefetch degree, enabling multiple prefetchers to complement each other on mixed applications. Our extensive evaluation, utilizing the ChampSim simulator, demonstrates that RL-CoPref can effectively adapt to various workloads and system configurations, optimizing prefetch control. On average, RL-CoPref achieves 76.15% prefetch coverage, having 35.50% IPC improvement, outperforming state-of-the-art individual prefetchers by 5.91–16.54% and outperforming SBP, a state-of-the-art (non-RL) prefetch controller, by 4.64%.

Integrative Taxonomy Clarifies the Historical Flaws in the Systematics and Distributions of Two Osteobrama Fishes (Cypriniformes: Cyprinidae) in India

The taxonomy and geographical distributions of Osteobrama species have historically posed challenges to ichthyologists, leading to uncertainties regarding their native ranges. While traditional taxonomy has proven valuable in classification, the utility of an integrated approach is restricted for this particular group due to limitations in combining information from biogeography, morphology, and genetic data. This study addresses the taxonomic puzzle arising from the recent identification of Osteobrama tikarpadaensis in the Mahanadi and Godavari Rivers, casting doubt on the actual distribution and systematics of both O. tikarpadaensis and Osteobrama vigorsii. The research reveals distinctions among specimens resembling O. vigorsii from the Krishna and Godavari riverine systems. Notably, specimens identified as O. vigorsii from the Indian Museum exhibit two pairs of barbels, while those from the Godavari River in this study are identified as O. tikarpadaensis. Inter-species genetic divergence and maximum likelihood phylogeny provide clear delineation between O. vigorsii and O. tikarpadaensis. The study suggests that O. vigorsii may be limited to the Krishna River system in southern India, while O. tikarpadaensis could potentially extend from the Mahanadi River in central India to the Godavari River in southern India. Proposed revision to morphological features for both species, accompanied by revised taxonomic keys, aim to facilitate accurate differentiation among Osteobrama congeners. The data generated by this research provide a resource for future systematic investigations into cyprinids in India and surrounding regions. Further, the genetic diversity information obtained from various riverine systems for Osteobrama species will be instrumental in guiding aquaculture practices and formulating effective conservation action plans.

Maximizing Penetration Testing Success with Effective Reconnaissance Techniques Using ChatGPT

Background/Objective: The study investigates the integration of ChatGPT, a generative pretrained transformer language model into the reconnaissance phase of penetration testing. The research aims to enhance the efficiency and depth of information gathering during critical security assessments offering potential improvements to traditional approaches.
 Research Problem: The research study addresses the challenge of optimizing the reconnaissance phase in penetration testing. It seeks to provide a solution by exploring the capabilities of ChatGPT in extracting valuable data, such as various aspects of the digital footprint or infrastructure of a system or an organization. The scope of the research relies in demonstrating how ChatGPT can contribute to the planning phase of penetration testing, guiding the selection of tactics, tools, and techniques for identifying and mitigating potential risks that could be used to assist with securing Internet accessible assets of a system or an organization.
 Methodology: The research adopts a case study methodology to assess the effectiveness of ChatGPT in reconnaissance. Tailored questions are formulated to extract specific information relevant to penetration testing. The study highlights the importance of prompt engineering emphasizing the need for carefully constructed questions to ensure usable results.
 Results: The research showcases the ability of ChatGPT to provide diverse and insightful reconnaissance information. The extracted data includes IP address ranges, domain names, vendor technologies, SSL/TLS ciphers, and network protocols. The information gathering improves efficiency of the reconnaissance phase aiding penetration testers in planning subsequent phases of the assessment.
 Discussion: The research study extends to the broader field of cybersecurity where artificial intelligence language models can play a valuable role in enhancing the success of reconnaissance in penetration testing. The research suggests that integrating ChatGPT into penetration testing can bring about positive changes in the efficiency and depth of information obtained during reconnaissance.
 Conclusion: The results of the study determine that incorporating ChatGPT in the reconnaissance phase significantly benefits penetration testers by offering valuable insights and streamlining subsequent assessment planning. The results affirm ChatGPT as a pivotal tool in maximizing success in penetration testing, contributing to ongoing advancements in cybersecurity practices.

