Key Performance Indicators Research Articles

Performance prediction and manipulation strategy of a hybrid system based on tubular solid oxide fuel cell and annular thermoelectric generator

Abstract Tubular solid oxide fuel cells (TSOFCs) generate high-grade waste heat during operation, but the existing waste heat recovery technologies designed for flat solid oxide fuel cells cannot be directly applied to TSOFC due to the geometry mismatch. To efficient harvest the waste heat, a new geometry-matching hybrid system including TSOFC and annular thermoelectric generator (ATEG) is synergistically integrated to evaluate the performance upper limit. A mathematical model is formulated and verified to describe the hybrid system by considering various thermodynamic-electrochemical irreversible effects. Key performance indicators are established to assess the potential performance. Calculations show that the peak power density and corresponding efficiency of the proposed system are enhanced by 20.39 % and 13.89 %, respectively, compared to a standalone TSOFC. Furthermore, the exergy destruction rate is reduced by 7.04 %. Extensive sensitivity analyses indicate that higher operating temperatures enhance the system’s performance, while larger electrode tortuosity negatively affects it. Additionally, various optimization paths of ATEG are explored to improve the system performance, including considerations such as the number of thermocouples, leg radial width, leg thickness, or annular shape parameter. The three-objective optimization yields an efficient design solution for the entire system, offering valuable insights for its design and operation.

Enhancing Cognitive Radio Network Through the Novel Optimization Process and Deep Learning Model in the Transverse System

The dynamic distribution of spectrum, made possible by next-generation networks that utilise Cognitive Radio technology, helps to alleviate spectrum shortages. Additionally, these networks may be dynamically operated to save energy. The innovative cloud-sharing-decision-based method for optimising cognitive radio networks' wireless networks is introduced in this research. Optimisation was achieved via 3 key performance indicators: spectrum utilisation, power utilisation, and human revelation. Researcher find the best key performance indicators for a realistic India’s suburban situation. An optimisation technique in the cloud-based design for architecture simultaneously decreases networks power usage for about 27.6%, mean worldwide revelation by 34.4%, & spectrum utilisation for about 34.6% when related to a standard Cognitive Radio net. For the areas of networks power consumption (4.9%), spectrum utilisation (7.4%), and global exposure (4.4%), method outperforms the old design, even in the worst-case optimisation scenario.

Developing a cross-functional team coordination framework: A model for optimizing business operations

In today's complex business environment, effective cross-functional team coordination is crucial for optimizing operations and achieving organizational goals. This study introduces a comprehensive framework designed to enhance cross-functional team coordination through structured operational management strategies. By integrating key principles of collaboration, communication, and accountability, the framework aims to bridge departmental silos and promote a culture of teamwork. The proposed model is structured around three core components: communication channels, collaborative tools, and performance metrics. First, it emphasizes the establishment of robust communication channels that facilitate real-time information sharing and feedback among team members from diverse functions. This fosters transparency and aligns objectives, ensuring that all team members are on the same page. Second, the framework advocates for the implementation of collaborative tools that streamline project management and task allocation. These tools enable team members to track progress, manage workloads, and share resources effectively, thereby enhancing overall productivity. By utilizing technology that supports remote collaboration, organizations can also adapt to the increasing trend of virtual teams, ensuring that geographical barriers do not hinder coordination. Finally, the model incorporates performance metrics that allow teams to evaluate their effectiveness and make data-driven decisions. By establishing clear key performance indicators (KPIs) related to team outcomes, organizations can continuously monitor progress and identify areas for improvement. This iterative approach not only optimizes current operations but also fosters a culture of continuous improvement within cross-functional teams. The framework is designed for applicability across various industries, demonstrating its versatility in addressing the unique challenges faced by organizations. By adopting this structured approach to cross-functional coordination, businesses can enhance their operational efficiency, reduce project delays, and improve overall team morale. This study contributes to the existing literature on operational management by providing a practical model that organizations can implement to optimize their business operations through improved cross-functional teamwork.

