Search Results (59)

Background: Anterior cruciate ligament (ACL) injuries are increasing in young athletes and many are related to non-contact, spontaneous mechanical fatigue-related ruptures. The objective of this narrative review is to identify and synthesize the anatomical, histological, physiological, and biomechanical basis of extracellular matrix (ECM) factors that contribute to ACL injuries and suggest ways to decrease their occurrence. Methods: The primary investigator searched PubMed, Web of Science, and Google Scholar database titles and abstracts using search phrases with Boolean operators: “anterior cruciate ligament” OR “ACL”, OR “cranial cruciate ligament” AND “disease”; “anterior cruciate ligament” OR “ACL”, OR “cranial cruciate ligament” AND “spontaneous rupture” OR “non-contact injury”; and “anterior cruciate ligament” OR ACL, OR cranial cruciate ligament” AND “crosslink”, “collagen” OR “extracellular matrix”; and “anterior cruciate ligament” OR “ACL”, OR “cranial cruciate ligament” AND “microtrauma”, OR “sudden” OR “fatigue failure”. The primary investigator and a sports orthopedic surgeon reviewed titles and abstracts of diverse evidence sources. From these identified sources, the study team performed full text reviews, selected contributing articles, performed Strength of Recommendation Taxonomy (SORT) grading, and synthesized the following themes: A Hostile Environment, ACL Strain, and Poor Nutrient Delivery; Accumulative ACL Microtrauma and Mechanical Failure; The ACL Differs From Other Ligaments; Collagen, the ECM, and ACL Mechanobiology; Crimps and ACL ECM Stretch; Crosslinks Improve ECM Mechanical Properties; The Delicate Collagen Synthesis and Degradation Balance; Exercise Training and the ACL; Can Nutraceuticals Help Restore the Balance?; Training Induced ACL Hypoxia; Estrogen and the Female Athlete; Counting Pitches or Counting Collagen Fiber Ruptures; and Restoring A Positive Anabolic–Catabolic Collagen Balance. Results: Regular exercise training within a physiologically safe loading range is vital to ACL ECM health. However, low or moderate evidence suggested that poor blood supply, slow metabolism, and a hypoxic environment may unbalance anabolic and catabolic homeostasis. Active rest and recovery concepts that prevent youth baseball shoulder and elbow injuries may help prevent non-contact ACL injuries. Conclusions: More prescriptive active rest and recovery intervals and neuromuscular control training may restore the anabolic–catabolic balance that increases mature crosslink density and improves ACL ECM strength. Confirmatory studies are needed to better establish therapeutic intervention mode(s), timing, dosage, and frequency optimization. Full article

The genus Xylaria comprises numerous species, particularly prevalent in tropical ecosystems such as those of Ecuador. Despite its ecological importance, the taxonomy of the genus remains challenging, and much of its diversity in the Neotropics remains under-documented. This study provides a preliminary characterization of the Xylaria diversity at the Estación Científica San Francisco, an Andean biodiversity hotspot in Southern Ecuador. Through an integrated approach including detailed macro- and micro-morphological descriptions and nuclear ribosomal DNA (nrDNA ITS and LSU) phylogenetic analyses, 20 Xylaria specimens were examined. As a result, ten species were recognized: Xylaria adscendens, X. cf. anisopleura, X. apiculata, X. curta, X. enterogena, X. fissilis, X. globosa, X. aff. telfairii, X. tuberoides, and X. aenea, the latter representing a new record for Ecuador. The phylogenetic analysis presented here serves as a preliminary systematic positioning of these specimens within the genus rather than a comprehensive global reconstruction. While these ribosomal markers provided preliminary insights into species relationships, partial incongruence with morphospecies highlights the evolutionary complexity of certain lineages and underscores the need for future multilocus studies. Furthermore, four additional phylotypes found in their anamorphic state are documented, suggesting that local diversity exceeds current records. By providing detailed morphological documentation supported by preliminary barcode data from a poorly sampled region, this study contributes vital information to the global understanding of Xylaria and underscores the importance of Southern Ecuador as a reservoir of fungal diversity. Full article

