Search Results (472)

Expanding the capacity of Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) beyond species identification to strain typing becomes a new challenge in clinical microbiology. This study demonstrated a specific identification of Streptococcus agalactiae sequence type 1 (ST1) by a manual decision tree and automatically ranking from the newly added MTPPs library, which has not been previously reported. The mass spectra of 25 STs (277 isolates) were generated. The presence and absence of specific peaks were combined to create a decision tree for manual identification. Three peaks at 3127, 5914, and 6252 in combination with m/z 3368 and 6281 were used for primary identification of ST1. However, to differentiate ST1 and ST314, five additional peaks were required. For the automatic system, the MTPP of all isolates was divided into three training–testing ratios of 40:60, 50:50, and 60:40. All categories revealed excellent accuracy rates of above 90% for ST1 identification. The 60:40 group showed the highest overall performance, in which sensitivity was observed at 83.9 to 96.8%, and specificity reached up to 100.0% for both the top two and the top three matches. In conclusion, we propose that the MTPP from MALDI-TOF is a potential model for speedy bacterial typing, crucial in epidemiology, prevention, and patient management. Full article

The study undertook a comprehensive review and comparative analysis of natural language processing techniques for news article classification, with a particular focus on Java language libraries. The dataset comprised an excess of 200,000 items of news metadata sourced from The Huffington Post. The traditional algorithms based on mathematical statistics and deep machine learning were evaluated. The libraries chosen for tests were Apache OpenNLP, Stanford CoreNLP, Waikato Weka, and the Huggingface ecosystem with the Pytorch backend. The efficacy of the trained models in forecasting specific topics was evaluated, and diverse methodologies for the feature extraction and analysis of word-vector representations were explored. The study considered aspects such as hardware resource management, implementation simplicity, learning time, and the quality of the resulting model in terms of detection, and it examined a range of techniques for attribute selection, feature filtering, vector representation, and the handling of imbalanced datasets. Advanced techniques for word selection and named entity recognition were employed. The study compared different models and configurations in terms of their performance and the resources they consumed. Furthermore, it addressed the difficulties encountered when processing lengthy texts with transformer neural networks, and it presented potential solutions such as sequence truncation and segment analysis. The elevated computational cost inherent to Java-based languages may present challenges in machine learning tasks. OpenNLP model achieved 84% accuracy, Weka and CoreNLP attained 86% and 88%, respectively, and DistilBERT emerged as the top performer, with an accuracy rate of 92%. Deep learning models demonstrated superior performance, training time, and ease of implementation compared to conventional statistical algorithms. Full article

With the growing emphasis on healthy eating and nutrition management in modern society, food image recognition has become increasingly important. However, it faces challenges such as large intra-class differences and high inter-class similarities. To tackle these issues, we present a Food Feature Fusion Network (FFFNet), which leverages a multi-head cross-attention mechanism to integrate the local detail-capturing capability of Convolutional Neural Networks with the global modeling capacity of Vision Transformers. This enables the model to capture key discriminative features when addressing such challenging food recognition tasks. FFFNet also introduces self-supervised clustering, generating pseudo-labels from the feature space distribution and employing a clustering objective derived from Kullback–Leibler divergence to optimize the feature space distribution. By maximizing similarity between features and their corresponding cluster centers, and minimizing similarity with non-corresponding centers, it promotes intra-class compactness and inter-class separability, thereby addressing the core challenges. We evaluated FFFNet across the ISIA Food-500, ETHZ Food-101, and UEC Food256 datasets, attaining Top-1/Top-5 accuracies of 65.31%/88.94%, 89.98%/98.37%, and 80.91%/94.92%, respectively, outperforming existing approaches. Full article

Point cloud segmentation underpins various applications in geospatial analysis, such as autonomous navigation, urban planning, and management. Kernel Point Convolution (KPConv) has become a de facto standard for such tasks, yet its fixed geometric kernel limits the modeling of fine-grained contextual relationships—particularly in heterogeneous, real-world point cloud data. In this paper, we introduce the adaptation of a Local Contextual Attention (LCA) mechanism for the KPConv network, with reweighting kernel coefficients based on local feature similarity in the spatial proximity domain. Crucially, our lightweight design preserves KPConv’s distance-based weighting while embedding adaptive context aggregation, improving boundary delineation and small-object recognition without incurring significant computational or memory overhead. Our comprehensive experiments validate the efficacy of the proposed LCA block across multiple challenging benchmarks. Specifically, our method significantly improves segmentation performance by achieving a 20% increase in mean Intersection over Union (mIoU) on the STPLS3D dataset. Furthermore, we observe a 16% enhancement in mean F1 score (mF1) on the Hessigheim3D benchmark and a notable 15% improvement in mIoU on the Toronto3D dataset. These performance gains place LCA-KPConv among the top-performing methods reported in these benchmark evaluations. Trained models, prediction results, and the code of LCA are available in a GitHub opensource repository. Full article

