Search Results (4,294)

Managing resource use in cloud and edge environments is crucial for optimizing performance and efficiency. Traditionally, this process is performed with detailed knowledge of the available infrastructure while being application-specific. However, it is common that users cannot accurately specify their applications’ low-level requirements, and they tend to overestimate them—a problem further intensified by their lack of detailed knowledge on the infrastructure’s characteristics. In this context, resource orchestration mechanisms perform allocations based on the provided worst-case assumptions, with a direct impact on the performance of the whole infrastructure. In this work, we propose a resource orchestration mechanism based on intents, in which users provide their high-level workload requirements by specifying their intended preferences for how the workload should be managed, such as prioritizing high capacity, low cost, or other criteria. Building on this, the proposed mechanism dynamically assigns resources to applications through a Reinforcement Learning method leveraging the feedback from the users and infrastructure providers’ monitoring system. We formulate the respective problem as a discrete-time, finite horizon Markov decision process. Initially, we solve the problem using a tabular Q-learning method. However, due to the large state space inherent in real-world scenarios, we also employ Deep Reinforcement Learning, utilizing a neural network for the Q-value approximation. The presented mechanism is capable of continuously adapting the manner in which resources are allocated based on feedback from users and infrastructure providers. A series of simulation experiments were conducted to demonstrate the applicability of the proposed methodologies in intent-based resource allocation, examining various aspects and characteristics and performing comparative analysis. Full article

To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

This study aims to investigate the role of Industry 4.0 (I4.0) technologies in facilitating the transition towards a circular economy (CE) in the manufacturing sector, exploring four key circular economy strategies—reuse, repair, refurbishment, and remanufacturing. This study combines a comprehensive literature review with case studies of ten manufacturing organisations from various sectors, including electronics, information and communication technologies, and the household and furniture industries. The research focuses on three main areas: the adoption of circular strategies, the challenges associated with implementing Industry 4.0 technologies, and the role of these technologies in enabling the transition to a circular economy. Data were collected through ten interviews with managers responsible for sustainability, corporate social responsibility, or circular economy projects and initiatives, as well as through documentary analysis of archival materials. The study found that organisations typically adopt multiple circular strategies, with repair being the most prevalent strategy across all sectors and adopted in every case analysed. However, the adoption of I4.0 technologies faces challenges such as scalability issues, digital expertise shortages, and outdated infrastructure. Advanced adopters of I4.0 technologies benefit from robust delivery systems supported by collaborative networks, which enhance knowledge transfer and development among stakeholders. Full article

Arrhythmias significantly contribute to morbidity and mortality in patients with congenital heart disease (CHD). While postoperative factors predisposing to arrhythmias are well-established, early electrophysiological alterations in pediatric CHD remain poorly understood. This review summarizes current knowledge on postnatal cardiac maturation, conduction-system development, and electrophysiological abnormalities in pediatric patients with and without CHD. Importantly, arrhythmia prevalence, mechanisms, and clinical relevance are systematically discussed across three pediatric groups, including healthy children and patients with unrepaired and repaired CHD. Understanding developmental arrhythmogenic mechanisms may facilitate early risk stratification, guide clinical management decisions, and improve long-term outcomes for pediatric patients with CHD. This review discusses the complex interplay between cardiac maturation, congenital defects, and arrhythmogenesis. It also outlines future directions that include noninvasive monitoring, selective intraoperative mapping, animal model studies, and standardized data collection to improve early risk stratification and long-term outcomes in children with CHD. Full article

