Search Results (81,055)

This study investigates the essential competencies for engineers in the context of digital transformation, with the aim of proposing a refined framework to guide professional development across career levels. A mixed-methods, sequential approach was adopted: (1) a systematic literature review, conducted between 2014 and 2024, which identified 46 competencies organized into seven dimensions; (2) a quantitative survey with 392 engineers who self-assessed their level of mastery for each competency; (3) semi-structured interviews with 20 company representatives, who validated and contextualized the essential competencies according to hierarchical levels (junior, mid-level, and senior); (4) data triangulation, resulting in a final competency model by career level. The findings reveal a widespread deficit in digital competencies, regardless of hierarchical level. In total, 33 competencies assessed by career level showed statistically significant differences in employer perceptions and were identified as progressive throughout the career trajectory. Analysis of self-assessments and interviews indicates that for early-career engineers, there is a strong emphasis on personal and basic cognitive competencies. For mid-level engineers, the data show a significant valuation of social competencies. Senior engineers are perceived as having accumulated experience across all seven mapped dimensions. This study offers a practical model that can be used by educational institutions, companies, and professionals to align education, market demands, and career planning. Full article

Lactylation, a recently identified post-translational modification (PTM) triggered by excessive lactate accumulation, has emerged as a crucial regulator linking metabolic reprogramming to pathological processes in liver diseases. In hepatic contexts, aberrant lactylation contributes to a range of pathological processes, including inflammation, dysregulation of lipid metabolism, angiogenesis, and fibrosis. Importantly, lactylation has been shown to impact tumor growth, metastasis, and therapy resistance by modulating oncogene expression, metabolic adaptation, stemness, angiogenesis, and altering the tumor microenvironment (TME). This review synthesizes current knowledge on the biochemical mechanisms of lactylation, encompassing both enzymatic and non-enzymatic pathways, and its roles in specific liver diseases. From a therapeutic perspective, targeting lactate availability and transport, as well as the enzymes regulating lactylation, has demonstrated promise in preclinical models. Additionally, combinatorial approaches and natural compounds have shown efficacy in disrupting lactylation-driven pathways, providing insights into future research directions for hepatic diseases. Although the emerging role of lactylation is gaining attention, its spatiotemporal dynamics and potential for clinical translation are not yet well comprehended. This review aims to synthesize the multifaceted roles of lactylation, thereby bridging mechanistic insights with actionable therapeutic strategies for liver diseases. Full article

The escalating challenge of resistance to insecticides among agricultural and public health pests poses a significant threat to global food security and vector-borne disease control. This review synthesizes current understanding of the molecular mechanisms underpinning resistance, including well-characterized pathways such as target-site mutations affecting nicotinic acetylcholine receptors (nAChRs), acetylcholinesterase (AChE), voltage-gated sodium channels (VGSCs), and γ-aminobutyric acid (GABA) receptors, and metabolic detoxification mediated by cytochrome P450 monooxygenases (CYPs), esterases, and glutathione S-transferases (GSTs). Emerging resistance mechanisms are also explored, including protein sequestration by odorant-binding proteins and post-transcriptional regulation via non-coding RNAs, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Focused case studies on Aedes aegypti and Spodoptera frugiperda illustrate the complex interplay of genetic and biochemical adaptations driving resistance. In Ae. aegypti, voltage-gated sodium channel (VGSCs) mutations (V410L, V1016I, F1534C) combined with metabolic enzyme amplification confer resistance to pyrethroids, accompanied by notable fitness costs and ecological impacts on vector populations. In S. frugiperda, multiple resistance mechanisms, including overexpression of cytochrome P450 genes (e.g., CYP6AE43, CYP321A8), target-site mutations in ryanodine receptors (e.g., I4790K), and behavioral avoidance, have rapidly evolved across global populations, undermining the efficacy of diamide, organophosphate, and pyrethroid insecticides. The review further evaluates integrated pest management (IPM) strategies, emphasizing the role of biopesticides, biological control agents, including entomopathogenic fungi and parasitoids, and molecular diagnostics for resistance management. Taken together, this analysis underscores the urgent need for continuous molecular surveillance, the development of resistance-breaking technologies, and the implementation of sustainable, multifaceted interventions to safeguard the long-term efficacy of insecticides in both agricultural and public health contexts. Full article

