Search Results (49)

The rapid proliferation of artificial intelligence (AI) has pushed hyperscale data center rack densities beyond 100 kW, driving facility power requirements to the gigawatt scale. As developers attempt to deploy these massive Zettascale compute loads across US wholesale electricity markets, they encounter severe transmission planning bottlenecks, multi-year interconnection delays, and escalating grid transient stability risks. This paper presents a generalizable techno-economic framework for evaluating grid-tied, behind-the-meter (BTM) energy architectures as a means of bypassing these constraints. The framework is demonstrated through a detailed case study in the Electric Reliability Council of Texas (ERCOT), selected for its rapid data center growth and evolving large-load regulatory environment. Using a scenario-based comparative approach, this study models the feasibility of transitioning from pure-grid reliance to hybrid, on-site generation across a three-phase deployment pathway scaling from 25 MW to 250 MW. Six distinct microgrid configurations are evaluated, integrating baseload technologies—including Enhanced Geothermal Systems (EGSs), Small Modular Reactors (SMRs), and Reciprocating Internal Combustion Engines (RICEs)—with a tiered-performance Battery Energy Storage System (BESS) combining high C-rate lithium-ion units and repurposed electric vehicle batteries. System viability is assessed through two primary metrics: the Levelized Cost of Energy (LCOE) and the Avoided Loss of Load Probability (ALOLP). The results indicate that the blended LCOE scenario ranges from $64.50/MWh (Geothermal + Solar PPA) to $94.20/MWh (SMR-anchored), compared to a $75.00/MWh pure-grid baseline. The 100% Geothermal configuration achieves a scenario-dependent ALOLP exceeding 99.9%, while gas-dependent configurations range from 58.0% to 91.2%. These findings suggest that geographic siting co-optimized with localized generation offers a viable pathway for balancing regulatory compliance, capital cost, and Uptime Tier IV operational resilience in early-stage data center development across constrained grid environments. Full article

Background: Congenital hearing loss occurs in about 2 of every 1000 newborns, of which half probably have a genetic origin. In syndromic patients, hearing impairment often results from craniofacial malformations affecting the outer and middle ear. Anatomical limitations such as microtia or external auditory canal atresia often preclude conventional air-conduction hearing aids, leaving bone-conduction devices as one viable option. However, surgical intervention in such patients is challenging. This study aimed to evaluate the audiological outcomes, safety, and effectiveness of the Bonebridge BCI 602 implant in 10 patients with genetic syndromes. Methods: The case series was made up of 10 patients aged 6–45 years, each diagnosed with a congenital syndrome affecting the external and/or middle ear. All cases involved surgical implantation of the Bonebridge system. Audiological outcomes were evaluated in free-field conditions on the day of sound processor activation and at 3–6 months follow-up via pure-tone and speech audiometry. Results: All surgical procedures were completed without serious adverse events, and the incidence of postoperative complications was low. Audiological outcomes showed clinically significant hearing improvement in all patients following Bonebridge implantation. Post-implantation hearing thresholds ranged from 25 to 40 dB HL, with notable gains in speech perception in both quiet and noisy environments. Conclusions: The Bonebridge implant appears to be a safe and effective option for auditory rehabilitation in patients with hearing loss associated with various genetic syndromes involving craniofacial malformation. However, this complex patient population requires individual assessment, interdisciplinary evaluation, and careful surgical planning. Full article

Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article

Although care and gender mainstreaming are increasingly recognized as key dimensions of sustainable urban planning, an analysis of their implementation in Mexico reveals the conceptual and material limitations of anthropocentric approaches to care within public space projects. In this article, we argue that ecofeminist and posthumanist perspectives on care help foreground the spatial and environmental dimensions of Everyday Care Environments (ECEs), highlighting ecosystemic interdependencies that remain largely overlooked in research focused on domestic, feminized, and family-based aspects of care work. Through qualitative research based on documentary analysis of local urban planning instruments and gender initiatives in Mexico City (CDMX) in the last 25 years, this article identifies persistent gaps in the integration of care work, safety, mobility, and intersectional perspectives into sustainable urban policy and practice. The findings offer insights for developing planning strategies capable of creating ECE that foster More-than-Human socio-environmental understandings of care, while advancing nature-based and ecosystem-oriented approaches to spatial justice. Full article

