Search Results (1,455)

Background: Intensive Care Unit (ICU) readmission and in-hospital mortality are critical indicators of patient outcomes following ICU discharge. Patients readmitted to the ICU often face worse prognosis, higher healthcare costs, and prolonged hospital stays. Identifying high-risk patients is essential for optimizing post-ICU care and resource allocation. Methods: This two-phase study included the following: (1) a retrospective analysis of ICU survivors in a mixed medical–surgical ICU to identify risk factors associated with ICU readmission and in-hospital mortality, and (2) a prospective validation of a newly developed predictive model: the Worse Outcome Score (WOScore). Data collected included demographics, ICU admission characteristics, severity scores (SAPS II, SAPS III, APACHE II, SOFA), interventions, complications and discharge parameters. Results: Among 1.190 ICU survivors, 126 (10.6%) were readmitted to the ICU, and 192 (16.1%) died in hospital after ICU discharge. Key risk factors for ICU readmission included Diabetes Mellitus, SAPS III on admission, and ICU-acquired infections (Ventilator-Associated Pneumonia (VAP) and Catheter-Related Bloodstream Infection, (CRBSI)). Predictors of in-hospital mortality were identified: medical admission, high SAPS III score, high lactate level on ICU admission, tracheostomy, reduced GCS at discharge, blood transfusion, CRBSI, and Acute Kidney Injury (AKI) during ICU stay. The WOScore, developed based on the results above, demonstrated strong predictive ability (AUC: 0.845 derivation, 0.886 validation). A cut-off of 20 distinguished high-risk patients (sensitivity: 88.1%, specificity: 73.0%). Conclusions: ICU readmission and in-hospital mortality are influenced by patient severity, underlying comorbidities, and ICU-related complications. The WOScore provides an effective, easy-to-use risk stratification tool that can guide clinicians in identifying high-risk patients at ICU discharge and guide post-ICU interventions, potentially improving patients’ outcomes and optimizing resource allocation. Further multi-center studies are necessary to validate the model in diverse healthcare settings. Full article

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

This study investigates the optimization of electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al₂O₃ nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz). Analysis of Variance (ANOVA) was used to statistically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were implemented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al₂O₃-enhanced dielectrics. This investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality. Full article

The large-scale utilization of wind and solar energy is crucial for achieving carbon neutrality targets. However, as extensive wind and solar power generation is integrated via power electronic devices, the inertia level of power systems continues to decline. This leads to a significant reduction in the system’s frequency regulation capability, posing a serious threat to frequency stability. Optimizing the system is an essential measure to ensure its safe and stable operation. Traditional optimization approaches, which separately optimize transmission and distribution systems, may fail to adequately account for the variability and uncertainty of renewable energy sources, as well as the impact of inertia changes on system stability. Therefore, this paper proposes a two-layer optimization method aimed at simultaneously optimizing the operation of transmission and distribution systems while satisfying minimum inertia constraints. The upper-layer model comprehensively optimizes the operational costs of wind, solar, and thermal power systems under the minimum inertia requirement constraint. It considers the operational costs of energy storage, virtual inertia costs, and renewable energy curtailment costs to determine the total thermal power generation, energy storage charge/discharge power, and the proportion of renewable energy grid connection. The lower-layer model optimizes the spatiotemporal distribution of energy storage units within the distribution network, aiming to minimize total network losses and further reduce system operational costs. Through simulation analysis and computational verification using typical daily scenarios, this model enhances the disturbance resilience of the transmission network layer while reducing power losses in the distribution network layer. Building upon this optimization strategy, the model employs multi-scenario stochastic optimization to simulate the variability of wind, solar, and load, addressing uncertainties and correlations within the system. Case studies demonstrate that the proposed model not only effectively increases the integration rate of new energy sources but also enables timely responses to real-time system demands and fluctuations. Full article

