Search Results (2,662)

Background/Objectives: Pseudomonas aeruginosa is an opportunistic pathogen frequently implicated in healthcare-associated infections, particularly ventilator-associated pneumonia and other device-related infections. The global emergence of carbapenem-resistant P. aeruginosa (CRPA) represents a major clinical challenge due to its limited therapeutic options and high mortality rates. Methods: Relevant clinical data were obtained from medical records. Isolates were identified via 16S PCR, and antimicrobial susceptibility testing was performed using the Vitek2 Compact system following CLSI guidelines. Carbapenemase genes (blaGES, blaKPC, blaIMP, blaNDM, blaVIM) were detected via PCR. Clonal relationships were determined via RAPD-PCR, and some sequence types were assigned according to the global P. aeruginosa MLST database. Results: In this study, 40 non-duplicate CRPA isolates were collected from 35 patients in a tertiary-care hospital in Mexico. Most isolates originated from adult patients, predominantly from tracheal aspirates (32.5%) and urine cultures (25.0%). Mechanical ventilation was the most common invasive device associated with infection, and the overall mortality rate reached 14.3%. Antimicrobial susceptibility testing showed that 95% of isolates exhibited a multidrug-resistant phenotype, with high resistance rates to ciprofloxacin (70.0%) and β-lactams. Carbapenemase genes were detected in 55% of isolates, mainly bla_IMP, bla_GES, and bla_VIM, either alone or in combination. Notably, this is the first report of ST309 (bla_IMP), ST411 (bla_GES + bla_IMP), and ST167 (bla_GES + bla_VIM) carrying carbapenemase genes in Mexico. Conclusions: These findings highlight the persistence and genetic diversity of CRPA circulating in hospital settings and emphasize the urgent need for strengthened genomic surveillance and infection control programs to prevent the spread of these high-risk multidrug-resistant clones. Full article

Heating, ventilation, and air-conditioning (HVAC) systems dominate energy consumption in hot-climate buildings, where maintaining occupant comfort under extreme outdoor conditions remains a critical challenge, particularly under emerging time-of-use (TOU) electricity pricing schemes. While deep reinforcement learning (DRL) has shown promise for adaptive HVAC control, existing approaches often suffer from comfort violations, myopic decision making, and limited robustness to uncertainty. This paper proposes a comfort-first hybrid control framework that integrates Soft Actor–Critic (SAC) with a Cross-Entropy Method (CEM) refinement layer, referred to as SACEM. The framework combines data-efficient off-policy learning with short-horizon predictive optimization and safety-aware action projection to explicitly prioritize thermal comfort while minimizing energy use, operating cost, and peak demand. The control problem is formulated as a Markov Decision Process using a simplified thermal model representative of commercial buildings in hot desert climates. The proposed approach is evaluated through extensive simulation using Saudi Arabian summer weather conditions, realistic occupancy patterns, and a three-tier TOU electricity tariff. Performance is assessed against state-of-the-art baselines, including PPO, TD3, and standard SAC, using comfort, energy, cost, and peak demand metrics, complemented by ablation and disturbance-based stress tests. Results show that SACEM achieves a comfort score of 95.8%, while reducing energy consumption and operating cost by approximately 21% relative to the strongest baseline. The findings demonstrate that integrating comfort-dominant reward design with decision-time look-ahead yields robust, economically viable HVAC control suitable for deployment in hot-climate smart buildings. Full article

