Search Results (265)

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

Mediterranean island cities face unique challenges in implementing smart city initiatives due to fragmented governance structures, seasonal economic pressures, and evolving societal expectations. This study investigates how strategic aspirations and public discourse shape the feasibility of smart city policies in insular contexts. Specifically, it combines SOAR (Strengths, Opportunities, Aspirations, Results) analysis with the Overton Window framework to examine both the strategic capacities and normative acceptability of technological interventions. The Overton Window, a model originally developed in political theory, is applied here to evaluate how public and policy acceptance of smart technologies, ranging from digital governance systems to AI-based mobility, varies across different islands. While this study draws on cross-case comparisons of multiple Mediterranean island contexts, the primary data were collected in Athens, Greece, through surveys and focus groups with citizens and stakeholders. The findings reveal disparities in institutional maturity, stakeholder coordination, and levels of citizen support. This study concludes that successful smart city transformation requires both strategic coherence and alignment with evolving public values. It proposes the ‘Ecopolis’ model as a conceptual planning framework that integrates sustainability, citizen participation, and data-driven governance in tourism-dependent island settings. Full article

Surface charge accumulation is considered one of the key factors that lead to unexpected insulator flashover failures in gas-insulated switchgear (GIS). With the existence of metal particles, charge accumulation characteristics on insulator surfaces become intricate in eco-friendly gases under AC voltage. In this study, the surface charge behavior on a down-scaled 252 kV AC GIS basin insulator model with a linear metal particle adhered to the HV electrode on the convex surface in compressed air (80%N₂/20%O₂) and C₄F₇N/CO₂ mixtures was investigated. After applying an AC voltage of 40 kV for 5 min, the charge densities on both surfaces were measured, and the effect of the metal particle and gas parameters was discussed. The results showed that charge spots were induced by metal particles on the insulator surfaces, and the polarities of which varied with the gas atmosphere. A decrease in maximum charge density was detected with an increase in C₄F₇N proportion at 0.1 MPa, and soar of which was observed at 0.5 MPa. With an increase in gas pressure, the maximum charge density increased in both atmospheres. The total quantity of charges showed similar behavior to the charge densities. It is indicated that the high electronegativity of C₄F₇N molecules presents a competing relationship in charge accumulation as the pressure increases. Full article

Cloud security automation has emerged as a critical solution for organizations facing increasingly complex cybersecurity challenges in cloud environments. This study examines the current state of cloud security automation, focusing on its role in symmetry between threat detection and response capabilities. Through analysis of recent market trends and technological developments, this paper explores key technologies, including Security Information and Event Management (SIEM), Extended Detection and Response (XDR), and Security Orchestration, Automation, and Response (SOAR) platforms. The integration of artificial intelligence and machine learning has transformed these systems, enabling real-time threat detection and automated response mechanisms. The research examines real-world applications and highlights that organizations implementing automated security solutions have demonstrated improved incident response times and reduced security breaches. However, challenges remain in terms of the complexity of integration and symmetry between automation and human expertise. As the global AI cybersecurity market is projected to reach $134 billion by 2030, the future of cloud security automation lies in advanced AI-driven solutions and improved threat intelligence integration. Even though cloud platforms are widely used, existing security tools have challenges in identifying real-time threats, the integration of heterogeneous data sources, and actionable intelligence generation. The majority of current solutions are not designed for cloud-native platforms and do not scale or evolve. This paper overcomes these challenges by introducing a scalable and extensible cloud security architecture, which uses sophisticated correlation and threat intelligence to provide increased detection accuracies as well as reduced response times for the challenging environment of advanced cloud-based infrastructures. This research aims to equip organizations with proven methods from real-world use cases and strategies that they can adopt to enable automated threat detection and response. Full article

