Performance Data Research Articles

A Novel Free-Air Diesel and Ozone Enrichment (FADOE) Research Platform

Air pollution is an escalating concern in the modern world, posing substantial threats to ecosystems processes. While the importance of comprehending the impact of pollutants on natural environments is evident, conducting rigorous field-based experiments presents formidable challenges. Elevating pollutant concentrations within open air environments in a controlled manner is complex. Nonetheless, such real-world experiments are invaluable for revealing the genuine influence of air pollutants on ecosystems and their functioning. Field-scale measurements have emerged as a pivotal avenue for advancing our understanding of the interactions between air pollutants and the natural world, providing unique insights into ecosystem dynamics, including critical processes like pollination and natural pest regulation. In atmospheric and ecological research, free-air exposure systems have proven effective in elevating carbon dioxide (CO2) and ozone (O3) concentrations, facilitating the exploration of their ecological consequences. Yet, nitrogen oxides (NOx), a class of pollutants with significant ecological and atmospheric relevance, have largely eluded field-based ecological investigations. This paper introduces the recently developed FADOE (Free-Air Diesel and Ozone Enrichment) platform, which allows the elevation of O3 and diesel exhaust (including NOx) within a field-scale context. Comprehensive information on the system's design, construction, and performance data from the 2023 summer season is presented.• Air pollution and ecosystem functioning• Elevated ozone and nitrogen oxides (NOx)• Free-air exposure systems for field scale measurements.

Examination of Operational Methods for a Low-Temperature Aquifer Thermal Storage Air Conditioning System Based on Operational Performance and Considerations of Thermal Storage and Pumping Volume Balance

Aquifer Thermal Energy Storage (ATES) systems are garnering attention as high-efficiency air conditioning technologies that contribute to the realization of a carbon-neutral society. This study focuses on an ATES system constructed in Japan, characterized by its complex geological conditions and thin aquifer layers. Detailed actual performance data measured over four years are presented, and performance analysis results show that a COP of 5 was achieved for the overall building cooling and heating system. In addition, the study provides a detailed analysis of the imbalance in heat quantity, remaining heat storage, and other factors based on actual data, identifying operational issues and summarizing specific improvement measures. Finally, as a proposal for a sustainable operational method to balance the cumulative heat storage and pumping volume of cold and hot water, specific operational procedures are summarized, including setting the return water temperature for the next season based on the dimensionless average pumping temperature of the previous season, and a flowchart is presented. There is no prior research that shows such specific operational procedures, and this can be considered an important achievement of this study.

Ultra-diluted complex additive in the diet reveals benefits in the intestinal tract of nursery-phase piglets.

This study aimed to assess an ultra-diluted (UD) complex, as a replacement for an antimicrobial growth promoter in diets, on growth performance, intestinal health, and inflammatory response of nursery piglets. The experiment lasted 37 days and involved 126 animals weaned at 21 ± 1.3 d, with an initial body weight of 5.62 ± 1.16kg. Piglets were assigned to six dietary treatments in a randomized block design with seven replicates and three piglets per pen as experimental unit. The treatments were: positive control (PC)- basal diet + 120mg/kg of chlorohydroxyquinoline; negative control (NC)- basal diet without additives; and NC containing 4.5; 6.0; 7.5 or 9.0kg of UD additive/ton diet. Performance data were calculated, and daily diarrhea was observed. Blood samples were collected for hematological analysis. At the end of the experiment, one animal per pen was slaughtered for organ weighing, pH, and the collection of intestinal samples for histopathology. Feces and cecal contents were collected for microbiological and antibiogram analyses. There was no difference in the performance between the treatments. Throughout the study, UD levels were equal to those of PC for diarrhea occurrence. Higher levels of UD complex led to higher total leukocyte counts. The 4.5 treatment showed a reduction in total and thermotolerant Enterobacteriaceae populations in piglet feces and an increase in lactic acid bacteria compared to PC. All treatments resulted in fewer duodenal histopathological alterations than those in the NC group. The use of UD additives, especially at 4.5kg/ton, is a good alternative to chlorohydroxyquinoline in piglet diets.

