Terms Of Performance Indicators Research Articles

Enhanced fault detection in polymer electrolyte fuel cells via integral analysis and machine learning

The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies.

A comprehensive comparison study on household solar-assisted heating system performance in the hot summer and cold winter zone in China

Distributed solar heating systems have high energy-saving potential as clean and energy-efficient production units in residential building applications. Solar energy resources are weak in hot summer and cold winter zone in China, which makes it difficult to completely rely on solar energy to heat residential buildings. A critical challenge is the lack of a theoretical basis for how to choose the solar-assisted heating system form. In this paper, seven solar-assisted heating systems are modeled and their performances are evaluated in six cities by using TRNSYS software. In terms of performance indicators, the solar fraction, the season performance factor, and the heat efficiency are used to thoroughly discuss the viability of using solar heating technologies in this zone. Based on the results, the average indoor air temperature was maintained between 16 °C and 20 °C, and the lowest solar fraction obtained was 10.1%. The carbon emission reduction in each city ranged from 60 kg to 400 kg, yielding good environmental benefits. According to the comprehensive comparison of the six evaluation indexes considered, the parallel or dual-source solar-assisted air source heat pump system can be used in a zone with medium solar energy resources. It is more suitable to use a dual-source solar-assisted air-source heat pump system in a zone with poor solar energy resources. The findings of this work can guide and promote the widespread application of distributed solar heating in household residential buildings in the hot summer and cold winter zone in China.

Performance Comparison of Directed Acyclic Graph-Based Distributed Ledgers and Blockchain Platforms

Since the introduction of the first cryptocurrency, Bitcoin, in 2008, the gain in popularity of distributed ledger technologies (DLTs) has led to an increasing demand and, consequently, a larger number of network participants in general. Scaling blockchain-based solutions to cope with several thousand transactions per second or with a growing number of nodes has always been a desirable goal for most developers. Enabling these performance metrics can lead to further acceptance of DLTs and even faster systems in general. With the introduction of directed acyclic graphs (DAGs) as the underlying data structure to store the transactions within the distributed ledger, major performance gains have been achieved. In this article, we review the most prominent directed acyclic graph platforms and evaluate their key performance indicators in terms of transaction throughput and network latency. The evaluation aims to show whether the theoretically improved scalability of DAGs also applies in practice. For this, we set up multiple test networks for each DAG and blockchain framework and conducted broad performance measurements to have a mutual basis for comparison between the different solutions. Using the transactions per second numbers of each technology, we created a side-by-side evaluation that allows for a direct scalability estimation of the systems. Our findings support the fact that, due to their internal, more parallelly oriented data structure, DAG-based solutions offer significantly higher transaction throughput in comparison to blockchain-based platforms. Although, due to their relatively early maturity state, fully DAG-based platforms need to further evolve in their feature set to reach the same level of programmability and spread as modern blockchain platforms. With our findings at hand, developers of modern digital storage systems are able to reasonably determine whether to use a DAG-based distributed ledger technology solution in their production environment, i.e., replacing a database system with a DAG platform. Furthermore, we provide two real-world application scenarios, one being smart grid communication and the other originating from trusted supply chain management, that benefit from the introduction of DAG-based technologies.

Modeling and Simulation of a High Temperature Proton Exchange Membrane Fuel Cell

High temperature (HT) (around 120-200°C) PEM FCs are predicted to be the next generation of PEMFCs particularly for hydrogen-powered automobiles and combined heat and power (CHP) systems because the water management can be simplified at such temperatures as only a single phase of water vapor needs to be considered. Additionally, the cooling system can be streamlined due to an increase in temperature gradient between the coolant and the FC stack. This will also allow easy recovery of waste heat that can be used as a practical heat source. Furthermore, the CO tolerance improves dramatically at high temperatures thereby allowing HT PEMFCs to utilize reformed and impure hydrogen.A single phase three-dimensional, steady-state, isothermal model for a single 5 HT PEM fuel cell with serpentine flow channels is implemented in COMSOL to investigate the effect of various operating conditions like temperature, back pressure and cathode flow rate and design parameters like catalyst layer loading, cathode GDL porosity, and flow field channels including parallel and interdigitated channels. Different performance indicators in terms of polarization curve, loss mechanisms, oxygen molar concentration, and anode and cathode pressure are represented for a complete overall analysis. In fact, the breakdown of different overpotential loss mechanism for HT PEMFC presented here is unique that not only quantifies these losses but also provides an accurate comparison with corresponding low temperature counterpart. The result from the computational model follows the experimental result very closely, thus, validating our model. The simulations stipulates that the performance of a HT PEMFC improves with increasing temperature, back pressure, and air flow rate. Increasing catalyst layer loading improves the performance up to a point after which it starts to drop at low voltages because of hindered gas diffusion. Similarly, a comparative study among different flow field channel is also presented indicating improved performance when going from parallel to interdigitated and serpentine channels. Furthermore, this study provides guidelines to optimize HT PEMFC performance through comprehensive parametric study.