A sales force effectiveness framework for enhancing healthcare access through pharmaceutical sales and training programs

This paper introduces a conceptual framework aimed at enhancing sales force effectiveness within the pharmaceutical industry, specifically focusing on improving healthcare access through optimized sales training programs and customer relationship management (CRM) systems. The proposed framework recognizes the critical role that pharmaceutical sales teams play in facilitating healthcare delivery, particularly during public health crises. It emphasizes the integration of effective training strategies and advanced CRM tools to enhance engagement with healthcare professionals, thereby improving product distribution and patient outcomes. The framework comprises three essential components Sales Force Training Enhancement, which focuses on developing targeted training programs that equip sales representatives with the necessary skills and knowledge to effectively communicate the value of pharmaceutical products to healthcare providers. This includes understanding clinical data, addressing healthcare providers’ concerns, and fostering a consultative selling approach. Secondly, CRM System Implementation, which underscores the significance of adopting robust CRM systems that streamline interactions between sales teams and healthcare professionals. By leveraging data analytics and customer insights, pharmaceutical companies can tailor their approach to meet the specific needs of healthcare providers, thereby enhancing relationship-building and communication. Thirdly, engagement and Outcome Measurement, which highlights the importance of measuring engagement levels and patient outcomes resulting from effective sales strategies. This component advocates for the use of key performance indicators (KPIs) to assess the impact of training and CRM initiatives on product distribution and healthcare access. The framework ultimately aims to foster a more collaborative environment between pharmaceutical sales teams and healthcare professionals, ensuring that patients receive timely access to essential medications and services. By focusing on the strategic integration of training and CRM systems, this framework presents a comprehensive approach to improving sales force effectiveness in the pharmaceutical sector. Future research and practical applications of this framework can further explore its implications for enhancing healthcare access, particularly in times of public health challenges.

A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses

In today’s rapidly evolving technological landscape, data-driven decision-making is critical for business growth and competitiveness. This paper presents a comprehensive operations management model that integrates Management Information Systems (MIS) to facilitate strategic decision-making in technology firms. The proposed model emphasizes the transformation of raw data into actionable insights, enabling organizations to optimize their operations and respond effectively to market dynamics. By systematically incorporating MIS into operational frameworks, the model provides a structured approach to data collection, analysis, and dissemination. It identifies key performance indicators (KPIs) that are essential for monitoring business performance and aligning operational activities with strategic objectives. Through case studies of leading tech companies, the model illustrates how data-driven methodologies can lead to improved forecasting, resource allocation, and overall operational efficiency. The framework is designed to be adaptable, allowing technology firms to customize the MIS components based on their unique business needs and market conditions. Key features of the model include real-time data analytics, predictive modeling, and user-friendly dashboards that enhance visibility across departments. These components empower decision-makers with timely and relevant information, fostering a culture of informed decision-making at all organizational levels. Moreover, the model addresses common challenges faced by tech businesses, such as data silos and integration issues. By promoting a centralized data repository and standardized reporting mechanisms, it enhances collaboration among teams and ensures consistency in data interpretation. The implications of this model extend beyond operational efficiency, influencing strategic initiatives such as product development, market expansion, and customer engagement. In conclusion, this data-driven operations management model not only supports strategic decision-making in technology firms but also positions them for sustainable growth in a competitive marketplace. By leveraging MIS effectively, organizations can harness the power of data to drive innovation and enhance overall business performance.

Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment.

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions.

Increase the speed of running 100 meters using the bench and skipping training methods

