Assessment Of Performance Indicators Research Articles

Performance of the Shallot Supply Chain in Kerinci Regency Jambi Province

This research aims to: (1) analyze the performance of shallot farming, (2) analyze the condition of the shallot supply chain using the FSCN (Food Supply Chain Network) discussion framework, (3) measure the performance of the shallot supply chain using the rating scale method and the assessment indicators adapted from SCOR (Supply Chain Operations Reference) model, and (4) formulate efforts to improve shallot supply chain performance by carrying out gap analysis and problem analysis first. The sampling method used was Simple Random Sampling and the next sample of supply chain actors was determined using a snowball sampling technique. The analysis method used is FSCN and the rating scale method with performance assessment indicators from the SCOR model.The results of the research show that the performance of shallot farming has followed good cultivation SOPs based on good cultivation norms (GAP), although not all activities have been carried out. The average area of shallot harvested land is 1.48 hectares. The varieties of shallots planted, brebes variety, thailan variety, indian variety peking variety, beko variety. shallot productivity is 20 tons per hectare with an average price of IDR 10,000/kg. Supply chain actors consist of farmer producers, village collectors, district collectors, wholesalers and retailers in stalls and local traditional markets. The performance of the shallot supply chain during in season is greater (score 3.57) than during off season (score 3.28). Efforts to improve the performance of the shallot supply chain by building an appropriate supply system, building partnerships, coordination and collaboration among supply chain actors as well as strengthening farmer institutions, overcoming the low availability of shallots, especially during the off season, increasing the availability of market information, and overcoming problems regarding mechanisms. distribution.

Higher Education Institution Clustering Based on Key Performance Indicators using Quartile Binning Method

The Key Performance Indicators of Higher Education Institutions (KPI-HEIs) are a crucial component of the internal quality assurance system that supports the achievement of excellence status for higher education institutions. Many private higher education institutions face challenges in independently analyzing the key performance assessment indicators of Private Higher Education Institutions (PHEIs), which often require complex methodological approaches and specialized expertise. The research aims to cluster PHEIs based on achieving key performance indicators (KPIs). Research the method used descriptive statistical methods and quartile binning techniques to analyze and cluster data based on the achievement of KPI-HEIs. The research results, based on descriptive statistical analysis, identified outliers in eight KPI-HEIs, along with a dominance of zero values in KPI 1, KPI 2, KPI 6, KPI 7, and KPI 8, with the highest proportion reaching 90.91% for KPI 8. Based on these findings, clustering using the quartile binning method resulted in four clusters of PHEIs based on KPIs: Cluster 1 consists of 19 institutions with poor, Cluster 2 consists of 14 institutions with fair achievement, Cluster 3 consists of 16 institutions with good achievement, and Cluster 4 consists of 17 institutions with very good achievement, which can serve as examples for other institutions. This research concludes that the quartile binning method successfully categorized private higher education institutions based on their achievement of KPIs into four clusters: poor, fair, good, and very good. This outcome demonstrates the effectiveness of the method in understanding the performance distribution of these institutions. It provides valuable insights for stakeholders to develop data-driven strategies aimed at enhancing educational quality.

Institutionalisation of sustainability performance measurement and reporting: Insights from Victoria (Australia) and New Zealand universities

This study explores the sustainability performance indicators (SPIs) universities disclose, and how institutional and resource dependence pressures influence the institutionalisation of sustainability performance measurement and reporting. We assess performance indicators (PIs) with quantitative or qualitative results reported against established targets and conducted a longitudinal cross-jurisdiction assessment of over 25,000 PIs reported by 16 Victorian (Australia) and New Zealand (NZ) universities from 2012 to 2021. Results from content and regression analyses provide evidence that SPI reporting is influenced by coercive, normative, and resource dependence pressures, with universities adopting an acquiescence strategy towards disclosure. SPIs account for a substantial proportion (54%) of total PIs and focus on economic and social performance. Environmental SPIs are typically absent unless mandated and SPIs that indicate the embedding of sustainability practices into core university activities are uncommon. Despite some innovative universities that voluntarily adopted sustainability reporting guidelines or disclosed SPIs beyond reporting mandates, these disclosure practices may not represent an improvement over currently accepted practices as they have not become widely diffused and institutionalised. Our study provides new insights on the state of university sustainability reporting and is relevant to standard-setters and regulators in advancing public sector sustainability reporting.