Diverse semantic information fusion for Unsupervised Person Re-Identification

Unsupervised Person Re-Identification (Re-ID) has achieved considerable success through leveraging various approaches that rely on hard pseudo-labels. Prior work mainly focused on improving the quality of pseudo-labels or enhancing the robustness of representation learning model. However, there has been little focus on exploring the contextual semantic information, which can reveal rich relations within samples and provide complementary knowledge to assist the hard pseudo-labels. To this end, we propose a novel method named FuseDSI to explore the potential to harness diverse contextual semantic information fusion. In addition to the hard pseudo labels, FuseDSI explores additional pair-wise semantic information and neighborhood semantic information within each mini-batch through online self-exploration. Furthermore, it leverages the explored semantic information as an additional supervisory signal to enhance robust representation learning. For these two types of contextual semantic information are dynamically estimated in an online manner based on the model’s status, they complement each other well with the hard pseudo-labels. One significant advantage of FuseDSI is its flexibility in combining various pseudo-labels-based methods. Moreover, since exploring the contextual semantic information requires no external elaborate module nor memory-consuming memory bank, it maintains the structure of baseline model with negligible impact on training time. Experimental studies on two widely used person ReID benchmark datasets (MSMT17, Market-1501) demonstrate that FuseDSI consistently improves the performance of baseline model and achieves the state-of-the-art results. Code is available at: FuseDSI.

Quantitative assessment of food safety interventions for Campylobacter spp. and Salmonella spp. along the chicken meat supply chain in Burkina Faso and Ethiopia

Rural and small-scale chicken farming is a major source of income in most African countries, and chicken meat is an important source of nutrients. However, chicken meat can be contaminated with Campylobacter spp. and Salmonella spp., pathogens with a high reported burden of foodborne illnesses. Therefore, it is essential to control these pathogens in chicken meat. Quantitative microbial risk assessments (QMRA) can aid the development of effective food safety control measures and are currently lacking in chicken meat supply chains in the African context. In this study, we developed stochastic QMRA models for Salmonella spp. and Campylobacter spp. in the chicken meat supply chain in Burkina Faso and Ethiopia employing the modular process risk model in @Risk software. The study scope covered chicken farming, transport, slaughtering, consumer handling, and consumption. Effectiveness of candidate interventions was assessed against baseline models' outputs, which showed that the mean annual Campylobacter spp. risk estimates were 6482 cases of illness per 100,000 persons and 164 disability adjusted life years (DALYs) per 100,000 persons in Burkina Faso, and 12,145 cases and 272 DALYs per 100,000 persons in Ethiopia. For Salmonella spp., mean annual estimates were 2713 cases and 1212 DALYs per 100,000 persons in Burkina Faso, and 4745 cases and 432 DALYs per 100,000 persons in Ethiopia. Combining interventions (improved hand washing plus designated kitchen utensils plus improved cooking) resulted in 75 % risk reduction in Burkina Faso at restaurants and 93 to 94 % in Ethiopia at homes for both Salmonella spp. and Campylobacter spp. For Burkina Faso, adding good hygienic slaughter practices at the market to these combined interventions led to over 91 % microbial risk reduction. Interventions that involved multiple food safety actions in a particular step of the supply chain or combining different interventions from different steps of the supply chain resulted in more risk reduction than individual action interventions. Overall, this study demonstrates how diverse and scanty food supply chain information can be applied in QMRA to provide estimates that can be used to stimulate risk-based food safety action in African countries.

Genetic Diversity of Bamboo (Yushinia alpina) Borer Larvae in the Mau Forest Complex, Kenya

Bamboo borer larvae have caused major losses of bamboo cover in natural forests and plantations. Lack of information on the fauna of bamboo trees has been cited as the contributing factor to poor management of bamboo stands. Genetic diversity information helps understand the effects of different fauna in guiding management plans. Genetic diversity information has recently become an important tool in conservation science. This paper aimed to determine the genetic diversity of the bamboo borer larvae in the Mau Forest complex in order to generate information that could guide the management and conservation of bamboo trees (Yushinia Alpina) in the Mau Forest Complex. The mitochondrial C oxidase Subunit 1 (COI) of 12 isolates was sequenced and analyzed. A similarity search of the bamboo borer larvae was carried out using the National Center for Biotechnology Information (NCBI) BLAST search to identify the larvae species. The genetic diversity and genetic pairwise distances were determined, and Tajimas D and Nei’s FU Fs statistics were calculated to estimate the population expansion that has occurred. The results showed genetic diversity (haplotype diversity 0.956) in the bamboo borer larvae population of the Mau Forest Complex. The nucleotide diversity (0.283) was found to be low. The similarity search showed that the bamboo borer larvae of Yushinia alpina belonged to four (4) species of noctuid larvae (Lepidoptera). The identity matches to the similar species scored an average of 94%. The Tajimas D (0.374) and FUs Fs (5.547) collectively indicated no rare excess mutations in the population. The results reveal high genetic diversity, which is key in the management of forest species

