Search Results (22,931)

Armed conflicts continue to severely impact human populations and essential infrastructure, particularly water supply systems. This study examines the Yechilla area, a high-intensity battle corridor during the Tigray (between 12°15′26″ 14°57′49″ N latitude; and 36°20′57″–39°58′54″ E longitude) war (2020–2022). Using Cochran’s formula, a representative sample of 89 water schemes was selected for onsite assessment. Additional data on damages to water offices, personnel, equipment, and related infrastructure were gathered through face-to-face interviews with local officials and water professionals, onsite visits, and reviews of governmental and non-governmental archives, and previous studies. The findings reveal that 48.3% of water schemes in the study area are non-functional (does not deliver water), which is a significant increase from pre-war non-functionality rates of approximately 7.1% regionally and 21.1% nationally. Despite the Pretoria peace agreement, non-functionality levels remain critically high two years after conflict. Damage includes partial impairments, lack of technical and spare part support, complete destruction, and looting of water scheme components. The widespread destruction of civilian water infrastructure during the Tigray conflict underscores the insufficiency of existing international legal frameworks, such as the International Humanitarian Law and International Water Law, which are inadequately protecting civilians and their property. Understanding the broader consequences of armed conflicts requires examining the indirect effects and the complex interactions within and between social, economic, and environmental systems. These interconnected impacts are essential to fully grasp how conflict affects livelihoods and human security on a wider scale. Full article

This scoping review investigates the use of fuzzy logic in serious games. Articles were searched in nine databases: ACM Digital Library, IEEE Xplore, IOPscience, MDPI, PubMed, ScienceDirect, Springer, Wiley, and Web of Science. The search retrieved 494 articles published between January 2020 and February 2025, of which 28 met the inclusion criteria. Specifically, four research questions were addressed, focusing on the taxonomy of serious games that use fuzzy logic, the characteristics of game design, the purpose and implementation of the fuzzy logic system within the game, and the experiments conducted in the studies. Results reported that 80% of the studies focused on educational serious games, while 20% addressed health applications. Mouse, keyboard, and smartphone touch screen were the most widely used interaction methods. The adventure genre was the most widely implemented in the studies (35.71%). Fuzzy logic was mainly used for adjusting game difficulty, followed by providing tailored feedback in the game. Mamdani inference was the most widely used inference method in the studies. Although 79% of the studies involved human participants in their experiments, 57% did not perform any statistical analysis of their results. Full article

Unlike traditional control systems that rely on physical input devices, facial gesture-based interaction offers a contactless and intuitive method for operating autonomous systems. Recent advances in computer vision and deep learning have enabled the use of facial expressions and movements for command recognition in human–robot interaction. In this work, we propose a lightweight, real-time facial gesture recognition method, GhostNet-BiLSTM-Attention (GBA), which integrates GhostNet and BiLSTM with an attention mechanism, is trained on the FaceGest dataset, and is integrated with a 3D robot simulation in Unity. The system is designed to recognize predefined facial gestures such as head tilts, eye blinks, and mouth movements with high accuracy and low inference latency. Recognized gestures are mapped to specific robot commands and transmitted to a Unity-based simulation environment via socket communication across machines. This framework enables smooth and immersive robot control without the need for conventional controllers or sensors. Real-time evaluation demonstrates the system’s robustness and responsiveness under varied user and lighting conditions, achieving a classification accuracy of 99.13% on the FaceGest dataset. The GBA holds strong potential for applications in assistive robotics, contactless teleoperation, and immersive human–robot interfaces. Full article

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

The epicardium plays a pivotal role in heart development, regeneration, and disease response through its contributions to multiple cardiac lineages and its dynamic paracrine signaling. Recent advances in lineage tracing, single-cell technologies, and, particularly, human pluripotent stem cell (hPSC)-derived cardiac organoid models have illuminated the cellular heterogeneity, developmental plasticity, and intercellular crosstalk of epicardial cells with other cardiac cell types. These models have revealed conserved and divergent mechanisms of epicardial function across species, offering new insights into epicardial–myocardial–endothelial–immune interactions and the regulation of cardiac repair. This review highlights recent key findings from developmental and regenerative studies, integrating them with emerging data from human cardiac organoids to provide an updated framework for understanding epicardial biology and its therapeutic potential. Full article

