Performance Indicators Research Articles

An Evaluation Framework of Industry 4.0’s Impact on SDGs and its Application to AI Reporting

Digital technologies of Industry 4.0 (I4.0) are key drivers of sustainable development. However, addressing the impact of I4.0 on Sustainable Development Goals (SDGs) requires overcoming the challenge of lacking suitable assessment tools. Despite sustainability reporting serves to assess how companies face sustainability challenges, communicate efforts, and promote a sustainable corporate culture, a critical need arises for a comprehensive tool that clarifies the impact of I4.0 technologies on the economic, environmental, and social dimensions of sustainability. This paper proposes a conceptual framework that builds on existing literature regarding how I4.0 technologies contribute to SDGs and leverages the recognized linking between SDGs and Global Reporting Initiative (GRI) Standards to improve the reporting of SDG progress. Thus, the proposed framework identifies the GRI-based Key Performance Indicators necessary for a thorough understanding of how innovation stemming from I4.0, significantly impacts SDGs. Through a content analysis of sustainability reports of innovative companies in the electronic field, the study shows how the proposed framework can be applied in practice. Focusing on Artificial Intelligence (AI), the application case allows a comprehensive understanding of the transformative potential of AI solutions towards sustainable development.

Modeling Resilience of Metro-Based Urban Underground Logistics System Based on Multi-Layer Interdependent Network

The metro-based underground logistics system (M-ULS) has been identified as an effective solution to urban problems resulting from the expansion of urban freight traffic. However, there is a paucity of current research that examines the resilience of a M-ULS in the context of unexpected events during operations. Therefore, this paper presents a methodology for assessing the resilience of the M-ULS. The method considers the propagation paths of various failures in a multi-layered, interdependent network that includes topology, functionality, facilities, and information, as well as network performance indicators based on network freight flow and logistics timeliness. The effectiveness of the method is demonstrated using the case of the Nanjing Metro. The results show that the type of disruption, the duration, and the direction of train travel all have a significant impact on the resilience of the M-ULS. The method proposed in this paper provides a scientific basis for the assessment and optimization of M-ULS resilience and also offers new insights into the use of urban rail transit to promote the sustainable development of urban logistics.

An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems

This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems.

Impact of sports vision training on visuomotor skills and shooting performance in elite skeet shooters

IntroductionVision serves as a critical channel for athletes to acquire information during competitions and constitutes a vital component of their competitive ability. Through scientifically designed sports visual training, specific visual skills can be enhanced, thereby assisting athletes in achieving optimal performance in competitive settings. This study aim to explore the visuomotor abilities and shooting performance of skeet shooters through Sports Vision Training (SVT).MethodsTwenty elite skeet shooters were recruited and randomly assigned to an experimental group (EXP, n = 10) and a control group (CON, n = 10). The EXP underwent 6-week of SVT on Senaptec Seneory Station, twice a week, while the CON completed an equivalent workload of target-tracking training.ResultVisuomotor skills testing showed significant improvements in Near- Far Quickness, Perception Span, and Eye-hand Coordination in the EXP (p < 0.05), with no changes in the CON. Comparative post-test results between the two groups showed significant differences in N/F Q, Target Capture, Perception Span, Eye-hand Coordination, and Go/ No Go (p < 0.05). In shooting performance indicators, EXP shooters showed a highly significant improvement in hit accuracy (p < 0.01), with a similar difference compared to the CON. Additionally, they exhibited a highly significant improvement in shotgun-mounting reaction time (p < 0.01). Kinematic indicators of shotgun movement during the firing process for shot 2 showed significant differences in peak velocity (p < 0.01), X-axis (p = 0.033) and Y-axis (p = 0.001) displacement.ConclusionSVT can enhance visuomotor abilities in skeet shooters and has a positive impact on their shooting technique. This is primarily manifested in shorter shotgun-mounting reaction time and improved efficiency in action at shot 2, effectively improving their shooting accuracy.