Decision-making in the multiphase optimization strategy: Applying decision analysis for intervention value efficiency to optimize an information leaflet to promote key antecedents of medication adherence.

Advances in the multiphase optimization strategy (MOST) have suggested a new approach, decision analysis for intervention value efficiency (DAIVE), for selecting an optimized intervention based on the results of a factorial optimization trial. The new approach opens possibilities to select optimized interventions based on multiple valued outcomes. We applied DAIVE to identify an optimized information leaflet intended to support eventual adherence to adjuvant endocrine therapy for women with breast cancer. We used empirical performance data for five candidate leaflet components on three hypothesized antecedents of adherence: beliefs about the medication, objective knowledge about AET, and satisfaction with medication information. Using data from a 25 factorial trial (n = 1603), we applied the following steps: (i) We used Bayesian factorial analysis of variance to estimate main and interaction effects for the five factors on the three outcomes. (ii) We used posterior distributions for main and interaction effects to estimate expected outcomes for each leaflet version (32 total). (iii) We scaled and combined outcomes using a linear value function with predetermined weights indicating the relative importance of outcomes. (iv) We identified the leaflet that maximized the value function as the optimized leaflet, and we systematically varied outcome weights to explore robustness. The optimized leaflet included two candidate components, side-effects, and patient input, set to their higher levels. Selection was generally robust to weight variations consistent with the initial preferences for three outcomes. DAIVE enables selection of optimized interventions with the best-expected performance on multiple outcomes.

An Advance Learning Platform for Slow Learners

LearnIt is an innovative educational system crafted to identify and support slow learners through advanced technological solutions. It employs the MERN stack (MongoDB, Express.js, React.js, Node.js) to offer a personalized learning environment tailored to meet individual student needs. This system not only enhances the educational experience for students but also provides tools to facilitate effective communication between teachers and parents, ensuring a cohesive support network for the learner. Additionally, LearnIt empowers educators with data-driven insights, allowing for informed decision-making and targeted interventions. The traditional educational model often overlooks the unique needs of slow learners, resulting in disengagement and poor academic performance. LearnIt addresses this challenge by integrating comprehensive assessment tools and personalized learning strategies within its platform. By identifying slow learners early, the system provides timely interventions that can significantly improve their learning trajectory. The use of the MERN stack ensures that LearnIt is both scalable and responsive. The assessment and monitoring module is at the core of LearnIt’s functionality. The development of LearnIt involved a detailed analysis of the educational needs of slow learners, incorporating feedback from educators, parents, and the students themselves. This user-centered approach ensured that the system's design and functionality were closely aligned with real-world requirements. Key modules of LearnIt include user management, assessment and monitoring, and communication tool. The user management module handles registration, authentication, and role-based access control, ensuring secure and personalized experiences for all users. The assessment and monitoring module is at the core of LearnIt’s functionality. It includes tools for creating and administering assessments, tracking student performance, and generating detailed reports. This module helps in identifying slow learners by analyzing their performance data and providing actionable insights to educators. With these insights, teachers can develop customized learning plans that address the specific needs of each student, fostering an inclusive and effective educational environment. Communication is another critical component of LearnIt. The platform includes features that facilitate seamless communication between teachers, parents, and students. This ensures that parents are kept informed about their child's progress and can collaborate with teachers to provide consistent support. Effective communication helps in building a supportive learning ecosystem, which is essential for the success of slow learners. The implementation of LearnIt has shown promising results. Preliminary studies indicate that the system significantly improves the identification and support of slow learners. LearnIt represents a significant advancement in the field of educational technology. By focusing on the needs of slow learners and leveraging the capabilities of the MERN stack, it offers a comprehensive solution that enhances learning outcomes. The system's personalized approach, combined with robust data analytics and effective communication tools, ensures that every student receives the support they need to thrive. As we continue to refine and expand LearnIt, its impact on educational equity and student success will undoubtedly grow, offering new opportunities for learners who have been historically underserved by traditional educational models. Keywords: Slow learners, education, literacy, mental development, intelligence.