The informational relevance of social accounting for the management of financial institutions. A case study: Banca Transilvania Financial Group

This paper analyzes the relevance of social accounting information for managing financial institutions, using Banca Transilvania Financial Group (BTFG) as a case study. It explores how social accounting data can enhance decision-making processes within these institutions. Social information from BTFG’s annual integrated reports was used to construct a social balance sheet, and financial data was collected to calculate economic value added (EVA) and social value added (SVA). Research question include: Does social accounting represent a lever for substantiating the managerial decision in financial institutions? Results show that SVA is a valuable indicator for financial institution managers, reflecting the institution’s contributions to social well-being, environmental impact, and community support. Policy implications suggest regulatory bodies should mandate the inclusion of social accounting metrics in financial reporting standards to encourage socially responsible practices, enhance transparency, and incentivize institutions achieving high SVA. This paper contributes to the literature by demonstrating the practical application of social accounting in financial institutions and highlighting the importance of SVA as a managerial tool. It aligns with existing research on integrating corporate social responsibility (CSR) metrics into financial decision-making, enhancing the understanding of combining social and economic indicators for comprehensive performance assessment The abstract covers motivation, methodology, results, policy implications, and contributions to the literature.

Integrating standardized indices and performance indicators for better drought assessment in semi-arid coastal aquifers

Aquifers in arid and semi-arid coastal regions, such as along the Mediterranean rim, are severely affected by droughts. The natural decrease in water levels is often exacerbated by excessive abstraction, resulting in both degradation of water quality and the risk of seawater intrusion. In these regions it is crucial to conduct thorough monitoring of wells, employing a wide range of indicators to forecast and mitigate the consequences of decreased precipitation and intensified pumping. This study proposes an analysis and monitoring methodology involving the calculation of performance indicators based on the Standardized Groundwater level Index (SGI), supplemented with information on the optimal accumulation time of the Standardized Precipitation Evapotranspiration Index (SPEI). Atmospheric reanalysis datasets and in-situ groundwater level observations are used together to derive the groundwater system memory and find consistent optimal SPEI accumulation times at each individual location. The inverse of memory derived from the autocorrelation of the SGI is used to estimate each well's ability to recover from drought conditions. This value provides the most reliable indication of resilience and sustainability. In the Algarve, the average regional variability of groundwater level is well captured by the SPEI-12 index. However, groundwater memories and optimal SPEI accumulation times are spatially very heterogeneous varying between SPEI-5 and SPEI-48. Wells with shorter memories (<6 months) demonstrate greater sustainability, whereas those with longer memories (>16 months), whether situated inland or along the coast, exhibit lower resilience and lower sustainability. Coastal wells with groundwater levels close to sea level, exhibiting minimal resilience, are of particular concern and require intensified monitoring efforts to adapt management strategies.

Machine-learning-derived online prediction models of outcomes for patients with cholelithiasis-induced acute cholangitis: development and validation in two retrospective cohorts