Time Domain Design of a Marine Target Tracking System Accounting for Environmental Disturbances

Environmental disturbances represent significant challenges to the performance and accuracy of autonomous systems, especially in marine environments, where their impact varies based on disturbance severity and the employed guidance law. This paper comprehensively investigates a marine target tracking system using time-domain simulations incorporating realistic environmental disturbances. Three guidance laws and four key performance indicators are analysed to evaluate system performance under disturbed and ideal conditions. A robust and systematic evaluation pipeline is developed and applied to a case study featuring a scaled tugboat model. This approach provides a reliable method to assess tracking accuracy and robustness in adverse conditions. The results are selected from a wide range of possibilities to show the effect of the disturbances on the selected target tracking motion control scenario with two manoeuvres and two environmental conditions. The results are measured through the selected key performance indicators, and several phases are identified for each manoeuvre to extend the analysis not only to the global KPI values but also to the partial values of defined phases. They reveal the quantitative effects of environmental disturbances, exposing different system behaviours and trends. These findings demonstrate the effectiveness of the proposed pipeline in quantifying tracking system performance, delivering useful understandings of the system under environmental disturbances. The broader implications of this study are substantial, offering enhanced predictive accuracy for the performance of the analysed systems, particularly in the context of target tracking. Furthermore, introducing numerical key performance indicators facilitates a more rigorous comparison of different system characteristics, enabling informed decisions in designing and optimising autonomous operations in challenging environments.

Design of logistics indicators for monitoring the COVID-19 vaccination process in Colombia : Logistic indicators for vaccination process.

The vaccine supply chain (VSC) integrates all activities from production to dispensing, characterized by complexity and low responsiveness. This study aims to define key performance indicators (KPIs) to evaluate the logistic and operational performance during the COVID-19 National Vaccination Plan in Colombia. This study employed the design science research (DSR) methodology to develop a dashboard for monitoring COVID-19 vaccination progress. DSR is a problem-solving paradigm focused on conceiving, testing, and evaluating artifacts to tackle specific challenges. The dashboard integrated a set of Key Performance Indicators (KPIs) that incorporated three dimensions: epidemiological, humanitarian, and logistical. Both, the dashboard and the KPIs underwent validation through three iterative DSR cycles, in collaboration with both private and public health stakeholders. A set of 38 validated KPIs, grouped into eight categories, was consolidated into an online dashboard. This tool enabled real-time tracking of the National Vaccination Plan at both national and departmental levels, providing stakeholders with a comprehensive view of the vaccine supply chain. It also allowed for the assessment and comparison of the execution and effectiveness of public policies. These KPIs supported decision-making from a logistical standpoint, enhancing the planning, preparation, and continuous improvement of mass vaccination processes, thereby increasing their potential impact even further.

HPB SO68 - Evaluating the Outcome of Laparoscopic Cholecystectomy Against Key Performance Indicator (KPI) of the British Benign Upper GI Surgical Society (BBUGSS)

Abstract Background Laparoscopic cholecystectomy (LC) presents inherent challenges and potential for significant complications, influenced by both patient factors and surgical expertise. Recognizing this variability, the British Benign Upper GI Surgical Society (BBUGSS) has established Key Performance Indicators (KPIs) aimed at enhancing LC outcomes. This study conducts an audit of LC outcomes in a single centre district general hospital (DGH) in accordance with BBUGSS KPIs and thus aims to determine if quality LC by modern standards is achievable in a DGH. Method The present retrospective observational study examined all adult LC conducted between August 2022 and June 2024 by two specialized biliary surgeons at a single surgical unit within a DGH, which also serves as a referral center of biliary cases from affiliated hospitals. The analysis encompassed preoperative assessments, intraoperative observations, and postoperative outcomes, with a focus on aligning these findings against the benchmarks established by the BBUGSS KPIs. Results 141 patients were included; median age was 54 years (IQR 41-64) and 59.6% were female. 37.6% were emergency cases, 44.2% were for acute cholecystitis, 22.7% for gallstone pancreatitis and 63.1% underwent intra-operative cholangiogram. KPIsBBUGSS StandardOur resultDay case for elective cases50%42%Readmission (within 30 days)<10%4.3%Laparoscopic cholecystectomy for acute cholecystitis (within 7 days)>35%97%Laparoscopic cholecystectomy for gallstone pancreatitis (within 2 weeks)>95%100%Conversion to open<5%0.7%Morbidity<10%1.4%Bile leak after elective surgery<1.5%0%Retained bile duct stones<2.5%0%Bile duct injury<0.3%0%Mortality<0.1%0% Conclusion This study shows that with the exception of day-case rate in elective LC, all KPIs were satisfactorily achieved, indicating that high quality LC is achievable within a DGH. The majority of readmissions within 30 days were related to inadequate postoperative analgesia. We have since enhanced pain management pathways. It is hoped that this will also improve same day discharge for elective cases to achieve BBGUGS standard. We are committed to conducting annual audits to monitor and optimize surgical outcomes and would encourage other centers to consider adopting the same approach.

