Search Results (2,817)

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant errors, and inefficiencies. This article focuses on addressing this deficiency by designing a computer vision desktop application for automated monitoring of PPE use. This system uses lightweight YOLOv8 models, developed to run on the local system and operate even in industrial locations with limited network connectivity. Using a Lean UX approach, the development of the system involved creating empathy maps, assumptions, product backlog, followed by high-fidelity prototype interface components. C4 and physical diagrams helped define the system architecture to facilitate modifiability, scalability, and maintainability. Usability was verified using the System Usability Scale (SUS), with a score of 87.6/100 indicating “excellent” usability. The findings demonstrate that a user-centered design approach, considering user experience and technical flexibility, can significantly advance the utility and adoption of AI-based safety tools, especially in small- and medium-sized manufacturing operations. This article delivers a validated and user-centered design solution for implementing machine vision systems into manufacturing safety processes, simplifying the complexities of utilizing advanced AI technologies and their practical application in resource-limited environments. Full article

As voice cloning technology rapidly advances, the risk of personal voices being misused by malicious actors for fraud or other illegal activities has significantly increased, making the collection of speech data increasingly challenging. To address this issue, this study proposes a data augmentation method based on XText-to-Speech (XTTS) synthesis to tackle the challenges of small-sample, multi-class speech recognition, using profanity as a case study to achieve high-accuracy keyword recognition. Two models were therefore evaluated: a CNN model (Proposed-I) and a CNN-Transformer hybrid model (Proposed-II). Proposed-I leverages local feature extraction, improving accuracy on a real human speech (RHS) test set from 55.35% without augmentation to 80.36% with XTTS-enhanced data. Proposed-II integrates CNN’s local feature extraction with Transformer’s long-range dependency modeling, further boosting test set accuracy to 88.90% while reducing the parameter count by approximately 41%, significantly enhancing computational efficiency. Compared to a previously proposed incremental architecture, the Proposed-II model achieves an 8.49% higher accuracy while reducing parameters by about 98.81% and MACs by about 98.97%, demonstrating exceptional resource efficiency. By utilizing XTTS and public corpora to generate a novel keyword speech dataset, this study enhances sample diversity and reduces reliance on large-scale original speech data. Experimental analysis reveals that an optimal synthetic-to-real speech ratio of 1:5 significantly improves the overall system accuracy, effectively addressing data scarcity. Additionally, the Proposed-I and Proposed-II models achieve accuracies of 97.54% and 98.66%, respectively, in distinguishing real from synthetic speech, demonstrating their strong potential for speech security and anti-spoofing applications. Full article

Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with 328 university students from four districts in Lima, Peru, using an online survey to measure technostress. Confirmatory factor analysis (CFA) was performed to assess the psychometric properties of the TS4US scale, resulting in a refined model with two latent factors and thirteen validated items. Findings indicate that 28% of students experience high technostress levels, while 5% report very high levels, though no significant associations were found between technostress and sociodemographic variables such as campus location, employment status, gender, and academic level. The TS4US instrument had been previously validated in Chile; this study confirms its structure in a new sociocultural context, reinforcing its cross-cultural applicability. These results highlight the need for sustainable strategies to mitigate technostress in higher education, including institutional support, digital literacy programs, and policies fostering a balanced technological environment. Addressing technostress is essential for promoting sustainable education (SDG4) and enhancing student well-being (SDG3). This study directly contributes to the achievement of Sustainable Development Goals 3 (Good Health and Well-being) and 4 (Quality Education) by providing validated tools and evidence-based recommendations to promote mental health and equitable access to digital education in Latin America. Future research should explore cross-country comparisons and targeted interventions, including digital well-being initiatives and adaptive learning strategies, to ensure a resilient and sustainable academic ecosystem. Full article