Cholelithiasis-induced acute cholangitis (CIAC) is an acute inflammatory disease with poor prognosis. This study aimed to create machine-learning (ML) models to predict the outcomes of patients with CIAC. In this retrospective cohort and ML study, patients who met the both diagnosis of 'cholangitis' and 'calculus of gallbladder or bile duct' according to the International Classification of Disease (ICD) 9th revision, or met the diagnosis of 'calculus of bile duct with acute cholangitis with or without obstruction' according to the ICD 10th revision during a single hospitalization were included from the Medical Information Mart for Intensive Care database, which records patient admissions to Beth Israel Deaconess Medical Center, MA, USA, spanning June 1, 2001 to November 16, 2022. Patients who were neither admitted in an emergency department nor underwent biliary drainage within 24h after admission, had an age of less than 18, or lost over 20% of the information were excluded. Nine ML methods, including the Logistic Regression, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Adaptive Boosting, Decision Tree, Gradient Boosting Decision Tree, Gaussian Naive Bayes, Multi-Layer Perceptron, and Support Vector Machine were applied for prediction of in-hospital mortality, re-admission within 30 days after discharge, and mortality within 180 days after discharge. Patients from Zhongda Hospital affiliated to Southeast University in China between January 1, 2019 and July 30, 2023 were enrolled as an external validation set. The area under the receiver operating characteristic curve (AUROC) was the main index for model performance assessment. A total of 1156 patients were included to construct models. We performed stratified analyses on all patients, patients admitted to the intensive care unit (ICU) and those who underwent biliary drainage during ICU treatment. 13-16 features were selected from 186 variables for model training. The XGBoost method demonstrated the most optimal predictive efficacy, as evidenced by training set AUROC of 0.996 (95% CI NaN-NaN) for in-hospital mortality, 0.886 (0.862-0.910) for re-admission within 30 days after discharge, and 0.988 (0.982-0.995) for mortality within 180 days after discharge in all patients, 0.998 (NaN-NaN), 0.933 (0.909-0.957), and 0.988 (0.983-0.993) in patients admitted to the ICU, 0.987 (0.970-0.999), 0.908 (0.873-0.942), and 0.982 (0.971-0.993) in patients underwent biliary drainage during ICU treatment, respectively. Meanwhile, in the internal validation set, the AUROC reached 0.967 (0.933-0.998) for in-hospital mortality, 0.589 (0.502-0.677) for re-admission within 30 days after discharge, and 0.857 (0.782-0.933) for mortality within 180 days after discharge in all patients, 0.963 (NaN-NaN), 0.668 (0.486-0.851), and 0.864 (0.757-0.970) in patients admitted to the ICU, 0.961 (0.922-0.997), 0.669 (0.540-0.799), and 0.828 (0.730-0.925) in patients underwent biliary drainage during ICU treatment, respectively. The AUROC values of external validation set consisting of 61 patients were 0.741 (0.725-0.763), 0.812 (0.798-0.824), and 0.848 (0.841-0.859), respectively. The XGBoost models could be promising tools to predict outcomes in patients with CIAC, and had good clinical applicability. Multi-center validation with a larger sample size is warranted. The Technological Development Program of Nanjing Healthy Commission, and Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds.

Digital Developmental Advising Systems for Engineering Students Based on Accreditation Board of Engineering and Technology Student Outcome Evaluations

The purpose of this research is to examine the benefits and limitations of the implementation of novel digital academic advising systems based on the principles of authentic outcome-based education (OBE) using automated collection and reporting processes for Accreditation Board for Engineering and Technology (ABET) student outcomes data for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools®, including an advising module based on assessment methodology incorporating the faculty course assessment report, performance indicators, and hybrid rubrics classified according to the affective, cognitive, and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of a) detailed students’ outcomes and performance indicator information and b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the faculty course assessment report using performance indicators and hybrid rubrics can provide comprehensive and realistic outcome data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions, and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better-performing outcomes to reinforce their natural affinity for learning specialized competencies to help them pursue related and successful career paths.

Asparagopsis taxiformis as a Novel Antioxidant Ingredient for Climate-Smart Aquaculture: Antioxidant, Metabolic and Digestive Modulation in Juvenile White Seabream (Diplodus sargus) Exposed to a Marine Heatwave.

The increasing frequency and duration of marine heatwaves (MHWs) due to climate change pose severe threats to aquaculture, causing drastic physiological and growth impairments in farmed fish, undermining their resilience against additional environmental pressures. To ensure sustainable production that meets the global seafood demand and animal welfare standards, cost-effective and eco-friendly strategies are urgently needed. This study explored the efficacy of the red macroalga Asparagopsis taxiformis on juvenile white seabream Diplodus sargus reared under optimal conditions and upon exposure to a MHW. Fish were fed with four experimental diets (0%, 1.5%, 3% or 6% of dried powdered A. taxiformis) for a prophylactic period of 30 days (T30) and subsequently exposed to a Mediterranean category II MHW for 15 days (T53). Biometric data and samples were collected at T30, T53 and T61 (8 days post-MHW recovery), to assess performance indicators, biomarker responses and histopathological alterations. Results showed that A. taxiformis supplementation improved catalase and glutathione S-transferase activities and reduced lipid peroxidation promoted by the MHW, particularly in fish biofortified with 1.5% inclusion level. No histopathological alterations were observed after 30 days. Additionally, fish biofortified with 1.5% A. taxiformis exhibited increased citrate synthase activity and fish supplemented with 1.5% and 3% showed improved digestive enzyme activities (e.g., pepsin and trypsin activities). Overall, the present findings pointed to 1.5% inclusion as the optimal dosage for aquafeeds biofortification with A. taxiformis, and confirmed that this seaweed species is a promising cost-effective ingredient with functional properties and great potential for usage in a climate-smart context.