Insights into Health Program Implementation and Evaluation in Iran: A Brief Review of National Healthcare Programs in Iran

Background: Healthcare program monitoring can play a key role in evaluating their implementation and identifying their strengths and weaknesses. Methods: This study was performed with the exploratory sequential mixed methods design in 2023 at Tehran, Iran. The information was categorized into indicators, refining, analysis, interpretation, reporting, and proposed challenges and recommendations. Results: Medical Sciences Department of Public Health (TUMSDoPH) evaluation of its public health programs revealed a comprehensive approach covering a wide spectrum from disease management to health promotion, emphasizing electronic health records, targeted disease interventions, and strategies for non-communicable diseases, alongside efforts to enhance community health. Conclusion: There is a need for initiating “the monitoring committee” under the supervision of the deputy chancellor for public health to monitor key performance indicators and policies to discover the problems of each part of the health system and provide recommendations.

Examining Strategies Developed by Insurance Companies for Addressing Carbon Emissions in the Automotive Supply Chain in the UK

The automotive supply chain is one of the top eight value chains that cause 50% of global emissions. Despite its significance, limited literature has researched the role of insurance companies in addressing automotive supply chain emissions. This research explores strategies developed by insurance companies for addressing carbon emissions in the automotive supply chain in the UK. It employs a qualitative multiple case study approach and conducts in-depth analysis of main drivers, barriers, and strategies in four insurance companies in addressing automotive supply chain emissions. It finds that cost savings and competitive advantage, changing mindset, impending regulation, market changes, and increased connectedness are the main drivers. But further progress is slowed down by five main barriers: ‘the complexity of tracking and quantifying emissions’, ‘conflicts of interest in the supply chain’, ‘skill shortage’, ‘lack of accountability’, and ‘profit prioritisation’. To overcome this, the study establishes five main strategies for insurance companies to follow: ‘circular business model with green parts and repair-over-replace methodologies’, ‘supply chain collaboration’, ‘quantifying emissions and setting key performance indicators’, ‘higher weighting for ESG in tenders and policies’, and ‘education and awareness’. If followed correctly, businesses will be able to achieve ‘emission reductions’, ‘gain competitive advantage’, and ‘reduce costs in the supply chain’. Taking into account these findings and the academic literature, this study develops a framework for insurance companies to mitigate automotive supply chain emissions. This is one of the first papers to study carbon emissions in automotive supply chains from the perspective of the insurance industry. It provides practical implications for the insurance industry in developing carbon emission strategies in automotive supply chains.

Designing a Human Resource Management System for Performance Assessment using Behaviorally Anchored Rating Scales (BARS) for Operators at PT. Polyland Indoteknik Bandung

Employee performance appraisal is a crucial aspect of enhancing organizational productivity and effectiveness. This research aims to develop a more robust performance appraisal system for factory operators at PT. Polyland Indoteknik Bandung employs the Behaviorally Anchored Rating Scales (BARS) method. Through an analysis of the existing appraisal system, this study found that the sole use of Key Performance Indicators (KPIs) is insufficient to measure complex behavioral aspects. Therefore, BARS is proposed as a more comprehensive method. The designed BARS system incorporates specific behavioral indicators relevant to the job of factory operators. Additionally, this research outlines an implementation strategy involving socialization, integration with existing systems, feedback provision, and periodic evaluation. The results indicate that BARS can improve the quality of performance appraisals and provide more constructive feedback to employees.