(1) Background: Bees of the genus Apis play a fundamental role in ecosystems thanks to their pollination activities and their long evolutionary history. This has resulted in species diversifying and spreading across Asia, Africa, and Europe. This review contextualises the genus within biogeographic and evolutionary frameworks, emphasising the importance of understanding the origins, adaptations, distribution and differences between species. (2) Methods: Recent studies on the biology, taxonomy and ecology of Apis species were analysed, including research on social behaviour, communication, genetics, morphology and environmental adaptations, as well as contributions using modern evolutionary and phylogeographic analytical methods. (3) Results: The gathered evidence shows that anthropogenic factors, including climate change, habitat loss, intensive agriculture, pollutants, competition with other bees and the spread of parasites and pathogens, significantly affect the stability of Apis populations and increase the vulnerability of wild species. (4) Conclusions: This review emphasises the importance of integrating ecological, genetic and management knowledge to develop effective conservation strategies that aim to reduce the impact of human activities and preserve the resilience of Apis species and the vital ecosystem services they provide. Full article

Beetles (Coleoptera) represent one of the most diverse insect groups and play vital ecological roles, yet their accurate identification is often challenging due to morphological similarities among taxa. DNA barcoding has emerged as a powerful and reliable tool for species-level identification and biodiversity monitoring. In this study, we established a local DNA barcode reference database for beetles in the Liancheng Nature Reserve, Gansu Province, China. From May to August 2024, beetle specimens were collected and identified using both morphological traits and DNA barcoding. Three species delimitation methods—Automatic Barcode Gap Discovery (ABGD), Assemble Species by Automatic Partitioning (ASAP), and Bayesian Poisson Tree Processes (bPTP)—were employed as complementary analytical tools, and phylogenetic relationships were inferred from cytochrome c oxidase subunit I (COI) sequences. A total of 164 COI sequences (650 bp) were obtained, representing 126 beetle species from 95 genera and 20 families. DNA barcoding successfully resolved morphologically ambiguous taxa, with many sequences reported here for the first time. Phylogenetic analysis revealed that species within the same genus formed cohesive clades before clustering at the family level, confirming the species-level discriminative power of the COI gene. Collectively, these findings demonstrate that COI-based DNA barcoding is a powerful complement to traditional taxonomy. The establishment of this preliminary reference database provides a valuable molecular resource for beetle identification and a practical tool to support biodiversity conservation, resource management, and long-term monitoring in the Liancheng Nature Reserve. Full article

This paper reviews recent progress in fault detection, reliability analysis, and predictive maintenance methods for grid-connected solar photovoltaic (PV) systems. With the rising adoption of solar power globally, maintaining system reliability and performance is vital for a sustainable energy supply. Common faults discussed include panel degradation, electrical issues, inverter failures, and grid disturbances, all of which affect system efficiency and safety. While traditional diagnostics like thermal imaging and V-I curve analysis offer valuable insights, they mostly detect issues reactively. New approaches using Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) enable real-time monitoring and predictive diagnostics, significantly enhancing accuracy and reliability. This study represents the introduction of a consolidated decision framework and taxonomy that systematically integrates and evaluates the fault types, symptoms, signals, diagnostics, and field-readiness across both plant types and voltage levels. Moreover, this study provides quantitative benchmarks of performance metrics, energy losses, and diagnostic accuracies of 95% confidence intervals. Adopting these advanced techniques promotes proactive management, reducing operational risks and downtime, thus reinforcing the resilience and sustainability of solar power infrastructure. Full article

Network slicing (NS) plays a vital role in enabling flexible and efficient resource allocation, tailored to diverse use cases and network domains. This survey paper explores the synergy between NS and Artificial Intelligence (AI), emphasizing how Machine Learning (ML) techniques can address challenges across the slice life-cycle. A key contribution of this work is an in-depth analysis of AI and primarily ML applications in each phase of the slice life-cycle, delving into their specific tasks and discussing the techniques applied to these tasks. Furthermore, we present a taxonomy based on different slicing criteria, offering a structured perspective to enhance understanding and implementation. Full article