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

The COVID-19 pandemic and the Russia–Ukraine war have struck the world’s energy markets. This study analyzed how the recent unstable fossil fuel markets impacted the Japanese electricity contracts, classified as extra-high-, high-, and low-voltage contracts. Multiple structural break tests were conducted to endogenously determine breaks affecting electricity prices during January 2019 to November 2022. By incorporating the effects of these breaks in the autoregressive distributed lag (ARDL) model, the study analyzed the effects of natural gas, coal, and crude oil prices on the types of electricity contract prices. The results of the analyses indicated a surge in electricity prices for low- and high-voltage contracts driven by an increase in natural gas. The results imply the importance of providing proper financial support to mitigate the effects of soaring electricity prices and implementing policies to diversify the electricity generation mix in Japan. Full article

For topology optimization problems under harmonic excitation in a frequency band, a large number of displacement and adjoint displacement vectors for different frequencies need to be computed. This leads to an unbearable computational cost, especially for large-scale problems. An effective approach, the Second-Order Arnoldi (SOAR) method, effectively solves the response and adjoint equations by projecting the original model to a reduced order model. The SOAR method generalizes the well-known Krylov subspace in a specified frequency point and can give accurate solutions for the frequencies near the specified point by using only a few basis vectors. However, for a wide frequency band, more expansion points are needed to obtain the required accuracy. This brings up the question of how many points are needed for an arbitrary frequency band. The traditional reduced order method improves the accuracy by uniformly increasing the expansion points. However, this leads to the redundancy of expansion points, as some frequency bands require more expansion points while others only need a few. In this paper, a bisection-based adaptive SOAR method (ASOAR), in which the points are added adaptively based on a local error estimation function, is developed to solve this problem. In this way, the optimal number and position of expansion points are adaptively determined, which avoids the insufficient efficiency or accuracy caused by too many or too few points in the traditional strategy where the expansion points are uniformly distributed. Compared to the SOAR, the ASOAR can deal with wide low/mid-frequency bands both for response and adjoint equations with high precision and efficiency. Numerical examples show the validation and effectiveness of the proposed method. Full article

The evolving landscape of cybersecurity threats demands the modernization of Security Operations Centers (SOCs) to enhance threat detection, response, and mitigation. Security Orchestration, Automation, and Response (SOAR) platforms play a crucial role in addressing operational inefficiencies; however, traditional no-code SOAR solutions face significant limitations, including restricted flexibility, scalability challenges, inadequate support for advanced logic, and difficulties in managing large playbooks. These constraints hinder effective automation, reduce adaptability, and underutilize analysts’ technical expertise, underscoring the need for more sophisticated solutions. To address these challenges, we propose a hyper-automation SOAR platform powered by agentic-LLM, leveraging Large Language Models (LLMs) to optimize automation workflows. This approach shifts from rigid no-code playbooks to AI-generated code, providing a more flexible and scalable alternative while reducing operational complexity. Additionally, we introduce the IVAM framework, comprising three critical stages: (1) Investigation, structuring incident response into actionable steps based on tailored recommendations, (2) Validation, ensuring the accuracy and effectiveness of executed actions, (3) Active Monitoring, providing continuous oversight. By integrating AI-driven automation with the IVAM framework, our solution enhances investigation quality, improves response accuracy, and increases SOC efficiency in addressing modern cybersecurity threats. Full article

Background/Objective: Identifying patterns of diagnostic imaging workflow parallel to the influence of certain variables, such as pathology guidelines over time, provides valuable insight for clinical decision making. This study presents a recurring trend of initial imaging orders and follow-ups, up to the diagnosis of pancreatic neuroendocrine tumors (pNETs), across two decades, with scans which led to pathological investigation. Methods: Three readers evaluated common conventional imaging among initial and follow-up studies for lesion detection and localization. Inter-reader and intra-reader analyses were controlled as contributing factors to the imaging diagnostic trend. Results: Our results show that CT was the prominent initial scan in pNET workup, likely due to their wide availability, high spatial resolution, and rapid acquisition, with a sufficient detection rate throughout both decades, regardless of technical advances. However, MRI scans also gained soaring popularity, especially among syndromic patients, likely due to follow-up and anatomical surgery precision. Conclusions: Newer modalities may be eventually useful and only requested for pNETs staging and further treatment. Full article