Biomethane production through the power to gas concept: A strategy for increasing the renewable sources exploitation and promoting the green energy transition

Direct catalytic methanation of biogas using hydrogen from electrolysis is a promising pathway to store the electricity from renewable power plants according to the Power to Gas concept. This type of methanation offers technical and economic advantages over the methanation of carbon dioxide separated from biogas, since it eliminates the upgrading step.This work is focused on the sizing and performance analysis of a Power to Biomethane plant based on biogas direct catalytic methanation process. The plant consists of two main sections: i) the Renewable Island, in which an anaerobic digestion plant and a renewable power plant (Photovoltaic plant or Wind farm) supply the biogas and the electricity; ii) the Biomethane Production Island, in which the hydrogenation of the carbon dioxide in the biogas, in presence of methane, occurs.This study, by means of an optimization procedure based on a multi-objective approach, aims to evaluate the optimal size of the Power to Biomethane plant that assures the maximum biomethane production and the maximum exploitation of the renewable electricity. Two case studies, referring to the installation of a Photovoltaic plant and a Wind farm as renewable power plants, have been analyzed.Results have highlighted that, for obtaining an annual biomethane production in the range of 2550–3150 kNm3 and by starting from a biogas availability of 500 Nm3/h, the sizes of the renewable power plants must be in the range 15–20 MW and in the range 19–23 MW for the Wind farm and the Photovoltaic plant, respectively. In terms of performance indicators, even if the PV plant has the highest electricity storage factor (up to 69 %), the Wind-based plant has both the highest plant load factor (up to 81%) and the equivalent full load operating hours (up to 6081), so that it is more favorable for the development of the Power to Biomethane concept.

Performance Evaluation of a Magnetic Resonance Imager with Evidence-Based Decision Making Tools.

Evidence-based decision-making tools were used to evaluate the performance of a 16-year-old 1.5T Magnetic Resonance Imager (MRI) that has been fully dedicated to clinical service in a public hospital in Mexico. The MRI age highlights the importance of regular performance evaluations to ensure that the equipment is still functioning optimally, even when the system went through an important software and hardware updated. The use of Multiple-Criteria Decision-Making is an effective method to evaluate the performance of complex systems like MR imagers. A technical global indicator was set, revealing that only 50% of available time has been used is interesting and could be further investigated to determine if there are any factors limiting the usage of the system. The good performance indicators in terms of productivity, availability, and technical aspects are encouraging and suggest that the updated software and hardware have had a positive impact on the overall performance of the system.Finally, the use of evidence-based decision-making tools to evaluate the performance of the MR imager was a successful approach and can serve as a model for other healthcare facilities to follow.Clinical Relevance- MRI is a non-invasive diagnostic test that uses a magnetic field and radiofrequency waves to obtain detailed images of internal structures of the body. Unlike X-rays and CT scans, which use ionizing radiation, MRI does not expose patients to harmful radiation, making it a safe option for diagnostic imaging. The use of MRI a valuable tool for characterizing lesions, detecting abnormalities, and monitoring disease progression and it is widely used in several medical fields, including neurology, oncology, and orthopedics, among others. Therefore, ensuring that the MRI system is working optimally is crucial for providing reliable and accurate results, which can lead to timely and appropriate treatment for patients.

