Search Results (9,832)

The increasing penetration of distributed energy resources (DERs) such as solar photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) in low-voltage (LV) and medium-voltage (MV) distribution networks is reshaping traditional grid operations. This shift introduces challenges including voltage violations, thermal overloading, and power quality issues due to bidirectional power flows. Hosting capacity (HC) assessment has become essential for quantifying and optimizing DER integration while ensuring grid stability. This paper reviews state-of-the-art HC assessment methods, including deterministic, stochastic, time-series, and AI-based approaches. Techniques for enhancing HC—such as on-load tap changers, reactive power control, and network reconfiguration—are also discussed. A key focus is the emerging concept of dynamic operating envelopes (DOEs), which enable real-time allocation of HC by dynamically adjusting import/export limits for DERs based on operational conditions. The paper examines the benefits, challenges, and implementation of DOEs, supported by insights from Australian projects. Technical, regulatory, and social aspects are addressed, including network visibility, DER uncertainty, scalability, and cybersecurity. The study highlights the potential of integrating DOEs with other HC enhancement strategies to support efficient, reliable, and scalable DER integration in modern distribution networks. Full article

The crystal structure of the hydrated form of 7-chloro-4-(4-methyl-1-piperazinyl)quinoline (BPIP) was determined by single-crystal X-ray diffraction analysis. This study revealed a one-dimensional supramolecular network stabilized by hydrogen bonding interactions between BPIP and water molecules. This compound represents one-half of a piperaquine molecule, a member of the 4-aminoquinoline class of antimalarial treatments, currently employed as a partner agent in modern combination therapies. As a simplified structural analog, BPIP can serve as a critical model system for probing the intermolecular interactions, physicochemical properties, and structural behavior of the parent compound. As a result, conducting a thorough solid-state characterization of BPIP is critical for gaining insight into its physical properties and verifying the material’s identity and purity. Full article

Sport in Spain during the dictatorship of Francisco Franco (1939–1975) underwent significant evolution across three distinct political phases: autarky, the technocratic stage, and late Francoism. Each of these periods was characterized by different approaches and uses of sport within the regime’s political structure. In the early years, sport was primarily employed as a tool for propaganda and social control, aligning with the authoritarian values of the state. Subsequently, with the rise of technocrats in the 1960s, reforms were implemented to promote the structural development of the sports system, fostering its modernization and the creation of specialized institutions. Finally, in the late Francoist period, sport became an instrument for international projection, as Spain increased its participation in international competitions and hosted sporting events. This entry analyzes the primary governmental initiatives for the organization and promotion of sport during the Franco regime, with particular attention to the administrative roles played by figures such as José Antonio Elola-Olaso and Juan Antonio Samaranch in the evolving structure of the Spanish sports system. Through an analysis based on documentary sources, it provides a comprehensive overview of Francoist sports policies, their objectives, and their impact on Spanish society. In this regard, sport under Franco’s rule was not only a means of political control but also laid the foundation for the later professionalization and globalization of Spanish sport. Full article

The management of power supply and distribution is becoming increasingly challenging because of the significant increase in energy demand brought on by global population growth. Buildings are estimated to be accountable for 40% of the worldwide use of energy, which underlines how important accurate demand estimation is for the design and construction of electrical infrastructure. In this respect, transmission and distribution network planning must be adjusted to ensure a smooth transition to the National Interconnected System (NIS). A technical and analytical scientific approach to a modern neighbourhood in Ecuador called “the Nuevo Samborondón” case study (NSCS) is laid out in this article. Collecting geo-referenced data, evaluating the current electrical infrastructure, and forecasting energy demand constitute the first stages in this research procedure. The sector’s energy behaviour is accurately modelled using advanced programs such as 3D design software for modelling and drawing urban architecture along with a whole building energy simulation program and geographical information systems (GIS). For the purpose of recreating several operational situations and building the distribution infrastructure while giving priority to the current urban planning, an electrical system model is subsequently developed using power system analysis software at both levels of transmission and distribution. Furthermore, seamless digital substations are suggested as a component of the nation’s electrical infrastructure upgrade to provide redundancy and zero downtime. According to our findings, installing a 69 kV ring is a crucial step in electrifying NSCS and aligning electrical network innovations with urban planning. The system’s capacity to adjust and optimize power distribution would be strengthened provided the algorithms were given the freedom to react dynamically to changes or disruptions brought about by distributed generation sources. Full article