Commit messages are vital for traceability, maintenance, and onboarding in modern software projects, yet their quality is frequently inconsistent. Recent large language models (LLMs) can transform code diffs into natural language summaries, offering a path to more consistent and informative commit messages. This paper makes two contributions: (i) it provides a systematic survey of automated commit message generation with LLMs, critically comparing prompt-only, fine-tuned, and retrieval-augmented approaches; and (ii) it specifies a transparent, agent-based evaluation blueprint centered on CommitBench. Unlike prior reviews, we include a detailed dataset audit, preprocessing impacts, evaluation metrics, and error taxonomy. The protocol defines dataset usage and splits, prompting and context settings, scoring and selection rules, and reporting guidelines (results by project, language, and commit type), along with an error taxonomy to guide qualitative analysis. Importantly, this work emphasizes methodology and design rather than presenting new empirical benchmarking results. The blueprint is intended to support reproducibility and comparability in future studies. Full article

The widespread adoption of Artificial Intelligence (AI) in critical domains, such as healthcare, finance, law, and autonomous systems, has brought unprecedented societal benefits. Its black-box (sub-symbolic) nature allows AI to compute prediction without explaining the rationale to the end user, resulting in lack of transparency between human and machine. Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures. To address this concern, explainability is of paramount importance, which has triggered the emergence of Explainable Artificial Intelligence (XAI) as a vital research area. XAI is aimed at enhancing transparency, trust, and accountability of AI models. This survey presents a comprehensive overview of XAI from the dual perspectives of challenges and opportunities. We analyze the foundational concepts, definitions, terminologies, and taxonomy of XAI methods. We then review several application domains of XAI. Special attention is given to various challenges of XAI, such as no universal definition, trade-off between accuracy and interpretability, and lack of standardized evaluation metrics. We conclude by outlining the future research directions of human-centric design, interactive explanation, and standardized evaluation frameworks. This survey serves as a resource for researchers, practitioners, and policymakers to navigate the evolving landscape of interpretable and responsible AI. Full article

Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration. Full article

The Phalaenopsis genus, a horticulturally vital group within the Orchidaceae, dominates global floriculture markets through strategic cultivar innovation, scalable propagation, and data-driven cultivation. This review systematically examines the breeding, propagation, cultivation management, and potential applications of Phalaenopsis while providing insights into future research directions. The main contents include the following: Breeding innovations—This review outlines the taxonomy of the Phalaenopsis genus and highlights its intergeneric hybridization potential, which offers vast opportunities for developing novel horticultural varieties. By establishing clear breeding objectives, researchers employ diverse breeding strategies, including conventional crossbreeding and biotechnological approaches (e.g., mutation breeding, ploidy manipulation, genetic transformation, and CRISPR/Cas9 editing). Propagation and cultivation management—Analyses of Phalaenopsis tissue culture protocols covering explant selection, media optimization, and regeneration systems are summarized. Key factors for efficient cultivation are discussed, including temperature, light, water, nutrient management, cultivation medium selection, and integrated pest/disease management. Scientific environmental control ensures robust plant growth, synchronized flowering, and high-quality flower production. Emerging applications—Phalaenopsis exhibits promising applications in functional bioactive compound extraction (e.g., antioxidants and antimicrobial agents). This review summarizes current advancements in Phalaenopsis breeding, cultivation, and potential applications. Based on technological progress and market demands, future research directions are proposed to support the sustainable development of the Phalaenopsis industry. Full article