Identify Cricket Shots using Machine Learning

Cricket shot detection is a game-changing technology that offers deep insights into player performance and match data, completely changing the way the sport is played. The main elements and importance of cricket shot detection systems are explored in this abstract. Using computer vision and machine learning techniques, the system examines video footage of cricket matches to accurately detect and classify every shot made by batsmen. Shot types (such as cover drive, pull, or leg glance), shot trajectories, and success rates are among the important data it retrieves. Numerous stakeholders in the cricketing ecosystem find diverse uses for cricket shot detection. It provides coaches and professional athletes with an unmatched post-match analysis tool that helps with strategic planning and performance enhancement.The method is used by team analysts to create winning strategies by gaining insight into opponents' shot patterns. Shot detection provides compelling visualizations and real-time shot labels in the broadcasting domain, enhancing the viewing experience. While talent scouts and cricket organizations use technology to find players and nurture talent, cricket enthusiasts profit from comprehensive shot data. This abstract highlight cricket shot detection's potential and adaptability, highlighting how it can revolutionize the cricket industry. Technology keeps improving the game, empowering players, and enthralling spectators with a deeper comprehension of the sport. Several sports have received a lot of attention and popularity recently. Many people were pining for live sports action during the height of the recent outbreak because there were no sporting activities. With millions of devoted fans who watch the games with emotion, cricket is one of the most respected sports in India. Enticed by the game, fans frequently conduct in-depth evaluations of certain players, focusing on their skills and shot choices. A greater number of people are interested in assessing players' performances in order to make wise choices for fantasy teams, especially in light of the popularity of fantasy leagues and related services. Automation presents a potential solution to the significantly time-consuming and manual process of detecting cricket batters' shots. In order to accomplish its goals, this study uses deep learning in the form of Convolutional Neural Networks (CNNs) to present an efficient method for evaluating cricket strokes

EXAMINING DIFFERENT USES OF IT FOR ENHANCING SERVICE INNOVATION PERFORMANCE

ABSTRACT Information technology (IT) is integral to service innovations, yet few efforts investigate how its different use might lead to different innovation outcomes, from a finer-grained perspective. Using the dynamic capabilities of the firm as a framework, we consider IT as an essential enabler of distinct service innovation activities and then use service-dominant logic to augment this high-level framework. The proposed model specifies how IT enables distinct innovation activities, through essential relationships that are associated with different innovation outcomes (radicalness and volume) and firm competitiveness. Our empirical test uses both survey data and accounting-based performance data from major Taiwanese service firms. According to the results, the strategic use of IT in planning activities is more relevant to innovation radicalness. To achieve innovation volume, strategically using IT for both planning and implementation is essential. Overall, customer cocreation strengthens these relationships and, more importantly, IT-enabled knowledge renewal in post-launch refinement activities has a significant, direct effect on firm competitiveness and positively moderates the relationship of innovation radicalness and firm competitiveness.

Performance prediction for network-level flexible pavements using an optimised ANN approach

ABSTRACT Considering that the road network shares traffic flow as a system, this study aimed to predict the network-level flexible pavement performance, using an optimised artificial neural network (ANN) approach to predict the pavement performance of each individual road, and the Ford-Fulkerson algorithm to determine the weight coefficients of roads. ANNs were developed to predict distress conditions, functional conditions and structural performance using the inputs of the pavement age and structural, traffic and climatic conditions. ANNs were trained with Long-Term Pavement Performance (LTPP) program data, and almost all the coefficient of determination values between ANN outputs and measured results are larger than 0.9. This ANN approach was optimised by establishing grey models to provide predictive short-term performance data for training ANNs, and employing Kalman Filter to modify the long-term performance prediction. Applications in typical LTPP sections validated the effectiveness of the optimisation method. This study used the reduction in road network capacity due to the assumed unavailability of a certain road to quantify its role (weight coefficient), which was calculated using the Ford-Fulkerson algorithm. This network-level performance prediction approach was applied in a 25-expressways network in Shanghai, which validated the good generalisation capabilities of ANNs and feasibility of the Ford-Fulkerson algorithm.