HPB SO70 - Evaluating the Outcome of Laparoscopic Common Bile Duct Exploration Against Key Performance Indicator (KPI) of the British Benign Upper GI Surgical Society (BBUGSS)

Abstract Background Treatment of common bile duct (CBD) stone(s) can be challenging with a risk of significant complications. Laparoscopic common bile duct exploration (LCBDE) displays superior outcomes when compared to endoscopic retrograde cholangiopancreatography (ERCP). Acknowledging the impact of patient variables and surgical proficiency, the British Benign Upper GI Surgical Society (BBUGSS) developed Key Performance Indicators (KPIs) to optimize LCBDE outcomes. The present study comprises an audit of LCBDE outcomes with respect to the BBUGSS KPIs and aims to determine the feasibility and quality of LCBDE that can be achieved in a district general hospital (DGH) with a recently developed LCBDE service. Method This retrospective observational study examined all adult patients who underwent LCBDE between August 2022 and June 2024 by two specialized biliary surgeons at a single surgical unit within a DGH. This DGH also serves as a referral center for biliary cases from affiliated hospitals. The preference in our surgical unit is single stage management (laparoscopic cholecystectomy and simultaneous LCBDE) of CBD stones in surgically fit patients. The analysis included preoperative assessments, intraoperative observations, and postoperative outcomes, with respect to the published BBUGSS KPIs for LCBDE. Results 60 patients were eligible for inclusion; median age was 56 years (IQR 40-67) and 40 (66.7%) were female. 33 (55%) were emergency cases. 52 (86.7%) were transcystic exploration; of these 8 (15.4%) utilised basket-in-catheter and 44 (84.6%) utilised choledochoscopes. Median post-operative stay for transcystic exploration was 1 day (IQR 0-2) and for cholechodotomy 8 days (IQR 5-9). Key Performance IndicatorBBUGSS StandardOur outcomeConversion rates<10%0%Bile duct clearance rates>85%91.7%Bile Leak Rates<5%0%Returns to theatre<5%0%Readmission rates (within 30 days)<15%6.7%Overall Morbidity<15%3.3%Overall Mortality<1%0% Conclusion This study indicates that the BBUGSS KPIs for LCBDE are satisfactorily achieved in our centre. Adequate ductal clearance with minimal morbidity was achieved. Our outcomes are likely attributed to the recent commencement of a specialist biliary service and utilisation of predominantly transcystic approach, with the aid of electrohydraulic lithotripsy where needed, as opposed to choledochotomy. Furthermore, the present study suggests a high quality LCBDE service is feasible in a DGH setting if there is sufficient clinical and managerial motivation. We suggest that single stage management of bile duct stones model should be considered in other similar centres.

Analysis of Industrial Performance Indicators Effects On Enhance The Competitiveness of The Food Sector and The Possibility Of Support in The Iraqi Economy (2003-2020)

This study was aimed on analyze the competitiveness of the food industry sector, and showed the level of industrial and competitive performance of these industries, and the possibility of supporting and enhancing their competitiveness in the Iraqi economy for the period 2003-2020. Competitiveness is considered one of the most important indicators of the efficiency of the food industry tool in local and foreign markets, as competitiveness represents the ability of productive sectors of different types to distribute their products and increase their sales level. Therefore, the food industry in Iraq is considered one of the most important sectors of the manufacturing industries that suffer from problems and obstacles including a decrease in the level of competitive and industrial performance. It is believed that the problem of the research is the weak competitiveness of the food industry in Iraq due to the decline in the quality of manufactured products and the weak level of competitiveness of these products compared to imported products. The results of the study showed a decrease and fluctuation in the contribution of the manufacturing sector to the gross domestic product (GDP) in Iraq. The economic policy was not providing the possibility of an investment climate to support and enhance the private sector in industrial investment. The indicators of production requirements such as labor productivity, worker productivity, and the value added index of the food industries sector began to fluctuate between decrease and increase, and to measure the extent of the reciprocal effects between all parts of the standard model and the extent of its contribution to developing the competitive capacity of this sector.

Social protection comparison across the world: a study of fiscal, structural and institutional performance discrepancies