Analysis of Child Safety Monitoring System using the SPSS Method

A wearing children's safety system functions as a smart gadget. Accurate and exact real-time location can be used to identify a child's surroundings. This system includes an ambient temperature display, an SOS light, and a distress buzzer. It aids them in locating their child. The system is simple to design. The programmer is designed to establish an approach that is user-friendly on both sides. GPS-equipped smart phones should be available to both parents and kids. The child's location, call history, message history, and communication via their smartphone are all tracked using this software. Safety monitoring systems use a person, rather than a mechanical system (safety monitor), to warn when roofs are six feet or more below the lowest level and are in danger of falling. A safety supervisor must be a competent person to recognize fall hazards and warn workers about them. The device has IoT tracking and a GSM module that allows you to monitor the child at all times. It has countless sensors connected to a CPU and is used to detect the right signals like heart rate, temperature and other dangers and alert the parents. A programmer called system monitoring software keeps track of, logs, and examines all levels of the IT ecosystem's system resources, from operating systems and electronics to the underlying system applications and services, to running user software. At the top Today, 80% of all child abuse cases reported around the globe. The remaining 26% were boys, with 74% being girls. In this world, a daughter goes missing every 40 seconds. Children If one's children's future is the foundation of one's nation, is affected, it affects overall development Nation. Due to abuses, children's emotional and mental stability is affected Destroying and future endeavors. The things that happen to these defenseless kids are not their fault. Parents are therefore accountable for raising their own children. But, since their children's future and career are not considered, parents are compelled to want for money. So, it is challenging to understand them. There are kids everywhere. We offer a setting in our system where this issue is able to be resolved effectively. It makes it simple for parents to follow their kids in real time as if they were close to them and they focus on their own business without any manual intervention. Ratio studies are statistical analyses of data from appraisals and property valuations. Nearly all states utilise them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators. Evaluation parameters taken as Temperature sensor, Pulse sensor, GPS, GSM and Web camera. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Software Engineering Defect Prediction the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

Task reliability index for operator performance and failure probability assessment in control room simulators

Main control room simulators are an important information source for improving the empirical basis of human reliability analysis (HRA). Both quantitative and qualitative observations provide valuable insight on the operators’ ability to successfully perform their tasks. However, relevant portions of this evidence are not used in typical approaches for estimation of human error probabilities (HEP), which are based on failure counts. With small sample sizes and the operator performance levels representative for nuclear power plants, very little information enters the probability estimates. This paper presents a novel approach to formalize the collection and use of reliability-relevant evidence from simulators. The methodology treats task reliability with the plant-centred orientation characteristic of the systems or Probabilistic Safety Assessment (PSA) perspective as well as a human-centred view of task performance based in human factors engineering. These are combined to form a Task reliability index (TRI) able to distinguish finer performance variations and consider indications of potential failures. Bayesian updating of HEP distributions is suggested by translating the TRI data into pseudo-failures and trials, preserving the convenient coupling of Beta-Binomial distributions in the Bayesian analysis. The approach is demonstrated using data from the International HRA Empirical Study and is shown to produce results that are comparable to those obtained in the empirical study, which were based on comprehensive expert analyses.