Glacial lake outburst floods (GLOFs) represent increasingly common and high-magnitude geohazards across the cryosphere of the Tibetan Plateau, particularly under ongoing climate warming and glacier retreat. This study combines multi-temporal remote sensing imagery and detailed Flo-2D hydrodynamic modeling to investigate the erosive dynamics of the 2020 Jinwuco GLOF in Southeastern Tibetan Plateau. Key conclusions include: (1) The 2.35 km-long flood routing channel exhibits pronounced non-uniformity in horizontal curvature, channel width, and cross-sectional shape, significantly influencing flood propagation; five representative cross-sections divide the channel into six distinct segments. (2) Prominent lateral erosion occurred proximally to the dam, attributable to extreme erosive forces and high sediment transport capacity during peak discharge, with horizontal channel curvature further amplifying local impact and erosion. (3) Erosion rates were highest near the dam and in downstream narrow segments, while mid-reach sections with greater width experienced lower erosion. (4) Maximum flow depths reached 28.12 m in topographically confined reaches, whereas peak velocities occurred in upstream and downstream curved sections. (5) The apparent critical erosive shear stress of bank material is controlled not only by soil strength but also by flood dynamics and pre-existing channel morphology, indicating strong feedback between flow dynamics, channel morphology, and critical erosive shear stress of bank material. This study provides a generalized and transferable framework for analyzing GLOF-related erosion in data-scarce high-altitude regions, offering critical insights for hazard assessment, regional planning, and risk mitigation strategies. Full article

Background: Aneurysmal subarachnoid hemorrhage (SAH) is over twice as common in females compared to males, who may also experience more severe hemorrhages and worse outcomes. Differences in SAH severity, susceptibility to delayed cerebral ischemia (DCI), and treatment responsiveness may underlie this disparity. This study evaluated sex-based differences in DCI timing, severity, treatment responsiveness, and outcomes after SAH. Methods: We analyzed 650 consecutive SAH patients admitted to RWTH Aachen University Hospital (2006–2021). SAH severity was assessed via the (World Federation of Neurological Surgeons) WFNS and modified Fisher scales. DCI-related infarction was defined as new infarcts on CT not present initially or within 48 h post-aneurysm occlusion. Endovascular rescue therapy (ERT) was used for treatment-resistant DCI. Outcomes were assessed at discharge and 12 months using the modified Rankin Scale (mRS). Generalized linear mixed-effects models adjusted for confounders. Results: Of 650 patients, 455 (70%) were female. DCI rates did not differ significantly between sexes (41.5% female vs. 36.4% male; p = 0.361). DCI-related infarction occurred in 19.4% of patients, with no sex-based differences in infarct volume (median 115 mL; p = 0.670) or location. ERT use was similar in females (22.4%) and males (23.9%; p = 0.825). Lower age, poor-grade SAH, and higher mFisher scores were associated with DCI and poor outcomes, but sex was not an independent predictor. Conclusions: Female sex was not associated with more severe SAH, a higher incidence of DCI, or more severe DCI manifestations. Although small effect sizes may become statistically significant in larger cohorts, our findings indicate that such effects are unlikely to be driven by differences in DCI timing, infarct size, or treatment responsiveness. Full article

Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (T_on) and pulse off time (T_off) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B₄C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R²) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors. Full article

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters is essential for improving machining outcomes, it is also important to consider the trade-offs between different performances metrics, such as material removal rate and part accuracy. Part accuracy in terms of dimensional and geometric deviations from nominal values was rarely considered in the literature, if not by the authors. Balancing these factors remains a challenge in the field of EDM. Therefore, this work aims to carry out a multi-objective optimisation of both MRR and part accuracy. A Ni-based alloy (Inconel-625) was used that is widely used in creep-resistant turbine blades and vanes and turbine disks in gas turbine engines for aerospace and defence industries. Four performance indices were optimised simultaneously: two related to the performance of the EDM process and two connected with the form deviations of the manufactured surfaces. Multi-hole copper electrodes having different diameters and three process parameters were varied during the experimental tests. Grey relational analysis and the Adaptive Neuro-Fuzzy Inference System method were used for optimisation. Grey relational analysis found that the following values of the process parameter—0.16 mm of multi-hole electrode diameter, 12 Amperes of Peak current, 200 µs of pulse on time and 0.2 kg/m² as dielectric pressure—produce the optimal performance, i.e., a material removal rate of 0.099 mm³/min, an electrode wear rate of 0.0002 g/min, a circularity deviation of 0.0043 mm and a cylindricity deviation of 0.027 mm. From the experimental examination using multi-hole electrodes, it is concluded that the material removal rate increases and the electrode wear rate decreases because of the availability of higher spark discharge areas between the electrode and work material interface. The Adaptive Neuro-Fuzzy Inference System models showed minimum mean percentage error and, therefore, better performance in comparison with regression models. Full article

