Search Results (294)

Exposure to per- and polyfluoroalkyl substances (PFASs) can cause detrimental health effects. The consumption of contaminated food is viewed as a major exposure pathway for humans, but the relationship between agriculture and PFASs has not been investigated thoroughly, and it is becoming a pressing issue since health advisories are continuously being reassessed. This semi-systematic literature review connects the release, environmental fate, and agriculture uptake of PFASs to enhance comprehension and identify knowledge gaps which limit accurate risk assessment. It focuses on the heavily agricultural state of Minnesota, USA, which is representative of the large Midwestern US Corn Belt in terms of agricultural activities, because PFASs have been monitored in Minnesota since the beginning of the 21st century. PFAS contamination is a complex issue due to the over 14,000 individual PFAS compounds which have unique chemical properties that interact differently with air, water, soil, and biological systems. Moreover, the lack of field studies and monitoring of agricultural sites makes accurate risk assessments challenging. Researchers, policymakers, and farmers must work closely together to reduce the risk of PFAS exposure as the understanding of their potential health effects increases and legacy PFASs are displaced with shorter fluorinated replacements. Full article

Male-factor infertility accounts for approxiamately half of all infertility cases globally, yet therapeutic options remain limited for individuals with no retrievable spermatozoa, such as those with non-obstructive azoospermia (NOA). In recent years, artificial gametogenesis has emerged as a promising avenue for fertility restoration, driven by advances in two complementary strategies: organotypic in vitro spermatogenesis (IVS), which aims to complete spermatogenesis ex vivo using native testicular tissue, and in vitro gametogenesis (IVG), which seeks to generate male gametes de novo from pluripotent or reprogrammed somatic stem cells. To evaluate the current landscape and future potential of these approaches, a narrative, semi-systematic literature search was conducted in PubMed and Scopus for the period January 2010 to February 2025. Additionally, landmark studies published prior to 2010 that contributed foundational knowledge in spermatogenesis and testicular tissue modeling were reviewed to provide historical context. This narrative review synthesizes multidisciplinary evidence from cell biology, tissue engineering, and translational medicine to benchmark IVS and IVG technologies against species-specific developmental milestones, ranging from rodent models to non-human primates and emerging human systems. Key challenges—such as the reconstitution of the blood–testis barrier, stage-specific endocrine signaling, and epigenetic reprogramming—are discussed alongside critical performance metrics of various platforms, including air–liquid interface slice cultures, three-dimensional organoids, microfluidic “testis-on-chip” devices, and stem cell-derived gametogenic protocols. Particular attention is given to clinical applicability in contexts such as NOA, oncofertility preservation in prepubertal patients, genetic syndromes, and reprocutive scenarios involving same-sex or unpartnered individuals. Safety, regulatory, and ethical considerations are critically appraised, and a translational framework is outlined that emphasizes biomimetic scaffold design, multi-omics-guided media optimization, and rigorous genomic and epigenomic quality control. While the generation of functionally mature sperm in vitro remains unachieved, converging progress in animal models and early human systems suggests that clinically revelant IVS and IVG applications are approaching feasibility, offering a paradigm shift in reproductive medicine. Full article

Background/Objectives: Weight gain is frequently observed during and following breast cancer therapy. Women with overweight/obesity have poorer breast cancer prognoses and are more likely to develop comorbidities. The present study describes the development and qualitative assessment of the acceptability of the NutriLife study, a lifestyle weight management intervention with dietetic counseling and digital tools for breast cancer survivors (BCSs). Methods: The intervention was developed using the Medical Research Council (MRC) framework, informed by a systematic literature review and stakeholder input. Acceptability was assessed using the Theoretical Framework of Acceptability (TFA). A total of 22 BCSs with overweight/obesity participated in focus groups, and 5 dietitians/nutritionists specializing in breast cancer in Greece participated in semi-structured interviews. The data were further analyzed using thematic analysis. Results: Stakeholders assessed the intervention as acceptable across all TFA constructs. The intervention was characterized as supportive, easily adaptable, time-efficient, well-organized, beneficial, and professionally driven, with potential barriers including limited personal time, inadequate digital literacy, insufficient self-care, and lack of commitment. Gradually increasing goals may be helpful and less stressful, while educational resources enhance focus on these objectives, thus encouraging intervention participation. Ensuring confidentiality was perceived as central to promoting health. Conclusions: The evidence-based, co-participatory design of the NutriLife intervention was perceived as acceptable by the participating stakeholders and will be pilot-tested in a randomized controlled trial. Full article