Quantitative indicators for environmental and social sustainability performance assessment of the supply chain

AbstractIn the era of globalization, supply chains are becoming less transparent, facing pressing sustainability challenges such as the inappropriate use of natural resources, poor working conditions, and environmental degradation. This paper addresses these issues by presenting a pioneering sustainability assessment framework aimed at increasing transparency and accountability in global supply chains. Emerging from a systematic literature review and insights from the Global Reporting Initiative (GRI), the framework comprises 91 robust performance indicators: 36 environmental and 55 social. These indicators, a mix of quantitative and semi-quantitative measures, provide a comprehensive tool for assessing the sustainability performance of supply chain actors across a range of sectors. The framework not only facilitates companies in measuring their own and their suppliers’ sustainability performance but also enhances their capacity to effectively communicate their environmental and social progress to stakeholders. Additionally, it is designed to seamlessly integrate with Industry 4.0 technologies, enabling more dynamic assessments.

Revolutionizing heat distribution: A method for harnessing industrial waste heat with supra-regional district heating networks

In both practice and literature, there is a lack of a concept for a supra-regional district heating network that efficiently transports heat from renewable and industrial waste heat sources to multiple heat sinks and regional district heating networks, like the high-voltage electricity transmission network in the electricity sector. This paper addresses this gap by presenting a novel method for the basic design of a supra-regional district heating network and its evaluation, along with key performance indicators for assessment. The method highlights essential data requirements and the derivation process necessary to enable the integration of such a heating network. Additionally, it describes how a basic design of such a network can be made feasible and evaluated. This method is then applied to a case study to demonstrate its implementation for a real-world application. Furthermore, this case study aims to either demonstrate or provisionally disprove the general feasibility of a supra-regional district heating network. The results indicate that the implementation of such a network has a positive impact on the CO2 balance and primary energy demand. The case study further demonstrates the technical feasibility of such a network, showing that a high linear heat density can be achieved through integration and that temperature levels within the network can be maintained adequately. This study confirmed that the developed method can effectively assess whether further investigations into implementing a supra-regional district heating network in a specific region are warranted. Additionally, the method offers a guideline on how to initially design such a network.

Ceramic Materials in dentistry using the SPSS Method

Dental ceramics can be used in a variety of root canal therapy, such as inlays, bone grafting, crowns, and bridges, to replace earthenware (PFM) systems with everything systems. Dental crowns, dental composite components, and dentures are all made of porcelain and glass-ceramic materials, which are together referred to as dental ceramics. Conventional dental porcelain is feldspar-based and contain sizable amounts of kaolin (Al2O32SiO22H2O), quartz, and feldspar (KAlSi3O8). Rocks containing mica and iron are known to contain feldspar, a grey crystalline mineral. Ceramics are any of the countless hard, brittle, heat, corrosion-resistant substances produced by molding and thereafter scorching an inorganic, non-metallic substance like clay to high temperatures. Common examples include earthenware, pottery, and brick. Dental ceramics that are made of Lucite feldspathic material are very attractive and frequently utilized. Inlays, on lays, partial crowns, crowns, and veneers for metals and ceramics are among their clinical indications. Ceramics are resistant to high temperatures, effective thermal insulators, and have minimal thermal expansion. For uses like as lining industrial furnaces and sealing space shuttles, it makes really good thermal barriers. Dental ceramics are explained within a framework that makes it simple to comprehend how they developed. symptoms and composition. Engineering assessments of efficacy of treatment are discussed, and research is done. Behavior of all earthenware systems clinically. The usage of dental ceramics is discussed from a practical standpoint. Emphasizing what they know but also how we know it while maximizing beauty and endurance. Reviewing the history of ceramics' use in dentistry is helpful. This account has three purposes: (1) to warn professionals Pottery and improved ceramics were created to remedy the issue; (2) to increase actual issues or restoration diversity; and (3) give a soft backdrop in nature and ceramic science. The use of ceramics has always represented the adoption of "high technology" and "Craftsmanship". Ratio studies are statistical analyses of data from appraisals and property valuations. Nearly all states utilize them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators. Evaluation parameters: Ceramic materials in dentistry, Glass-ceramics, predominantly glassy ceramics, Substructure ceramics, Particle-filled glasses and Polycrystalline ceramics. Result: The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Characteristics of sisal fiber the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach

This paper introduces a new analytical technique using a direct synthesis strategy to develop centralised proportional–integral (PI) controllers for multivariable processes. The current method design controller is focused on attaining the desired closed-loop response for multi input multi output (MIMO) processes that involve multiple time delays. The conventional multivariable PI controller is obtained by approximating the ideal multivariable controller by the Maclaurin series expansion. This is accomplished by choosing the desired closed-loop response order as the system order plus two. Subsequent analysis investigates the proposed method’s efficacy in designing multivariable PI controllers. Servo and regulatory problems studies were conducted to find the effectiveness of the proposed method and compared it with other methods recently reported in the literature, as well as assessment of performance indices like integral absolute error (IAE) and integral square error (ISE). The proposed controller's robustness is assessed by plotting the inverse maximum singular value against frequency, both input and output multiplicative uncertainties.

Human Resource Functions: Examining Insights from ABC Research Organization

Human resource management (HRM) functions are critical to ensuring an organization's success and sustainability. These functions encompass various activities designed to manage an organization’s employees. This paper examined human resource management functions; recruitment, training, as well as compensation to handle an organization’s most valuable asset in the face of firmness ecological challenges. Drawing on the Resources-Based and Human Capital theories, a case study design was employed in this research and the data were gathered by semi-structured interviews with three participants. Thematic analysis revealed a recruitment focus on targeted sourcing and networking, training priority placed upon mentorship and tailored programmes, as well as compensation alignment with commitment, length of service, and market analysis for an ABC organisation. It was recommended that the HRM should foster further improvement through evaluations and audits continually, enhanced bias training in audit processes and increase transparency of compensation as well as setting Key Performance Indicators (KPIs) for HR assessment.

A novel framework for analyzing the impact of COVID-19 on entrepreneurial financial firms in Jordan

PurposeCOVID-19 has rendered many firms' business models, strategies and performance vulnerable, including entrepreneurial financials. Some managed to survive, while others drowned in the epidemic swamp. This study offers an exceptional model to fill the gap.Design/methodology/approachEmploying a rigorous qualitative design, the study utilizes a novel framework that integrates institutional theory (IT) and corporate entrepreneurial strategy (CES). Semi-structured interviews were conducted, and thematic analysis identified key themes: external environment, institutional environment and organizational response, CES and performance and survival.FindingsThe study reveals the dual nature of the external and institutional environment, acting as both facilitators and barriers for entrepreneurial financial firms (EFFs). It highlights the robust CES exhibited by these firms during the pandemic, demonstrating their adept balancing and integration of different CES components in their organizational response. The EFFs employ a mix of financial and nonfinancial indicators for performance assessment, yielding varied outcomes based on contextual factors.Practical implicationsEFFs and stakeholders are guided to adapt their business models, balance institutional pressures, implement CES and evaluate performance. It advocates collaboration within the entrepreneurial finance ecosystem and leveraging opportunities emerging from the pandemic, including new market segments, technologies, innovations and regulatory changes.Originality/valueThis topic is underexplored in many emerging economies. Fresh perspectives and rigor frameworks are developed on how EFFs navigate and capitalize on the pandemic under uncertainties.

Estimating the as-built thermal performance of dwellings using simulated on-board data: From ideal to limited monitoring

Advancement in non-intrusive monitoring of energy usage in households presents an ideal opportunity to get insight into the as-built performance of buildings and assess performance indicators such as the Heat Loss Coefficient (HLC) of the building fabric. The availability of in-use monitored data contributes to bridging the energy performance gap while avoiding costly and exhaustive dedicated measurement campaigns. Methodologies to assess the building performance by combining on-board monitoring and statistical data-driven models are widely accepted. However, their unsupervised and automated applicability on a large scale, with limited data and no insight into the building characteristics and occupants’ behaviour still needs to be investigated. Therefore, this work aims at assessing the reliability and performance of statistical black-box AutoRegressive with eXogenous input (ARX) models on artificial measurement campaigns starting from idealistic setups and progressing towards limited ones. In this investigation, the uncertainty in the estimate inherited from inputs, such as different heat sources or weather data, and their post-processing is combined with the uncertainty originating from different monitoring packages, which results in a thorough overview of the statistical modelling limitations. Results show that disaggregation of the final heating usage poses a major challenge. In cases where gross gas usage is measured, and the system efficiency and domestic hot water consumption are not taken into account, the HLC can deviate by 49% in low-performing houses and by 106% in well-performing houses. However, by applying data pre-processing methods the discrepancy can be lowered to 8% and 23% respectively, but with high uncertainty. On the other hand, the installation of an additional heat meter could yield deviations from the target of 26% for low-performing and 11% for well-performing dwellings without data manipulation, both with low uncertainties.

