Assessment Of Performance Indicators Research Articles

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.

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 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.

Latar Belakang Pendidikan CEO, Pengalaman CEO dan Kinerja Perusahaan (Studi Pada Perusahaan yang Terdaftar di Indeks LQ45, Indonesia)

This study aims to determine the effect of the CEO expertise (with the proxies of educational background and experience) on company performance (with the proxies of return on assets (ROA), return on equity (ROE), and dividend payout ratio (DPR)). This study employs quantitative method, in which the data are analyzed by multiple linear regression. The samples include companies listed in the LQ45 index of the Indonesia Stock Exchange for the 2016-2019 period. The results of the study exhibit that the CEO educational background affects ROA and ROE, except DPR. It is suggested that further studies consider not only educational background and experience but also work relations and gender, and use other performance assessment indicators such as PBV.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

System-of-systems approach to spatio-temporal crowdsourcing design using improved PPO algorithm based on an invalid action masking

Spatio-temporal crowdsourcing (STC) is a typical case of complex system-of-systems (SoSs) design, wherein the primary objective is to allocate real-time tasks to suitable groups of workers. Over time, the STC allocation has gradually evolved into a dynamic matching involving three distinct entities: tasks, workers, and workplaces. Aiming at addressing the problems of poor convergence, slow response and sparse actions caused by the spatial complexity and time dynamics of the STC, this paper proposes an improved proximal policy optimization algorithm based on an invalid action masking (IAM-IPPO) for the SoSs design of the STC. Initially, the ternary dynamic matching (TDM) of tasks, workers and workplaces in the STC is described. Furthermore, the STC allocation is formulated as a Markov decision process, with the corresponding definition of state space, action space, and reward mechanism. On this basis, an invalid action masking (IAM) method is mainly introduced to update the policy-based network of proximal policy optimization (PPO), realizing sampling only from valid actions to masking invalid action selection. Subsequently, the algorithmic framework of IAM-IPPO is elaborated upon, and the model is trained to generate an effective allocation scheme. Comparative experiments are conducted on authentic datasets, aiming to assess performance indicators of the presented approach. The findings demonstrate a substantial enhancement in performance for the IAM-IPPO algorithm compared to other baselines, which is helpful in exploring excellent design schemes of the crowdsourcing SoSs, especially in dynamic large-scale cases.

Optimization of Urban Science and Technology Innovation Performance Assessment Model - Based on VAR Methodology Perspective

Abstract Conventional science and technology performance assessment methods and indicators can no longer meet the current needs of urban science and technology innovation performance assessment, and innovative research is urgently needed. In this study, the four elements of innovation input, innovation process, innovation output, and innovation guarantee are taken as the first-level indicators of the urban science and technology innovation performance assessment index system. The G1 entropy value correction method is used to assign weights to the urban science and technology innovation performance assessment index system to reduce subjective arbitrariness. Finally, the specific situation of innovation elements and innovation performance in S city was selected, and the influence relationship between innovation elements and innovation performance assessment was analyzed using a vector autoregressive model. The results show that the method of assessing the city’s science and technology innovation performance based on the VAR model is simple and easy to operate, the confidence coefficient of the assessment model is more than 0.7, and the mean value of the validity CVR is 0.703. The assessment results have a high degree of credibility and validity and effectively promote the region’s scientific and technological self-reliance and self-improvement.