Background and Study Aim. Running 100 meters requires optimal speed, strength, and physical endurance. Running speed is often a key indicator of physical ability and athletic performance. However, not everyone achieves optimal speed and physical ability in running the 100 meters. Many factors influence a student's running performance, including the training methods used. The aim of the research is to determine the increase in speed for running 100 meters using bench and skipping training methods. Material and Methods. This research is an experimental study aiming to find cause and effect relationships in one or more experimental groups through different training treatments. The design used is a two-group pretest-posttest design. The participants were male students actively involved in sports activities, capable of performing running techniques well, and willing to participate in the training sessions. Initially, students underwent a pretest to determine their treatment group by ranking the pretest scores. This allowed the formation of two groups: one group of 15 students participating in bench climbing exercises, and another group of 15 students engaging in skipping exercises, using ordinal pairing. The instrument used for the 100-meter running test is the 100-meter running test. Results. Based on the results of hypothesis testing using pretest and posttest t-tests, the 100-meter running speed after bench up and down training was 4.621. The pretest and posttest data for 100-meter running speed with skipping training was 4.790. For the posttest, the running speed for 100 meters with bench up and down training and skipping training was 4.240. The two-way p-value was 0.000, which is less than 0.05, indicating a significant difference in the 100-meter running speed before and after the exercise. Conclusions. Bench climbing exercises can increase the strength of the primary leg muscles used in sprinting, such as the quadriceps, hamstrings, and calves. These exercises improve body balance and coordination, which are crucial for efficient running posture and technique. Meanwhile, skipping can enhance explosive power and the ability of leg muscles to generate power quickly and efficiently. Skipping also improves coordination between hands and feet, aiding in maintaining rhythm and efficiency in running movements. Overall, bench climbing exercises are more effective in improving 100-meter running performance compared to skipping exercises.

Data Delivery Indicators in EIDA: Designing a Consistent Metrics System in a Distributed Services Environment

The European Integrated Data Archive (EIDA) is a data service within Observatories and Research Facilities for European Seismology (ORFEUS) which distributes seismological waveform data across, presently, 12 federated data centres, so-called EIDA nodes. EIDA is also integrated into the European Research Infrastructure EPOS (European Plate Observing System). The increasing need of quantifying the scientific impact of such Research Infrastructures at all levels of operations has led EIDA to develop a unified system able to provide global indicators for data delivery across all EIDA nodes. To achieve this goal, EIDA implemented a system where the numbers are collected and aggregated by each EIDA node and ingested in a centralised database, accessible through an online API. The successful implementation of the system stems from carefully identifying the relevant indicators for well-defined groups of users. The implementation combines simplicity and robustness whilst respecting the distributed design of EIDA. The architecture and implementation choices may be of interest to other multi-site data distribution research infrastructures that face the challenge of providing meaningful key performance indicators.

A Multi‐Trip Vehicle Routing Problem With Release Dates and Interrelated Periods

ABSTRACTThis article proposes a new multi‐trip vehicle routing problem variant, originally motivated by a practical application, namely a car components distribution to repair centers (warehouses). The problem is named the multi‐trip vehicle routing with release dates and interrelated periods (MTVRP‐RDIP). The routes may start at different pre‐defined periods, called departure periods, and may have different durations. Thus, two routes that start at different periods may be active at the same period, leading to the so‐called interrelated periods. Moreover, a route may only start at a period if a vehicle is available, and the availability of the vehicle depends not only on the existing vehicles but also on the active routes at that time period. The clients are classified according to their importance and represent warehouses that require car components that are made available throughout the time horizon, thus leading to the consideration of release dates. Delays in satisfying client orders and not serving orders in the time horizon result in penalty costs that must be minimized. The objective to minimize also includes routing costs and vehicle utilization costs. Two metrics are defined to compute an order's delay, each leading to a different mixed integer linear model. Computational results showed that one model clearly outperforms the other and even this one is only suited to address the smallest instances. A matheuristic based on a rolling‐horizon process that iteratively solves the better‐performing model with fewer periods was designed to tackle the largest instances. The matheuristic can provide feasible solutions for all test instances in an efficient manner that are, on average, better than the ones provided by the model in most of the compared key performance indicators.

Measuring the Influence of Industrialization in Deep Energy Renovations: A Three-Case Study Utilizing Key Performance Indicators (KPIs)

Existing buildings in the European Union account for 40% of its energy consumption. To significantly reduce this impact, annual deep energy renovation rates should triple by the end of the 2020s. However, the lack of automation in the construction industry has hindered energy renovation efforts. Horizon Europe’s INPERSO project (Industrialised and Personalised Renovation for Sustainable Societies) aims to create a user-centered energy rehabilitation method based on industrialized technologies and systems, enhancing efficiency and building performance. To bridge the gap between predictions and real-world outcomes, the 22 project partners—using a multi-criteria decision analysis (MCDA) process—devised a list of key performance indicators (KPIs) for evaluating rehabilitation based on economic, energy, environmental, social, and technological factors. Adopting a human-centric approach, these project partners aim to minimize the technologies’ environmental impact while optimizing users’ comfort and experience. The indicators are designed to evaluate performance at every stage of the renovation process, enabling continuous feedback and user engagement and ultimately ensuring that projected energy savings are met throughout the building’s lifespan. The KPIs selected for INPERSO provide a solid framework for evaluating and monitoring sustainable renovation. However, challenges such as administrative reluctance and user disruption must be addressed to further boost the adoption of deep energy renovations.