PurposeSocial protection states the public-mandated (strategies and programmes) to address the vulnerability and risk among poor and near-poor households. Social protection must not only help people meet their basic needs but also contribute to the long-term well-being and broader societal goals of equity, social justice and empowerment. The role of social protection in achieving these goals has not been adequately documented. This paper examines the performance of the social protection mechanism from the perspective of the regional structural and institutional performance indicators. Social protection outlay dynamics are synthesized in the structural and institutional conducts.Design/methodology/approachA world regional comparability is made by considering the panel dataset for the time interval 1995–2020. In empirics, the unconditional and conditional ranks are constructed, and regression analyses are made subject to (1) the fiscal constraint of social protection, (2) structural performance indicators and (3) the institutional performance indicators of the targeted regions across the globe. The fully modified-ordinary least square (FM-OLS) method is used to construct the ranks.FindingsThe rank analysis demonstrates that the developed regions (i.e. Europe, Central Asia and North America) have relatively good welfare standards and fiscal capacity for social protection drives compared to the developing/underdeveloped regions (i.e. Sub-Saharan Africa and South Asia). The structural/institutional performance indicators have shown their significance in determining/utilizing their budget for social protection and maintaining welfare standards. Interestingly, most developed regions are relatively reluctant to maintain the structural/institutional performance for the determination/execution of the funds for social protection as compared to the developing and underdeveloped regions.Practical implicationsIn policy discourse, this research suggests that governments must make some market operations to enhance their regions’ structural and institutional performance to get better outcomes of social protection spending in the form of attractive welfare standards.Originality/valueMultiple studies have been done considering the social protection dynamics at nano, micro and macro levels, while this study considered the mega dataset across the globe to analyse the social protection dynamics in consideration of structural and institutional performances.

Techno-Economic Analysis of BAU-STR Dryer for Rice Drying: An Approach to Accelerate Adoption

Postharvest food loss and waste offset worldwide agricultural productivity and food security. Insufficient drying and storage are the prominent drivers of food loss and waste in underdeveloped countries. Mechanical grain drying systems have distinct benefits over sun drying but are inaccessible to underserved communities due to high capital costs and energy demand. This study evaluated the techno-economic and financial performance of a half-ton-capacity BAU-STR dryer. The moisture extraction rate, drying rate, drying efficiency, and energy consumption were used as technical performance indicators. In contrast, the net present value (NPV), internal rate of return (IRR), benefit–cost ratio (BCR), and payback period were considered economic performance indicators. The technical performance analysis results revealed that the moisture content of rice was reduced from 19.5% to 13.5 ± 0.15% in 4.0 h with an average drying rate of 1.5%/h and a drying efficiency of 75.1%. The financial performance analysis resulted in a drying cost, NPV, IRR, BCR, and PBP of USD 0.96 per 100 kg of grain, USD 3018, 135%, 3.0, and 0.73 yr., respectively, when the annual use was 240 h. If the yearly use of the dryer increased from 240 to 720 h, a higher NPV, IRR, and BCR, as well as a lower payback period and drying cost, could be achieved. Adopting a BAU-STR dryer for drying grain (rice and corn) among underserved communities could play a key role in postharvest food loss and waste.

Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving

Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Radar, in particular, operates with electromagnetic waves and remains effective under a variety of weather conditions. It uses point cloud technology to map the objects in front of you, making it easy to group these points to associate them with real-world objects. Numerous clustering algorithms have been developed and can be integrated into radar systems to identify, investigate, and track objects. In this study, we evaluate several clustering algorithms to determine their suitability for application in automotive radar systems. Our analysis covered a variety of current methods, the mathematical process of these methods, and presented a comparison table between these algorithms, including Hierarchical Clustering, Affinity Propagation Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mini-Batch K-Means, K-Means Mean Shift, OPTICS, Spectral Clustering, and Gaussian Mixture. We have found that K-Means, Mean Shift, and DBSCAN are particularly suitable for these applications, based on performance indicators that assess suitability and efficiency. However, DBSCAN shows better performance compared to others. Furthermore, our findings highlight that the choice of radar significantly impacts the effectiveness of these object recognition methods.

Relationship Between School Climate and Teacher Job Satisfaction: A Meta-Analytic Study

It is said that a professional teacher is a teacher who performs well. One indicator of a teacher's good performance is that it can be seen from the high level of job satisfaction of the teacher. It is expected that teachers who have high job satisfaction can increase their professionalism in their work. Several previous studies said a relationship exists between school climate and teacher job satisfaction. Unlike previous research, this study aims to determine the strength of the relationship or the magnitude of the difference between school climate and job satisfaction through a meta-analysis method with ten highly reputable articles. The results show that school climate positively correlates with teacher job satisfaction. The advice is to obtain information about administration and management in developing a school climate. This is because the school climate can facilitate teacher job satisfaction by increasing teacher professionalism in performance to produce quality students following the goals of Indonesian education stated in the curriculum regarding educational goals.