Multi-target optimization management of reservoirs plays a crucial role in balancing multiple scheduling objectives, thereby contributing to watershed sustainability. In this study, a model was developed for the multi-target optimization scheduling of water–sediment, ecological, and socioeconomic objectives of reservoirs with multi-dimensional scheduling needs, including flood control, sediment discharge, ecological protection, and socio-economic development. After obtaining the Pareto solution set by solving the optimization model, a decision model based on cumulative prospect theory (CPT) was constructed to select optimal scheduling schemes, resulting in the development of a multi-target decision framework for reservoirs. The proposed framework not only mitigates multi-target conflicts among water–sediment, ecological, and socioeconomic objectives but also quantifies the different preferences of decision-makers. The framework was then applied to six cascade reservoirs (Longyangxia, Liujiaxia, Haibowan, Wanjiazhai, Sanmenxia, and Xiaolangdi) in the Yellow River basin of China. A whole-river multi-target decision model was developed for water–sediment, ecological, and socioeconomic objectives, and the cooperation–competition dynamics among multiple objectives and decision schemes were analyzed for wet, normal, and dry years. The results demonstrated the following: (1) sediment discharge goals and ecological goals were somewhat competitive, and sediment discharge goals and power generation goals were highly competitive, while ecological goals and power generation goals were cooperative, and cooperation–competition relationships among the three objectives was particularly pronounced in dry years; (2) the decision plans for abundant, normal, and low water years were S293, S241, and S386, respectively, and all are consistent with actual dispatch conditions; (3) compared to local models, the whole-river multi-target scheduling model achieved increases of 71.01 × 10⁶ t in maximum sediment discharge, 0.72% in maximum satisfaction rate of suitable ecological flow, and 0.20 × 10⁹ kW·h in maximum power generation; and (4) compared to conventional decision methods, the CPT-based approach yielded rational results with substantially enhanced sensitivity, indicating its suitability for selecting and decision-making of various schemes. This study provides insights into the establishment of multi-target dispatching models for reservoirs and decision-making processes for scheduling schemes. Full article

Background and Objectives: Laryngeal cancer imposes a disproportionate burden on speech, airway protection and long-term quality of life. Contemporary population-based data for Central and Eastern Europe remain scarce, and the post-pandemic trajectory is uncertain. Materials and Methods: We performed a nationwide, retrospective ecological time-series study using Romanian mortality registers and hospital-discharge files for 2017–2023. Crude and age-standardised mortality rates (ASMRs) were calculated, county-level indirect standardisation and spatial autocorrelation assessed and joinpoint regression quantified temporal trends. Forecasts to 2040 combined Holt–Winters/ARIMA models with Elliott-wave heuristics anchored to Fibonacci retracements. Results: In 2023, 798 laryngeal cancer deaths yielded a crude mortality of 3.65/100,000 (95% CI 3.41–3.91). Male mortality (7.07/100,000) exceeded female mortality 18-fold. Rural residents experienced a higher rate than urban counterparts (4.26 vs. 3.04/100,000), a difference unchanged after indirect age standardisation. National ASMR fell by 3.7% annually (p < 0.01), yet five counties formed a high-risk corridor (standardised mortality ratios 1.59–1.82); Moran’s I = 0.27 (p < 0.01) indicated significant spatial clustering. Pandemic-era surgical throughput collapsed by 48%, generating a backlog projected to persist beyond 2030. Ensemble forecasting anticipates a doubling of discharges and mortality between 2034 and 2037 unless smoking prevalence falls by ≥30% and radon exposure is curtailed. Conclusions: Although overall laryngeal cancer mortality in Romania is declining, the pace lags behind Western Europe and is threatened by geographic inequities and pandemic-related care delays. Aggressive tobacco control, radon-remediation policies and expansion of surgical and radiotherapeutic capacity are required to avert a forecasted surge in the next decade. Full article