Plastic, a ubiquitous part of our daily lives, has become a global necessity, with annual production exceeding 300 million tons. However, the accumulation of synthetic polymers in our environment poses a pressing global challenge. To address this urgent issue, fungi have emerged as potential agents for plastic degradation. In our previous manuscript, ‘A Review of the Fungi That Degrade Plastic’, we explored the taxonomic placement of plastic-degrading fungi across three main phyla: Ascomycota, Basidiomycota, and Mucoromycota. In this review, we built upon that foundation and aimed to further explore the taxonomic relationships of these fungi in a comprehensive and detailed manner, leaving no stone unturned. Moreover, we linked metabolic activity and enzyme production of plastic-degrading fungi to their taxonomy and summarized a phylogenetic tree and a detailed table on enzyme production of plastic-degrading fungi presented here. Microbial enzymes are key players in polymer degradation, operating intra-cellularly and extra-cellularly. Fungi, one of the well-studied groups of microbes with respect to plastic degradation, are at the forefront of addressing the global issue of plastic accumulation. Their unique ability to hydrolyze synthetic plastic polymers and produce a wide range of specific enzymes is a testament to their potential. In this review, we gather and synthesize information concerning the metabolic pathways of fungi involved in the degradation of plastics. The manuscript explores the diverse range of specific enzymes that fungi can produce for plastic degradation and the major pathways of plastic metabolism. We provide a listing of 14 fungal enzymes (Esterase, Cutinase, Laccase, Peroxidases, Manganese peroxidase, Lignin peroxidase, Oxidoreductases, Urease, Protease, Lipase, Polyesterase, Dehydrogenase, Serine hydrolase, and PETase) involved in pathways for plastic degradation alongside the relevant fungi known to produce these enzymes. Furthermore, we integrate the fungi’s enzyme-producing capabilities with their taxonomy and phylogeny. Taxonomic and phylogenetic investigations have pinpointed three primary fungal classes (Eurotiomycetes, Sordariomycetes (Ascomycota), and Agaricomycetes (Basidiomycota)) as significant plastic degraders that produce the vital enzymes mentioned earlier. This paper provides a foundational resource for recognizing fungal involvement in the biodegradation of synthetic polymers. It will ultimately advance fungal biotechnology efforts to address the global issue of plastic accumulation in natural environments. Full article

Students with disabilities often face challenges in participating in classroom activities with normal students. Assistive technologies powered by Artificial Intelligence (AI) or Machine Learning (ML) can provide vital support to ensure inclusive and equitable learning environments. In this paper, we identify AI or ML-powered inclusive education tools and technologies, explore the factors required for developing personalized learning plans using AI, and propose a real-time personalized learning framework. We have identified inclusive education tools and technology driven by AI or ML as well as factors impacting the creation of AI-based personalized learning based on our exploration of Google Database, blog sites, company sites, tools, and techniques used in different centers. This study proposes a system model that includes engagement and adaptive learning components. The system uses Bloom’s taxonomy to continuously track the learner’s development. We identified a comprehensive list of AI- or ML-powered inclusive education tools and technologies and determined key factors for developing personalized learning plans, including emotional state, student progress, preferences, learning styles, and outcomes. Based on this research, AI-based inclusive education has the potential to improve educational experiences for students with disabilities by creating a more equitable and inclusive learning environment. Full article

Amaranthus, a genus in Amaranthaceae, is divided into three subgenera—Amaranthus, Acnida, and Albersia—and contains approximately 70 to 80 species. Understanding its phylogenetic relationships is essential for species classification, genetic diversity assessment, and evolutionary studies. This knowledge is vital for improving Amaranthus utilization in crop improvement and managing the ecological impacts of invasive weeds. In this study, we analyzed the chloroplast genomes of 27 Amaranthus species across all three subgenera to characterize their genomic features and construct a comprehensive phylogenetic tree. Our aim was to elucidate the phylogenetic relationships within the genus and evaluate interspecific affinities among the subgenera. We also addressed the taxonomic ambiguity surrounding A. bouchonii and A. powellii to determine their distinct species within the genus. Chloroplast genome sizes ranged from 149,949 to 150,818 bp, with GC content varying between 36.52% and 36.63%. Comparative structural analyses confirmed highly conserved quadripartite structures, gene content, and organization, comprising 87 protein-coding genes, 37 tRNAs, and 8 rRNAs. Repeat and codon usage analyses revealed conserved repeat patterns and a preference for codons ending in A or U. Selection pressure analysis indicated a predominantly purifying selection, with matK showing signs of positive selection, particularly in A. spinosus. Phylogenetic analysis of 80 protein-coding genes confirmed the monophyly of subgenus Amaranthus but found Alberisa and Acnida to be paraphyletic. Despite their morphological similarity, A. bouchonii and A. powellii were placed in separate clades within subgenus Amaranthus, with A. bouchonii clustering with A. retroflexus, and A. powellii aligning with the A. hybridus complex. Additionally, we identified 16 variable regions as potential molecular markers for species identification. Our study provides the most comprehensive Amaranthus chloroplast genome dataset to date, offering new insights into its evolutionary relationships and valuable genomic resources for taxonomy, germplasm management, and invasive risk assessment. Full article