Constrained Device Performance Benchmarking with the Implementation of Post-Quantum Cryptography

Advances in quantum computers may pose a significant threat to existing public-key encryption methods, which are crucial to the current infrastructure of cyber security. Both RSA and ECDSA, the two most widely used security algorithms today, may be (in principle) solved by the Shor algorithm in polynomial time due to its ability to efficiently solve the discrete logarithm problem, potentially making present infrastructures insecure against a quantum attack. The National Institute of Standards and Technology (NIST) reacted with the post-quantum cryptography (PQC) standardization process to develop and optimize a series of post-quantum algorithms (PQAs) based on difficult mathematical problems that are not susceptible to being solved by Shor’s algorithm. Whilst high-powered computers can run these PQAs efficiently, further work is needed to investigate and benchmark the performance of these algorithms on lower-powered (constrained) devices and the ease with which they may be integrated into existing protocols such as TLS. This paper provides quantitative benchmark and handshake performance data for the most recently selected PQAs from NIST, tested on a Raspberry Pi 4 device to simulate today’s IoT (Internet of Things) devices, and provides quantitative comparisons with previous benchmarking data on a range of constrained systems. CRYSTALS-Kyber and CRYSTALS-Dilithium are shown to be the most efficient PQAs in the key encapsulation and signature algorithms, respectively, with Falcon providing the optimal TLS handshake size.

Impact Of Visual Content In LinkedIn Marketing Strategies

In today's digital world, professional networking sites like LinkedIn require novel approaches to catch attention and encourage interaction. This research looks at the effect of visual content on LinkedIn's marketing strategy. The study investigates how using visuals like photos, infographics, and videos affects important metrics including user engagement, brand recognition, and lead generation. It investigates how visual content satisfies the platform's algorithm, potentially enhancing organic reach and exposure. Furthermore, the study looks at the psychological elements of visual content consumption on LinkedIn. It investigates how images may improve information retention, elicit emotions, and promote brand storytelling to create a more compelling marketing message. The study intends to establish a link between the utilization of visual material and effective LinkedIn marketing outcomes by analyzing user behavior and content performance statistics. The findings might be useful for people and organizations looking to improve their LinkedIn presence and achieve marketing objectives. Keywords: LinkedIn marketing strategies, Visual content impact, User Engagement, Brand awareness, Lead generation, Organic reach, Content performance data, psychological aspects, Information retention, Brand storytelling

The Gender Pay Gap in Academia: Evidence from the Beedie School of Business

We analyzed gender pay gap in academia using detailed performance data of all faculty members at the Beedie School of Business, Simon Fraser University, during 2012–2022. Although we initially observed a small average pay gap in favor of male academics, we found that female academics received higher remuneration compared to their male counterparts, once we controlled for research and teaching productivity, prior education and work experience, ethnicity, and various academic appointments. Our results provide an insight into possible sources of gender bias and highlight the need to control for teaching and research performance when investigating gender pay gaps.

Balanced Mix Design Plus for Mixtures that Contain Recycled Asphalt Pavement

Recent efforts have been made to include mixture performance tests at the mix design stage using so-called “balanced” mix design (BMD). This paper evaluates the use of two mixtures that contain intermediate and high recycled asphalt pavement contents in the context of a BMD approach referred to as balanced mix design plus (BMD+). BMD+ utilizes the dynamic modulus, cyclic fatigue, and stress sweep rutting tests and mechanistic models and has three tiers of design based on the amount of performance testing required. As part of the BMD+ approach, index–volumetrics relationships (IVRs) and performance–volumetrics relationships (PVRs) based on the four-corner concept were developed for the two mixtures and verified using the performance data from seven other volumetric conditions. The developed PVRs were used to perform BMD+ Tier 3 analysis of the two mixtures, whereas the IVRs based on two points at the target air void content of 4% were used to conduct BMD+ Tier 2 analysis. The results indicate that the use of BMD+ Tier 2, which requires performance tests at only two conditions and without pavement simulations, yields longer lasting mixtures compared with Tier 1, which follows the Superpave volumetric mix design method. It was also found that Tier 3 requires more testing and pavement simulations but leads to even further gains in predicted life duration compared with Tier 2.