Losses VS Opportunities: Principles of Lean Management in the Academic Field

Currently, the concept of lean management is increasingly becoming the focus not only of representatives of the production sector, but also of educational and scientific organizations, healthcare organizations, government and regional authorities, etc. In this article, the authors show that the implementation of lean management principles and tools in the activities of academic organizations allows you to “restart” many processes and reduce the loss of quality and productivity by several times without significant costs. The first part of the article describes the main stages of the formation of the lean management concept and the contribution of Russian scientists (A.K. Gastev, I.V. Rebelsky (the basics of lean mental labor)) to the development of both methodological and practical recommendations that currently constitute the core of the philosophy of lean manufacturing and management. The central place in the article is devoted to the features and classification of losses in relation to both the activities of the organization as a whole and the activities of scientific and pedagogical workers (along with the “classical” classification of losses developed by T. Ono and J. Liker, ten features were emphasized to identify losses in the academic field). Among the reasons that led to an increasing interest in the use of lean management technologies in the academic field, the authors highlight the following ones: strengthening management and formalization in assessing performance indicators, strengthening information asymmetry, changing the level and quality of communication of employees, updating the problem of precarization of scientific and pedagogical workers’ employment and gradual blurring of the boundaries between work and rest. In the final part of the article, a set of recommendations was formed to identify losses in the activities of scientific and pedagogical workers, and an algorithm for their elimination was proposed.

Studying Spatial Correlation and Regional Differences in Local Government Debt Performance Using Spatial Econometric Models

Abstract The magnitude and type structure of local government debt are first analyzed in this work. Then, using the “4E” evaluation theory, a local government debt performance assessment index system is built. Second, an entropy-TOPSIS technique is used to calculate the risk index of local government debt performance, and a spatial econometric model is used to examine the geographical spillover impact of local government debt performance across different areas. Finally, the heterogeneity of local government debt management was evaluated using the Malmquist index. The findings demonstrate that the hypothesized national effect is often superior to the sub-regional effects, with a debt ratio regression result of -0.065. Despite the overall local government debt coefficient being -0.032, the direct impact coefficient for the eastern area is -0.025.

Treatability of high-strength real sheep slaughterhouse wastewater using struvite precipitation coupled Fenton's oxidation: The MAPFOX process

Struvite (MAP, magnesium ammonium phosphate hexahydrate, MgNH4PO4.6H2O) precipitation-aided Fenton's OXidation (MAPFOX process) was explored in the treatment of high-strength real sheep slaughterhouse wastewater (RSSW) for the first time under a comprehensive soft-computing-based modeling study. The experimental results showed that under the highest-efficiency conditions (chemical combination of MgCl2.6H2O + NaH2PO4.2H2O, a molar ratio of Mg2+:NH4+-N:PO43–P = 1.2:1:1, a reaction pH of 9.0 ± 0.10, [NH4+-N]0 = 240 ± 20 mg/L, and a reaction time of 15 min), MAP precipitation could effectively remove more than 80 %, 60 %, 55 %, and 70 % of color, total chemical oxygen demand (TCOD), soluble COD (SCOD), and ammonium nitrogen (NH4+-N) from the raw RSSW. The results of the Fenton's oxidation used as the post-treatment unit of the proposed MAPFOX system indicated that the integrated advanced oxidation process (AOP) was able to reduce the residual pollutant levels in the MAP-pretreated RSSW to the relevant discharge standards. Under the subsequent condition of [Fe2+]0 = 90 mmol/L, [H2O2]0 = 180 mmol/L, reaction pH = 3.25, and total reaction time = 60 min, more than 97 % of color, TCOD, SCOD, and NH4+-N could be removed from the RSSW via the Fenton's oxidation after the MAP-based physicochemical treatment. According to SEM micrographs, surface morphology of dehydrated struvite exhibited irregular-shaped and overlapping sharp-edged particles of various sizes with an average size of about 50.9 μm. The Fourier Transform Infrared (FTIR) spectroscopy confirmed the active functional groups and type of bonds for the high-strength RSSW-oriented struvite (heated) within the spectral range of 4000–450 cm−1. Thermogravimetric Analysis (TGA), Derivative Thermogravimetry (DTG), Differential Thermal Analysis (DTA), and Differential Scanning Calorimetry (DSC) of the dehydrated struvite revealed that the weight loss occurred in three temperature zones, the maximum weight loss rate of 0.252 mg/min was recorded at around 224 °C and at time of 20.83 min, and the sample had strong endothermic and medium exothermic peaks at about 241 °C and 679 °C, respectively. The predictive successes of the implemented soft-computing approaches were benchmarked in terms of various statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the support vector machines-Pearson VII universal kernel function (SVM-PUKF)-based model (correlation coefficient (CC) = 0.9999–1.0000), mean absolute error (MAE) = 0.0222–0.0389 %, mean absolute percentage error (MAPE) = 0.0270–0.0506 %, root mean squared error (RMSE) = 0.0258–0.0415 %, coefficient of variation of RMSE (CV(RMSE) = 0.0003–0.0008, Nash–Sutcliffe efficiency (NSE) = 0.9998–1.000, Legates and McCabe's index (LMI) = 0.9894–0.9952) over other data-intelligent models in predicting the pollutant removal efficiencies. The computational results also indicated that the narrowest uncertainty bands (±1.96Se = 0.0537–0.1483 %) and the lowest amounts of expanded uncertainty (U95 = 3.1224–5.3124 %) values for all efficiency sets were achieved for the applied SVM-PUKF-based strategy. This study demonstrated the first-ever and successful application of the proposed MAPFOX process in treatability of the RSSW and capability of the implemented soft-computing strategy in modeling a highly nonlinear treatment system.