MICROPRUDENTIAL ASPECT BEYOND RULE OF THUMB IN RURAL BANK INDUSTRY IN INDONESIA

Objective: This research aims to examine the microprudential aspects beyond traditional rule of thumb measures in the regulation of Rural Banks in Indonesia, particularly focusing on the implications of recent regulatory changes that have prompted Rural Bank mergers. Theoretical Framework: This research utilizes a framework that incorporates financial ratios as critical indicators of bank performance and health. It considers the influence of regulatory frameworks—especially those outlined in POJK regulations—on the operational effectiveness and stability of Rural Banks. Related to the forced corporate action, reason that administrative reform may not have increased merger activity as expected is that larger, combined activities are likely to be more readily able to absorb the significant costs of complying with administrative reform than smaller Rural Bank. Method: This research methodology used is a qualitative descriptive method. In conducting this study, researchers used primary data and secondary data. Primary data obtained from the results practitioners' perspective of interviews (in-depth interviews) with experts and practitioners, who have an understanding of the issues discussed in Rural Bank Industry so that we can presumably determine the real current situation using a Qualitative Descriptive method with a Phenomenology study approach. While secondary data was complemented by data collected from literature studies, from the Financial Service Authority (OJK) official website, Several Rural Bank Performance Report and Financial Report during 2019 - 2023, scientific articles, books, information from the mass media and other relevant sources of information. Result and Discussion: This study highlights that reliance solely on the rule of thumb can be misleading in assessing bank health. It shows that a detailed analysis of various financial ratios—such as capital adequacy, asset quality, earnings, and liquidity—provides a more comprehensive understanding of a bank's performance and risks associated with mergers. The banking industry those had time to familiarize itself with the subject and to put legislation into concrete action. Other findings indicate that the enforcement of regulations aimed at increasing capital adequacy and reducing systemic risk has led to forced mergers among Rural Banks. Research Implication: The study underscores the importance of integrating microprudential regulations into the operational frameworks of Rural Banks. It suggests that regulators should prioritize comprehensive assessments based on financial ratios rather than simplistic heuristic methods, especially in the context of forced mergers driven by regulatory pressures. Effective and appropriate use of the merger strategies forced by regulator (OJK) have not facilitated Rural Banks’ efforts exactly to earn above-average returns. However, even when pursued for value-creating reason, merger strategies are not problem-free. Reason for the use of merger strategies and potential problem with such strategies are suboptimum goals congruence because of management control problem – include lack of direction, motivational problem, and personal limitation (Merchant et al, 2017). Recommendation: The research recommends that Rural Banks systematically evaluate their financial health using detailed financial ratios and adapt to the new regulatory landscape that encourages mergers. It also urges regulators to refine their guidelines to include these metrics as essential tools for ongoing performance assessment. Enhanced training for bank management on interpreting and applying these financial ratios can improve overall operational effectiveness, particularly in the context of mergers. Merger is not just about consolidating financial aspects, but also harmonizing the combination of tangible (Numbers on Balance Sheet) and intangible assets (Human capital aspect).

Roadmap for Power Generation of Industrial Hot Extruded Bi2Te3-Based Thermoelectric Device in Real-World Heat Sources.

Recovering waste heat through thermoelectric (TE) technology is critical for enhancing energy efficiency. However, commercial devices often require n- and p-type thermoelectric units to have similar cross-sectional areas and conductivities, complicating optimization of their figure of merit (ZT). Moreover, real-world heat sources are not constant temperature sources, and only heat flux can be measured. Given these constraints, selecting appropriate parameters for TE devices to maximize their output power for specific heat sources is of significant scientific and practical importance. In this work, we studied a series of n- and p-type Bi2Te3-based TE materials, prepared using commercial hot extrusion techniques, with average conductivities of approximately 650 S cm-1, 820 S cm-1, 1050 S cm-1, and 1300 S cm-1 over the temperature range of 50-250 °C. These materials were assembled into four different thermoelectric devices (HE1, HE2, HE3, and HE4). Finite element simulations for devices HE1, HE2, HE3, and HE4 (TE leg: 1.4 mm × 1.4 mm × 1.6 mm, device area: 40 mm × 40 mm) showed maximum power densities of 3953.5 W m-2 and 1300.8 W m-2 at 67,000 W m-2 and 33,500 W m-2 heat fluxes, respectively. The fabricated devices achieved maximum power densities of 2907.1, 3014.0, 2910.9, and 2525.4 W m-2 at 67,000 W m-2 heat flux, with discrepancies attributed to interface thermal resistance. Based on these findings, we have plotted output performance roadmaps for TE devices with different average electrical conductivities under various heat flux densities (67,000 W m-2 and 33,500 W m-2) as functions of external load and TE leg height. These roadmaps enable rapid design of TE devices tailored to specific external heat source conditions and cost requirements, thereby maximizing waste heat recovery. This study's use of electrical conductivity as a performance indicator for final device output power density offers direct guidance for practical waste heat recovery applications.