One of the challenges post-COVID-19 is reducing the negative impacts on quality of life, performance, and independence in activities of daily living. Assessing functional dependence in adults one year after acute infection can help to understand the long-term consequences, evaluate the impact on quality of life, plan rehabilitation and healthcare, identify the most vulnerable groups, measure the socioeconomic impact, and support public policies and clinical decisions. Objectives: The objectives of this study are as follows: (a) to assess the prevalence of functional dependence in Brazilian adults with COVID-19; (b) to analyze the association between the study variables; and (c) to determine the factors associated with functional dependence. Methods: This was an observational, cross-sectional study with 987 adults (18 to 59 years old) living in the State of Paraná (Brazil) hospitalized for COVID-19 between March and December 2020. Data were collected by telephone 12 months after the acute infection using an instrument to retrieve sociodemographic and health information, and a functional dependence scale to assess dependence before COVID-19 retrospectively (using participant recall information) and at the time of the interview. Data were analyzed using penalized logistic regression after imputing missing data. Data were analyzed using penalized logistic regression after imputing missing data. Results: Functional dependence after COVID-19 was 5.0% and was associated with low levels of education, not having a partner, living with someone, not owning a home, experiencing job changes, requiring care, obesity, smoking, multimorbidity, ICU admission in the acute phase, use of invasive ventilation, or having Long COVID. Individuals who required care or used invasive ventilation support were, respectively, 9.3 and 6.5 times more likely to develop dependence after COVID-19. Despite adjustment for multiple factors, the magnitude of the observed effects warrants cautious interpretation, as unmeasured or residual confounding effects may still be present. Sample recall bias due to collection after 12 months and the presence of the alpha variant without COVID-19 vaccination coverage may limit data generalization. Conclusions: The results highlight the need to emphasize the public health implications of identifying functional dependence. In this vein, it is necessary to implement preventive measures, identify and monitor more vulnerable groups, plan rehabilitation programs, and develop public health policies. Full article

This study explores the intersection of sociocultural factors, particularly privacy, with energy consumption patterns in residential buildings in Riyadh, Saudi Arabia. While cultural values around privacy have long been recognised as influential in residential design, the impact of these values on energy consumption is underexplored. This research aims to fill this gap by examining how privacy needs, residents’ preferences, and open layouts affect energy efficiency, particularly in terms of natural light and ventilation. A mixed-methods approach was employed, including semi-structured interviews with engineers, data collected from 108 respondents via an online survey, a case study of a residential building in Riyadh, and building performance simulations using IES software. The study also assessed actual energy consumption data and indoor lighting as potential implications of privacy concerns, causing changes in behavioural control of systems (e.g., windows, blinds, lighting, etc.). It focuses on the relationship between privacy needs, energy use, and natural daylight distribution. The IES simulation results for the studied residential building show an annual energy consumption of 24,000 kWh, primarily due to cooling loads and artificial lighting caused by privacy measures applied by the residents. The findings reveal that privacy-driven design choices and occupant behaviours, such as the use of full window shutters, frosted glazing and limited window operation, significantly reduce daylight availability and natural ventilation, leading to increased reliance on artificial lighting and air conditioning. This study highlights the need for human-centric design approaches that address the interplay between sociocultural factors, particularly reinforcing cultural sensitivity, and building performance, offering insights for future sustainable housing developments in Riyadh and similar contexts. Full article

Mosque buildings have symbolic significance, which makes them ideal candidates for implementing energy-efficient building design strategies. Mosques located in hot climates face several challenges in achieving thermal comfort while meeting energy efficiency requirements due to their distinct architectural features and intermittent occupancy patterns. Addressing these challenges requires integrating innovative energy-efficient retrofit strategies that cater to the characteristics of existing contemporary mosque buildings. Thus, this study provides a review of these approaches, considering both passive and active strategies. Passive strategies include thermal insulation, glazing upgrades, and shading improvements, while active ones include Heating, Ventilation, and Air Conditioning (HVAC) zoning and smart control, lighting upgrades, and the integration of photovoltaic panels. The findings highlight the potential of combining both passive and active retrofitting measures to achieve substantial energy performance improvements while addressing the thermal comfort needs of mosque buildings in hot climates. However, more research is needed on smart control systems and advanced building materials to further enhance energy performance in mosque buildings. By adopting these strategies, mosques can serve as models of energy-efficient design, promoting sustainability and resilience in their communities. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