Pengaruh Penerapan Green Accounting Terhadap Sustainable Development Dengan Ukuran Perusahaan Sebagai Variabel Intervening

This study aims to test and analyze the effect of implementing green accounting as measured through environmental performance assessment indicators on sustainable development as measured through the GRI G4 index with company size as the intervening variable. This study uses a quantitative approach. The research sample consisted of 8 companies from 93 population of basic materials sector companies on the Indonesia Stock Exchange (IDX) for the 2017-2021 period which were determined using a purposive sampling technique. Data analysis techniques were performed using the Partial Least Square (PLS) method with the help of SmartPLS 4.0 software. This research data is secondary data in the form of annual reports and sustainability reports obtained from the official website of each company. The results of the study found that green accounting has a positive and significant influence on sustainable development and company size. In addition, with regard to sustainable development it was found that company size did not have a significant effect, either as an independent variable or an intervening variable.
 Keywords: Green Accounting, Company Size, Sustainable Development

Analyzing of Water Quality Management using SPSS Method

In order to maintain water quality, it is typically essential to authorise the release of hazardous compounds, for which surface water waste discharge must be monitored. To make sure that the water is safe to consume for people, animals, and other marine life, it is crucial to monitor water quality. It's crucial for ports to measure water quality in order to comprehend environmental effects and protect marine life. Non-point resource pollution, streams of pollution both direct and indirect, management of watersheds, erosion, and sediments, non-point sources of pollution, and integrated management of water resources,contamination of groundwater and waste water management. Depending on whether or not the water is suitable for drinking or swimming, water quality refers to the state of the water, including its chemical, physical, and biological characteristics. The quality of water can be assessed by taking groundwater samples to undergo lab analysis or by utilising probes that can continuously or intermittently record data. Information on water quality is used by the water sector to manage water resources. Understanding what is happening beneath, where and how quickly water is moving, which geochemical processes are taking place, and recognising distinct sources of water are all made easier by the quality of the water. The necessity to upgrade civilization's plumbing has an impact on water quality as well. One of the natural resources that is present in appropriate amounts is water. It is a crucial component of life on Earth. It is frequently used for drinking, cleaning, cooking, bathing, irrigation, and other residential and commercial purposes. For water utilities dealing with rising energy costs, a lack of water supplies, and strict regulatory requirements, water as well as energy resources are crucial.Early in the 1990s, the idea of an interwoven Water and Energy Quality Management Software (EWQMS) was established as a framework for operational optimization for simultaneously addressing issues with water quality, water supply, and energy management. In order to build an EWQMS, about twenty water utilities interfaced existing control systems with external or internal software optimization packages. Due to increased utilisation of cheaper tariff times and improved operating efficiencies, utilities with an installed EWQMS have reported operating cost savings of 8e15%, which has resulted in a decrease in energy consumption of 6e9%. This paper presents the current state of understanding on the usual structural features and operational tactics of EWQMS, and both advantages and disadvantages are considered. The analysis also identifies the gaping holes that should spur new research initiatives and outlines the difficulties faced during the installation and deployment of EWQMS. 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. Energy and water quality management, system, Water utilities, Energy efficiency, Water quality and Demand forecasting. 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.