This study offers a comprehensive examination of the scientific output related to the integration of Artificial Intelligence (AI) in education using qualitative research methods, which is an emerging intersection that reflects growing interest in understanding the pedagogical, ethical, and methodological implications of AI in educational contexts. Grounded in a theoretical framework that emphasizes the potential of AI to support personalized learning, augment instructional design, and facilitate data-driven decision-making, this study conducts a Systematic Literature Review and bibliometric analysis of 630 publications indexed in Scopus between 2014 and 2024. The results show a significant increase in scholarly output, particularly since 2020, with notable contributions from authors and institutions in the United States, China, and the United Kingdom. High-impact research is found in top-tier journals, and dominant themes include health education, higher education, and the use of AI for feedback and assessment. The findings also highlight the role of semi-structured interviews, thematic analysis, and interdisciplinary approaches in capturing the nuanced impacts of AI integration. The study concludes that qualitative methods remain essential for critically evaluating AI’s role in education, reinforcing the need for ethically sound, human-centered, and context-sensitive applications of AI technologies in diverse learning environments. Full article

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of diverse algorithms that optimize output values based on varied input parameters. In this context, we identify the main topics related to cellular networks and machine learning algorithms in order to pinpoint areas where the optimization of parameters is crucial. Furthermore, the wide range of available algorithms often leads to confusion and disorder during classification processes. It is crucial to note that next-generation networks are expected to require reduced latency times, especially for sensitive applications such as Industry 4.0. Research Question: An analysis of the existing literature on mobile network load balancing methods was conducted to identify systems that operate using semi-automatic, automatic and hybrid algorithms. Our research question is as follows: What are the automatic, semi-automatic and hybrid load balancing algorithms that can be applied to next-generation mobile networks? Contribution: This paper aims to present a comprehensive analysis and classification of the algorithms used in this area of study; in order to identify the most suitable for load balancing optimization in next-generation mobile networks, we have organized the classification into three categories, automatic, semi-automatic and hybrid, which will allow for a clear and concise idea of both theoretical and field studies that relate these three types of algorithms with next-generation networks. Figures and tables illustrate the number of algorithms classified by type. In addition, the most important articles related to this topic from five different scientific databases are summarized. Methodology: For this research, we employed the PRISMA method to conduct a systematic literature review of the aforementioned study areas. Findings: The results show that, despite the scarce literature on the subject, the use of load balancing algorithms significantly influences the deployment and performance of next-generation mobile networks. This study highlights the critical role that algorithm selection should play in 5G network optimization, in particular to address latency reduction, dynamic resource allocation and scalability in dense user environments, key challenges for applications such as industrial automation and real-time communications. Our classification framework provides a basis for operators to evaluate algorithmic trade-offs in scenarios such as network fragmentation or edge computing. To fill existing gaps, we propose further research on AI-driven hybrid models that integrate real-time data analytics with predictive algorithms, enabling proactive load management in ultra-reliable 5G/6G architectures. Given this background, it is crucial to conduct further research on the effects of technologies used for load balancing optimization. This line of research is worthy of consideration. Full article

Climate change is affecting farmers’ livelihoods and their ability to adapt. Therefore, solutions for adaptation and resilience are required. The objective of the work was to assess how nature-based solutions contribute to improving the adaptive capacity of farmers, taking coffee production in Mexico as a case study. It followed the theoretical approach of the Sustainable Livelihoods Framework, which involves identifying the capacities, resources, and activities that a population possesses, considering the following six dimensions: natural, social, human, economic, physical, and political. A rapid systematic review was carried out to identify measurement indicators for each dimension. A semi-structured survey was constructed to collect information on the indicators in the field. The surveys were administered to a sample of 60 randomly selected farmers who utilized various management types incorporating nature-based solutions, including diversified polyculture, simple polyculture, and simplified shade. In addition, farmers who do not use nature-based solutions and who grow coffee in full sun were considered. An index of adaptive capacity was then calculated for each coffee agroecosystem assessed, and finally, actions were proposed to strengthen the livelihood dimensions and increase the adaptive capacity of farmers. It was found that farmers using the management types diverse polyculture and simple polyculture had an average value of the adaptive capacity index classified as high (15.06 and 11.61, respectively). Farmers using the simplified shade management type had an average index value classified as medium (8.59). Whereas, farmers producing coffee in full sun were classified with low adaptive capacity in the average index value (−0.49). The results obtained in this research can contribute to informed government decision making (local, state, or federal) in generating policies to improve or design nature-based solutions in the agricultural sector, thereby increasing the adaptive capacity of producers in the face of climate variability. Full article

A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as inspection, cleaning, maintenance, and rescue while maintaining stable adhesion to the surface. Its applications span various sectors, including industrial maintenance, marine engineering, and aerospace manufacturing. This paper provides a systematic review of the physical principles and scalability of various attachment methods used in wall-climbing robots, with a focus on the applicability and limitations of different attachment mechanisms in relation to robot size and structural design. For specific attachment methods, the design and compatibility of motion and attachment mechanisms are analyzed to offer design guidance for wall-climbing robots tailored to different operational tasks. Additionally, this paper reviews localization and path planning methods for wall-climbing robots, comparing graph search, sampling-based, and feedback-based algorithms to guide strategy selection across varying environments and tasks. Finally, this paper outlines future development trends in wall-climbing robots, including the diversification of locomotion mechanisms, hybridization of attachment systems, and advancements in intelligent localization and path planning. This work provides a comprehensive theoretical foundation and practical reference for the design and application of wall-climbing robots. Full article