Accurate prediction of building cooling load is crucial for enhancing energy efficiency and optimizing the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems. To improve predictive accuracy, we propose a hybrid Grey Wolf Optimizer-Convolutional Neural Network–Long Short-Term Memory (GWO-CNN-LSTM) prediction model. A 3D model of the building was first developed using SketchUp, and its cooling load was subsequently simulated with EnergyPlus and OpenStudio. The Grey Wolf Optimizer (GWO) algorithm is employed to automatically tune the hyperparameters of the CNN-LSTM model, thereby improving both training efficiency and predictive performance. A comparative analysis with other models demonstrates that the proposed model effectively captures both long-term temporal patterns and short-term fluctuations in cooling load, outperforming baseline models such as Long Short-Term Memory (LSTM), Genetic Algorithm-Convolutional Neural Network-Long Short-Term Memory (GA-CNN-LSTM), and Particle Swarm Optimization-Convolutional Neural Network–Long Short-Term Memory (PSO-CNN-LSTM). A comparative analysis with other models demonstrates that the proposed model effectively captures both long-term temporal patterns and short-term fluctuations in cooling load, outperforming baseline models such as LSTM, GA-CNN-LSTM, and PSO-CNN-LSTM. The GWO-CNN-LSTM model achieves an R² of 0.9266, with MAE and RMSE of 218.7830 W and 327.4012 W, respectively, representing improvements of 35.0% and 27.0% in MAE and RMSE compared to LSTM, and 20.8% and 16.3% compared to GA-CNN-LSTM. Full article

Ventilated façade systems are being increasingly used in energy-efficient building envelopes due to their configurational flexibility and potential to reduce thermal bridging. This study focuses on the experimental evaluation of anchoring components used in such systems, specifically examining the effect of various thermal insulation pads and internal inserts on the system’s mechanical, thermal, and fire performance. A series of laboratory tests was carried out to assess the static behavior of aluminum brackets under both tensile (suction wind load) and compressive (pressure wind load) forces. The results demonstrate that the use of thermal pads and inserts does not lead to any significant degradation of the mechanical capacity of the anchoring brackets, confirming their structural reliability. Additional thermal testing revealed that the use of insulating materials significantly reduces heat transfer through the brackets. Fire resistance tests were conducted to compare the performance of different types of insulation pads under elevated temperatures. The findings indicate that the choice of pad material substantially influences both fire integrity and thermal performance. This study confirms the potential of incorporating optimized insulating pads and inserts into façade brackets to enhance the thermal and fire performance of ventilated façades without compromising their structural behavior. Full article

Background and Objectives: Acute respiratory failure (ARF) has a heterogeneous course in the emergency department (ED), and early prediction of noninvasive mechanical ventilation (NIMV) failure is difficult. The PaCO₂–ETCO₂ gap reflects ventilation–perfusion mismatch and increased physiologic dead space; however, the prognostic value of its short-term change during NIMV is unclear. This study evaluated baseline, post-treatment, and delta (post–pre) PaCO₂–ETCO₂ gap values for predicting intubation, intensive care unit (ICU) admission, and mortality in ED patients with ARF receiving NIMV. Materials and Methods: This prospective observational study enrolled adults (≥18 years) treated with NIMV in a tertiary ED. Exclusion criteria included GCS < 15, intoxication, pneumothorax, trauma, pregnancy, gastrointestinal bleeding, need for immediate intubation/CPR, or incomplete data. ETCO₂ was recorded within the first 3 min of NIMV and at 30 min; concurrent arterial blood gases provided PaCO₂. The PaCO₂–ETCO₂ gap was calculated at both time points and as delta. Outcomes were intubation, ICU admission, and mortality. ROC analyses determined discriminatory performance and cutoffs using the Youden index. Results: Thirty-four patients were included (50% female; mean age 73.26 ± 10.07 years). Intubation occurred in 9 (26.5%), ICU admission in 20 (58.8%), and mortality in 10 (29.4%). The post-treatment gap and delta gap were significantly higher in intubated patients (p = 0.007 and p = 0.001). For predicting intubation, post-treatment gap > 10.90 mmHg yielded AUC 0.807 (p = 0.007; sensitivity 77.8%, specificity 76.0), while delta gap > 2.90 mmHg yielded AUC 0.982 (p = 0.001; sensitivity 88.9%, specificity 92.0). Delta gap also predicted ICU admission (cutoff > 0.65 mmHg; AUC 0.746, p = 0.016) and mortality (cutoff > 2.90 mmHg; AUC 0.865, p = 0.001). Conclusions: In ED ARF patients receiving NIMV, an increasing PaCO₂–ETCO₂ gap—especially the delta gap—was associated with higher risks of intubation, ICU admission, and mortality, supporting serial CO₂ gap monitoring as a practical early warning marker of deterioration. Full article