Exploring the potential of roof greening for low-carbon landscapes.

As one type of urban artificial ecosystems, roof greening exhibits carbon source/sink characteristics during their life cycle. The carbon cycle mechanism is complex. The lack of exhaustive carbon performance quantification methods and assessment indicators hinders the promotion and implementation of green roof urban decarboni-zation. Focusing on the quantification of roof greening low-carbon landscape potential, we analyzed the internal carbon cycle mechanism of green roof systems and explored four carbon reduction and sink pathways (P1-P4): biogenic carbon sink, embodied carbon, operational carbon, and bioenergy supply. Based on the dual performance indicators of normalized value of carbon emissions and carbon payback time, we summarized the normalized value measurement method of each pathway. The potential and characteristics of each pathway were quantified by extracting data from the literature. The results showed that the quantified potential values for P1 to P4 were 9.54, -2.26, 2.96 and 0.35 kg CO2·m-2·a-1, respectively, and that the potential values for each pathway were strongly influenced by plant types, climate, and other factors. The imperfect base database and the heterogeneity of assessment scenarios impacted the accuracy of the measurements. The integrated low carbon landscape potential of extensive green roofs was discussed in sub-scenarios, with the 40-year-life cycle integrated carbon reduction ranging from 92.24 to 433.42 kg CO2·m-2 and the carbon payback period ranging from 5 to 14 years. Finally, we summarized the problems in the assessment to facilitate future updates and improvements.

Comprehensive performance assessment of energy storage systems for various application scenarios based on fuzzy group multi criteria decision making considering risk preferences

The energy storage (ES) is an indispensable flexible resource for green and low-carbon transformation of energy system. However, ES application scenarios are complex. Therefore, scientifically assessing the applicability of different energy storage systems in various scenarios is prominent for the development of ES industry. This paper proposes an integrated fuzzy-GMCDM (group multi criteria decision making) method for achieving the above objective. In the proposed method, the comprehensive performance assessment index system consists of 10 indicators from the perspectives of technology, economy, efficiency and environment based on fuzzy Delphi model. The optimal aggregation weights can reflect the total judgments of all experts are determined by Bayesian Best-Worst method. Fuzzy cumulative prospect theory is employed to maximize the use of objective data, and prioritize the rankings of alternatives under various scenarios considering risk preferences of decision-makers. The results show that this method can provide decision-makers with the optimal solutions for ES selection in various scenarios such as renewable energy consumption and ancillary services. Furthermore, risk preferences with respect to the 4 aspects, affect the optimal choice and the rankings of ES systems, especially for the flywheel ES, sodium‑sulfur battery and vanadium redox flow battery.

A systematic literature review: Setting a basis for smart and sustainable city performance measurement

AbstractThe growing amount of data and the inventive solutions that arise from it create opportunities to construct and manage smart sustainable cities. Before attempting to establish appropriate solutions in this regard, it is crucial to clearly grasp what smart and sustainable cities are and the aspects around which they are built. The purpose of this study is to critically analyze and evaluate the studies that have been conducted on smart and/or sustainable cities and to provide a basis for the measurement of smartness and sustainability of cities. In this context, using Systematic Literature Review (SLR) methodology, the smart city, sustainable city, and smart sustainable city concepts are firstly defined. After that, the indicators for the assessment of the smartness and sustainability of cities and the selection processes of these indicators are analyzed. According to the analysis, the studies were grouped based on the use of (i) selection criteria, (ii) selection methods/tools, and (iii) models used and 14 criteria were deduced from the literature for the evaluation of performance assessment indicators: measurability, availability, completeness, relevance, independence, reliability, currency, responsiveness, simplicity, representativeness, cost‐effectiveness, consistency, comparability, and consensus. This study aims to support other studies and applications of authorities on creating and assessing a smart and sustainable city (i.e., the indicators) that want to join the 2030 Agenda but face problems regarding their cities' specific conditions. The first step towards creating a smart sustainable city can be taken by determining the most suitable indicators for the city.

