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25 pages, 5136 KB  
Article
A Data-Driven Battery Energy Storage Regulation Approach Integrating Machine Learning Forecasting Models for Enhancing Building Energy Flexibility—A Case Study of a Net-Zero Carbon Building in China
by Zesheng Yang, Dezhou Kong, Zhexuan Chen, Zhiang Zhang, Dengfeng Du and Ziyue Zhu
Buildings 2025, 15(19), 3611; https://doi.org/10.3390/buildings15193611 - 8 Oct 2025
Abstract
Building energy flexibility is essential for integrating renewables, optimizing energy use, and ensuring grid stability. While renewable and storage systems are increasingly used in buildings, poorly designed storage strategies often cause supply-demand mismatches, and a comprehensive, indicator-based assessment approach for quantifying flexibility remains [...] Read more.
Building energy flexibility is essential for integrating renewables, optimizing energy use, and ensuring grid stability. While renewable and storage systems are increasingly used in buildings, poorly designed storage strategies often cause supply-demand mismatches, and a comprehensive, indicator-based assessment approach for quantifying flexibility remains lacking. Therefore, this study designs customized energy storage regulation strategies and constructs a comprehensive energy flexibility assessment scheme to address key issues in supply-demand coordination and energy flexibility evaluation. LSTM and Rolling-XGB methods are used to predict building energy consumption and PV generation, respectively. Based on battery safety constraints, a data-driven battery energy storage system (BESS) model simulates battery behavior to evaluate and compare building energy flexibility under two scenarios: (1) uncoordinated PV-BESS, and (2) coordinated PV-BESS with load forecasting. A practical validation was conducted using a net-zero-carbon building as the case study. Simulation results show that the data-driven BESS model improves building energy flexibility and reduces electricity costs through optimized battery sizing, tailored storage strategies, and consideration of local time-of-use tariffs. In the case study, local energy coverage reached 62.75%, surplus time increased to 34.77%, and costs were cut by nearly 40% compared to the PV-only scenario, demonstrating the significant benefits brought by the proposed BESS model that integrates load forecasting and PV generation prediction features. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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15 pages, 992 KB  
Article
Triterpene and Caffeoylquinic Acid Constituents Contribute to the Cognitive-Enhancing, but Not Anxiolytic, Effects of a Water Extract of Centella asiatica in Aged Mice
by Wyatt Hack, Lucas Kuhnau, Jesus Martinez, Luke C. Marney, Jaewoo Choi, Arshia R. Sohal, Seiji Koike, Thuan Nguyen, Claudia S. Maier, Amala Soumyanath and Nora E. Gray
Nutrients 2025, 17(19), 3171; https://doi.org/10.3390/nu17193171 - 8 Oct 2025
Abstract
Background/objectives: A water extract of the plant Centella asiatica (CAW) has been shown to improve cognitive deficits in aged mice when administered for 5 weeks in drinking water. However, the contribution of the constituent compounds within CAW to the beneficial effects of the [...] Read more.
Background/objectives: A water extract of the plant Centella asiatica (CAW) has been shown to improve cognitive deficits in aged mice when administered for 5 weeks in drinking water. However, the contribution of the constituent compounds within CAW to the beneficial effects of the extract remains unelucidated. This study evaluated the effects of triterpene (TT) and caffeoylquinic acids (CQA) found within CAW, on learning, cognitive flexibility, memory, and anxiety-like behaviors in aged C57BL6 mice. Methods: Eighteen-month-old male and female C57BL6 mice were administered either TT, CQA, or the combination (TT+CQA) in their drinking water for a total of 5 weeks, at concentrations corresponding to their presence in CAW. During the final two weeks of treatment learning, executive function, memory, and anxiety were assessed. Results: Aged mice of both sexes showed significant improvement in learning when treated with TT and CQA separately and in combination. Treatment with TT also improved cognitive flexibility in aged mice of both sexes, but CQA and the combination of TT+CQA did not alter cognitive flexibility in aged male mice. There was no effect on recognition memory or anxiety in any of the treatment groups (TT, CQA, TT+CQA) relative to mice treated with the vehicle control although there was a trend towards improved recognition memory with TT treatment. Conclusions: These results suggest that the TT and CQA present in CAW likely contribute to its previously reported amelioration of age-related cognitive changes, especially in learning and cognitive flexibility, while other constituents may be responsible for CAW’s anxiolytic effects. Full article
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18 pages, 440 KB  
Article
Supporting Employment After Cancer: A Mixed-Methods Evaluation of a Vocational Integration Programme for Childhood, Adolescent, and Young Adult Cancer Survivors
by Margherita Dionisi-Vici, Anna Schneider-Kamp, Ilenia Giacoppo, Alessandro Godono, Eleonora Biasin, Antonella Varetto, Emanuela Arvat, Francesco Felicetti, Giulia Zucchetti and Franca Fagioli
Curr. Oncol. 2025, 32(10), 564; https://doi.org/10.3390/curroncol32100564 - 8 Oct 2025
Abstract
Childhood, adolescent, and young adult cancer (CAYAC) survivors often face challenges entering the workforce due to long-term physical, cognitive, and psychological late effects, defined as chronic health conditions resulting from cancer and its treatments. This study evaluated a vocational integration programme that addresses [...] Read more.