Silver nanoparticles (AgNPs) have emerged as promising agents in cancer diagnostics and/or therapy, demonstrating a lot of possible pharmacological actions. However, understanding the pharmacokinetics and safety profiles of nanoparticles, which is crucial for their clinical application, still raises many questions. Studies indicate that AgNPs can accumulate in tumour tissues, improving drug delivery and specificity. However, their interaction with biological systems necessitates thorough safety evaluations. Classical methods for assessing AgNPs’ safety include cytotoxicity assays, genotoxicity tests, and histopathological examinations. However, novel techniques are emerging, such as advanced imaging and biomarker analysis, offering more precise toxicity assessments. Prediction models, including computational simulations and in silico analyses, are being developed to forecast AgNPs’ toxicity profiles. These models aim to reduce reliance on animal testing and expedite the evaluation process. To mitigate potential risks associated with nanoparticle-based therapies, strategies such as surface modification, controlled release systems, and targeted delivery are being explored. These methods aim to enhance therapeutic efficacy while minimizing adverse effects. The main aim of this review article is to describe AgNPs from the point of view of their pharmacokinetic/toxicokinetic profile in the light of modern knowledge. Special attention will be given to novel methods for assessing the safety and toxicity profiles of AgNPs, providing insights into their interactions with cancer therapies and their potential clinical applications. Full article

Maternal overnutrition and targeted supplements during pregnancy strongly affect fetal development in beef cattle, influencing gene expression, tissue development, and productivity after birth. As modern feeding practices often result in cows receiving energy and protein above requirements, understanding the balance between adequate nutrition and overconditioning is critical for sustainable beef production. This review synthesizes findings from recent studies on maternal overnutrition and supplementation, focusing on macronutrients (energy, protein, methionine) and key micronutrients (e.g., selenium, zinc). It evaluates the timing and impact of supplementation during different gestational stages, with emphasis on fetal muscle and adipose tissue development, immune function, and metabolic programming. The role of epigenetic mechanisms, such as DNA methylation and non-coding RNAs, is also discussed in relation to maternal dietary inputs. Mid-gestation supplementation promotes muscle growth by activating muscle-specific genes, whereas late-gestation diets enhance marbling and carcass traits. However, maternal overnutrition may impair mitochondrial efficiency, encourage fat deposition over muscle, and promote collagen synthesis, reducing meat tenderness. Recent evidence highlights sex-specific fetal programming differences, the significant impact of maternal diets on offspring gut microbiomes, and breed-specific nutritional responses, and multi-OMICs integration reveals metabolic reprogramming mechanisms. Targeted trace mineral and methionine supplementation enhance antioxidant capacity, immune function, and reproductive performance. Precision feeding strategies aligned with gestational requirements improve feed efficiency and minimize overfeeding risks. Early interventions, including protein and vitamin supplementation, optimize placental function and fetal development, supporting stronger postnatal growth, immunity, and fertility. Balancing nutritional adequacy without excessive feeding supports animal welfare, profitability, and sustainability in beef cattle systems. Full article

Artificial intelligence has experienced tremendous growth in various areas of knowledge, especially in computer science. Distributed computing has become necessary for storing, processing, and generating large amounts of information essential for training artificial intelligence models and algorithms that allow knowledge to be created from large amounts of data. Currently, cloud services offer products for running distributed data training, such as NVIDIA Deep Learning Solutions, Amazon SageMaker, Microsoft Azure, and Google Cloud AI Platform. These services have a cost that adapts to the needs of users who require high processing performance to perform their artificial intelligence tasks. This study highlights the relevance of distributed computing in image processing and classification tasks using a low-scalability distributed system built with devices considered obsolete. To this end, two of the most widely used libraries for the distributed training of deep learning models, PyTorch’s Distributed Data Parallel and Distributed TensorFlow, were implemented and evaluated using the ResNet50 model as a basis for image classification, and their performance was compared with modern environments such as Google Colab and a recent Workstation. The results demonstrate that even with low scalability and outdated distributed systems, comprehensive artificial intelligence tasks can still be performed, reducing investment time and costs. With the results obtained and experiments conducted in this study, we aim to promote technological sustainability through device recycling to facilitate access to high-performance computing in key areas such as research, industry, and education. Full article