Nonlinear inertia and its effect within an X-shaped mechanism – Part II: Nonlinear influences and experimental validations

In this study, the effects of nonlinear inertia are further investigated and verified with prototyping and experimental validation. The new inertia unit integrated within a well-designed X-shaped mechanism can produce 3 different typical nonlinear inertia forms which have different nonlinear influence on dynamic response. A critical parametric indicator (Ratio of Inertia) is thus proposed to identify and indicate different types of nonlinearities and a new performance assessment indicator (F-stability) is proposed to assess the performance improvement compared to different nonlinearity with respect to linear counterparts in a dynamic excitation sense. A method for testing different nonlinear inertia is also proposed. It is uniquely revealed that (a) the Ratio of Inertia can be well used to tune different types of inertia nonlinearities; (b) the symmetrical nonlinear inertia (HCHE-type) is generally better in vibration isolation with smaller peak/anti-peak values, peak/anti-peak frequencies (∼ 1 Hz and 3∼4Hz in our prototype), and faster decay rate (∼ -50dB/Dec with our prototype) but higher high-frequency transmissibility compared to the two asymmetric nonlinearities; (c) the nonlinear inertia contributes significantly to tune the interactive force between vibration source and object in the low frequency range (<10Hz in our prototype) and obviously helpful and robust to stronger excitations. All the theoretical analysis results are verified with our developed prototype. These results should for the first time provide a comprehensive and in-depth understanding of nonlinear inertia and its effects in different cases and thus definitely shed light on various engineering applications.

Effects of Characteristics of Sisal Fiber using the SPSS Method

The distinguishing qualities or traits of anything are its characteristics. They can be viewed as traits that set one person or thing apart from another. For instance, the chameleon's ability to blend in is one of its traits. Soft, straight, and golden in color, sisal fibers are. Sisal can have long or short fibers because it is extremely coarse and rigid. Because of its strength, toughness, stretch ability, compatibility with some colors, and resilience to degradation in saline water, sisal is prized for use as cordage. Sisal can be fashioned into carpets, mats, and a variety of handicrafts in addition to its traditional applications as rope, ropes, string, and yarn. Sisal fiber is a tough, robust material with low flexibility that is obtained from freshly cut leaves of the plant. It is crucial in the production of materials like carpeting, coarse purses, maritime rope (where strong resistance to saltwater is required), and cordage. Research significance: In recent years, materials engineers and material scientists have paid close attention to sisal-fiber reinforced composites as a type of environmental composites. Effects of fiber surface treatment on fiber compressive properties and fiber-matrix are discussed in this article. Tensile as well as single-strand strand pull tests were used, respectively, to assess the interfacial characteristics. For assessing the implies that the average shear resistance of composite laminates, a short beam shearing test was also carried out. High density polyethylene (HDPE), vinyl ester, and epoxy were selected as the matrix materials. Two different fiber surface-treatment techniques, chemical bonding and oxidation, were used to boost interfacial strength. According to the results, different fiber surface-treatment techniques had varying effects on the tensile strength of sisal fiber, as well as the strength and bonding characteristics of the fiber-matrix interface. As a result, this work can provide important knowledge on jute fiber-polymer matrix compound interface design. Method: 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: Fiber diameter (mm), Water content (%), Tensile strength(MPa), Tensile modulus(GPa) and Elongation at break (%) 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. Conclusion: 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.