Biomaterials can be defined as materials that interact positively with living tissues, restoring compromised functions, or enhancing tissue regeneration. Currently, biomaterial research often relies on a “trial-and-error method”, involving numerous experiments driven largely by experience. This strategy leads to a substantial waste of resources, such as manpower, time, materials, and finances. Optimizing the process is therefore essential. A recent and promising approach to this challenge involves artificial intelligence (AI), as demonstrated by the growing number of studies in this field. AI algorithms rely on data and empower computers with decision-making capabilities, mimicking aspects of the human mind and solving complex tasks with little to no human intervention. Due to their potential, AI and its derivatives are now widely used both in everyday life and in scientific research. In biomaterials science, AI models enable data analysis, pattern recognition, and property prediction. The aim of this review article is to highlight the key results achieved through the application of AI in the field of polymers for biomedical applications and, more broadly, in the development of advanced biomaterials. An overview will be provided on how an AI algorithm works, the differences between traditional programming and AI-based approaches, and their main limitations. Finally, the core topic will be addressed by categorizing biomaterials according to material class. Full article

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation method struggles with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user-detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed with scalability and inductive capabilities in mind, avoiding the need for full-graph training. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science. Full article

Trust plays a pivotal role in individuals’ interactions with technological systems, and those incorporating artificial intelligence present significantly greater challenges than traditional systems. The current landscape of higher education is increasingly shaped by the integration of AI assistants into students’ classroom experiences. Their appropriate use is closely tied to the level of trust placed in these tools, as well as the strategies adopted to critically assess the accuracy of AI-generated content. However, scholarly attention to this dimension remains limited. To explore these dynamics, this study applied the POTDAI evaluation framework to a sample of 132 engineering and social sciences students at Camilo José Cela University in Madrid, Spain. The findings reveal a general lack of trust in AI assistants despite their extensive use, common reliance on inadequate verification methods, and a notable skepticism regarding professors’ ability to detect AI-related errors. Additionally, students demonstrated a concerning misperception of the capabilities of different AI models, often favoring less advanced or less appropriate tools. These results underscore the urgent need to establish a reliable verification protocol accessible to both students and faculty, and to further investigate the reasons why students opt for limited tools over the more powerful alternatives made available to them. Full article

The killer whale (Orcinus orca) is a crucial predator in marine ecosystems, affecting prey populations and overall ecosystem health. Since May 2020, Iberian killer whales in the Strait of Gibraltar have interacted unusually with pleasure boats, posing significant maritime safety challenges. Recognized as critically endangered by the IUCN, a conservation plan for these whales has been approved in Spain. This study analyzes media coverage of these interactions, as media can shape public opinion and influence policies regarding human–wildlife conflicts. A total of 107 news articles published between June 2022 and September 2024 in Spanish media were examined, focusing on the interactions between Iberian killer whales and boats. The research included six variables from prior studies to enhance understanding of media representation and its effects on conservation management. Findings suggest that media coverage often limits comprehension of orca behavior and their vulnerable status. Full article

The karst landscape of Trenggalek Regency, located in several sub-districts including Dongko, Kampak, and Watulimo, is shaped by the Wonosari Formation and is characterized by springs and underground rivers. Due to water scarcity in the region, local communities heavily depend on these natural water sources. This study assesses the groundwater quality of 16 springs and 20 underground rivers to evaluate their suitability for consumption and associated health risks. Using the groundwater quality index (GWQI), human health risk assessment (HHRA), and statistical methods, various physicochemical parameters were analyzed, including pH, total dissolved solids (TDS), electrical conductivity (EC), and concentrations of iron (Fe²⁺), manganese (Mn²⁺), calcium carbonate (CaCO₃), and sulfate (SO₄). Water generally meets the World Health Organization standards for safe drinking. However, correlation analysis reveals notable mineral dissolution and possible anthropogenic influence. TDS strongly correlates with EC (r = 0.97), while Fe²⁺ shows significant relationships with Mn and TDS. Conversely, CaCO₃ shows a negative correlation with EC and TDS, suggesting alternative sources beyond rock weathering. The HHRA indicates higher non-carcinogenic health risks from Fe²⁺ contamination in underground rivers compared to springs. The study’s novelty comes in its integrated assessment of groundwater quality and health hazards in Trenggalek’s karst region, which uses GWQI, HHRA, and statistical analysis to show geochemical interactions and highlight iron-related health issues in underground rivers. Full article