Glaciers play a crucial role in shaping global hydrology and biogeochemical cycles, yet their climate-forced dynamic impact on chemical denudation and solute yields remain poorly understood. This study compiled data on 40 well-documented cationic denudation rates (CDR) from glaciers across Northwest America, the Svalbard/Arctic Canada, Iceland, Greenland, Europe, China-Tibet, Antarctica, and the Himalayas, revealing substantial spatial variability. CDRs ranged from 46 to 4160 meq m⁻² yr⁻¹. Northwest American and Himalayan glaciers exhibited the highest CDRs, with the Himalayan denudation rate exceeding the global average by more than fourfold. The exceptionally high mean chemical weathering intensity (CWI) of 801 meq m⁻³ from the Himalayan glaciers indicate a wide range of geochemical and climatic conditions within the region, while Northwest American and Greenland glaciers show comparatively lower mean intensities (273 and 247 meq m⁻³, respectively) suggesting a consistent geochemical regime. Northwest American glaciers had the highest specific discharge rates, while Svalbard/Arctic Canada glaciers had the lowest, reflecting regional disparities influenced by climatic and geological factors. A Bonferroni post hoc test highlighted significant differences in specific discharge between Northwest American glaciers and two other basins, emphasizing their distinct hydrological behavior. Predictive modeling revealed a statistically significant but weak relationship between CDR and specific discharge (R² = 57%), suggesting that much of the variability in CDR cannot be explained by specific discharge alone. A regression coefficient of 382 meq m⁻² yr⁻¹ indicates that CDR increases with glacier discharge, although basin-specific analyses showed minimal variation in this relationship across regions. Svalbard/Arctic Canada, Antarctic, Greenlandic, Icelandic, and European Alpine glaciers displayed lower CDRs, which varied depending on underlying lithology, with higher rates observed in carbonate and basaltic terrains compared to other lithologies. We hypothesize that glacier retreat enhances the downward progression of the weathering reaction front, increasing CDR, particularly in rapidly retreating glaciers. Full article

Being a greenhouse gas, SF₆ has significant potential to cause global warming. No alternative gas has been found so far that meets the required criteria. Ongoing research has narrowed down the candidates to some relatively environmentally friendly elementary gases such as N₂, CO₂, and their mixtures with a small percentage of SF₆ (10–20%). Streamers are important and play a deterministic role in the breakdown phenomenon. The inception and growth of streamer discharge depend on the generation of free electrons. Various ionization sources, including field ionization, Auger release of electrons, photoionization, and electron detachment from negative ions, have been employed in dielectric media. In this work, field ionization is considered a free-electron generation mechanism for streamer initiation. In field ionization, neutral molecules produce free electrons when extremely high electric fields are present near the needle electrode. A 3D particle model with field ionization is then used to investigate positive streamer initiation in SF₆/N₂ and SF₆/CO₂ for different mixing ratios at 1 and 5 bar. It was observed that for both mixtures, the number and the apparent length of streamer branching decreased with increasing SF₆ concentration and were minimal at 100% SF₆. The number of branches and the apparent length of streamers were higher in the case of SF₆/CO₂ compared with SF₆/N₂ mixtures, indicating a higher ionization rate for the SF₆/CO₂ mixture. With increasing pressure, the branching and length of the streamers for both mixtures decreased significantly. Although the field-ionization model is only suitable for very high electric fields in the vicinity of the needle tip, its validity is still questionable for uniform fields and at lower pressures. Full article