Vehicle-to-Grid (V2G) technology enables electric vehicles (EVs (Unless otherwise specified, Electric Vehicles (EVs) in this study refer to the totality of BEVs, PHEVs, and other battery-equipped vehicles that have the potential to participate in V2G)) to interact with renewable energy sources, positioning it as a key driver of energy system decentralization. While V2G holds significant potential for enhancing grid stability and economic efficiency, its large-scale deployment requires a robust economic assessment. However, existing research predominantly focuses on technical feasibility, lacking comprehensive economic evaluations due to the complexity of V2G system architectures. To bridge this gap, we propose the BSTP (Business-Stakeholders-Technology-Policy) V2G economic evaluation framework and the VRR (Value Realization Rate) methodology, employing a Semi-Systematic Co-Design Approach. This framework systematically characterizes the evolution of V2G business models, the interactions among key stakeholders, the influence of technological and policy factors, and the criteria for economic feasibility assessment. Furthermore, we identify a “Big Models, No Trials” issue in V2G economic research, where large-scale theoretical models lack empirical validation. To address this challenge and ensure the practical applicability of our framework, we define six core challenges that must be resolved for a rigorous economic evaluation of V2G. Our findings provide a structured foundation for future research and policy development, offering insights that could accelerate the transition to decentralized energy systems. Full article

The global expansion of road networks and the aging of infrastructure have intensified the need for efficient pavement distress detection technologies to ensure road safety and sustainability. While traditional manual inspections are time consuming and labor-intensive, recent advances in automated systems have improved detection precision. However, challenges persist, including limited accuracy, poor generalization across datasets, and high computational demands for pixel-level segmentation. This review systematically examines the evolution of pavement distress detection, covering three key phases: manual inspection, semi-automated systems, and non-destructive automated methods. We analyze advancements in image acquisition (e.g., 2D to 3D, ground to aerial platforms) and processing techniques (e.g., threshold-based segmentation to deep learning), highlighting critical trade-offs between speed, accuracy, and scalability. Our findings reveal that, while modern systems excel in controlled environments, their real-world performance remains inconsistent due to varying imaging conditions and underrepresented distress types. To address these gaps, we propose four future directions: (1) enhancing environmental adaptability through multi-sensor datasets, (2) optimizing datasets via self-supervised learning, (3) deploying lightweight models on edge devices for real-time analysis, and (4) integrating predictive maintenance frameworks. These strategies aim to shift pavement management from reactive repairs to proactive, data-driven decision making, ultimately supporting smarter infrastructure systems. Full article

Background and aims: Recent advances in acute stroke interventions have highlighted the importance of accurate determination of infarct volume in the evaluation of acute stroke patients, carrying important prognostic and therapeutic implications for treatment planning, outcome prediction, and evaluation of the success of therapeutic interventions. However, there is no consensus on the methodologies employed to measure cerebral infarct volume. We aimed to assess the reproducibility and reliability of methods employed in the clinical determination of infarct volume in acute ischaemic stroke. Methods: We carried out a systematic review of studies assessing methodologies for the determination of infarct volume in the acute phase (<24 h). We searched Medline PubMed, Scopus, Cinahl, Cochrane Library, Web of Science, and Embase for studies examining image-based diagnosis of acute ischaemic stroke < 24 h by CT or MRI. Data on patient cohorts, imaging type, time from symptoms onset, methodologies and quantification strategies, rater reliability, accuracy, sensitivity, and specificity were compared. Results: We identified eighteen eligible studies with a total of 3298 ischaemic stroke patients assessing a variety of manual, semi-automated, and fully-automated methods. The ABC/2 method was found to be highly reliable, reproducible, and accurate, and provides the best manual estimate of infarction, but has a tendency to under- or overestimate infarct volume. Semi-automated and automated approaches with user refinement showed excellent inter-rater and intra-rater correlation. However, differences in operating algorithms and lack of standardisation of image acquisition parameters, quality, and format may impact performance and reproducibility. Conclusions: Of all methods, automated and semi-automated approaches utilising rater judgment and refinement represent the most robust approaches, with semi-automated tools demonstrating consistent and repeatable results. We recommend a standardised reporting of study methodologies for the accurate interpretation and comparison of efficacy of therapeutic interventions and patient outcomes, especially in a multi-centre setting. This may allow for more effective evaluation of stroke therapies and accelerate ischaemic stroke treatment decisions. Full article