Artificial intelligence is comprehensively transforming the food safety governance system by integrating modern technologies and building intelligent control systems that provide rapid solutions for the entire food supply chain from farm to fork. This article systematically reviews the core applications of AI in the orbit of food safety. First, in the production and quality control of primary food sources, the integration of spectral data with AI efficiently identifies pest and disease, food spoilage, and pesticide and veterinary drug residues. Secondly, during food processing, sensors combined with machine learning algorithms are utilized to ensure regulatory compliance and monitor production parameters. AI also works together with blockchain to build an immutable and end-point traceability system. Furthermore, multi-source data fusion can provide personalized nutrition and dietary recommendations. The integration of AI technologies with traditional food detection methods has significantly improved the accuracy and sensitivity of food analytical methods. Finally, in the future, to address the increasing food safety issues, Food Industry 4.0 will expand the application of AI with lightweight edge computing, multi-modal large models, and global data sharing to create a more intelligent, adaptive and flexible food safety system. Full article

Background: The ongoing clinical challenge of managing hemoglobin levels in chronic dialysis patients is exacerbated by the gap between growing patient needs and the limited availability of nephrologists. Clinical nurse specialists (CNSs) have been contributing to the successes of modern healthcare systems across Europe, but there is a limited understanding of specific mechanisms by which CNSs can support and improve patient outcomes in renal diseases. Objectives: Responding to previous calls, this intervention study evaluated the role of nephrology CNSs in dialysis patient care. Methods: This intervention study employed erythropoiesis-stimulating agents (ESAs) to investigate whether a personalized, tailored protocol led by nephrology CNSs could improve the hemoglobin balance compared to the conventional standard of care led by nephrologists. Thirty-nine patients who met the inclusion criteria with a preset hemoglobin value between 10.5–12 g/dL completed the study. Results: There were no significant differences in hemoglobin levels between patients managed by nephrologists and those overseen by CNSs. Hemoglobin variability remained unchanged after protocol implementation, while key dialysis quality indicators (e.g., iron saturation, urea reduction) remained within safety limits. Notably, ESA-related medical adjustments were significantly reduced, requiring half as many modifications over 12 study points. Conclusions: A CNS-led personalized protocol effectively maintained dialysis patient parameters within established safety thresholds. These findings reinforce the critical role of CNSs in enhancing the efficiency, effectiveness, and safety of hemoglobin management in this high-risk population. Policy implications are discussed. Full article

The use of computer-aided engineering (CAE) tools has become essential in modern design processes, significantly streamlining mechanical design tasks. The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. A key focus lies in understanding and mitigating the detrimental effects of cavitation on injector surfaces, as it can reduce the injector lifespan and induce material degradation. By combining advanced numerical finite element tools with algorithmic optimization, these adverse effects can be effectively mitigated. The incorporation of computational tools enables efficient numerical analyses and rapid, automated modifications of injector designs, significantly enhancing the ability to explore and refine geometries. The primary goal remains the minimization of cavitation phenomena and the improvement in injector performance, while the collaborative use of specialized software environments ensures a more robust and streamlined design process. Specifically, using the simulated annealing algorithm (SA) helps identify the optimal configuration that minimizes cavitation-induced effects. The proposed approach provides a robust set of tools for engineers and researchers to enhance injector performance and effectively address cavitation-related challenges. The results derived from this integrated framework illustrate the effectiveness of the optimization methodology in facilitating the development of more efficient and reliable injector systems. Full article

In video transmission, maintaining high visual quality under variable network conditions, including bandwidth and efficiency, is essential for optimal viewer experience. Channel errors or malicious attacks during transmission can cause degradation in video quality, affecting its secure transmission and putting its confidentiality and integrity at risk. This paper presents a novel approach to enhancing objective video quality by integrating an energy-efficient forward error correction (FEC) technique into video encoding and transmission processes. Moreover, it ensures that the video contents remain secure and unintelligible to unauthorized parties. This is achieved by combining H.264/AVC syntax-based encryption and decryption algorithms with error correction during the video coding process to provide end-to-end confidentiality. Unlike traditional error correction strategies, our approach dynamically adjusts redundancy levels based on real-time network conditions, optimizing bandwidth utilization without compromising quality. The proposed framework is evaluated across full reference objective video quality metrics, demonstrating significant improvements in the peak signal-to-noise ratio (PSNR) and PSNR₆₁₁ of the recovered videos. Experiments are carried out on multiple test video sequences with different video resolutions having various characteristics, i.e., colors, motions, and structures, and confirm that the FEC-based solution effectively detects and corrects packet loss and transmission errors without the need for retransmission, reducing the impact of channel noise and accidental disruptions on visual quality in challenging network environments. This study contributes to the development of resilient video transmission systems with reduced computational complexity of the codec and provides insights into the role of FEC in addressing quality degradation in modern multimedia applications where low latency is crucial. Full article