The impact of a multidisciplinary team approach on the management of patients diagnosed with complex colorectal polyps.

Large nonpedunculated colorectal polyps (LNPCPs) have a greater than average risk of malignancy, incomplete resection/recurrence and complications associated with treatment. Appropriate management of these lesions is crucial to prevent cancer and reduce harm. The British Society of Gastroenterologists (BSG) and the Association of Coloproctologists of Great Britain and Ireland (ACPGBI) published guidelines for the management of LNPCPs in 2015. The aim of this work was to evaluate the case mix and outcomes from a single centre's multidisciplinary approach to managing such lesions against the key performance indicators (KPIs) set within these national guidelines. Cohort study from a single centre over a 2-year period, January 2020 to December 2022. After exclusions, a total of 229 cases were discussed. Most complex polyps were treated endoscopically, with conservative management recommended in 22 cases where patients had significant comorbidities and were unlikely to benefit from therapy. The overall surgical intervention rate (including transanal endoscopic microsurgery/per anal excision) was 14%. Of the cases treated endoscopically, there was residual polyp detected in 3.9% at 12-months' follow-up. Complications were rare. There was a single case of perforation following endoscopic submucosal dissection and no significant postprocedure bleeds. There were no perforations following endoscopic mucosal resection, although postpolypectomy bleeding requiring admission occurred in 4%. Sixty-eight per cent of patients were treated within 56 days of multidisciplinary team (MDT) discussion, despite the timeframe being within a period of huge disruption due to the global COVID pandemic. KPI standards published by the BSG were therefore met. MDT management of complex polyps (LNPCPs) is effective and meets the standards set by national guidance. Patient outcomes following treatment for complex polypectomy are improved when the most appropriate procedure is performed by the most appropriate operator. Discussion at a complex polyp MDT can facilitate this approach.

Experiments and FE Modeling on the Seismic Behavior of Partially Precast Steel-Reinforced Concrete Squat Walls

This paper proposed an innovative precast steel-reinforced concrete (PPSRC) squat wall to simplify on-site construction. In PPSRC squat shear walls, the hollowly precast RC wall panel can be assembled on-site through the pre-erected steel shapes, and the boundary cores will be filled using fresh concrete together with the slab system. The seismic performance of PPSRC squat walls, influenced by different construction techniques (cast-in-place vs. precast) and steel ratios, was examined through pseudo-static experiments on three specimens. Some key performance indicators, including hysteretic behavior, skeleton curves, stiffness degradation, energy dissipation, and load-carrying capacity, were analyzed in detail. The test results indicated that all the PPSRC squat walls failed in typical shear failure, and no significant slippage between the precast and fresh concrete sections was observed during the loading process, indicating that the composite action could be fully achieved via the novel throat connectors. In addition, the PPSRC squat walls could achieve comparable seismic performance compared with that of cast-in-place SRC shear walls (the peak load of the PPSRC squat wall only increased by 0.26% compared with the control specimen), and the load-carrying capacity and deformability could be enhanced by increasing the steel ratio in the boundary elements. Finally, an elaborate finite element model was developed and validated using ABAQUS software. The parametric analysis of the concrete strengths of precast and cast-in-place parts and the axial load was conducted further to investigate the seismic performance of PPSRC squat walls.

Improving Patient Experience and Team Engagement with Environmental Services: Application of Watson's Theory of Human Caring.

Patient experience and team engagement are key performance indicators in organizational strategic planning. Health-care organizations have a seldom-used resource within acute care settings that expands the ability to improve patients' experiences of their care as well as their own team engagement. Environmental services (ES) staff, the second largest health-care workforce, spend an average of 10 min per patient day interacting with patients and families. Few studies highlight how nursing theory may influence patient experience outside of the nursing role. This paper explores how ES staff at a non-profit rural 974-bed academic medical center impacted patient experience using nursing theory to promote a caring environment. Applying Watson's Theory of Human Caring and Caritas Processes© with ES team members beginning at leadership levels increased team engagement and improved patient experience. ES team members benefited from a professional nursing framework and from being integrated as a valued partner in advancing human caring.