This study examines the longitudinal relationship and interactions among comprehensive water management, human development, and fragility. The seventeen Middle Eastern countries were examined for the period from 1996 to 2023. The Human Development Index (HDI) and Fragile States Index (FSI) were considered as a proxy for human development and fragility. In addition, the Water Poverty Index (WPI) was thoroughly assessed using classical and improved methods to measure multidisciplinary water management. Findings highlight that “Resources” and “Environment” are the most critical components of WPI. Iran performed the most consistently across WPI versions, whereas Palestine performed the worst. “Capacity,” “Environment,” and “Access” are the most influential components of HDI. FSI was found to be the most sensitive to “Capacity” and “Environment”, which contribute to both human development and stability. This study provides empirical evidence to inform SDG 6 implementation by demonstrating the linkage between WPI components and progress in human development. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Iron metabolism and chronic inflammation are two interrelated processes that significantly influence the initiation and progression of CRC. Iron is essential for cell proliferation, but its excess promotes oxidative stress and DNA damage, while inflammation driven by cytokine-regulated pathways accelerates tumourigenesis. We therefore conducted this narrative review to collate the available evidence on the link between iron homeostasis and inflammatory signalling in CRC and highlight potential diagnostic and therapeutic applications. Methods: This narrative review of preclinical and clinical studies explores the molecular and cellular pathways that connect iron regulation and inflammation to CRC. Key regulatory molecules, such as the transferrin receptor (TFRC), ferroportin (SLC40A1), ferritin (FTH/FTL), hepcidin, and IL-6, were reviewed. Additionally, we summarised the findings of transcriptomic, epigenomic, and proteomic studies. Relevant therapeutic approaches, including iron chelation, ferroptosis induction, and anti-inflammatory strategies, were also discussed. Results: Evidence suggests that CRC cells exhibit altered iron metabolism, marked by the upregulation of transferrin receptor (TFRC), downregulation of ferroportin, and dysregulated expression of ferritin. Inflammatory mediators such as IL-6 activate hepcidin and STAT3 signalling, which reinforce intracellular iron retention and oxidative stress. Increased immune evasion, epithelial proliferation, and genomic instability appear to be linked to the interaction between inflammation and iron metabolism. Other promising biomarkers include ferritin, hepcidin, and composite gene expression signatures; however, their clinical application remains limited. Although several preclinical studies support the use of targeted iron therapies and combination approaches with anti-inflammatory agents or immunotherapy, there is a lack of comprehensive clinical validation confirming their efficacy and safety in humans. Conclusion: Although preclinical studies suggest that iron metabolism and inflammatory signalling form an interconnected axis closely linked to CRC, translating this pathway into reliable clinical biomarkers and effective therapeutic strategies remains a significant challenge. Future biomarker-guided clinical trials are essential to determine the clinical relevance and to establish precision medicine strategies targeting the iron–inflammation crosstalk in CRC. Full article