In light of the soaring growth of pumped hydro energy storage (PHES) plants in China in recent years, there is an urgent need for a comprehensive understanding of their developmental trajectory and the identification of their multidimensional impacts. This paper reviews the development of PHES in China and highlights its various impacts. Despite the relatively late start of PHES development in China, the country has recently ranked first worldwide with an aggregated installed capacity of 50.94 GW in operation in 2023. These plants are primarily distributed in North China, East China, and South China, contributing to the safe and stable operation of regional power grids. Furthermore, over 300 plants are under construction or in the planning stage across the whole country, aiming to support large-scale renewable energy development and facilitate a sustainable energy transition. However, it is important to recognize that such extensive PHES development requires significant land resources, which can lead to disturbances in local ecosystems and affect nearby residents. Additionally, environmental emissions may arise from a life-cycle perspective. Finally, several countermeasures are proposed to enhance sustainable PHES development in China. They include strengthening the rational planning of new plants to optimize their spatial distribution, refining the engineering design of new plants, and exploring avenues for sharing the benefits of PHES development with a broad spectrum of local residents. Full article

Blockchain technology has emerged as a transformative solution for secure, immutable, and decentralized data management across diverse domains, including economics, healthcare, and supply chain management. Given its soaring adoption, it is crucial to assess the suitability of various blockchain platforms for specific applications. This study evaluates the performance of Hyperledger Fabric (HF) and private Ethereum (Geth) to analyze their scalability (node count), throughput (transactions per second (TPS)), and latency (measured in milliseconds). A benchmarking tool was developed in-house to assess the execution of key smart contract functions—QueryUser, CreateUser, TransferMoney, and IssueMoney—under varying transaction loads (10–1000 transactions) and network sizes (2–16 node count). The results indicate that HF performs significantly better than private Ethereum in terms of invoke functions, achieving up to 5× throughput and up to 26× lower latency. However, private Ethereum excels in query operations because of its account-based ledger model. While Hyperledger Fabric scales efficiently within moderate transaction volumes, it experiences concurrency limitations beyond 1000 transactions, whereas private Ethereum processes up to 10,000 transactions, albeit with performance fluctuations due to gas fees. The findings offer valuable insights into the strengths and tradeoffs of both platforms, informing optimal blockchain selection for enterprise applications that require high transaction efficiency. Full article

Following World War II, the swift economic growth in construction and the soaring demand in urban regions led to the excessive extraction of natural resources like fossil fuels, minerals, forests and land. To tackle significant global challenges, including the consumption of natural resources, air pollution and climate change, radical changes have been suggested over the past decades. As part of this strategic initiative, prioritizing sustainability in construction has emerged as a crucial focus in the design of all projects. In order to identify the most environmentally sustainable reinforced concrete (RC) slab system, this research investigates the carbon emissions associated with various slab systems, including solid, voided slabs and precast floor systems. The results demonstrate that beam and slab floor and solid slabs have the highest embodied carbon due to the significant use of concrete and related materials, whereas voided slabs and two-way joist floors exhibit lower carbon emissions. The results indicate that the two-way joist system is the most environmentally advantageous option. For precast floor systems, post-tensioned concrete and hollow-core slabs demonstrate the lowest embodied carbon levels. This research provides practical recommendations for architects and engineers aimed at enhancing sustainable design methodologies. It emphasizes the importance of incorporating low-carbon materials as well as pioneering flooring technologies in upcoming construction initiatives to support the achievement of global sustainability objectives. Full article