In an e-learning environment, question levels are based on Bloom’s Taxonomy (BT), which normally classifies a course’s learning objectives into diverse levels. As per the previous literature, the assessment procedure lacks accuracy and results in redundant keywords when automatically assigning Bloom’s taxonomic categories using a keyword-based approach. These assessments are considered challenging as far as e-learning-based students are concerned, as the text feed is the only instrumental testing part. Student assessments are limited to multiple-choice questions and lack an evaluation of students’ text-based input. This paper proposes a natural-language processing-based intelligent deep-learning model that relies on parametric cognitive assessments. By applying class labels to students’ descriptive responses, the proposed approach helps classify a variety of questions mapped to BT levels. The first contribution of this work is a compiled dataset of the assessment items from 300 students, who were tested on 20 questions at each level. Each level is calculated by combining the responses from all students, resulting in 6000 questions per cognitive level for a total of 36,000 records. The second contribution is the development of an intelligent model based on a recurrent neural network (RNN), which not only predicts Bloom’s question level but also learns it over further iterations. The students’ text-based answers are accessed to gauge performance using a refined question pool gathered through the RNN model. The student dataset is mapped and tested using the NLP model for further classification of the students’ cognitive levels. This assessment is related to the formulation of questions and the compilation of Episode 2 for assessment. The third contribution is the comparison and demonstration of the improvements in learning using a parametric cognitive-based assessment in an episodic manner. Improved classification accuracy was attained by adding more processing layers based on the iterative, RNN-based learning model to achieve the vital threshold difference. The cognitive based questions pool classification achieved by RNN results in 98% accuracy. The resulting student assessments, based on performance, increased to an accuracy ratio of 92.16% and a precision ratio of 92.36% at an aggregate level based on the Random Forest classifier. We claim that our work serves as an initiative for effective student evaluations in interactive and e-learning-based environments when handling other types of inputs, like mathematical, graphical, and multimodal inputs. Full article

The history of religions during the nineteenth and early twentieth centuries has been closely tied to the classification of Indigenous religions. However, recent scholarship in the field of religion has increasingly drawn on the work of subaltern and postcolonial historiography as a way of disrupting the European canon and dislodging Indigenous and non-western ways of knowing and being from the tyranny of the classical taxonomies of religion. Recent approaches to religious diversity have been challenged for reproducing imperial hierarchies of religion—assuming an accommodationist approach to Indigenous religions while also rendering invisible the internal diversity, fluidity, and adaptive orientations within Indigenous religions. In this paper, I contend that in the postcolonial context, Indigenous religions uncouple themselves from traditional taxonomies of religion, and, in particular, I propose religious complexity as a suitable framework and approach for accounting, contending with, and reporting on religious change in postcolonial South Africa. I explore questions about how to account for, ‘classify’, or ‘measure’ change related to everyday African Indigenous religious efforts and practices in the aftermath of and in response to colonialism, where conventional ideas about religious authority and affinity are displaced by Indigenous practices that can variously be described as simultaneously vital, viral, or feral. Full article