Background: Primary hepatocellular carcinoma (HCC) and liver metastases differ in origin, progression, and therapeutic response, yet a direct high-resolution spatial comparison of their tumor microenvironments (TMEs) within the liver has not previously been performed. Methods: We applied high-definition spatial transcriptomics to fresh-frozen specimens of one HCC and one liver metastasis (>16,000 genes per sample, >97% mapping rates) as a proof-of-principle two-specimen study, cross-validated in human proteomics and patients’ survival datasets. Transcriptional clustering revealed spatially distinct compartments, rare cell states, and pathway alterations, which were further compared against an independent systemic dataset. Results: HCC displayed an ordered lineage architecture, with transformed hepatocyte-like tumor cells broadly dispersed across the tissue and more differentiated hepatocyte-derived cells restricted to localized zones. By contrast, liver metastases showed two sharply compartmentalized domains: an invasion zone, where proliferative stem-like tumor cells occupied TAM-rich boundaries adjacent to hypoxia-adapted tumor-core cells, and a plasticity zone, which formed a heterogeneous niche of cancer–testis antigen–positive germline-like cells. Across both tumor types, we detected a conserved metabolic program of “porphyrin overdrive,” defined by reduced cytochrome P450 expression, enhanced oxidative phosphorylation gene expression, and upregulation of FLVCR1 and ALOX5, reflecting coordinated rewiring of heme and lipid metabolism. Conclusions: In this pilot study, HCC and liver metastases demonstrated fundamentally different spatial architectures, with metastases uniquely harboring a germline/neural-like plasticity hub. Despite these organizational contrasts, both tumor types converged on a shared program of metabolic rewiring, highlighting potential therapeutic targets that link local tumor niches to systemic host–tumor interactions. Full article

Human–machine trust has shifted over the past three decades from trust in automation to trust in AI, while research paradigms, disciplines, and problem spaces have expanded. Centered on AI in healthcare, this narrative review offers a longitudinal synthesis that traces and compares phase-specific changes in theory and method, providing design guidance for human-AI systems at different stages of maturity. From a cross-disciplinary view, we introduce an Interdisciplinary Human-AI Trust Research (I-HATR) framework that aligns explainable AI (XAI) with human–computer interaction/human factors engineering (HCI/HFE). We distill three core categories of determinants of human-AI trust in healthcare, user characteristics, AI system attributes, and contextual factors, and summarize the main measurement families and their evolution from self-report to behavioral and psychophysiological approaches, with growing use of multimodal and dynamic evaluation. Finally, we outline key trends, opportunities, and practical challenges to support the development of human-centered, trustworthy AI in healthcare, emphasizing the need to bridge actual trustworthiness and perceived trust through shared metrics, uncertainty communication, and trust calibration. Full article

Human cytomegalovirus (HCMV) is a complex pathogen that encodes a diverse array of proteins essential for its survival and replication within host organisms. Among these proteins, a noteworthy group comprises four chemokine-like G protein-coupled receptors (cellular GPCRs), which play pivotal roles in the virus’s evasion of the host immune response and the establishment of persistent infections. Of particular interest is pUS28, recognized as one of the most extensively studied viral GPCRs (vGPCRs). This receptor has attracted significant attention for its potential as a target for innovative antiviral therapies aimed at addressing HCMV-related diseases. In contrast, pUS27 has not been as thoroughly characterized, presenting a potentially promising avenue for antiviral intervention. The relative scarcity of research surrounding pUS27 underscores an exciting opportunity for further exploration, as a deeper understanding of its functions and mechanisms may reveal novel strategies for combating HCMV infections. This review seeks to synthesize recent advancements in our understanding of pUS27, elucidating its biological roles, interactions, and potential implications for therapeutic development. We will also highlight critical gaps in the existing literature that warrant further investigation, underscoring the need for a more comprehensive understanding of this understudied receptor. By delving into the complexities of pUS27, we aim to inspire future research initiatives that could lead to the development of novel antiviral treatments, thereby enhancing our overall understanding of HCMV pathogenesis. Importance: The study of vGPCRs is essential for understanding how viruses like HCMV manipulate host cell signaling and evade immune responses. While pUS28 has been extensively studied due to its broad chemokine binding and signaling activity, its lesser-known homolog, pUS27, warrants closer attention. Likely arising from a gene duplication event, pUS27 shares approximately 31% sequence identity with pUS28 and is conserved across HCMV strains, suggesting an important functional role. By focusing on pUS27, we may uncover shared mechanisms that allow therapies to effectively target both pUS28 and pUS27, potentially leading to more potent antiviral treatments. The implications of studying pUS27 are profound, as it could play a pivotal role in improving our approaches to combating HCMV and enhancing our overall understanding of immune evasion strategies. Full article