The aim of this research is to understand the events and experiences that contribute to the development of top leaders who are capable of thinking of their organization pedagogically and strategically to respond to present-day challenges. The uniqueness of the objective itself justified the choice of narrative research based on the interdependent relationship between leaders and institutions. Methodologically, the autobiographical narrative was used as the method and data collection technique. We studied the life stories of two headmasters from two school clusters in Portugal, as well as the dynamics of their leadership. The analysis of the life stories was complemented by a chronotopography, documentary analysis, focus groups with middle managers and interviews with members of the Portuguese Ministry of Education. The analysis of the data collected through the life narratives enabled a series of milestones to be identified that, due to their authors’ ability to reflect, were decisive in their professional development, namely, further education; initial training; experience in management bodies and lifelong learning; the participation in the Educational Territories of Priority Intervention programme, the Pedagogical Innovation Pilot Project and school networks. Full article

With the increasing demand for urban rail transit capacity, shield tunneling has become the predominant method for constructing underground metro systems in densely populated cities. However, the spatial interaction between shield tunnels and adjacent retaining structures poses significant engineering challenges, potentially leading to excessive ground settlement, structural deformation, and even stability failure. This study systematically investigates the deformation behavior and associated risks of retaining systems during adjacent shield tunnel construction. An orthogonal multi-factor analysis was conducted to evaluate the effects of grouting pressure, grout stiffness, and overlying soil properties on maximum surface settlement. Results show that soil cohesion and grouting pressure are the most influential parameters, jointly accounting for over 72% of the variance in settlement response. Based on the numerical findings, a Bayesian network model was developed to assess construction risk, integrating expert judgment and field monitoring data to quantify the conditional probability of deformation-induced failure. The model identifies key risk sources such as geological variability, groundwater instability, shield steering correction, segmental lining quality, and site construction management. Furthermore, the effectiveness and cost-efficiency of various grouting reinforcement strategies were evaluated. The results show that top grouting increases the reinforcement efficiency to 34.7%, offering the best performance in terms of both settlement control and economic benefit. Sidewall grouting yields an efficiency of approximately 30.2%, while invert grouting shows limited effectiveness, with an efficiency of only 11.6%, making it the least favorable option in terms of both technical and economic considerations. This research provides both practical guidance and theoretical insight for risk-informed shield tunneling design and management in complex urban environments. Full article

Wildlife rescue centres face considerable challenges in promoting animal welfare and enhancing the care and housing conditions of animals under professional supervision. These challenges are further compounded by the diversity of species admitted, each with distinct specific needs. In Colombia and other Latin American countries, primates are among the most frequently rescued and behaviourally complex mammalian taxa, requiring particular attention. In response, this study aimed to assess the content validity of proposed animal welfare indicators for Cebus albifrons through a Delphi consultation process and to develop two species-specific assessment protocols: a daily-use tool for keepers and a comprehensive protocol for professional audits. A panel of 23 experts in primate care and rehabilitation participated in two consultation rounds to evaluate and prioritise the indicators based on their content validity, perceived reliability, and practicality. Indicators were classified as either animal-based (direct measures) or resource- and management-based (indirect measures). After each round, experts received summarised feedback to refine their responses and facilitate consensus building. Of the 39 initially proposed indicators, 28 were validated for inclusion in the extended protocol and 10 selected for the daily-use checklist. Among these, 20 indicators in the extended protocol and 6 in the daily protocol were resource- or management-based—such as adequate food provision, physical enrichment, and habitat dimensions—highlighting their practical applicability and relevance in identifying welfare issues and risk factors. Although these indirect indicators were more numerous, the top-ranked indicators in both protocols were animal-based, including signs of pain, affiliative behaviours, and abnormal repetitive behaviours. These are essential for accurately reflecting the animals’ welfare state and are therefore critical components of welfare assessment in captive non-human primates. This study demonstrates that welfare assessment tools can be effectively tailored to the specific needs of wildlife rescue centres, providing a robust foundation for enhancing welfare practices. These protocols not only offer practical approaches for assessing welfare but also underscore the importance of embedding animal welfare as a priority alongside conservation efforts. Future research should aim to refine these tools further, assess their implementation, and evaluate inter- and intra-observer reliability to ensure consistency across different settings. Full article