Blockchain-enabled cyber-physical system for construction site management: A pilot implementation

Prefabricated construction has become one of the most innovative methods to address the pressing housing demands. However, conventional prefabricated construction projects often suffer from challenges of managing numerous paper-based documentation, non-standardized management and intricate stakeholders in construction site management (CSM). The current design approaches lack sufficient consideration of the activity-based user relationships. To facilitate the system framework design and implementation for CSM, this study proposes a generic Cyber-physical Centered (CCD) Design, illustrating an effective and systematic approach to identify user-activity relationships in a cross-collaborative context. The consideration of entity, value chain and user-activity relationships facilitate identity management and value chain management services. The proposed approach was then utilized to a pilot project to design and implement the Blockchain-enabled Cyber-physical Site Management System (BCSMS) integrated with Digital Work Supervision System (DWSS), incorporating considerations on multi-source information flow and user-centric applications design. The implemented BCSMS decentralized two types of fundamental documents in CSM - Request for Inspection and Survey Checking (RISC) form and site diary. It addresses information fragmentation and asymmetry in a cross-collaborative environment caused by manual-input and error-prone records. The proposed system was evaluated by two important system performance indicators in terms of average response time and transaction per second. The system performance test shows that the accountability of BCSMS is significant compared to DWSS, given the p-value 0.01. Blockchain effectively improves DWSS with enhanced data submission, identity management and data-driven site performance evaluation functions. This study also qualitatively analyzed the pros and cons of proposed blockchain-enabled cyber-physical system (CPS) from society-economy-technology aspects for technical practitioners, construction stakeholders and governments.

Experimental analysis of power battery preheating system based on thermoelectric elements

This paper designs a battery thermal management system (BTMS) for the cooling/heating of battery modules based on thermoelectric cooling (TEC) and liquid cooling (LC) plates. By utilizing the experimental rig for the thermal performance test built, the heating process of cell modules is evaluated, thus enabling electric vehicles (EVs) to be operated in low-temperature conditions. A comparison is made upon bottom heating with or without coolant passage, and combined bottom/top heating, to determine the optimal operation mode. Subsequently, the reasonable operating voltages of each TEC unit and the top cooling fan in the system are obtained based on the key performance indicators in terms of temperature rise rate, the temperature difference between cells, and power consumption. Applying a thermoelectric system with 6 V on the bottom and 9 V on the top as well as a fan voltage of 10 V, the battery pack can be efficiently heated from −5°C to 5 °C within 824 s. It is worth noting that the total power consumption of the system is only 81.2 W, of which the bottom TEC consumes 31.21 W, the top TEC uses 47 W, and the electronic fan requires 2.994 W, demonstrating the overall effectiveness and energy efficiency of the system.

SYSTEM OF ENTERPRISE EFFICIENCY MANAGEMENT

The efficiency of activity is one of the conditions for the success and survival of the enterprise. Managing this efficiency is a complex and multifaceted process. That is why the question of creating a system for managing the efficiency of the enterprise is urgent. The purpose of the article is to define and justify the components of such a management system.
 The following methods were used to achieve the goals of the article: the information on the definition of the concept of "management of enterprise efficiency" was analyzed and synthesized, and it was established that the authors consider this phenomenon in different ways. Some authors describe this concept as a complex integrated process, others associate it only with the activity of the employee and working conditions. In addition, there are differences in the interpretations of the concept related to the result of management. Some believe that it should be a financial result, while others believe that the result should be the provision of established parameters. With the help of a logical and systematic approach, it was determined that the system of performance indicators should consist of separate groups of indicators that correspond to a balanced system. In the course of the research, the synthesis method, graphical and analytical methods were also widely used to form the structure of the enterpriseʼs efficiency management system.
 In the article, the components of the companyʼs performance management system were further developed, which, unlike the existing ones, are divided into groups corresponding to the aspects of the Balanced Scorecard in terms of performance indicators, and include all types of company resources in terms of the support system.
 The proposed improvements will stabilize the process of managing the efficiency of the enterprise and create clearer conditions for its implementation.
 The obtained results, consisting in the development of an improved system of management of the efficiency of the enterpriseʼs activity, allow further research on the development of the system of performance indicators and the mechanism of selecting tools for the implementation of efficiency management.