Background: Breast cancer mortality and long-term survival are influenced by the unpredictability of recurrences, which cause significant diagnostic and therapeutic challenges for oncology teams. The risk of local and distant recurrence is higher in advanced stages and in the first two years following initial treatment. Accurate staging and continuous monitoring of recurrence are crucial for effective therapy planning. Indicators of recurrence, such as luminal subtype, disease stage, age, and treatment choice, can provide new knowledge and improve patient disease-free and overall survival rates. Methods: We conducted a retrospective cohort study of patients with stage I-III invasive breast cancer at a regional-based institution. The study population consisted of 98 patients with distant and locoregional recurrences from a large cohort of 744 patients diagnosed and treated at our institution between 2007 and 2024. Data on previous treatment for breast cancer, disease stage, molecular subtype, initial size and location of the tumor in the breast, lymph node status, living environment, and type of recurrence were recorded retrospectively. Results: The recurrence patterns in 98 patients included local recurrence in 25 (25.5%), distant recurrence in 70 (71.4%), and both local and distant recurrence in three (3.1%). Our study showed that patients diagnosed with stage II (40.8%) or stage III (55.1%) breast cancer, as well as those with the luminal B subtype (43.87%), were more likely to experience recurrence. The majority of patients affected by recurrent disease were postmenopausal women aged between 51 and 70 years (32 cases aged 51–60 years and 34 cases aged 61–70 years). Tumors measuring between 2 and 5 cm were more likely to produce distant single-organ recurrence (26 cases). More cases were associated with urban areas (77 cases). Conclusions: In menopausal women, most causes of local breast cancer recurrence are related to advanced stage at diagnosis and luminal B subtype. Patient age, primary tumor location in the CSE, and previous adjuvant treatment with aromatase inhibitors may affect the risk of recurrence. Comprehensive studies on recurrence in postmenopausal women can provide a more precise understanding of the extent of disease in such patients. Full article

Innovation has become a pivotal factor in driving economic growth for cities and regions. Urban innovation spaces are urban spaces where innovative economic and industrial activities, such as research, teaching, and high-tech manufacturing, are clustered. They have become hot research topics in recent years. Evaluating the performance of urban innovation spaces to promote rational resource allocation and enhance land development potential has become a critical task in urban planning. However, existing studies suffer from insufficient depth of research scales and a lack of quantitative indicators and data analysis. In response to the above gaps, this study constructed a framework for evaluating the performance of urban innovation spaces from 25 indicators of five major types, including core elements of innovation, entrepreneurship support institutions, service facilities, external environments, and diversities, aiming to quantify the performance heterogeneity of innovation spaces at the micro scale. This study took Harbin as an example and employed the entropy, kernel density estimation, and entropy-weighted TOPSIS methods, identifying four high-scoring areas of innovation spaces—the Science and Technology Innovation City area, the High-tech Industrial Development area, the core area of the old city, and the Harbin Veterinary Research Institute area—which were divided into three types: the Entrepreneurial leading area, Environmental Support area, and Balanced Development area. Finally, this study analyzed the interaction between each indicator. It was found that the correlation between the core elements of innovation and the indicators of entrepreneurship support institutions was strong and had a high degree of importance. The correlation of different types of service facility indicators is quite different, and the external environment indicators and diversity indicators are mainly affected by other indicators, especially the core elements of innovation and entrepreneurship support institutions. This paper provides a valuable tool for the performance evaluation of urban innovation spaces for researchers and urban planning decision makers. Full article