Background: The primary effect of Botulinum toxin (BoNT) is to cause weakness in the injected muscles by inhibiting the release of acetyl choline from presynaptic nerve terminals. Its effect on sensorimotor integration (SMI) has largely been confined to small studies. The aim of this review is to highlight effect of BoNT on SMI in the context of Parkinson’s disease (PD), Cervical dystonia (CD), and Writer’s cramp (WC). Methods: Using keywords “Botulinum toxin” and “sensorimotor integration” or “Freezing of gait (FOG)” or ‘Tremor”or “Cervical dystonia” or “Parkinson’s disease”, or “Writer’s cramp”, PubMed database was searched for relevant articles supporting our view. The abstracts of all resultant articles (case reports, case series, randomized trials, observational studies) were reviewed to look for evidence of effects of botulinum toxin on SMI. The relevant articles were charted in excel sheet for further full text review. Results: In FOG, chronic BoNT injections may alter central motor patterns with inclusion of alternative striatal systems, cerebellum, and its connections. In tremor, the afferent proprioceptive input may be modified with reduction of intracortical facilitation and increment of intracortical inhibition. In CD, BoNT can restore disorganized cortical somatotrophy, the key pathophysiology behind cervical dystonia. Similarly, in WC, both the deficient sensory system and abnormal reorganization of the sensorimotor cortex may be altered following chronic BoNT injections. Conclusions: There is preliminary evidence that BoNT may modulate SMI in PD, CD, and WC by altering inputs from the muscle spindles in short term and modifying circuits/particular anatomic cerebral cortices in the long term. Properly conducted randomized trials comparing BoNT with placebo or prospective large-scale studies to look for effect on various surrogate markers reflective of changes in SMI should be the next step to confirm these findings. Targeting the system of afferents like spindles and golgi tendon organs in muscles may be a better way of injecting BoNT, with lower amounts of toxin needed and potential for lesser side-effects like weakness and atrophy. However, this needs to be proven in controlled trials. Full article

Industry 5.0 introduces a new paradigm where digital technologies support sustainable and human-centric industrial development. Within this context, resilience-based maintenance (RBM) emerges as a forward-looking maintenance strategy focused on system adaptability, fault tolerance, and recovery capacity under uncertainty. This article presents a systematic literature review (SLR) on RBM in the context of Maintenance 5.0. The review follows the PRISMA methodology and incorporates bibliometric and content-based analyses of selected publications. Key findings highlight the integration of AI methods, such as machine learning and digital twins, in enhancing system resilience. The results demonstrate how RBM aligns with the pillars of Industry 5.0, sustainability, and human-centricity, by reducing resource consumption and improving human–machine interaction. Research gaps are identified in AI explainability, sector-specific implementation, and ergonomic integration. The article concludes by outlining directions for developing Maintenance 5.0 as a strategic concept for resilient, intelligent, and inclusive industrial systems. Full article

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly rely on single-modal inputs and suffer from poor recognition stability under complex field conditions, a multimodal recognition framework has been proposed. This framework integrates RGB imagery, thermal infrared imaging, and environmental sensor data. A cross-modal attention mechanism, environment-guided modality weighting strategy, and decoupled recognition heads are incorporated to enhance the model’s robustness against small targets, intermodal variations, and environmental disturbances. Evaluated on a high-complexity multimodal field dataset, the proposed model significantly outperforms mainstream methods across four key metrics, precision, recall, F1-score, and mAP@50, achieving 91.5% precision, 89.2% recall, 90.3% F1-score, and 88.0% mAP@50. These results represent an improvement of over 6% compared to representative models such as YOLOv8 and DETR. Additional ablation studies confirm the critical contributions of key modules, particularly under challenging scenarios such as low light, strong reflections, and sensor data noise. Moreover, deployment tests conducted on the Jetson Xavier edge device demonstrate the feasibility of real-world application, with the model achieving a 25.7 FPS inference speed and a compact size of 48.3 MB, thus balancing accuracy and lightweight design. This study provides an efficient, intelligent, and scalable AI solution for pest surveillance and biological control, contributing to precision pest management in agricultural ecosystems. Full article