Managing Change in Digital Media Companies: Addressing Unrealistic KPIs and Employee Burnout

This paper explores the role of communication and conflict resolution in managing change within digital media companies, with a focus on addressing unrealistic Key Performance Indicators (KPIs) and the 996 work culture. By applying insights from Michael Elwood Roloff's research on conflict and communication, the paper discusses the dynamics between mid-level leaders, employees, and top management. It presents strategies to manage employee resistance, enhance collaboration, and realign organizational goals with realistic expectations, promoting a healthier and more productive work environment.

Enhancing Distribution Efficiency Through OTIF Performance Evaluation

Today’s market with more frequent but smaller deliveries complicates the realization of logistics processes and activities. Customers expect products within 24 h or 48 h. This poses a logistics challenge for companies in fulfilling the expected time window. In these situations, companies can either perform self-distribution or they can outsource it. When outsourcing, companies must define key performance indicators (KPIs) to measure the efficiency of the distribution process. One such KPI is On-Time In-Full (OTIF). OTIF fulfillment is increasingly becoming a fundamental requirement not only in distribution but also throughout the entire supply chain. Two essential prerequisites for successful outsourcing collaboration are ensuring that orders are delivered on time and in full. A review of the existing literature revealed that no studies simultaneously address this complex issue. This was exactly the main motive for developing a model that simultaneously takes into account the utilization of logistics resources on one hand, and OTIF on the other when calculating the efficiency. The model developed in this paper was tested on the data of a company operating in the Serbian market. The results showed that seven of the company’s warehouses are inefficient. A two-stage matrix analysis with the three most important indicators (operational efficiency, on-time delivery, and in-full delivery) was performed based on the obtained results. Other than the obvious scientific contributions, the proposed model can help companies increase their resource utilization efficiency as well as OTIF fulfillment.

The mediating role of HRM outcomes in the relationship between employee development and company performance results: A study with a sustainable HRM perspective on Central European MNCs

AbstractThe main goal of the article is to determine the mediating role of human resources management (HRM) outcomes in the relationships between employee development (ED) and company performance results and to establish whether there are any identifiable regularities in this scope in the pre‐pandemic and pandemic period of COVID‐19, the latter being understood as a crisis period, in the headquarters (HQs) and foreign subsidiaries of multinational companies (MNCs). Additionally, the article aims to undertake an interpretation of the obtained research data from the perspective of sustainable HRM. The empirical research encompassed 200 MNCs with headquarters in Central Europe. The research encompassed 200 MNCs headquartered in Central Europe. For statistical analysis, the Partial Least Squares Structural Equation Modeling was used. The results indicate that HRM outcomes serve as a positive intermediary and this mediation is stronger in crisis times. The findings enrich both theory and practice, providing a solid framework for incorporating sustainable HRM into organizational strategic management, including ED, especially in MNCs, to boost performance and resilience. An innovative aspect is the application of employee key performance indicators as the efficiency index to study variable effects.

Configurational Design of a Hybrid System Based on an Air–Water Heat Pump and Biomass Boiler for a Rural Dwelling

Hybrid energy systems combine multiple energy sources and storage technologies to enhance performance and meet diverse energy needs. Hybrid heat pump systems are particularly suitable for heating and cooling buildings in rural areas. Air-source heat pumps have two well-known disadvantages during the coldest period of the year, when the building’s heating load is at its peak: the heat pump’s capacity is reduced and it needs to perform defrost cycles. A potential solution is to size the heat pump to cover only a portion of the peak load and to use a second heat generator in a hybrid heat pump system. There is a gap in the literature regarding the configurational analysis of hybrid heat pump (HHP) systems, particularly in terms of combining heat pumps and biomass boilers, and evaluating their efficiency, economic aspects, and environmental impact. Thus, in this research, a dynamic model of a HHP system, consisting of an air-to-water heat pump paired with a biomass boiler as a backup, is presented. Various configurations of the HHP system have been developed to evaluate key performance indicators, such as efficiency, emissions, operational costs, and other relevant factors. The findings of this paper indicate that the energy performance of HHP systems is significantly affected by the system layout, heat pump size, cut-off temperature, and the control algorithm used to activate the heat generators. Moreover, series operation of HHP systems is not only more efficient than parallel operation but also results in lower emissions and reduced operation costs. As expected, the energy loss associated with defrost cycles significantly impacts the overall performance of a hybrid system based on an air-source heat pump. Finally, the impact of the cut-off temperature on the key parameters in the configuration analysis was examined, and the optimal performance of the HHP system, in terms of minimizing operational costs and emissions, was depicted using a heat map diagram.