Strategic Analysis in the Public Sector using Semantic Web Technologies

This article addresses the complex challenges that public organizations face in designing, implementing, and evaluating their strategies, where public interest and regulatory compliance often intertwine with strategic objectives. This research investigates the application of ontologies in the field of public sector strategy management to enhance the capacity of organizations to make informed data-driven decisions, efficiently allocate resources, and effectively navigate the intricate landscape of the public sector. The LNEC - National Laboratory for Civil Engineering’s strategy is used as an exploratory case study. Semantic web technologies are used to perform strategy analysis, including validating the strategy formulation, and supporting the strategy execution by assessing performance indicators, verifying the design of cause-and-effect relationships between strategic objectives, and monitoring and empirically validating these relationships. The increased interoperability of these technologies enables information sharing across systems and organizations. Following the strategy analysis, recommendations are provided, leading to a more robust and data-driven strategic management approach, enabling accurate, traceable, and continuous monitoring of an organization’s strategy. Theoretical and practical implications are discussed, along with limitations and future work. This research offers a blueprint for public sector organizations seeking to optimize their strategies, foster transparency, and deliver more effective services to the public they serve.

Business Management Goal Setting and Performance Appraisal Strategies of Development

The purpose of this paper is to explore the issue of management optimization in construction engineering cost consulting enterprises, and to provide scientific and reasonable management methods and strategies for enterprises to improve their management level and overall competitiveness by studying and analyzing the formulation of goal setting and performance evaluation strategies. This paper adopts the method of combining literature review and case analysis to conduct research. Firstly, through reviewing relevant literature, we understand the relevant theories and practical experience of goal setting and performance evaluation; then, combined with the actual situation of engineering cost consulting enterprises, we conduct an in-depth analysis and research on the goal setting and performance evaluation strategy of enterprises; finally, through the method of case study, we conduct an empirical research on the management optimization of enterprises, and verify the effectiveness and feasibility. The findings of this paper include the following: 1. Key elements of goal-setting and core indicators of performance assessment for engineering cost consulting firms were summarized; 2. Based on the results of the questionnaire survey and the case study, a performance appraisal strategy for different positions and levels is proposed; 3. Based on the results of the empirical study, the effectiveness and feasibility of the goal-setting and performance assessment strategies were verified. Through researching and analyzing the management optimization problems of construction engineering cost consulting enterprises.

A Consolidated MCDM Framework for Overall Performance Assessment of Listed Insurance Companies Based on Ranking Strategies

The main objective of this study is to analyze the performance of non-life insurance companies operating in the Turkish insurance industry with a hybrid model including Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) methods. For this purpose, the performance assessment indicators, consisting of fourteen sub-criteria in three dimensions are taken into account for comparing five insurance companies traded on the Borsa Istanbul (BIST) over five consecutive years (2015 to 2019). Subsequently, year-wise rankings are aggregated using the Borda count (BC) procedure. The results of PFAHP indicate that service network is the most important main criterion (dimension) for performance assessment of non-life insurance companies, followed by stock market performance and financial ratios that come in the second and third ranks, respectively. Furthermore, the results of MAIRCA based on BC procedure reveal that Halk Sigorta, a state-owned insurance company, is the most successful company in terms of selected performance indicators in the period examined. A comprehensive sensitivity analysis is performed in order to test stability and the robustness of the results from the proposed framework, and the results of sensitivity analysis confirms the rationality and robustness of the suggested integrated MCDM framework. As a result, the suggested assessment framework can be applied by different decision-making groups in the industry as a valuable and practical decision-making tool for monitoring and improving the performance of insurance companies. Finally, some of managerial implications are also discussed.