Ladder Use Ability, Behavior and Exposure by Age and Gender.

This study aimed to quantify and compare ladder use ability and behavior in younger and older men and women from three ladder use behavior experiments. The experimental tasks comprised (1) changing a lightbulb on a household stepladder under two cognitive demands (single and dual task), (2) clearing a simulated roof gutter on a straight ladder and (3) querying ladder choice in different exigency scenarios. Ladder use ability and behavior data were captured from recorded time, performance, motion capture and user choice data. In addition, this study surveyed ladder use frequency and habitual behaviors. The experimental findings indicate that older adults require more time to complete ladder tasks; younger adults display riskier ladder use behaviors; men and women display similar ladder use ability; and men are more willing to climb riskier ladders. The survey found older adults to report more frequent ladder use than younger adults, and men use straight ladders more frequently than women. These results suggest that the reported higher ladder fall rates experienced by older adults and men are linked to increased ladder use exposure and riskier ladder choice. This knowledge can help guide population-specific interventions to reduce ladder falls in both young and older people.

AN ANALYSIS OF BEST PERFORMING STOCKS OF BANKING SECTOR THROUGH TECHNICAL ANALYSIS

This research conducts an in-depth analysis of the best performing stocks within the banking sector using technical analysis methods. Through the examination of historical price data, trading volumes, and various technical indicators, the study aims to identify patterns and trends that contribute to the success of these stocks. Key factors such as moving averages, support and resistance levels, and momentum indicators are analyzed to determine the stocks' performance and potential future movements. The findings provide valuable insights into the effectiveness of technical analysis in predicting stock performance within the banking sector. Keywords: banking sector, technical analysis, stock performance, and historical price data, trading volumes, technical indicators, moving averages, support and resistance levels, momentum indicators.

Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer.

Cancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects. This prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes. Machine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics. The individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

Tracking earmarked funding to international organizations: Introducing the earmarked funding dataset

AbstractOver the past two decades, donor funding of international organizations (IOs) such as the United Nations, has shifted from core resources toward earmarked contributions. While core resources provide IOs with discretion how to achieve their mandates, earmarked contributions require IOs to focus their assistance on specific themes, sectors, countries, or projects, as determined by the donors. Our understanding of the drivers and effects of earmarked funding has been limited by the lack of systematic data. To address this gap, we introduce the Earmarked Funding Dataset. The dataset contains 342,812 earmarked aid activities from nearly 50 donors with over 340 IOs with a mandate in international development from 1990 to 2020. We first explore the data descriptively to cast light on the evolution of the multilateral development system. We then use the data to examine whether and how donors financially reward well-performing IOs. To that end, we combine our data with newly available performance data synthesized from Multilateral Organization Performance Assessment Network (MOPAN) evaluations of 32 IOs in 2009–2020. Using difference-in-difference estimations, we find a significantly positive relationship between outcome performance and earmarked contributions. In contrast, performance does not seem to affect core resources. These patterns suggest that donors reward well-performing multilaterals with more funding, albeit based on development outcomes which are not easily contractible. From a policy perspective, these findings suggest that performance-based approaches to aid allocation—as they are currently practiced—upload the financial risks of organizational reform processes to international organizations.