Existing office buildings in Australia contribute to 24% of the nation’s electricity consumption and 10% of greenhouse gas emissions, with energy use projected to rise by 84%. Meeting the 2050 sustainability target and United Nations (UN) 17 Sustainable Development Goals (SDGs) requires improving sustainability within existing office buildings. This systematic review examines net zero energy and climate resilience strategies in these buildings by analysing 74 studies from scholarly literature, government reports, and industry publications. The literature search was conducted across Scopus, Google Scholar, and Web of Science databases, with the final search in early 2025. Studies were selected based on keywords and research parameters. A narrative synthesis identified key technologies, evaluating the integration of net zero principles with climate resilience to enhance energy efficiency through HVAC modifications. Technologies like heat pumps, energy recovery ventilators, thermal energy storage, and phase change materials (PCMs) have been identified as crucial in reducing HVAC energy usage intensity (EUI). Lighting control and plug load management advancements are examined for reducing electricity demand. This review highlights the gap between academic research and practical applications, emphasising the need for comprehensive field studies to provide long-term performance data. Current regulatory frameworks influencing the net zero transition are discussed, with recommendations for policy actions and future research. This study links net zero performance with climate adaptation objectives for existing office buildings and provides recommendations for future research, retrofit planning, and policy development. Full article

This study addresses the thermal management challenges of marine gas turbine enclosures by proposing an innovative optimization of the air intake design, enhancing thermal management capabilities without mechanical restructuring. Through Computational Fluid Dynamics (CFD), the research systematically optimizes key parameters including cooling air inlet pressure, positioning, and enclosure inlet diameter. The results demonstrate that elevating the cooling air inlet pressure to 300 Pa enhanced the entrainment ratio (η) by 9.55% and increased the pressure loss coefficient (PLC) by 2.06% compared to the baseline case (P_in = 0 Pa). An enclosure inlet diameter of 1100 mm optimizes entrainment efficiency (η = 0.331) and minimizes internal temperatures. The multi-objective optimization identifies the globally optimal configuration (D = 800 mm, P_in = 300 Pa, L = 1.6 m), which improves the entrainment ratio by 31.7% (η = 0.399) and reduces the average temperature at key monitoring points (T₁–T₅) by up to 14 K compared to the baseline, albeit with a marginal increase in PLC. This optimal configuration ensures that all local temperatures remain within the operational limit of 355 K. This research provides a theoretical foundation for enhancing marine power system performance and offers evidence-based guidance for engineering applications. Full article