Nanophotonics, the study of light–matter interactions at the nanometer scale, has emerged as a transformative field that bridges photonics and nanotechnology. Using engineered nanomaterials—including plasmonic metals, high-index dielectrics, two-dimensional (2D) materials, and hybrid systems—nanophotonics enables light manipulation beyond the diffraction limit, unlocking novel applications in sensing, imaging, and quantum technologies. This review provides a comprehensive overview of recent advances (post-2020) in nanophotonic materials, fabrication methods, and their cutting-edge applications. We first discuss the fundamental principles governing nanophotonic phenomena, such as localized surface plasmon resonances (LSPRs), Mie resonances, and exciton–polariton coupling, highlighting their roles in enhancing light–matter interactions. Next, we examine state-of-the-art fabrication techniques, including top-down (e.g., electron beam lithography and nanoimprinting) and bottom-up (e.g., chemical vapor deposition and colloidal synthesis) approaches, as well as hybrid strategies that combine scalability with nanoscale precision. We then explore emerging applications across diverse domains: quantum photonics (single-photon sources, entangled light generation), biosensing (ultrasensitive detection of viruses and biomarkers), nonlinear optics (high-harmonic generation and wave mixing), and integrated photonic circuits. Special attention is given to active and tunable nanophotonic systems, such as reconfigurable metasurfaces and hybrid graphene–dielectric devices. Despite rapid progress, challenges remain, including optical losses, thermal management, and scalable integration. We conclude by outlining future directions, such as machine learning-assisted design, programmable photonics, and quantum-enhanced sensing, and offering insights into the next generation of nanophotonic technologies. This review serves as a timely resource for researchers in photonics, materials science, and nanotechnology. Full article

Individual identification of Holstein cattle is crucial for the intelligent management of farms. The existing closed-set identification models are inadequate for breeding scenarios where new individuals continually join, and they are highly sensitive to obstructions and alterations in the cattle’s appearance, such as back defacement. The current open-set identification methods exhibit low discriminatory stability for new individuals. These limitations significantly hinder the application and promotion of the model. To address these challenges, this paper proposes a prototype network-based incremental identification framework for Holstein cattle to achieve stable identification of new individuals under small sample conditions. Firstly, we design a feature extraction network, ResWTA, which integrates wavelet convolution with a spatial attention mechanism. This design enhances the model’s response to low-level features by adjusting the convolutional receptive field, thereby improving its feature extraction capabilities. Secondly, we construct a few-shot augmented prototype network to bolster the framework’s robustness for incremental identification. Lastly, we systematically evaluate the effects of various loss functions, prototype computation methods, and distance metrics on identification performance. The experimental results indicate that utilizing ResWTA as the feature extraction network achieves a top-1 accuracy of 97.43% and a top-5 accuracy of 99.54%. Furthermore, introducing the few-shot augmented prototype network enhances the top-1 accuracy by 4.77%. When combined with the Triplet loss function and the Manhattan distance metric, the identification accuracy of the framework can reach up to 94.33%. Notably, this combination reduces the incremental learning forgetfulness by 4.89% compared to the baseline model, while improving the average incremental accuracy by 2.4%. The proposed method not only facilitates incremental identification of Holstein cattle but also significantly bolsters the robustness of the identification process, thereby providing effective technical support for intelligent farm management. Full article

Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints. Full article

Nitrogen (N) fertilizer management in winter wheat production faces challenges from volatilization losses and sub-optimal application strategies. This is particularly problematic in the Southern Great Plains, where environmental conditions during top-dressing periods favor N losses. This study evaluated the effects of a fertilizer placement method, enhanced-efficiency fertilizers, and application timing on grain yield and protein concentration (GPC) across six site-years in Oklahoma (2016–2018). Treatments included broadcast applications of untreated urea and SuperU^® (urease/nitrification inhibitor-treated urea). These were compared with subsurface placement using single-disc and double-disc drilling systems, applied at 67 kg N ha⁻¹ during January, February, or March. Subsurface placement increased the grain yield by 324–391 kg ha⁻¹ compared to broadcast applications at sites with favorable soil conditions. However, responses varied significantly across environments. Enhanced-efficiency fertilizers showed limited advantages over untreated urea. Benefits were most pronounced during February applications under conditions favoring volatilization losses. Application timing effects were more consistent for GPC than for the yield. Later applications (February–March) increased GPC by 0.8–1.2% compared to January applications. Treatment efficacy was strongly influenced by soil pH, equipment performance, and post-application environmental conditions. This indicates that N management benefits are highly site-specific. These findings demonstrate that subsurface placement can improve nitrogen use efficiency (NUE) under appropriate conditions. However, success depends on matching application strategies to local soil and environmental factors rather than adopting universal recommendations. Full article