Childhood, adolescent, and young adult cancer (CAYAC) survivors often face challenges entering the workforce due to long-term physical, cognitive, and psychological late effects, defined as chronic health conditions resulting from cancer and its treatments. This study evaluated a vocational integration programme that addresses these barriers and promotes psychosocial well-being. The multidisciplinary intervention combined career guidance, soft-skills training, and a paid internship. Using a mixed-method design with questionnaires and semi-structured interviews, we assessed feasibility, satisfaction, and psychosocial outcomes. Thirteen participants (mean-age-at-diagnosis: 12.9 years, SD 5.2; mean-age-at-interview: 27.2 years, SD 5.3) reported over 40 late effects, mostly of moderate severity. Health-Related Quality of Life (HRQoL), measured by the SF-12, showed a Physical Component Score mean of 45.2 (SD 9.1) and a Mental Component Score mean of 43.5 (SD 11.2), indicating greater psychological impact. The programme received high satisfaction ratings (mean 8.3/10) and was described as motivating and valuable, enhancing self-confidence and career prospects. Social support emerged as a key facilitator, while participants noted the need for flexibility and individualised pacing. Despite a limited sample size and potential recruitment bias, this study provides preliminary insights into the feasibility and perceived value of tailored vocational programmes, emphasising the importance of adaptable, socially supportive interventions for CAYAC survivors. Full article
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14 pages, 2299 KB  
Article
Innovative Compact Vibrational System with Custom GUI for Modulating Trunk Proprioception Using Individualized Vibration Parameters
by Debdyuti Mandal, John R. Gilliam, Sheri P. Silfies and Sourav Banerjee
Bioengineering 2025, 12(10), 1088; https://doi.org/10.3390/bioengineering12101088 - 7 Oct 2025
Abstract
Conventional vibrational systems associated with proprioception are mostly equipped with a single standard frequency and amplitude. This feature often fails to show kinesthetic illusion on different subjects, as different individuals respond to different frequencies and amplitudes. Additionally, different muscle groups may also require [...] Read more.
Conventional vibrational systems associated with proprioception are mostly equipped with a single standard frequency and amplitude. This feature often fails to show kinesthetic illusion on different subjects, as different individuals respond to different frequencies and amplitudes. Additionally, different muscle groups may also require the flexibility of frequencies and amplitudes. We developed a custom vibrational system that is equipped with flexible frequency and amplitude, adapted to a custom graphical user interface (GUI). Based on the user’s criteria, the proposed vibrational system enables a wide range of frequencies and amplitudes that can be swept under a single platform. In addition, the system uses small linear actuators that are wearable and attach to the subject without the need for restrictive straps. The vibrational system was used to model trunk proprioceptive impairment associated with low back pain. Low back pain is the leading cause of disability worldwide. It is mostly associated with impaired postural control of the trunk. For postural control, the somatosensory system transmits proprioceptive (position sense) information from the sensors in the skin, joints, muscles, and tendons. Proprioceptive studies on trunk muscles have been conducted where the application of vibration at a set amplitude and frequency across all participants resulted in altered proprioception and a kinesthetic illusion, but not in all individuals. To assess the feasibility of the system, we manipulated the trunk proprioception of five subjects, demonstrating that the vibrational system is capable of modulating trunk proprioception and the value of customizing parameters of the system to obtain maximal deficits from individual subjects. Full article
(This article belongs to the Special Issue Low-Back Pain: Assessment and Rehabilitation Research)
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29 pages, 9465 KB  
Article
Modeling Seasonal Fire Probability in Thailand: A Machine Learning Approach Using Multiyear Remote Sensing Data
by Enikoe Bihari, Karen Dyson, Kayla Johnston, Daniel Marc G. dela Torre, Akkarapon Chaiyana, Karis Tenneson, Wasana Sittirin, Ate Poortinga, Veerachai Tanpipat, Kobsak Wanthongchai, Thannarot Kunlamai, Elijah Dalton, Chanarun Saisaward, Marina Tornorsam, David Ganz and David Saah
Remote Sens. 2025, 17(19), 3378; https://doi.org/10.3390/rs17193378 - 7 Oct 2025
Abstract
Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, [...] Read more.
Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article
(This article belongs to the Special Issue Remote Sensing in Hazards Monitoring and Risk Assessment)
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15 pages, 830 KB  
Article
Family Physicians’ Perspectives on Personalized Cancer Prevention: Barriers, Training Needs, Quality Improvements and Opportunities for Collaborative Networks
by Delia Nicoara, Cosmin Cristescu, Ioan Constantin Pop, Radu Alexandru Ilies, Niculina Nicoara, Alexander Olivier von Stauffenberg, Stefan Matei, Maximilian Vlad Muntean and Patriciu Achimas-Cadariu
J. Clin. Med. 2025, 14(19), 7073; https://doi.org/10.3390/jcm14197073 - 7 Oct 2025
Abstract
Background/Objectives: Family physicians are key stakeholders in the implementation of cancer prevention strategies, including risk factor assessment, lifestyle counseling, and early detection. Despite this, integration of personalized prevention into routine practice remains limited. This study aimed to explore family physicians’ perspectives on [...] Read more.
Background/Objectives: Family physicians are key stakeholders in the implementation of cancer prevention strategies, including risk factor assessment, lifestyle counseling, and early detection. Despite this, integration of personalized prevention into routine practice remains limited. This study aimed to explore family physicians’ perspectives on barriers, training needs, and collaboration opportunities in cancer prevention. Methods: A mixed-methods study was conducted using an exploratory sequential design. The qualitative phase involved semi-structured interviews with 12 family physicians from the North-West Region of Romania. Thematic analysis was employed to identify main challenges and opportunities. Findings informed the development of a structured online survey completed by 50 family physicians. Descriptive and comparative statistical analyses were applied to assess trends and subgroup differences. Results: Interviews and survey data revealed multiple barriers to cancer prevention in primary care: insufficient consultation time, limited access to diagnostic tools, administrative workload, and low patient health literacy. Physicians reported moderate familiarity with personalized prevention but expressed strong interest in further training, particularly through flexible and interactive learning formats. Collaboration with cancer centers was considered suboptimal; participants emphasized the need for streamlined referral pathways and improved communication. Conclusions: The study highlights systemic and educational gaps affecting cancer prevention efforts in family medicine. Tailored training programs, digital integration with cancer centers, and targeted policy adjustments are needed to enhance prevention capacity within primary care. Full article
(This article belongs to the Section Oncology)
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25 pages, 4775 KB  
Article
Methodology for Assessing the Technical Potential of Solar Energy Based on Artificial Intelligence Technologies and Simulation-Modeling Tools
by Pavel Buchatskiy, Stefan Onishchenko, Sergei Petrenko and Semen Teploukhov
Energies 2025, 18(19), 5296; https://doi.org/10.3390/en18195296 - 7 Oct 2025
Abstract
The integration of renewable energy sources (RES) into energy systems is becoming increasingly widespread around the world, driven by various factors, the most relevant of which is the high environmental friendliness of these types of energy resources and the possibility of creating stable [...] Read more.