Evaluating performance in the context of mobile telework: an attention-based view

This study is an empirical account of how line managers evaluate the performance of their subordinates in the context of mobile telework. Whilst the increasing use of new technologies affords certain advantages for line managers in remote performance evaluation (PE), it also results in a greater volume, fragmentation and variety of performance data, which can be challenging to manage. Adopting an attention-based view (ABV), we shed light on the role of technology in PE, elaborating on the kinds of attentional stimuli that are generated by technology as part of the broader socio-technical work environment, and how these together with the attentional perspectives of the manager influence attentional engagement, i.e. what managers direct their time, energy and effort on in PE. Our focus is on attention as something line managers do, in their immediate context. We contribute to the remote work and PE literature by showing how the interplay between two drivers of attentional engagement: attentional stimuli (different possible foci of attention in the external environment) and managers’ own attentional perspectives (the cognitive and motivational structures that influence what stimuli receive attention) influence the attentional engagement in PE of mobile teleworkers.

Airlines’ Corporate Growth and Environmental Conservation: Evidence from Global Carriers and Forwarders

This study explores how global airlines can achieve growth and environmental conservation using transport, financial performance, and carbon dioxide (CO₂) emissions data before, during, and after the COVID-19 pandemic from 2019 to 2022 with manually collected data for 38 leading international airlines. First, the regression analyses identify a clear range of turning points that airlines should consider in terms of Scopes 1 and 2 CO₂ emissions per employee and cargo ton-kilometers per employee (cargo ton-kilometers (CTK)/EMP) considering the environmental Kuznets Curve (EKC) hypothesis. Second, the deciding factors, including fundamental efforts by the airlines themselves, result from the interaction of three points, which have been encouraged and promoted in the airline industry in recent years. (1) Tighter emissions controls for air transport, (2) investors’ emphasis on environment, society, and governance (ESG), and (3) assessments and guidelines from ratings agencies and economic and environmental organizations. Third, increasing CTK/EMP to verified thresholds and taking an ESG-oriented approach can contribute to airlines’ combined achievement of growth and environmental conservation and related data will expand academic and policy-related research.

Stretched Thin: How a Misalignment Between Allocation and Valuation Underlies the Paradox of Diversity Achievement in Higher Education

Racial inequality is remarkably resilient in organizational and labor market contexts despite efforts to resolve it, which raises significant questions about the mechanisms underlying its persistence. We argue that organizational efforts that increase the inclusion of underrepresented racial groups in the short term may conceal an emergent mechanism that paradoxically results in exclusion over time. The emergent mechanism stems from an acute misalignment between the scope of allocation in the matching process and the scope of valuation in the evaluation process, which ultimately increases voluntary and involuntary turnover among underrepresented racial groups. We examine this paradox through a revelatory case in higher education. Drawing on comprehensive administrative and research performance data from a large (R1) U.S. public university, we find that Black assistant professors are significantly more likely than their White colleagues to be allocated to non-standard positions, i.e., formally appointed in two academic departments with shared compensation. Our results demonstrate that such non-standard appointments are associated with a significant decline in research productivity, which remains central during the evaluation process. The end result is that jointly appointed assistant professors—among whom Blacks are disproportionately represented—experience lower likelihoods of retention.

A two-step identification framework for building thermal dynamic based on high fluctuation performance data: A case of office with variable refrigerant flow system

Thermal dynamics model plays a crucial role of advanced control in the intelligent building. Grey box method is widely used in the modeling of building thermal dynamics based on the operation data of air-conditioner. The performance of direct-expansion (DX) system fluctuated severely during its operation, which leads to the difficulties in identification and evaluation of the building thermal model. In this paper, a two-step identification framework for second-order thermal resistance-capacity network of building based on DX system data is proposed. A case study is conducted on an office building equipped with variable refrigerant flow system in Foshan, China. The results show that the proposed framework can identify the parameters rationally. The model achieves high accuracy for both indoor temperature (Mean absolute errors of 0.74°C) and cooling load prediction (Mean absolute errors of 4453.86W) in a short time interval of 10 minutes. Additionally, the best complexity of the grey-box model for case building and the data acquisition interval is determined. Finally, an improved set of evaluation metrics for thermal load evaluation is proposed and discussed.