Designing a structure-function alphabet of helix based on reduced amino acid clusters

Several simple secondary structures could form complex and diverse functional proteins, meaning that secondary structures may contain a lot of hidden information and are arranged according to certain principles, to carry enough information of functional specificity and diversity. However, these inner information and principles have not been understood systematically. In our study, we designed a structure-function alphabet of helix based on reduced amino acid clusters to describe the typical features of helices and delve into the information. Firstly, we selected 480 typical helices from membrane proteins, zymoproteins, transcription factors, and other proteins to define and calculate the interval range, and the helices are classified in terms of hydrophilicity, charge and length: (1) hydrophobic helix (≤43%), amphiphilic helix (43%∼71%), and hydrophilic helix (≥71%). (2) positive helix, negative helix, electrically neutral helix and uncharged helix. (3) short helix (≤8 aa), medium-length helix (9–28 aa), and long helix (≥29 aa). Then, we designed an alphabet containing 36 triplet codes according to the above classification, so that the main features of each helix can be represented by only three letters. This alphabet not only preliminarily defined the helix characteristics, but also greatly reduced the informational dimension of protein structure. Finally, we present an application example to demonstrate the value of the structure-function alphabet in protein functional determination and differentiation.

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

As landmines and other unexploded ordnances (UXOs) present a great risk to civilians and infrastructure, humanitarian demining is an essential component of any post-conflict reconstruction. This paper introduces the Minefield Observatory, a novel web-based datastore service that semantically integrates diverse data in humanitarian demining to comprehensively and formally describe suspected minefields. Because of the high heterogeneity and isolation of the available minefield datasets, extracting relevant information to determine the optimal course of demining efforts is time-consuming, labor-intensive and requires highly specialized knowledge. Data consolidation and artificial intelligence techniques are used to convert unstructured data sources and store them in an ontology-based knowledge database that can be efficiently accessed through a Semantic Web application serving as the Minefield Observatory user interface. The MINEONT+ ontology was developed to integrate diverse mine scene information obtained through non-technical surveys and remote sensing, such as aerial and hyperspectral satellite imagery, indicators of mine presence and absence, contextual data, terrain analysis information, and battlefield reports. The Minefield Observatory uses the Microdata API to embed this dataset into dynamic HTML5 content, allowing seamless usage in a user-centric web tool. A use-case example was provided demonstrating the viability of the proposed approach.

Socialization and Counseling About Health Traditional/Complementary Alternative Medicine for Mothers Pregnant

Introduction: Health traditional / CAM (Complementary and Alternative Medicine) has Lots of Mothers interested in pregnant to overcome several complaints and objections to comfort during pregnancy. Besides they feel worried about consuming therapy However attention exists several side effects requires enough information Objective: Know description effort counseling about health traditional or CAM to Mother Pregnant Method: studies literature about counseling /educational use of Health traditional or CAM for Mothers pregnant. Library data collection, or object research his research dug through diverse information bibliography. Using secondary data, via several related articles with health traditional /CAM. The analysis uses an annotation bibliography, which can be interpreted as a list of sources used in something study given a conclusion related to what is written in it Results: Review from the article as many as 5 and with the essence society uses a type of health method traditional / CAM to avoid the effect of side treatment Because the public considers treatment traditional/complementary relatively safer. There are many types of methods during pregnancy medicines and herbal plants. However, it means without risk rise, a pregnant must have competent knowledge about the utilization of therapy safe complement during pregnancy That reason CAM is used because CAM is easily obtained and cheaper costs. Conclusion: Socialization and Counseling health traditional /CAM required concerning utility type/modality Health Traditional /CAM safety or side effect necessary attention and condition Mother pregnant That Alone. Counseling involves husband, family, and energy health and midwives at the Community Health Center. Furthermore, more exploration carries on about the perception and meaning use health traditional. Next expected can increase synergy between service health conventional and health traditional/CAM.