This paper presents a case study of the Local Care Ecosystems developed by the provincial government of Gipuzkoa (Basque Country, Spain) to strengthen coordination between social services, health services, and community-based initiatives at the municipal level. The initiative seeks to personalize care, enhance service integration, and support community-based care with the overarching goal of improving the quality of life for older adults living at home. These ecosystems incorporate social, institutional, and technological innovations aimed at supporting individuals who are frail or vulnerable throughout the care cycle. At present, 18 Local Care Ecosystems are active, providing services to 1202 people over the age of 65 and 167 families. The model addresses a growing global challenge linked to population aging, which has led to increasing demand for care and support services that are often fragmented, under-resourced, and constrained by outdated regulatory frameworks. These structural issues can compromise both the quality and efficiency of care for dependent individuals. Based on the findings, the paper offers policy recommendations to support the transfer and adaptation of this model, with the aim of improving the well-being of older adults who wish to remain in their own homes. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The growing number of senior experts leaving the workforce (especially in more developed economies, such as in Europe), combined with the ubiquitous access to artificial intelligence (AI), is triggering organizations to review their knowledge transfer programs, motivated by both financial and management perspectives. Our study aims to contribute to the field by analyzing options to integrate intergenerational tacit knowledge transfer (InterGenTacitKT) with AI-driven approaches, offering a novel perspective on sustainable Knowledge and Human Resource Management in organizations. We will do this by building on previous research and by extracting findings from 36 in-depth semi-structured interviews that provided success factors for junior/senior tandems (JuSeTs) as one notable format of tacit knowledge transfer. We also refer to the literature, in a grounded theory iterative process, analyzing current findings on the use of AI in tacit knowledge transfer and triangulating and critically synthesizing these sources of data. We suggest that adding AI into a tandem situation can facilitate collaboration and thus aid in knowledge transfer and trust-building. We posit that AI can offer strong complementary services for InterGenTacitKT by fostering the identified success factors for JuSeTs (clarity of roles, complementary skill sets, matching personalities, and trust), thus offering organizations a powerful means to enhance the effectiveness and sustainability of InterGenTacitKT that also strengthens employee productivity, satisfaction, and loyalty and overall organizational competitiveness. Full article

Hand hygiene (HH) is essential for preventing disease transmission, particularly in schools where children are in close contact with other children. This study evaluated a school-based intervention on observed HH practices and hand cleanliness in six primary schools in Guatemala. Hand cleanliness was measured using the Quantitative Personal Hygiene Assessment Tool. The intervention included (1) HH behavior change promotion through Handwashing Festivals, and (2) increased access to HH materials at HH stations. Handwashing Festivals were day-long events featuring creative student presentations on HH topics. Schools were provided with soap and alcohol-based hand rub throughout the project to support HH practices. Appropriate HH practices declined from 51.2% pre-intervention to 33.1% post-intervention, despite an improvement in median Quantitative Personal Hygiene Assessment Tool scores from 6 to 8, indicating cleaner hands. Logistic regression showed higher odds of proper HH when an assistant was present. The decline in HH adherence was likely influenced by fewer assistants and changes in COVID-19 policies, while improvements in hand cleanliness may reflect observational bias. These findings emphasize the importance of sustained behavior change strategies, reliable HH material access, and targeted interventions to address gaps in HH practices, guiding school health policy and resource allocation. Full article

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article

Background/Objectives: International students pursuing higher education in the United States face unique challenges that increase their risk of food insecurity, including limited financial resources, employment restrictions, and cultural barriers. While food insecurity among domestic students has been widely studied, limited research focuses on the lived experiences of international graduate students. This study explores the challenges, perceptions, and coping strategies related to food insecurity among international graduate students at a large public university in North Texas. Methods: This qualitative, ethnographic study involved 20 semi-structured interviews with international graduate students who were clients of the university’s food pantry. Participants were recruited using purposive convenience sampling. Interviews focused on students’ experiences with food access, financial constraints, campus resources, and cultural food preferences. Data were analyzed using thematic coding in MAXQDA. Two standardized food insecurity measures—the USDA and FAO scales—were also administered and analyzed using SPSS. Results: Findings revealed that 85% of participants experienced limited access to nutritious and culturally appropriate foods, with 70% reporting hunger due to financial constraints. Themes included lack of cooking skills, limited campus food options, difficulty accessing familiar groceries, and limited job opportunities. Students expressed that food insecurity significantly impacted their physical health, mental well-being, and social lives, though many continued to prioritize academics over personal nourishment. Conclusions: Food insecurity among international graduate students is multifaceted, shaped by financial, cultural, and institutional barriers. Addressing this issue requires culturally sensitive interventions, improved access to diverse food options, tailored student support services, and institutional efforts to better understand and meet the needs of international students. Full article