K-HEALTHinAIR Open Access Platform: exploring user-profiles

Abstract Background There is a lack of awareness and accessible knowledge of IAQ effects on citizens’ health. The K-HEALTHinAIR project is working to provide a one-stop-shop for IAQ knowledge. With this aim, partners are co-designing with representatives of the quadruple helix a unique platform to allocate data, information, and relevant resources to research and foster IAQ in Europe. This platform is envisioned to store in an accessible and friendly manner datasets of concentrations of key indoor air pollutants and health parameters, standardized Key Performance Indicators, indoor location characterization information, IAQ recommendations and regulations and analytical models. Methods First, a stakeholders mapping was performed in all project pilot sites to identify the platform potential users. Second, partners identified with stakeholders which information, data and resources of the project would be of interest and how to present them. Findings were summarized in user-profiles according to the quadruple helix (academia, public administration, industry, and general society). Results A set of resources had been identified for each user profile. Each of them will be adapted and formatted according to the users’ needs before uploading it to the platform. The platform will allow users to access information according to their interest in an accessible a friendly manner. In this sense, a dashboard will be included in the website of the project for accessing the different modules of the platform: data management module and knowledge sharing module. The platform is being co-designed and will be tested in the project scenarios with representatives of the quadruple helix. Conclusions The K-HEALTHinAIR Open Access platform aims to be the one-stop-shop for IAQ knowledge.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

Association Between Delay to First Shock and Successful First-Shock Ventricular Fibrillation Termination in Patients With Witnessed Out-of-Hospital Cardiac Arrest.

In patients with out-of-hospital cardiac arrest who present with an initial shockable rhythm, a longer delay to the first shock decreases the probability of survival, often attributed to cerebral damage. The mechanisms of this decreased survival have not yet been elucidated. Estimating the probability of successful defibrillation and other factors in relation to the time to first shock may guide prehospital care systems to implement policies that improve patient survival by decreasing time to first shock. Patients with a witnessed out-of-hospital cardiac arrest and ventricular fibrillation (VF) as an initial rhythm were included using the prospective ARREST registry (Amsterdam Resuscitation Studies). Patient and resuscitation data, including time-synchronized automated external defibrillator and manual defibrillator data, were analyzed to determine VF termination at 5 seconds after the first shock. Delay to first shock was defined as the time from initial emergency call until the first shock by any defibrillator. Outcomes were the proportion of VF termination, return of organized rhythm, transportation with return of spontaneous circulation, and survival to discharge, all in relation to the delay to first shock. A Poisson regression model with robust standard errors was used to estimate the association between delay to first shock and outcomes. Among 3723 patients, the proportion of VF termination declined from 93% when the delay to first shock was <6 minutes to 75% when that delay was >16 minutes (Ptrend<0.001). Every additional minute in VF from emergency call was associated with 6% higher probability of failure to terminate VF (adjusted relative risk, 1.06 [95% CI, 1.04-1.07]), 4% lower probability of return of organized rhythm (adjusted relative risk, 0.96 [95% CI, 0.95-0.98]), and 6% lower probability of surviving to discharge (adjusted relative risk, 0.94 [95% CI, 0.93-0.95]). Every minute of delay to first shock was associated with a significantly lower proportion of VF termination and return of organized rhythm. This may explain the worse outcomes in patients with a long delay to defibrillation. Reducing the time interval from emergency call to first shock to ≤6 minutes could be considered a key performance indicator of the chain of survival.