Background/Objectives: Emergency Medical Services (EMS) clinicians in the US have high COVID-19 vaccine hesitancy rates and often do not receive primary vaccinations or subsequent boosters. The extent of booster attrition following initial vaccination and first booster dose in EMS clinicians is unknown. Our objective was to evaluate the prevalence and drivers of COVID-19 booster attrition in EMS clinicians. We hypothesized that booster attrition is common among EMS clinicians and associated with various EMS characteristics. Methods: This study was a cross-sectional analysis of nationally certified civilian EMS clinicians aged 18–85 years old. An electronic survey was distributed, which included an evaluation of vaccination status, booster acceptance, willingness to receive future boosters, perceived risk of contracting COVID-19 from the Understanding America Survey (8 items), and mistrust of healthcare organizations using the Medical Mistrust Index (MMI) (7 items). These data were combined with demographic and work-related characteristics from the National Registry of EMTs dataset. A multivariable logistic regression model (OR, 95% CI) was used to describe booster attrition associations between demographics, work-related characteristics, perceived risk, and medical mistrust. Results: A total of 1902 respondents met initial inclusion criteria. Within this group, 78% were COVID-19 vaccinated, and an additional 65% received a booster. Of these, 37% reported not planning to receive any other booster treatments following the first booster. Primary reasons for not continuing with subsequent boosters include confusion among experts on efficacy (59%), severe side effects (38%), the belief that COVID-19 is not a threat (26%), the belief in natural immunity (25%), and the belief that boosters are not required (23%). Odds of planning to receive another booster increased with receiving a flu vaccine (5.03, 3.08–8.22) and urban environment (1.96, 1.19–3.24, referent rural). In comparison, the odds of planning to receive another booster were lower for paramedics (0.56, 0.38–0.83, referent EMT) and fire agencies (0.53, 0.31–0.89, referent hospital). As the perceived risk of COVID-19 and medical mistrust decrease, the odds of planning to receive another booster increase (perceived risk: 1.98, 1.41–2.78; trust: 4.12, 3.21–5.28). Conclusions: The rate of booster attrition following receipt of one booster is high, at 37%. While there are associations with EMS demographic and workforce characteristics, further exploration is necessary to define the drivers and potential consequences of high booster attrition in the EMS clinician community. Full article

Facing the intensifying global climate change pressures and China’s strategic commitment to carbon peaking and carbon neutrality, this study focuses on the multiple challenges faced by the Sichuan-Chongqing region, the economic core of southwest China, in optimizing its energy structure, controlling carbon emissions, and exploring sustainable development pathways. The study uses the LEAP (Long-range Energy Alternatives Planning) model to simulate energy demand and carbon emission trends under different policies and innovative technologies by constructing various scenarios. By conducting a comparative analysis of the LEAP model’s projection results under four scenarios (baseline scenario, alleviative scenario, low-carbon scenario, and high-efficiency low-carbon scenario), this study quantifies the energy demand and carbon emission pathways in the Sichuan-Chongqing region. The results show that optimizing the energy structure and improving energy efficiency are key to achieving carbon neutrality in the Sichuan-Chongqing region. Under the high-efficiency low-carbon scenario, the region is expected to reach peak energy consumption by 2050 and achieve a significant reduction in carbon emissions by 2060, with emissions dropping to 58.1% of the total emissions in 2050 and falling below 25% of the base year’s emissions. The industry sector is expected to account for 77.6% of total emissions. This study highlights the positive impact of widespread clean energy adoption on carbon reduction and demonstrates the importance of industrial restructuring and low-carbon technological innovation, among other green technologies, in promoting economic and environmental sustainability. Furthermore, by quantitatively analyzing carbon emission pathways under different scenarios, the study provides quantitative support and policy references for Sichuan-Chongqing and other regions to implement more scientific emission reduction measures and carbon neutrality pathway planning. The findings contribute to advancing regional collaborative governance, enhancing the scientific rigor of policy implementation, and fostering global climate governance cooperation, ultimately contributing to the coordinated and sustainable development of the ecological environment, economy, and society, embodying the “Sichuan-Chongqing efforts”. Full article