The present study focuses on investigating the contribution of puppetry as a pedagogical and psychosocial tool in special education, addressing the literature gap in the systematic documentation of the experiences of special education teachers, concerning its use in daily teaching practice. The main objective is to capture the way in which puppetry enhances the learning, social and therapeutic support of students with complex educational and psychosocial needs. The study employs a qualitative phenomenological approach, conducting semi-structured interviews with eleven special education teachers who integrate puppetry into their teaching. Qualitative data were analyzed using thematic analysis. The findings highlight that puppetry significantly enhances cognitive function, concentration, memory and language development, while promoting the active participation, cooperation, social inclusion and self-expression of students. In addition, the use of the puppet acts as a means of psycho-emotional empowerment, supporting positive behavior and helping students cope with stress and behavioral difficulties. Participants identified peer support, material adequacy and training as key factors for effective implementation, while conversely, a lack of resources and time is cited as a key obstacle. The integration of puppetry in everyday school life seems to ameliorate a more personalized, supportive and experiential learning environment, responding to the diverse and complex profiles of students attending special schools. Continuous training for teachers, along with strengthening the collaboration between the arts and special education, is essential for the effective use of puppetry in the classroom. Full article

This study investigates the relationships between proactive personality, psychological capital, work engagement, work well-being, and innovative work behavior among employees in Saudi public authorities, based on the conservation of resources theory and the job demands-resources model. Using a sequential mediation model, data from 457 public employees were analyzed through structural equation modeling. The results show that a proactive personality and psychological capital significantly predict work engagement, but neither is significantly related to work well-being. Notably, while a proactive personality does not directly impact innovative work behavior, psychological capital does. Additionally, work well-being partially mediates the relationship between work engagement and innovative work behavior. These findings suggest that enhancing psychological capital and fostering engagement are key to promoting innovation. The mediating role of well-being highlights the importance of employee welfare in this process. This study provides practical implications for HR managers in the Saudi public sector and emphasizes strategies for building internal psychological resources. However, as data were collected from a single source, future research should include multiple key informants to enhance generalizability. This study builds on theory by demonstrating how proactive personality and psychological capital jointly stimulate innovative behavior through engagement and well-being, enriching the job demands-resources model with personal resource dynamics in public sector organizations. Full article

Media organizations are experiencing ongoing transformation, increasingly driven by the advancement of AI technologies. This development has begun to link journalists with generative systems and synthetic technologies. Although newsrooms worldwide are exploring AI adoption to improve information sourcing, news production, and distribution, a gap exists between resource-rich organizations and those with limited means. Since ChatGPT 3.5 was released on 30 November 2022, Greek media and journalists have gained the ability to use and explore AI technology. In this study, we examine the use of AI in Greek newsrooms, as well as journalists’ reflections and concerns. Through qualitative analysis, our findings indicate that the adoption and integration of these tools in Greek newsrooms is marked by the lack of formal institutional policies, leading to a predominantly self-directed and individualized use of these technologies by journalists. Greek journalists engage with AI tools both professionally and personally, often without organizational guidance or formal training. This issue may compromise the quality of journalism due to the absence of established guidelines. Consequently, individuals may produce content that is inconsistent with the media outlet’s identity or that disseminates misinformation. Age, gender, and newsroom roles do not constitute limiting factors for this “experimentation”, as survey participants showed familiarity with this technology. In addition, in some cases, the disadvantages of specific tools regarding qualitative results in Greek are inhibiting factors for further exploration and use. All these points to the need for immediate training, literacy, and ethical frameworks. Full article

Background/Objectives: This study explores the perspectives of mental health professionals on tailoring the Mentis Plus intervention to enhance positive mental health (PMH) in individuals experiencing First-Episode Psychosis (FEP). Although the Mentis Plus Program has been previously implemented in other contexts, it has not yet been applied to FEP care. Therefore, this study aimed to adapt the intervention for future implementation through expert consultation. Methods: A qualitative exploratory-descriptive design was employed. Data were collected via three focus groups comprising multidisciplinary professionals experienced in FEP care. Qualitative content analysis was used to examine the data. Results: Participants viewed the tailored Mentis Plus intervention as a valuable, recovery-oriented tool. Key recommendations included a flexible, group-based format with eight weekly sessions. Suggested intervention components encompassed gratitude journaling, emotional regulation techniques, and collaborative problem-solving exercises. Group delivery was highlighted as essential for mitigating isolation and promoting peer support. Practical implementation strategies included phased session structures and routine emotional check-ins. Identified barriers to implementation included the need for specialized training, limited therapeutic spaces, and the heterogeneity of participant needs. Facilitators included a person-centered approach, institutional backing, and sufficient resources. Conclusions: The findings support the feasibility and clinical relevance of a tailored Mentis Plus FEP Program—Brief Version. Expert-informed insights provide a foundation for adapting mental health interventions to early-psychosis care and inform future research and implementation strategies. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article