Background: Transcatheter aortic valve replacement (TAVR) is widely used in patients with severe aortic stenosis. The HALP (hemoglobin, albumin, lymphocyte, and platelet) score is an easily obtainable composite index that reflects nutritional status and systemic inflammation. Methods: In this single-center retrospective study, 140 patients who underwent TAVR between 1 April 2021, and 31 October 2024, were included. Patients were stratified according to the median HALP score (32.65) into low (<32.65)- and high (≥32.65)-HALP groups. In-hospital outcomes were mortality, bleeding requiring transfusion of >5 units of red blood cells, acute kidney injury (AKI), need for mechanical ventilation >24 h, and length of hospital stay. Associations between the HALP score and clinical outcomes were evaluated using multivariable regression analyses, and the discriminatory performance of HALP was assessed using receiver operating characteristic (ROC) curves. Results: Patients with low HALP scores had higher rates of in-hospital mortality (11.4% vs. 4.2%; p = 0.002), bleeding (28.6% vs. 5.7%; p < 0.001), AKI (11.4% vs. 2.9%; p < 0.001), and need for mechanical ventilation >24 h (25.7% vs. 14.4%; p = 0.002), as well as longer hospital stay (4.82 ± 1.50 vs. 3.62 ± 1.94 days; p = 0.001) compared with the high-HALP group. In multivariable models, a lower HALP score remained independently associated with all adverse in-hospital outcomes. ROC analysis showed good discriminatory ability of the HALP score for mortality (area under the curve [AUC] = 0.816; cut-off = 20.16), bleeding (AUC = 0.798; cut-off = 24.94), AKI (AUC = 0.737; cut-off = 26.21), and need for mechanical ventilation (AUC = 0.735; cut-off = 27.36). Conclusions: The HALP score is independently associated with adverse in-hospital clinical outcomes in patients undergoing TAVR and may serve as a simple and practical tool for early risk stratification in this population. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of building energy consumption and play a crucial role in maintaining indoor comfort. However, hidden faults in air-handling units (AHUs) often lead to energy waste and degraded performance, highlighting the importance of reliable fault detection and diagnosis (FDD). This study proposes a simulation-driven FDD framework that integrates a standardized prototype dataset and an independent evaluation dataset generated from a calibrated EnergyPlus model representing a target facility, enabling controlled experimentation and transfer evaluation within simulation environments. Training data were generated from the DOE EnergyPlus Medium Office prototype model, while evaluation data were obtained from a calibrated building-specific EnergyPlus model of a research facility operated by Company H in Korea. Three representative fault scenarios—outdoor air damper stuck closed, cooling coil fouling (65% capacity), and air filter fouling (30% pressure drop)—were systematically implemented. A Deep Belief Network (DBN) classifier was developed and optimized through a two-stage hyperparameter tuning strategy, resulting in a three-layer architecture (256–128–64 nodes) with dropout and regularization for robustness. The optimized DBN achieved diagnostic accuracies of 92.4% for the damper fault, 98.7% for coil fouling, and 95.9% for filter fouling. These results confirm the effectiveness of combining simulation-based dataset generation with advanced deep learning methods for HVAC fault diagnosis. The results indicate that a DBN trained on a standardized EnergyPlus prototype can transfer to a second, independently calibrated EnergyPlus building model when AHU topology, control logic, and monitored variables are aligned. This study should be interpreted as a simulation-based proof-of-concept, motivating future validation with field BMS data and more diverse fault scenarios. Full article

Thermal comfort and energy efficiency are two main goals of heating, ventilation, and air conditioning (HVAC) systems, which use about 40% of the total energy in buildings. This paper aims to predict optimal room temperature, enhance comfort, and reduce energy consumption while avoiding extra energy use from overheating or overcooling. Six Machine Learning (ML) models were tested to predict the optimal temperature in the classroom based on the occupancy characteristic detected by a Deep Learning (DL) model, You Only Look Once (YOLO). The decision tree achieved the highest accuracy at 97.36%, demonstrating its effectiveness in predicting the preferred temperature. To measure energy savings, the study used RETScreen software version 9.4 to compare intelligent temperature control with traditional operation of HVAC. Genetic algorithm (GA) was further employed to optimize HVAC energy consumption while keeping the thermal comfort level by adjusting set-points based on real-time occupancy. The GA showed how to balance comfort and efficiency, leading to better system performance. The results show that adjusting from default HVAC settings to preferred thermal comfort levels as well controlling the HVAC to work only if the room is occupied can reduce energy consumption and costs by approximately 76%, highlighting the substantial impact of even simple operational adjustments. Further improvements achieved through GA-optimized temperature settings provide additional savings of around 7% relative to preferred comfort levels, demonstrating the value of computational optimization techniques in fine-tuning building performance. These results show that intelligent, data-driven HVAC control can improve comfort, save energy, lower costs, and support sustainability in buildings. Full article

Passive building ventilation features, such as wind towers, can help meet rising cooling and ventilation demands in hot, arid regions. However, most prior studies rely on scaled models or isolate single design parameters, limiting holistic insight. This study conducts a full-scale, validated computational fluid dynamics (CFD) parametric analysis of wind tower geometry and its impact on ventilation efficiency in semi-enclosed spaces. Five geometric properties are investigated: tower shape, roof type, number of shafts, separator height, and number of louvres. Additionally, the sensitivity of the optimal configuration to wind speed, wind direction, and louvre orientation is assessed. Results from 88 CFD cases highlight strong interactions among design parameters and show that straight towers with curved roofs consistently perform best. Compared with a tower with six shafts, a flat internal roof, and downward-facing louvres, an optimized tower with four shafts, a convex internal roof, and upward-facing louvres increases airflow rate by a factor of 2.7 and occupied-zone air velocity by 45%, underscoring the importance of holistic geometric optimization. Full article