Rational management of scarce water resources is necessary. These resources are not utilised effectively. Therefore, the efficacy of irrigation management at the field level can be enhanced, and the irrigated areas can be expanded through rigorous irrigation management. By estimating water requirements in a straightforward, realistic, precise and feasible manner, achieving optimal water consumption for quality production and profitability is possible. In the context of the development of water resources in tropical and hot climates such as Ghana, estimating water demand assists farmers in planning and adjusting their requirements over time. This study assessed the water requirements of a greenhouse banana during the dry season to assure year-round cultivation, as Ghana has two primary seasons: wet and dry. The estimate was predicated using WSN and the JDL–Mivar data fusion model, which was dependent on the determination of perspiration. The results were contrasted with the existing literature, considering both climatic and biological data and other parameters during the cultivation period due to the model’s ability to fuse datasets. The study determined that the optimal indoor temperature for banana cultivation was 38.1 °C, while the minimum threshold was set at 21 °C. Significant differences and fluctuations in the maximal daily transpiration rates were observed in the water requirements for ‘WN’ values, which ranged from 25 to 50 m³/(ha·J). Banana plants require an intake of 10–20 litres of water per day during their growth season, according to the data collected from the WSN moisture sensor. The banana plants transpired between 100 and 600 kilogrammes of water for every kilogramme of dry matter produced during the humid climate, as indicated by the transpiration ratio, which ranged from 100 to 600. The Leaf Area Index (LAI) fluctuated from 3.3 in June to 4.89 in December. Our proposed method for monitoring bananas in a greenhouse will provide the cultivator with precise information about the bananas that are cultivated within the greenhouse environment. The optimal Leaf Area Index is between 3.6 and 4.5 for bananas to achieve their maximum yield potential. The relative humidity for bananas is typically around 80%, ranging from 65% to 75% during the night and approximately 80% during the day. Full article

Urbanization has undeniably improved human living conditions but has also significantly altered the natural landscape, leading to increased Urban Heat Island (UHI) effects. While many studies have examined these impacts in other countries, research on this topic in Sri Lanka remains limited. This study aimed to evaluate the effects of changes in built-up areas (BAs) and Vegetation Cover (VC) on UHI and environmental criticality (EC) in Matara cityCity, Sri Lanka, utilizing Landsat data. This study employed the commonly used remote-sensing (RS) indices such as the land surface temperature (LST), the UHI Index, and the Environmental Criticality Index (ECI). Various techniques were utilized including supervised image classification, Urban–Rural Gradient Zone (URGZ) analysis, grid-based analysis, UHI profiles, and regression analysis. The results revealed that built-up areas increased by 12.21 km², while vegetation cover decreased by 9.94 km², and this urban expansion led to a 2.7 °C rise in mean LST over 26 years. By 2023, newly developed BA showed the highest LST and environmental criticality, with mean LST values ranging from 25 °C to 21 °C in URGZs 1 to 15 near the city center, and lower values of 15 °C to 16 °C in URGZs 40 to 47 further from the core. The correlation analysis highlighted a strong positive relationship between the NDBI and LST, underscoring the significant impact of BA expansion on LST. Consequently, high-density built-up areas are experiencing high environmental criticality. To minimize these effects, planning agencies should prioritize green urban planning strategies, particularly in high LST and environmental criticality zones. This approach can also be applied to other cities to assess the UHI and LST phenomena, with the goal of protecting the natural environment and promoting the health of urban dwellers. Full article