With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. Full article

Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are primarily derived from expert experience and remain significantly limited. This substantially hinders advancements in power demand forecasting accuracy. Emerging multimodal learning approaches have demonstrated great promise in machine learning and AI-generated content (AIGC). In this paper, we propose, for the first time, a textual-knowledge-guided numerical feature discovery (TKNFD) framework for short-term power demand forecasting by interacting text modal data—a potentially valuable yet long-overlooked resource in the field of power demand forecasting—with numerical modal data. TKNFD systematically and automatically aggregates qualitative textual knowledge, expands it into a candidate feature-type set, collects corresponding numerical data for these features, and ultimately constructs four-dimensional multivariate source-tracking databases (4DM-STDs). Subsequently, TKNFD introduces a two-stage quantitative feature identification strategy that operates independently of forecasting models. The essence of TKNFD lies in achieving reliable and comprehensive feature discovery by fully exploiting the dual relationships of synonymy and complementarity between text modal data and numerical modal data in terms of granularity, scope, and temporality. In this study, TKNFD identifies 38–50 features while further interpreting their contributions and dependency correlations. Benchmark experiments conducted in Maine, Texas, and New South Wales demonstrate that the forecasting accuracy using TKNFD-identified features consistently surpasses that of state-of-the-art feature schemes by up to 36.37% MAPE. Notably, driven by multimodal interaction, TKNFD can discover previously unknown interpretable features without relying on prior empirical knowledge. This study reveals 10–16 previously unknown interpretable features, particularly several dominant features in integrated energy and astronomical dimensions. These discoveries enhance our understanding of the origins of strong randomness and non-linearity in power demand fluctuations. Additionally, the 4DM-STDs developed for these three regions can serve as public baseline databases for future research. Full article

Rice (Oryza sativa) is a globally important crop that plays a central role in maintaining food security. This scientific review examines the critical role of genetic disease resistance in protecting rice yields, dissecting at the molecular level how rice plants detect and respond to pathogen attacks while evaluating modern approaches to developing improved resistant varieties. The analysis covers single-gene-mediated and multi-gene resistance systems, detailing how on one hand specific resistance proteins, defense signaling components, and clustered loci work together to provide comprehensive protection against a wide range of pathogens and yet their production is severely impacted by pathogens such as Xanthomonas oryzae (bacterial blight) and Magnaporthe oryzae (rice blast). The discussion extends to breakthrough breeding technologies currently revolutionizing rice improvement programs, including DNA marker-assisted selection for accelerating traditional breeding, gene conversion methods for introducing new resistance traits, and precision genome editing tools such as CRISPR/Cas9 for enabling targeted genetic modifications. By integrating advances in molecular biology and genomics, these approaches offer sustainable solutions to safeguard rice yields against evolving pathogens. Full article

With the advancement of modern agriculture, intelligent laser robots driven by deep learning have emerged as an effective solution to address the limitations of traditional weeding methods. These robots offer precise and efficient weed control, crucial for boosting agricultural productivity. This paper provides a comprehensive review of recent research on laser weeding applications using intelligent robots. Firstly, we introduce the content analysis method employed to organize the reviewed literature. Subsequently, we present the workflow of weeding systems, emphasizing key technologies such as the perception, decision-making, and execution layers. A detailed discussion follows on the application of deep learning algorithms, including Convolutional Neural Networks (CNNs), YOLO, and Faster R-CNN, in weed control. Here, we show that these algorithms can achieve high accuracy in weed detection, with YOLO demonstrating particularly fast and accurate performance. Furthermore, we analyze the challenges and open problems associated with deep learning detection systems and explore future trends in this research field. By summarizing the role of intelligent laser robots powered by deep learning, we aim to provide insights for researchers and practitioners in agriculture, fostering further innovation and development in this promising area. Full article