Coral recruits demonstrate thermal resilience

Marine heatwaves are becoming more frequent during summer and pose a significant threat to coral reef ecosystems. Restoration efforts have the potential to support native coral populations and guard them against some degree of environmental change, while global action against climate change takes place. Interspecific hybridization is one approach through which resilient coral stock could be generated for restoration. Here we compared the performance of Acropora kenti and A. loripes hybrid and purebred coral recruits under a simulated thermal stress event. A. kenti eggs were successfully fertilized by A. loripes sperm to produce ‘KL’ hybrids, but no ‘LK’ hybrids could be produced from A. loripes eggs and A. kenti sperm. Despite corals in the elevated treatment accruing thermal stress (>12 degree heating weeks over 2 months) known to result in mass bleaching, both purebred and hybrid recruits showed no signs of stress under the simulated temperature regime, based on the performance indicators survivorship, size, color (a proxy of bleaching), and photochemical efficiency of photosystem II. Comparisons between the hybrids and purebreds studied here must be interpreted with caution because hybrid sample sizes were small. The hybrids did not outperform both of their purebred counterparts for any metrics studied here, demonstrating that there are limitations to the extent to which interspecific hybridization may boost the performance of coral stock. In general, the purebred A. loripes recruits performed best under both ambient and elevated conditions. The performance of the KL hybrid corals was similar to the maternal parental species, A. kenti, or not significantly different to either parental purebred species. The Symbiodiniaceae communities of the KL hybrids were characteristic of their maternal counterparts and may have underpinned the performance differences between the A. kenti/KL hybrid and A. loripes recruits.

Optimized evaluation of the quality of sensor video internet of things (VIOT) by the integration of big data and artificial intelligence

AbstractThe application of sensor video internet of things technology to large-scale integrated work can significantly improve the working quality of employees. However, the degree of improvement in working quality is still difficult to measure in a systematic, intelligent, stable, and accurate manner. local optimization and adjustment after evaluation are still relatively challenging, To address these issues, the study proposes a method of optimizing the evaluation of sensor video quality through the integration of big data and AI techniques. A large-scale integrated distance education system in the field of education and training with a certain application basis is adopted as a case. Including big data and AI techniques such as integrated intelligent agent modules, recommendation algorithms, and transaction optimization algorithms, a new agent-oriented system design with fast response speed, strong scalability, convenient local optimization, and greater stability is achieved. According to the network topology structure of the distance education system in colleges and universities, this paper uses queuing theory to analyze the system performance of the system. The focus of this paper is the quantitative relationship between system communication intensity ρ, user arrival rate λ, system channel capacity n and system waiting delay, blocking probability, average queue length, system throughput and other important performance indicators. In teaching evaluation, the key factor that affects the quality of classroom teaching, that is, Developing a comprehensive system for evaluating classroom instruction is crucial. By incorporating student feedback, leveraging data mining techniques, and harnessing computer technology, a holistic framework for gathering, analyzing, and generating actionable insights on teaching performance is established. This approach makes the evaluation process more systematic and evidence-based, identifying 12 key elements that influence classroom education standards. In the experimental section, the student assessment data sets I1 and I2 exhibit experimental values (statistics) that significantly exceed the thresholds, with a minimum support of 0.32 and a confidence level of 0.61. Moreover, the Boolean matrix is divided into 90 points. The rule U1Ua ≥ U2 is identified as a subset of {U1U2Ua} within the large item set, signifying a strong association rule. These findings confirm the robustness of the artificial intelligence model proposed in this paper for video quality prediction. The optimized sensor video quality evaluation method not only meets a satisfactory confidence level and matching value but also demonstrates good reliability and relevance in the evaluation criteria.