Sustainability in agribusiness is pivotal for addressing environmental challenges and ensuring long-term agricultural productivity, particularly in resource-constrained regions. This descriptive and exploratory study aims to develop and prioritize strategies to enhance the sustainability of greenhouse agribusiness in Hamedan Province, Iran, offering practical insights for policymakers and practitioners. We employed a comprehensive approach, integrating a systematic literature review with semi-structured interviews conducted with 18 purposively selected experts, including university faculty, agricultural researchers, and sector managers. Through SWOT analysis, we identified key internal strengths (e.g., year-round production potential) and weaknesses (e.g., high energy consumption), as well as external opportunities (e.g., access to export markets) and threats (e.g., reliance on imports). The analysis revealed that the most effective strategies for promoting sustainable greenhouse development are predominantly defensive, focusing on mitigating internal weaknesses and external threats. Using the TOWS matrix, we developed and prioritized strategic recommendations, including policy frameworks for organic production, a national sustainability support program, and cooperative marketing initiatives to improve market access. These strategies can serve as a roadmap for enhancing greenhouse sustainability in Hamedan and offer a replicable framework for similar semi-arid regions facing comparable challenges. Full article

Medical image segmentation, a critical task in medical image analysis, aims to precisely delineate regions of interest (ROIs) such as organs, lesions, and cells, and is crucial for applications including computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis. While fully supervised deep learning methods have demonstrated remarkable performance in this domain, their reliance on large-scale, pixel-level annotated datasets—a significant label scarcity challenge—severely hinders their widespread deployment in clinical settings. Addressing this limitation, this review focuses on non-fully supervised learning paradigms, systematically investigating the application of semi-supervised, weakly supervised, and unsupervised learning techniques for medical image segmentation. We delve into the theoretical foundations, core advantages, typical application scenarios, and representative algorithmic implementations associated with each paradigm. Furthermore, this paper compiles and critically reviews commonly utilized benchmark datasets within the field. Finally, we discuss future research directions and challenges, offering insights for advancing the field and reducing dependence on extensive annotation. Full article

The conceptual design phase in architecture requires both intuitive and iterative approaches, which traditional Building Information Modeling (BIM) workflows fail to support properly. BIM provides data-driven decision-making and project coordination but does not offer affective or experiential feedback capabilities. BIM and Virtual Reality (VR) integration offers a promising solution to improve user-focused spatial assessments during initial design phases. The research follows three distinct phases, including a Systematic Literature Review to identify BIM-based conceptual workflow limitations, semi-structured interviews with architects to understand practical challenges and expectations, and the development of a BIM-based framework combining immersive VR for affective and visuospatial evaluation. A testing phase of the proposed framework occurred in the pilot study. The current BIM workflows show significant deficiencies in their ability to support creative flexibility, user engagement, and experiential validation. The BIM-VR framework implemented in the pilot study showed improvements in spatial cognition, emotional engagement, and iterative design decision-making during the conceptual design phase. Early-stage architectural design evaluation becomes more effective through VR integration into BIM workflows because it provides real-time immersive user feedback. The proposed framework helps develop BIM tools that are more intuitive for humans while advancing user-informed design practices in the architecture, engineering, and construction industries. Full article

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Full article

Minor visual phenomena (MVP), such as visual illusions, pareidolias, feeling of presence, and passage hallucinations, are often experienced by patients with Lewy Body Disease (LBD), in addition to complex visual hallucinations (VH), even in the early stages of the disease. This systematic review aimed to provide an up-to-date literature review of the occurrence and prevalence of MVP in LBD and to assess their potential associations both with VH and visuoperceptual and visuospatial deficits. A systematic literature search was carried out in PubMed, Web of Science, APA PsycInfo, Scopus, and Cochrane Library, and a total of 44 articles were included. The included studies showed significant variability in the occurrence of MVP in the LBD population and in the assessment methods used, such as standardized scales (e.g., the noise pareidolia test), semi-structured interviews (e.g., the North-East Visual Hallucinations Interview), and clinical descriptions. Similarly to VH, MVP appears to be highly specific to LBD, helping in differential diagnosis from Alzheimer’s Disease. The overall relationship between MVP, VH, and visuoperceptual/visuospatial deficits remains unclear. Some studies found that MVP (especially pareidolic responses and presence of hallucinations) was positively correlated with VH, yet it is challenging to determine whether MVP can be considered a precursor of future VH development. Negative associations were reported between MVP (especially pareidolias) and visuoperceptual/visuospatial abilities. However, it is not clear whether these deficits serve as independent, exclusive factors in MVP occurrence or if they interact with VH as a contributing component. Gaining insight into the occurrence of these phenomena could prove beneficial for differential diagnosis, prognosis, and prediction of treatment outcomes in patients with LBD. Full article