Background: Aphasia, a common consequence of left-hemisphere stroke, significantly impairs communication and daily functioning. Various studies have explored language recovery but only few have focused on the predictors of recovery of functional communication in patients with stroke. Objective: To identify clinical and linguistic factors associated with functional communication outcomes in patients with post-stroke aphasia. Methods: We enrolled 61 patients with aphasia due to left-hemispheric stroke, admitted to post-acute neurorehabilitation centers. Patients underwent neuropsychological, functional, and language assessments at admission (T0) and discharge (T1). Language abilities were evaluated with the Brief Exam of Language—II (BEL-II), and functional communication was measured through caregiver-rated I-CETI scores. Depression, basic (ADL) and instrumental (IADL) activities of daily living were also assessed. Correlations and regression models were used to examine predictors of functional communication recovery (ΔCETI). Results: Significant improvements were observed in all language domains, functional independence, and mood symptoms from T0 to T1 (p < 0.003). Regression analysis showed that demographic and general clinical variables (e.g., age, etiology, dysphagia) were not significant predictors of ΔCETI. However, ADL score, comprehension skills (Token test and comprehension sub-score of BEL-II) were significantly associated with functional communication recovery (β = 0.51, β = 0.68 and β = 0.75, respectively; p < 0.05). Conclusions: Functional communication recovery in post-stroke aphasia is strongly associated with initial comprehension abilities and functional autonomy in basic life activities, rather than demographic or general clinical variables. These findings highlight the need for targeted interventions aimed at improving receptive language and the importance of including ecologically valid communication assessments in post-stroke rehabilitation protocols. Full article

The low-pressure edge of a pump-turbine runner blade is more prone to cavitation than other parts. The installation angle of the blade’s low-pressure edge is one of the key parameters affecting the cavitation performance of the pump-turbine. Based on the installation angle of the blade’s low-pressure edge obtained by the principle of normal outflow of the turbine runner, two other installation angles of the low-pressure edge are constructed by increasing the installation angle of the low-pressure edge toward the band direction. Three types of blades are designed based on the parametric design program of the pump-turbine runner. The Zwart cavitation model is adopted to carry out full-channel steady numerical simulations for the three runners. The efficiencies and internal flow fields of the draft tube under turbine operating conditions are compared. The cavitation characteristics in pump mode, the distribution of the turbulent flow field, and the pressure distribution on the blade surface are analyzed. The influence laws of the installation angle of the blade’s low-pressure edge on pump-turbine performance is summarized. A design method for anti-cavitation of Francis pump-turbine runners has been explored. The results show that the LP1 blade can achieve normal outflow under the turbine’s rated operating condition, but due to the large inflow attack angle under pump operating conditions, the cavitation performance in pump mode is very poor. By increasing the installation angle of the blade’s low-pressure edge toward the band direction, the efficiencies and cavitation performances of the pump mode can be improved. The LP3 blade reduces the inflow attack angle while optimizing the pressure distribution on the blade’s suction surface, thereby reducing the superimposed effect of two phenomena under large-discharge pump operating conditions with low cavitation numbers: flow separations on the pressure surface caused by inflow impact, and flow separations on the suction surface of adjacent blades caused by cavitation. As a result, the cavitation performance of the LP3 blade is significantly better than that of the LP1 and LP2 blades. The proposed anti-cavitation design method is simple and effective and can be applied to the research and modification design of Francis pump-turbine runners. Full article

A novel architectural design is proposed to optimize the thermal management of lithium-ion batteries (LiBs) through a software-enabled switching mechanism. This approach addresses critical challenges such as hot-spot generation, peak temperature rise, and uneven thermal distribution—issues commonly observed in conventional single-terminal battery modules (STBMs). The proposed dual-terminal configuration integrates an enhanced battery pack structure with a software-enabled switching algorithm that identifies the 50% depth of discharge (DoD) and toggles the current path between two terminals to supply the load. Correspondingly, the module also incorporates the division of four thermal zones and four regions concept in the battery module (BM). Experiments were conducted to evaluate the performance of the proposed model at five different C-rates: 0.5C, 0.75C, 1C, 1.25C, and 1.5C. The results demonstrate that the software-enabled dual-terminal switching (Se-DTS) consistently outperforms the STBM across three key aspects. First, in terms of peak temperature, Se-DTS achieved reductions of 19.33%, 17.83%, and 12.72% at C-rates of 1C, 1.25C, and 1.5C, respectively. Second, in thermal distribution, Se-DTS improved performance, with an 86.1% reduction at 1.25C. Third, regarding hot-spot reduction, improvements of 100% (regional level) and 72.22% (zonal level) were observed at 1.25C, while at 1.5C, an 80% improvement was achieved at the zonal level, without using a cooling system. Full article