Modern custom machine construction and automation projects face pressure to shorten innovation cycles, reduce durations, and manage growing system complexity. Traditional methods like Waterfall and V-Model have limits where end-to-end data traceability is vital throughout the product life cycle. This study introduces the implementation of a web application that incorporates a model-based design approach to assess its applicability and effectiveness in conceptual design scenarios. At the heart of this approach is the Case-Based Axiomatic Design Assistant (CADA), which utilizes Axiomatic Design principles to break down complex tasks into structured, analyzable sub-concepts. It also incorporates Case-Based Reasoning (CBR) to systematically store and reuse design knowledge. The effectiveness of the visual assistant was evaluated through expert-led assessments across different fields. The results revealed a significant reduction in design effort when utilising prior knowledge, thus validating both the efficiency of CADA as a model and the effectiveness of its implementation within a user-centric application, highlighting its collaborative features. The findings support this approach as a scalable solution for enhancing conceptual design quality, facilitating knowledge reuse, and promoting agile development. Full article

This article deals with the analysis and exploration of the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The authors point out that the implementation of advanced AI technologies into already functioning and often complex systems, such as enterprise resource planning (ERP), presents significant technical challenges and requires a well-thought-out integration strategy. The complexity arises from the need to align new solutions with existing processes, resources, and data. Using the example of a fuel distribution system, the authors present the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The article presents a comprehensive analysis of the smart upgrade of fuel delivery management (FDM) architecture by incorporating an AI app to solve complex problems, such as predicting demand or traffic flows, as well as correctly detecting near-miss events. Technological convergence enables the mutual pursuit of improving the management process by developing soft skills and expanding knowledge managers. The authors’ findings show that an important factor for successful convergence is horizontal and vertical matching of the human knowledge and artificial intelligence cooperation for archive max positive synergy. Some recommendations could be useful for tank truck operators as a starting point to predict demand patterns, smart route planning, etc., where an AI app could be very successful. Full article

Obesity is a complex, multifactorial disease and a growing global health challenge associated with type 2 diabetes, cardiovascular disorders, cancer, and reduced quality of life. The existing pharmacological therapies are characterized by their limited number and efficacy, and safety concerns further restrict their utilization. This review synthesizes extensive knowledge regarding the role of the endocannabinoid system (ECS) in the pathogenesis of obesity, as well as its potential as a therapeutic target. A thorough evaluation of preclinical and clinical data concerning endocannabinoid ligands, cannabinoid receptors (CB1, CB2), their genetic variants, and pharmacological interventions targeting the ECS was conducted. Literature data suggests that the overactivation of the ECS may play a role in the pathophysiology of excessive food intake, dysregulated energy balance, adiposity, and metabolic disturbances. The pharmacological modulation of ECS components, by means of CB1 receptor antagonists/inverse agonists, CB2 receptor agonists, enzyme inhibitors, and hybrid or allosteric ligands, has demonstrated promising anti-obesity effects in animal models. However, the translation of these findings into clinical practice remains challenging due to safety concerns, particularly neuropsychiatric adverse events. The development of novel strategies, including peripherally restricted compounds, hybrid dual-target agents, dietary modulation of endocannabinoid tone, and non-pharmacological interventions, promises to advance the field of obesity management. Full article

Backyard poultry systems (BPS) are the most widespread form of animal production worldwide, contributing to household economies and improving food availability. However, limited biosecurity measures and close human–animal interactions raise concerns regarding zoonotic disease transmission. In recent years, consumer-driven motivations have given rise to non-traditional BPS with differential attributes (BPS-DA), yet there is limited knowledge about their food production practices. This study aimed to characterize and compare practices across 25 BPS and 25 BPS-DA in the Metropolitan Region using surveys, interviews, and direct observations of egg collections and poultry slaughters. Eggs were the main animal product in both systems, with women primarily responsible for care. Poultry slaughter was reported exclusively in BPS (60%), generally performed under inadequate hygienic conditions and without veterinary oversight. These practices, (poultry slaughter, food production and handling), may considerably increase the risk of human exposure to zoonotic pathogens, such as avian influenza viruses. In contrast, BPS-DA prioritized birds as companion animals (60%), free-range rearing (68%), and hobby-based production (80%). While both systems showed limited biosecurity, significant differences were found in the use of dedicated footwear (p = 0.01; V = 0.35), egg collection sites (p = 0.04; V = 0.29), and refrigeration (p = 0.004; V = 0.41). Veterinary access was limited in both (32% in BPS; 44% in BPS-DA). These findings highlight critical gaps in health management and underscore the need for context-specific educational and regulatory strategies for safer backyard poultry production. Full article