Multiview adaptive attention pooling for image–text retrieval

Image–Text Retrieval (ITR) aims to bridge the heterogeneity gap between image and text and establish the retrieval ability between the two modalities. There is a large intraclass difference in visual language data; that is, the content of an image can be described from different views. Representing images and texts as single embedded features to measure similarity makes it difficult to capture the diversity and fine-grained information of modal features. Previous methods use cumbersome dilated convolution structures or stack multiple feature pooling operators to perform multiview learning of images, and then take the view with the highest similarity score to the text as the alignment result. This may lead to two problems: (1) The model becomes complex, and has poor scalability and limited feature learning ability; (2) The matching strategy with the highest similarity score may have the problem that text view features deliberately emphasize a certain area, resulting in sub-optimal matching. Therefore, we propose a multiview adaptive attention pooling (MVAAP) network, a simpler and more effective multiview global feature embedding method. Specifically, MVAAP learns the query for each view, extracts the salient image region of the view through an adaptive attention mechanism, and generates an optimal pooling strategy to aggregate it into the global feature of the view. Beyond that, we introduce multiview embedding of the text branch and consider the response between different views to improve the generalization ability of the model. Plenty of experiments on the two mainstream cross-modal datasets of MS-COCO and Flickr30K prove the accuracy and superiority of the method.

Humidity-mediated synaptic plasticity in Ag loaded porous SiOx based memristor for multimodal neuromorphic sensory system

Neuromorphic sensory system plays a critical role for human being to perceive, interact and even deduce with the external environment. Multimodal plasticity implementation of neuromorphic sensory system that can learn with diversified information empowers the development of environment-interactive artificial intelligence. In this work, we demonstrated a multimodal neuromorphic sensory system based on Ag loaded porous SiOx based memristor. The humidity-mediated synaptic plasticity behaviors were detailedly analyzed in the range of 10–90% relative humidity (RH). The humidity-mediated silver ion migration in porous SiOx memristors was studied by theoretical and experimental methods, and the mechanism of synergistic effect between porous micro-structure and ambient humidity was elucidated. A multimodal neuromorphic sensory system was finally constructed and the adaptive behavior of the human eye was also successfully simulated by taking advantage of this well-designed Au/Ag-SiOx/ITO memristor. The biomimetic intelligence demonstrated in our multimodal neuromorphic sensory devices and systems shows its potential in promoting the advancement in brain-like artificial intelligence.

THE ESSENCE OF MIND STRATEGY IN THE RELATIONSHIP WITH NATURE: HUMAN-NATURE-WISDOM UNITY

A systematic approach requires combining the knowledge of related subjects (e.g., natural sciences) into a whole and making conditions for the learners to research, draw conclusions, process abundant and diverse information, improve and change, complete their knowledge, i.e., eliminate common/traditional subject barriers, refuse narrow empiricism. The field of science education is wide, complex and diverse. It is not only the transmission of natural science knowledge but also ecological, environmental, antidrug, sexual, antinicotine education, healthy lifestyle training, etc. Today’s society is dealing with very relevant issues of nature protection, nature conservation, ecology, environmental protection, and other problems. All this requires a new approach from all of us, the efforts of various field specialists, eliminating all separating barriers. Global problems are a common concern for all of us. This is just because not a single problem is isolated from another, most of them are intertwined, fully determined, united in their nature. Therefore, knowing (clarifying) connections is complicated and hardly achievable. Their solution is possible only when they are comprehensively analysed by closely linking them together.

“We are the minority”: Latinx student-athletes experiences within their Predominantly White Institutions

Despite the burgeoning Latinx student population in the United States, a conspicuous gap exists in the academic literature, particularly within the National Collegiate Athletic Association (NCAA) context. Consequently, the central objective of this study was to redress this void by analyzing the racial experiences encountered by NCAA Division I Latinx student-athletes enrolled in Predominantly White Institutions (PWIs). To fulfill this objective, the researchers applied a qualitative phenomenological approach, seeking an in-depth comprehension of the significance of diversity practices within the distinct educational milieus inhabited by the study’s participants. Employing Latin Critical Race Theory (LatCrit) as the theoretical framework, the authors gathered data through six semi-structured interviews and supplemented this information with pertinent diversity and inclusion-related information derived from each participating university and the respective athletic programs. The study’s findings underscored the persistent issue of racial discrimination confronted by NCAA Division I Latinx student-athletes within the university setting. Remarkably, despite the extensive diversity, equity, and inclusion (DEI) trainings and councils within these institutions, Latinx student-athletes continued to grapple with racial disparities. Indeed, recurring themes from the data highlighted their significant reliance on familial support and campus resources as strategies for navigating and mitigating these challenges. In light of a substantial body of scholarly work that recognizes the critical importance of DEI in sport, this study serves as a poignant reminder of the pressing need for further academic and practical endeavors aimed at effectively addressing racial disparities.