The integration of renewable energy sources (RES) into energy systems is becoming increasingly widespread around the world, driven by various factors, the most relevant of which is the high environmental friendliness of these types of energy resources and the possibility of creating stable generation systems that are independent of the economic and geopolitical situation. The large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different methods of generation, each with its own characteristics. As a result, the issues of data collection and processing necessary for optimizing the operation of such energy systems are becoming increasingly relevant. The first stage of renewable energy integration involves building models to assess theoretical potential, allowing the feasibility of using a particular type of resource in specific geographical conditions to be determined. The second stage of assessment involves determining the technical potential, which allows the actual energy values that can be obtained by the consumer to be determined. The paper discusses a method for assessing the technical potential of solar energy using the example of a private consumer’s energy system. For this purpose, a generator circuit with load models was implemented in the SimInTech dynamic simulation environment, accepting various sets of parameters as input, which were obtained using an intelligent information search procedure and intelligent forecasting methods. This approach makes it possible to forecast the amount of incoming solar insolation in the short term, whose values are then fed into the simulation model, allowing the forecast values of the technical potential of solar energy for the energy system configuration under consideration to be determined. The implementation of such a hybrid assessment system allows not only the technical potential of RES to be determined based on historical datasets but also provides the opportunity to obtain forecast values for energy production volumes. This allows for flexible configuration of the parameters of the elements used, which makes it possible to scale the solution to the specific configuration of the energy system in use. The proposed solution can be used as one of the elements of distributed energy systems with RES, where the concept of demand distribution and management plays an important role. Its implementation is impossible without predictive models. Full article
(This article belongs to the Special Issue Solar Energy, Governance and CO2 Emissions)
19 pages, 2109 KB  
Article
Machine Learning Optimization of SWRO Membrane Performance in Wave-Powered Desalination for Sustainable Water Treatment
by Lukka Thuyavan Yogarathinam, Sani I. Abba, Jamilu Usman, Abdulhayat M. Jibrin and Isam H. Aljundi
Water 2025, 17(19), 2896; https://doi.org/10.3390/w17192896 - 7 Oct 2025
Abstract
Wave-powered desalination systems integrate reverse osmosis (RO) with renewable ocean energy, providing a sustainable and environmentally responsible approach to freshwater production. This study aims to investigate wave-powered RO desalination using supervised and deep machine learning (ML) models to predict the effects of variable [...] Read more.
Wave-powered desalination systems integrate reverse osmosis (RO) with renewable ocean energy, providing a sustainable and environmentally responsible approach to freshwater production. This study aims to investigate wave-powered RO desalination using supervised and deep machine learning (ML) models to predict the effects of variable feed flow on permeate recovery and salt rejection under dynamic hydrodynamic conditions. Multiple ML models, including Gaussian process regression (GPR), support vector machines (SVMs), multi-layer perceptron (MLP), linear regression (LR), and decision trees (DTs) were systematically assessed for the prediction of permeate recovery and salt rejection (%) using three distinct input configurations: limited physicochemical features (M1), flow- and salinity-related parameters (M2), and a comprehensive variable set incorporating temperature (M3). GPR achieved near-perfect predictive accuracy R2 values (~1.00) with minimal errors for permeate recovery and salt rejection, attributed to its flexible kernel and probabilistic design. MLP and SVM also performed well, though they showed greater sensitivity to feature complexity. In contrast, DT models exhibited limited generalization and higher error rates, particularly when key features were excluded. Sensitivity analyses revealed that feed pressure (FP) and brine salinity (BS) were dominant positive influencers of permeate recovery and salt rejection. In contrast, brine flow (BF) and permeate salinity (PS) had negative impacts. Full article
(This article belongs to the Special Issue Novel Methods in Wastewater and Stormwater Treatment)
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15 pages, 1082 KB  
Article
Effects of High-Intensity Interval Training on Functional Fitness in Older Adults
by André Schneider, Luciano Bernardes Leite, Fernando Santos, José Teixeira, Pedro Forte, Tiago M. Barbosa and António Miguel Monteiro
Appl. Sci. 2025, 15(19), 10745; https://doi.org/10.3390/app151910745 - 6 Oct 2025
Viewed by 170
Abstract
(1) Background: The global increase in life expectancy has generated growing interest in strategies that support functional independence and quality of life among older adults. Functional fitness—including strength, mobility, flexibility, and aerobic endurance—is essential for preserving autonomy during aging. In this context, physical [...] Read more.
(1) Background: The global increase in life expectancy has generated growing interest in strategies that support functional independence and quality of life among older adults. Functional fitness—including strength, mobility, flexibility, and aerobic endurance—is essential for preserving autonomy during aging. In this context, physical exercise, particularly High-Intensity Interval Training (HIIT), has gained attention for its time efficiency and physiological benefits. This randomized controlled trial aimed to evaluate the effects of a group-based HIIT program on functional fitness in older adults; (2) Methods: Functional outcomes were assessed before, during, and after a 65-week intervention using standardized field tests, including measures of upper and lower body strength, flexibility, aerobic endurance, and agility. This study was prospectively registered at ClinicalTrials.gov (NCT07170579); (3) Results: Significant improvements were observed in the HIIT group across multiple domains of functional fitness compared to the control group, notably in upper body strength, lower limb flexibility, cardiorespiratory endurance, and mobility; (4) Conclusions: These results suggest that HIIT is an effective and adaptable strategy for improving functional fitness in older adults, with the potential to enhance performance in daily activities and support healthy aging in community settings. Full article
(This article belongs to the Special Issue Sports, Exercise and Healthcare)
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18 pages, 7307 KB  
Article
Conic Programming Approach to Limit Analysis of Plane Rigid-Plastic Problems
by Artur Zbiciak, Adam Kasprzak and Kazimierz Józefiak
Appl. Sci. 2025, 15(19), 10729; https://doi.org/10.3390/app151910729 - 5 Oct 2025
Viewed by 165
Abstract
This paper presents the application of conic programming methods to the limit analysis of plane rigid-plastic problems in structural and geotechnical engineering. The approach is based on the formulation of yield criteria as second-order cone constraints and on the dual optimization problem, which [...] Read more.