Adoption of the use of robots in the foodservice industry has increased, and research in the quality of service provided by robots is required. Our research objective is to determine interrelationships among service quality, perceived value, restaurant image, satisfaction, and revisit intentions among customers at robotic restaurants. Data collection was conducted, with 342 South Korean restaurant consumers considered suitable to offer accurate responses to the survey questions. An online survey was employed to examine hypothesized relationships. Data analysis used descriptive statistics, confirmatory factor analysis, and structural equation modeling. Three dimensions of service quality (atmosphere quality, food quality, and interaction quality) at robotic restaurants were critical for higher perceived value by diners at robotic restaurants. Perceived value increases robotic restaurant image, customer satisfaction, and customer revisit intention. Additionally, findings reveal that robotic restaurant image is a positive predictor of satisfaction, and that satisfaction is a positive predictor of revisit intention. Robotic restaurants have become pervasive in hospitality service environments. Accordingly, theoretical and empirical findings about multiple dimensions of service quality in this context likely will be of interest to marketing researchers and practitioners for sustainable restaurant business. Full article

Therapeutic phlebotomy remains a key intervention in the management of erythrocytosis and iron overload disorders, particularly polycythemia vera (PV) and hereditary hemochromatosis. Despite its historical origins as an ancient practice, venesection continues to be recommended in international guidelines for the reduction of hematocrit and iron burden, thereby mitigating thrombotic and organ-related complications. However, the evolving landscape of targeted pharmacologic therapies is reshaping the therapeutic paradigm. This review examines the current role of therapeutic phlebotomy, with a particular focus on PV, outlining its physiological rationale, clinical benefits, and well-documented limitations—including iron deficiency, procedural burden, and incomplete hematocrit control between sessions. Comparative insights are provided between phlebotomy and red cell apheresis, highlighting differences in efficacy, tolerability, and accessibility. The emergence of disease-modifying agents—such as interferons, JAK inhibitors, hepcidin mimetics, and epigenetic modulators like givinostat and bomedemstat—promises more sustained hematologic control with the potential to reduce or eliminate the need for repeated phlebotomies. While phlebotomy remains indispensable in early-stage or low-risk PV, its future utility will likely shift toward complementary or bridge therapy in the context of individualized, pharmacologically driven strategies, redefining the role of phlebotomy in the era of precision medicine. Full article

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard ILP solvers. This is achieved by adopting the concept of contact graphs from classical continuous sphere packing problems to the present discrete context, resulting in a reduction technique for the models which enables their efficient solution as well as their decomposition to smaller subproblems. Based on our calculations, we provide a systematic summary of all lower and upper bounds for packings in the smallest Hamming spaces. The known results are reproduced, with some bounds found to be sharp, and the upper bounds improved in some cases. Full article

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

As governments pursue digital transformation to improve service delivery and administrative efficiency, AI chatbots have emerged as a promising innovation in smart public administration. However, their adoption remains limited, particularly in transitioning countries where institutional, organizational, and technological conditions are complex and evolving. This study aims to empirically examine the key aspects, challenges, and strategic implications of AI chatbots’ adoption in public administration of Uzbekistan, a transitioning government in Central Asia. The study offers a novel contribution by employing an extended technology–organization–environment (TOE) framework. Data were collected through a survey among 501 public employees and partial least squares structural equation modeling was used to analyze data. The results reveal that perceived usefulness, compatibility, organizational readiness, effective accountability, and ethical AI regulation are key enablers, while system complexity, traditional leadership, resistance to change, and concerns over data management and security pose major barriers. The findings contribute to the literature on effective innovation in public administration and provide practical insights for policymakers and public managers aiming to effectively implement AI solutions in complex governance settings. Full article

Endocrine-disrupting chemicals are substances capable of interfering with hormonal systems, potentially leading to adverse developmental, reproductive, neurological, and immune effects in both humans and wildlife. Various experimental models are currently available to assess the endocrine-disrupting potential of substances. However, in the context of cosmetic ingredients, the ban on animal testing for safety and efficacy evaluations in Europe and other regions necessitates the use of in vitro or in silico approaches. Concerns have been raised regarding the possible endocrine-disrupting properties of certain cosmetic compounds, prompting the development of a priority substance list that includes several ultraviolet (UV) filters. This review provides a comprehensive overview of the main methodologies employed to evaluate endocrine-disrupting effects, with a particular focus on different endocrine organs. It also compiles and analyzes literature data related to commonly used UV filters such as benzophenones, avobenzone, homosalate, octocrylene, octinoxate, and 4-methylbenzylidene camphor. A major limitation identified is the lack of validated in vitro methods for assessing disruptions in specific endocrine organs, such as the thyroid and pancreas. This gap hinders accurate interpretation of experimental results and highlights the urgent need for further research to clarify the safety profiles of UV filters and other cosmetic ingredients. Full article