The energy dissipation of hydraulic structures is crucial to the overall safety and stability of hydraulic engineering projects. In order to isolate energy dissipators from hydraulic engineering projects and address the issues of vibration damage caused by the discharge structures, a new type of vertical jet energy dissipator was developed by placing crushing needles at the nozzle of the vertical jet pipeline. The crushing needles were mainly used to break the high-energy jet into several smaller jets. As the air is mixed with the water flow, the mechanical energy of the water flow is converted into internal energy and dissipated in the air. The structural parameters of the vertical jet energy dissipators include the size and number of crushing needles. In this paper, the first-order and second-order statistical characteristics and energy dissipation rate of vertical jet energy dissipators under different structural parameters are studied by means of numerical simulation and a physical model test. The results show that, within the scope of this study, the energy dissipation rate of a vertical jet increases with the increase in Reynolds number, the number of crushing needles, and the size of crushing needles; and the energy dissipation rate of the vertical jet increases by 1.04 to 4.89 times compared with that without crushing needles. Under the same Reynolds number, the height of the jet decreases with the increase in the number of crushing needles and the size of the crushing needles. With the vertical development of the flow, the vertical average velocity of the vertical jet energy dissipator is getting smaller and smaller. Adding crushing needles will hinder the upward diffusion of the jet, reduce the height of the jet, and accelerate the attenuation of the jet velocity. As a statistic result regarding the fluid stress, the Reynolds stress along the axis shows a slow upward trend at the potential core, soars at the shear layer, and finally decreases at the end of the jet. The flow has a higher convective transportation intensity in the lateral direction than in other directions. The addition of crushing needles can, to some extent, affect the fluid transport in that area, thereby altering the pressure in the region and dissipating the mechanical energy of the flow-induced vibrations when the jet impacts the crushing needles. The vertical jet-type energy dissipator proposed in this study addresses key engineering challenges, such as terrain constraints and the need for flexible design solutions. Its ability to efficiently dissipate energy while maintaining adaptability makes it a valuable tool for hydraulic engineers designing energy dissipation systems. The conclusions of this study provide a theoretical basis for the application of vertical jet energy dissipators. Full article

The rapid evolution of Integrated Energy Systems (IESs) demands robust management of information transmission, which is critical for real-time monitoring, coordination, and operational efficiency. However, the increasing complexity and costs associated with information exchange necessitate effective pricing mechanisms to ensure economic sustainability and optimal resource allocation. This paper presents an evolutionary game-theoretic framework to analyze regulatory strategies governing information transmission within IES. In the context of market dynamics, both market regulators and communication network operators are considered as actors with bounded rationality, emphasizing their strategic interplay within the system. The main contributions include formulating a model that treats communication network operators as independent entities, enhancing the understanding of IES member diversity and interactivity. This study introduces evolutionary game dynamics, providing new insights into optimizing regulatory policies. This paper also innovates by considering asset utilization in defining effective assets, potentially curbing excessive investment by communication network operators and preventing information transmission prices from soaring. A case study is provided to reveal the dynamics and equilibrium states of the regulatory game, offering theoretical support for refining regulatory strategies in IESs. Full article

A significant segment of the inhabitants in Mexico have a high rate of malnutrition and obesity, especially in impoverished and segregated areas. This study analyzes the paradox of food swamps, food availability, and food’s ecological footprint to promote the creation of community gardens in Querétaro. This paper is segmented into four sections. It starts by recording the omnipresence of the Mexican chain “OXXO” convenience stores, which offer mainly processed foods. The second segment of the research depicts the miles traveled by Mexican crops to visualize their carbon footprint. The third portion explores the impact of urban agriculture in the 20th century on cities. The final section proposes designing and implementing community gardens in two marginalized neighborhoods (Tlacote and San Antonio–Bolaños) in Querétaro, Mexico, to foster healthier, more sustainable neighborhoods. The findings corroborate a soaring number of unhealthy food stores, elevated carbon footprints related to food production, and a community request for urban agriculture, including the regeneration of community public areas. The research emphasizes the impact of landscape urbanism, especially community gardens, to foster social, urban, and environmental regeneration. The study provides a scheme for advocating healthier lifestyles and more sustainable urban environments by focusing on food distribution, ecological services, and community engagement. Full article