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments. Full article

The intensive development of road transport has resulted in a significant increase in air pollution. This phenomenon is particularly noticeable in urban areas. This creates the need for analyses and forecasts of the scale and extent of future emissions of harmful substances into the environment. The aim of this study was to estimate the costs of the emission of toxic components of exhaust gases generated by all users of conventionally propelled vehicles travelling on a section of urban road in the next 25 years. The traffic study was carried out on an urban traffic route, playing a key role for road transport in the dimension of a given urban agglomeration. The traffic forecast for the analysed road section was based on the results of our own measurements carried out in April 2023 and external data from the General Directorate for Roads and Motorways. The results of the observations concerned six categories of vehicles for the morning and afternoon rush hours. Based on the data obtained, the generic structure of the vehicle population on the analysed section and the average daily traffic were determined. Using the methodology contained in the Blue Book of Road Infrastructure, parameters were calculated in the form of annual indicators of traffic growth on the analysed section, travel speed, and annual air pollution costs for selected vehicle categories, remembering at the same time that the Blue Book-based methodology does not distinguish between unit costs in relation to the type of emissions. The results of the study confirmed that there was an increase in the cost of toxic emissions for each vehicle category over the projected 25-year period. The largest increases were seen for trucks with trailers and passenger cars. In total, for all vehicle categories, emission costs nearly doubled from 2024 to 2046, from EUR 3,745,229 to EUR 7,443,384, due to the doubling of the number of vehicles resulting from the traffic forecast. The analyses presented here provide an answer to the question of what pollution costs may be faced by cities in which road transport will continue to be based on conventional types of propulsion. In addition, the research presented can be used to develop urban mobility transformation plans for the coming years, within the scope of the widely promoted smart city concept and the idea of electromobility, by pointing out to local authorities the direct economic benefits of these changes. Full article

Green spaces play a critical role in enhancing the urban environment, improving livability, and providing essential ecosystem services. A city should have at least 25% green space from an environmental and health point of view. However, quantitative estimation is required to assess the extent and pattern of green space changes for proper urban management. The present study aimed to identify and track the changes in urban green spaces within the Dhaka South City Corporation (DSCC) of Bangladesh over a 30-year period (i.e., 1991–2021). Geospatial techniques were utilized to analyze green space dynamics using Landsat 4–5 TM satellite images from 1991, 2001, and 2011 and Landsat 8 images from 2021. Supervised image classification techniques and Normalized Difference Vegetation Index (NDVI) analysis were performed to assess the urban green space dynamics in DSCC. The results of our study revealed a significant 36.5% reduction in vegetation cover in the DSCC area over the study period. In 1991, the green area coverage in DSCC was 46%, indicating a relatively healthy environment. By 2001, this coverage had declined sharply to 21.3%, further decreasing to 19.7% in 2011, and reaching a low of just 9.5% in 2021. The classified maps generated in the study were validated through field observations and Google Earth images. The outcomes of our study will be helpful for policymakers and city planners in developing and applying appropriate policies and plans to preserve and improve urban green spaces in DSCC in Bangladesh and other Asian megacities with high population density. Full article

This research explores the application of digital twin technologies to progress the United Nations’ Sustainable Development Goals (SDGs) within the water-energy-food-environment (WEFE) nexus management in urban refugee areas. The study in Irbid Camp utilizes a detailed 3D Revit model combined with real-time data and community insights processed through advanced machine learning algorithms. An examination of 450 qualitative interviews indicates an 80% knowledge level of water conservation practices among the community but only 35% satisfaction with the current management of resources. Predictive analytics forecast a 25% increase in water scarcity and an 18% surge in energy demand within the next ten years, prompting the deployment of sustainable solutions such as solar energy installations and enhanced rainwater collection systems. By simulating resource allocation and environmental impacts, the digital twin framework helps in planning urban development in line with SDGs 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). This investigation highlights the capacity of digital twin technology to improve resource management, increase community resilience, and support sustainable urban growth, suggesting its wider implementation in comparable environments. Full article

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM_2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km². The increase in PM_2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning. Full article