This paper presents the application of conic programming methods to the limit analysis of plane rigid-plastic problems in structural and geotechnical engineering. The approach is based on the formulation of yield criteria as second-order cone constraints and on the dual optimization problem, which directly provides collapse mechanisms and limit loads. Two benchmark examples are investigated. The first concerns a deep beam under uniform top pressure, analyzed with linear and quadratic finite elements. The results confirm the ability of the method to reproduce realistic collapse mechanisms and demonstrate the effect of mesh refinement and element type on convergence. The second example addresses the ultimate bearing capacity of a strip footing on cohesive-frictional soil. The numerical implementation was carried out in MATLAB using CVX with MOSEK as the solver, which ensures practical applicability and efficient computations. Different soil models are considered, including Mohr–Coulomb and two Drucker–Prager variants, and the results are compared with the classical Terzaghi solution. Additional elastoplastic FEM simulations carried out in a commercial program are also presented. The comparison highlights the differences between rigid-plastic optimization and incremental elastoplastic analyses, showing that both conservative and liberal estimates of bearing capacity can be obtained. The study shows that conic programming is an efficient and flexible framework for limit analysis of plane rigid-plastic problems, providing engineers with complementary tools for assessing ultimate loads, while also ensuring good computational efficiency. Full article
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25 pages, 1245 KB  
Article
Evaluating Cybersecurity Measures for Smart Grids Under Uncertainty: A Picture Fuzzy SWARA–CODAS Approach
by Betul Kara, Ertugrul Ayyildiz, Bahar Yalcin Kavus and Tolga Kudret Karaca
Appl. Sci. 2025, 15(19), 10704; https://doi.org/10.3390/app151910704 - 3 Oct 2025
Viewed by 194
Abstract
Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose [...] Read more.
Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article
(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)
40 pages, 1781 KB  
Article
Exponentiated Inverse Exponential Distribution Properties and Applications
by Aroosa Mushtaq, Tassaddaq Hussain, Mohammad Shakil, Mohammad Ahsanullah and Bhuiyan Mohammad Golam Kibria
Axioms 2025, 14(10), 753; https://doi.org/10.3390/axioms14100753 - 3 Oct 2025
Viewed by 122
Abstract
This paper introduces Exponentiated Inverse Exponential Distribution (EIED), a novel probability model developed within the power inverse exponential distribution framework. A distinctive feature of EIED is its highly flexible hazard rate function, which can exhibit increasing, decreasing, and reverse bathtub (upside-down bathtub) shapes, [...] Read more.
This paper introduces Exponentiated Inverse Exponential Distribution (EIED), a novel probability model developed within the power inverse exponential distribution framework. A distinctive feature of EIED is its highly flexible hazard rate function, which can exhibit increasing, decreasing, and reverse bathtub (upside-down bathtub) shapes, making it suitable for modeling diverse lifetime phenomena in reliability engineering, survival analysis, and risk assessment. We derived comprehensive statistical properties of the distribution, including the reliability and hazard functions, moments, characteristic and quantile functions, moment generating function, mean deviations, Lorenz and Bonferroni curves, and various entropy measures. The identifiability of the model parameters was rigorously established, and maximum likelihood estimation was employed for parameter inference. Through extensive simulation studies, we demonstrate the robustness of the estimation procedure across different parameter configurations. The practical utility of EIED was validated through applications to real-world datasets, where it showed superior performance compared to existing distributions. The proposed model offers enhanced flexibility for modeling complex lifetime data with varying hazard patterns, particularly in scenarios involving early failure periods, wear-in phases, and wear-out behaviors. Full article
(This article belongs to the Special Issue Probability, Statistics and Estimations, 2nd Edition)
17 pages, 1851 KB  
Article
A Method for Determining Medium- and Long-Term Renewable Energy Accommodation Capacity Considering Multiple Uncertain Influencing Factors
by Tingxiang Liu, Libin Yang, Zhengxi Li, Kai Wang, Pinkun He and Feng Xiao
Energies 2025, 18(19), 5261; https://doi.org/10.3390/en18195261 - 3 Oct 2025
Viewed by 192
Abstract
Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the [...] Read more.
Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R2 > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article
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22 pages, 605 KB  
Article
Urban Climate Integration Framework (UCIF): A Multi-Scale, Phased Model
by Spenser Robinson
Land 2025, 14(10), 1990; https://doi.org/10.3390/land14101990 - 3 Oct 2025
Viewed by 258
Abstract
Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical [...] Read more.
Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical resilience, decarbonization, and social/community engagement. The framework conceptualizes metropolitan and building scales as analytically distinct but operationally linked, allowing strategies to reflect the different systems, stakeholders, and capacities at each level. It also outlines a three-phase progression—Initial (assessment and goal setting), Readiness (planning and implementation), and Steady-State (monitoring and iterative adjustment)—to support staged, adaptive deployment. Each phase includes sample metrics and SMART goals that can be tailored to local context and tracked over time. By integrating theoretical insights with practical implementation tools, the framework offers a flexible yet rigorous approach for advancing urban sustainability. It emphasizes the importance of aligning technical interventions with institutional capacity and community participation to enhance effectiveness and equity. This model contributes to both planning theory and applied sustainability efforts by providing a structured pathway for cities to enhance climate readiness across systems and scales. Full article
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15 pages, 840 KB  
Article
External Validation and Comparative Performance of the T.O.HO. and S.T.O.N.E. Scoring Systems for Predicting Stone-Free Outcomes Following Flexible Ureteroscopy: Toward Personalized Preoperative Counseling
by Yuka Sugizaki, Takanobu Utsumi, Rino Ikeda, Naoki Ishitsuka, Takahide Noro, Yuta Suzuki, Shota Iijima, Takatoshi Somoto, Ryo Oka, Takumi Endo, Naoto Kamiya and Hiroyoshi Suzuki
J. Pers. Med. 2025, 15(10), 477; https://doi.org/10.3390/jpm15100477 - 2 Oct 2025
Viewed by 126
Abstract
Background/Objectives: The attainment of a stone-free (SF) condition is a fundamental indicator of successful outcomes after flexible ureteroscopy (fURS) for urinary stone disease. External confirmations of preoperative scores remain limited. We externally validated the T.O.HO. and S.T.O.N.E. scores in an independent Japanese [...] Read more.
Background/Objectives: The attainment of a stone-free (SF) condition is a fundamental indicator of successful outcomes after flexible ureteroscopy (fURS) for urinary stone disease. External confirmations of preoperative scores remain limited. We externally validated the T.O.HO. and S.T.O.N.E. scores in an independent Japanese cohort and examined calibration, decision curve utility, and threshold-guided use to support personalized planning. Methods: We retrospectively analyzed 361 consecutive patients treated with fURS from March 2018 to August 2023. Postoperative SF status was defined as the absence of residual calculi greater than 2 mm on non-contrast computed tomography performed within three months of surgery. Independent determinants of SF were identified using multivariable logistic regression, predictive performance was quantified by receiver operating characteristic analyses with DeLong’s test, and model calibration and decision curve analysis were additionally assessed. Results: Among the 361 patients, 255 (70.6%) achieved an SF state. A larger stone diameter, the presence of lower-pole calculi, and preoperative pyuria (positive urine WBC) were significant independent predictors of residual fragments. T.O.HO. demonstrated superior discrimination (AUC 0.86) compared with S.T.O.N.E. (AUC 0.77; p < 0.01) and surpassed individual predictors. Both scores showed acceptable calibration. Decision curve analysis demonstrated higher net benefit for T.O.HO. across clinically relevant thresholds. We provide clinically useful cut-offs (e.g., T.O.HO. ≤5: high SF probability; 6: trade-off discussion; ≥7: higher residual risk) to align actions with patient priorities. Conclusions: Beyond discrimination, a calibrated, threshold-aware use of T.O.HO. enables personalized preoperative counseling and shared decision-making, potentially reducing unnecessary staging and enhancing routine fURS planning. Full article
(This article belongs to the Section Personalized Medical Care)
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