Acid mine drainage (AMD) remains a critical environmental challenge in South Africa due to its severe impact on water quality, ecosystems and public health. Numerous studies on AMD management, treatment and resource recovery have been conducted over the past 20 years. This study presents a comprehensive review of research activities on AMD in South Africa from 1998 to 2025, highlighting key trends, emerging challenges and future directions. The study reveals a significant focus on passive and active treatment methods, environmental remediation and the recovery of valuable resources, such as iron, rare earth elements (REEs) and gypsum. A bibliometric analysis was conducted to identify the most influential studies and thematic research areas over the years. Bibliometric tools (Biblioshiny and VOSviewer) were used to analyse the data that was extracted from the PubMed database. The findings indicate that research production has increased significantly over time, with substantial contributions from top academics and institutions. Advanced treatment technologies, the use of artificial intelligence and circular economy strategies for resource recovery are among the new research prospects identified in this study. Despite substantial progress, persistent challenges, such as scalability, economic viability and policy implementation, remain. Furthermore, few technologies have moved beyond pilot-scale implementation, underscoring the need for greater investment in field-scale research and technology transfer. This study recommends stronger industry–academic collaboration, the development of standardised treatment protocols and enhanced government policy support to facilitate sustainable AMD management. The study emphasises the necessity of data-driven approaches, sustainable technology and interdisciplinary cooperation to address AMD’s socioeconomic and environmental effects in the ensuing decades. Full article

The integration of artificial intelligence into sustainable agriculture holds significant potential to transform traditional agricultural practices. This transformation of agricultural practices through AI directly intersects with several critical sustainable development goals, such as Climate Action (SDG13), Life Below Water (SDG 14), and Life on Land (SDG 15). However, such implementations are fraught with multifaceted challenges. This study explores the technological, organizational, and environmental challenges confronting top management in the agricultural sector utilizing the technological–organizational–environmental framework. As interest in AI-enabled sustainable initiatives continues to rise globally, this exploration is timely and relevant. The study employs an interpretive case study approach, drawing insights from a carbon sequestration project within the agricultural sector where AI technologies have been integrated to support sustainability goals. The findings reveal six key challenges: sustainable policy inconsistency, AI experts lacking farming knowledge, farmers’ resistance to change, limited knowledge and expertise to deploy AI, missing links in the existing system, and transition costs, which often hinder the achievement of long-term sustainability outcomes. This study emphasizes the importance of field realities and cross-disciplinary collaboration to optimize the role of AI in sustainability efforts. Full article

Background: Parents of children with type 1 diabetes play a key role in managing their child’s self-management, which can be stressful and burdensome. High involvement can lead to reactions such as emotional, cognitive, and physical exhaustion in parents. Understanding parents’ psychosocial impact due to their child’s disease is crucial for the family’s overall well-being. The purpose of this study was to describe stress and burden experienced by parents in families with children living with type 1 diabetes. Methods: This study utilized a qualitative approach, analyzing interviews with 16 parents of children aged 10 to 17 years living with T1D through qualitative content analysis. The data collection occurred between January and February 2025. Results: Managing a child’s Type 1 diabetes can be tough on family relationships, affecting how partners interact, intimacy, and sibling relationships. The constant stress and worry might leave parents feeling exhausted, unable to sleep, and struggling to think clearly, on top of the pain of losing a normal everyday life. The delicate balance between allowing a child with type 1 diabetes to be independent and maintaining control over their self-management renders these challenges even more demanding for the parents. Conclusions: Parents’ experiences highlight the need for robust support systems, including dependable school environments, trustworthy technical devices, reliable family and friends, and accessible healthcare guidance. These elements are essential not only for the child’s health and well-being but also for alleviating the emotional and practical burdens parents face. Full article

Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents a fundamental line of analysis in road safety research within the scientific community. Among these efforts, macro-level modeling plays a key role by enabling the analysis of the spatiotemporal relationships between diverse factors at an aggregated zonal scale. However, in cities like Bogotá, predicting short-term traffic crashes remains challenging due to the complexity of these spatiotemporal dynamics, underscoring the need for models that more effectively integrate spatial and temporal data. This paper presents a strategy based on deep learning techniques to predict short-term spatiotemporal traffic crashes in Bogotá using 2019 data on socioeconomic, land use, mobility, weather, lighting, and crash records across TMAU and TAZ zones. The results showed that the strategy performed with a model called SpatioConvGru-Net with top performance at the TMAU level, achieving $R^2$ = 0.983, MSE = 0.017, and MAPE = 5.5%. Its hybrid design captured spatiotemporal patterns better than CNN, LSTM, and others. Performance improved at the TAZ level using transfer learning. Full article