Postpartum uterine diseases such as metritis and endometritis impair reproductive performance and cause substantial economic losses in dairy cows worldwide. The multifactorial etiology, involving polymicrobial infections and complex host immune responses, poses diagnostic and therapeutic challenges. Traditional treatments rely on antibiotics, e.g., cephalosporins like ceftiofur and cephapirin, with broad-spectrum efficacy. However, emerging antimicrobial resistance, biofilm formation by pathogens such as Trueperella pyogenes, Fusobacterium necrophorum, and Escherichia coli, and bacterial virulence factors have reduced effectiveness of conventional therapies. Advances in systems biology, particularly proteomics, metabolomics, and microRNA (miRNA) profiling, have provided unprecedented insights into the molecular mechanisms underpinning uterine disease pathophysiology. Proteomic analyses reveal dynamic changes in inflammatory proteins and immune pathways, whereas metabolomics highlight shifts in energy metabolism and bacterial–host interactions. Furthermore, miRNAs have critical roles in post-transcriptional gene regulation affecting immune modulation, inflammation, and tissue repair, and also in modulating neutrophil function and inflammatory signaling. Uterine inflammation not only disrupts local tissue homeostasis but also compromises early embryo development by altering endometrial receptivity, cytokine milieu, and oocyte quality. Integration of multi-omics approaches, combined with improved diagnostics and adjunct therapies—including micronutrient supplementation and immunomodulators—offers promising avenues for enhancing disease management and fertility in dairy herds. This review synthesizes current knowledge on proteomics, metabolomics, and miRNAs in postpartum uterine diseases and highlights future directions for research and clinical applications. Full article

This paper presents preliminary findings on the effectiveness of a citizen science initiative that engages local communities in rural Kalimantan in collecting wildlife observations within their village forests. By leveraging the power of community participation, the initiative aims to build on local knowledge, promote sustainable management practices, and collect valuable data on species distribution. Through a combination of focus group discussions, training workshops, field surveys, and mobile app-based data collection from 2023 to 2025, the initiative successfully mobilized community members, particularly those with limited technological experience, to actively participate in biodiversity monitoring. We recently introduced a small ‘payment for wildlife observations’ system that significantly boosted observations. The initial results highlight the potential for citizen science to generate valuable species trend data and foster a sense of pride, ownership, and stewardship among community members. While the current manuscript does not provide statistical analyses of the wildlife data, we describe how we plan to overcome data biases that are inherent to opportunistic, unstructured survey efforts. The project continues, but the lessons learned thus far can inform future citizen science initiatives and contribute to the development of sustainable, long-term, low-cost and effective community-based conservation strategies in the region. Full article

Integrating human–robot collaboration (HRC) into construction sites has significantly enhanced efficiency and quality. However, it also introduces new or intensifies existing risks as it brings in new entities, relationships, and construction activities. Safety remains the top priority and a persistent concern in HRC systems. However, the current literature on human–robot collaboration safety (HRCS) is vast yet fragmented, and a systematic exploration of its status and research trends in the construction context is still lacking. This paper explores advances in HRCS over the past two decades through a mixed quantitative and qualitative analysis method. Initially, 287 related articles were identified by keyword-searching in Scopus, followed by bibliometric analysis using CiteSpace to uncover the knowledge structure and track emerging research trends. Subsequently, a qualitative discussion highlights achievements in HRCS across five dimensions: (1) optimization of remote intelligent machinery; (2) hazard analysis and risk assessment in HRCS; (3) digital twin for safety monitoring; (4) cognitive and psychological impacts; (5) organizational management perspective. This study quantitatively maps the scientific landscape of HRCS at a macro level and qualitatively identifies key research areas. It provides a comprehensive foundation for understanding the evolution of HRCS and exploring future research directions and applications. Full article

Hypophosphatemia, defined as serum phosphate levels below 2.5 mg/dL, is a common yet underrecognized electrolyte disturbance in critically ill patients, with prevalence estimates reaching up to 80%. This review explores the intricate bidirectional relationship between hypophosphatemia and hyperventilation, emphasizing its profound implications for respiratory function and critical care management. Hypophosphatemia impairs oxygen delivery by depleting 2,3-diphosphoglycerate (2,3-DPG), disrupts central respiratory drive, and weakens respiratory muscles, leading to hyperventilation, ventilatory failure, and prolonged mechanical ventilation. Conversely, hyperventilation exacerbates hypophosphatemia through respiratory alkalosis, triggering intracellular phosphate shifts and metabolic cascades that rapidly deplete serum levels. This cycle creates significant challenges for ventilator weaning and increases morbidity and mortality. Underlying mechanisms include impaired ATP synthesis, altered chemoreceptor sensitivity, and systemic inflammatory responses. Hypophosphatemia-induced hyperventilation manifests as unexplained tachypnea and respiratory alkalosis, often misdiagnosed as anxiety or pain, while hyperventilation-induced hypophosphatemia contributes to diaphragmatic dysfunction and poor ventilatory performance. Common precipitating factors include refeeding syndrome, diabetic ketoacidosis, continuous renal replacement therapy, and malnutrition. Complications extend beyond respiratory dysfunction to include cardiac depression, immune dysfunction, prolonged ICU stays, and increased healthcare costs. Current diagnostic approaches rely on serum phosphate measurements, which poorly reflect total body stores due to significant intracellular shifts. Emerging biomarkers such as fibroblast growth factor 23 (FGF23) and advanced monitoring technologies, including continuous phosphate tracking, may enhance recognition. Treatment strategies emphasize targeted phosphate repletion based on severity, with intravenous supplementation and ventilatory support tailored to minimize complications. Preventive measures, including risk stratification, prophylactic supplementation, and ventilator management, are critical for high-risk populations. Despite advances, knowledge gaps persist in optimizing monitoring and repletion protocols, understanding genetic variations, and identifying ideal phosphate targets for improved respiratory outcomes. This review provides a comprehensive framework for recognizing and managing hypophosphatemia’s impact on respiratory dysfunction in critically ill patients. Adopting evidence-based interventions and leveraging emerging technologies can significantly improve clinical outcomes, reduce ICU complications, and enhance recovery in this vulnerable population. Full article

Background/Objectives: Multi-cancer detection (MCD) tests have emerged as a promising tool to redefine the landscape of early cancer detection. Implementation of this novel technology will likely fall to primary care clinicians (PCC). The purpose of this study is to characterize and explore differences in PCCs perceptions and preferences towards MCD testing. Methods: Between March and May of 2023, this cross-sectional survey was administered to 281 PCCs, including physicians and advanced care providers practicing within an integrated healthcare system spanning five states. The survey collected data on self-reported characteristics, perceptions of MCD testing, and preferences for learning about MCD testing. Analysis was limited to those with no prior experience with MCD testing (N = 181, response rate 22.8%). Descriptive statistics summarized key variables and chi-square tests assessed differences in perceptions and preferences by key characteristics. Results: Most PCCs were interested in MCD testing (66.3%), but limited knowledge/awareness of MCD testing and confidence to manage patients with a positive test were observed, along with concerns around cost (76.7%) and misuse/poor implementation. The primary preferences for learning about MCD testing were online courses or classroom instruction (64.5%). Significant differences in perceptions and preferences for learning were observed by location, degree, and years in practice. Conclusions: PCCs in our study held positive views towards MCD testing, but gaps and variation in knowledge and confidence towards MCD testing and concerns around the cost and misuse/poor implementation were observed. While efforts to train and educate all PCCs on MCD testing is a critical first step, more research is needed to understand how best to support implementation tailored to individual and system-level needs and characteristics. Full article