Seismic performance of precast concrete walls with grouted sleeve connections using large-diameter bars

This paper proposes an innovative assembly technique using large-diameter bars anchored in several single-row, widely spaced grouted sleeves at the keyway-type horizontal joint to realize an indirect vertical connection between adjacent precast concrete (PC) walls, which has the advantages of high efficiency and ease of construction. To examine the effect of vertical connection strength (rebars' diameter at horizontal joints) on the seismic performance of PC shear walls, quasi-static cyclic loading tests on five full-size wall specimens, including one cast-in-place specimen and four PC specimens with grouted sleeve connections, are carried out. Seismic performance indicators in terms of failure modes, hysteresis characteristics, energy dissipation, ductility, and stiffness degradation of PC walls are investigated and compared with those of the cast-in-situ wall. Meanwhile, the method for predicting the bearing capacity of PC walls with large-diameter rebars is developed. The results show that the failure modes of cast-in-situ and precast walls are controlled by bending instead of shear, but there is an obvious difference in the crack propagation between the two types of walls. Carrying capacity, ductility factor, stiffness, and energy dissipation of PC walls are significantly enhanced by increasing the connection strength, by 44.1%, 23.9%, 72.1%, and 24.0%, respectively, when the connection strength reaches around 90%. Particularly, the PC wall's seismic performance is comparable to that of the cast-in-situ wall when the connection strength is 90%. The calculated results are well-matched with the experimental results, the proposed calculation method reliably predicts the bearing capacity of PC walls with large-diameter rebars. The assembly technique can be safely applied to multi-story PC wall panel structures in seismic zones.

A Smart Diseases Diagnosis and Classification Strategy of Electronic Healthcare Application Using Novel Hybrid Artificial Intelligence Approaches

In today’s world, the healthcare industry faces difficulties like a scarcity of healthcare professionals, ageing, and rising healthcare costs. Also the classification and decision making process using the data generated via electronic health sensors is of major concern. In the fields of research and medical services, artificial intelligence (AI) is widely employed. However, correct estimate for various illnesses is a significant issue. The implementation of a new hybrid artificial intelligence (AI)-based classifier for helping prediction diagnosis in patients with chronic cancer conditions is examined in this work. Unknown qualities are predicted and given using the Hierarchical Red deer optimization (HRDO) based feature extraction, which is based on realworld cases. The Self-Systemized Generative Fuzzy Algorithm (SSGFA), which finds irregularities in patient data and predicts sickness, is used to create the hybrid classification design. This study’s simulation analysis included datasets for colon, lung, and brain cancer illnesses. The new combination of classifiers’ better performance resulted in total classification with increased accuracy, precision, recall, and F-measure, respectively. In terms of performance indicators, the suggested strategy is also compared to traditional methods. This demonstrates the suggested classification model’s ability to appropriately categorize various illnesses information for categorization.

Decentralized Finance (DeFi) Projects: A Study of Key Performance Indicators in Terms of DeFi Protocols’ Valuations

Decentralized finance (DeFi) protocols use blockchain-based tools to mimic banking, investment and trading solutions and provide a viable framework that creates incentives and conditions for the development of an alternative financial services market. In this respect, they can be seen as alternative financial vehicles that mitigate portfolio risk, which is particularly important at a time of increasing uncertainty in financial markets. In particular, some DeFi protocols offer an automated, low-risk way to generate returns through a “delta-neutral” trading strategy that reduces volatility. The main financial operations of DeFi protocols are implemented using appropriate algorithms, but unlike traditional finance, where issues of value and valuation are commonplace, DeFis lack a similar value-based analysis. The aim of this study is to evaluate relevant DeFi performance metrics related to the valuations of these protocols through a thorough analysis based on various scientific methods and to show what influences the valuations of these protocols. More specifically, the study identifies how DeFi protocol valuations depend on the total value locked and other performance variables, such as protocol revenue, total revenue, gross merchandise volume and inflation factor, and assesses these relationships. The study analyzes the valuations of 30 selected protocols representing three different classes of DeFi (i.e., decentralized exchanges, lending protocols and asset management) in relation to their respective performance measures. The analysis presented in the article is quantitative in nature and relies on Granger causality tests as well as the results of a fixed effects panel regression model. The results show that the valuations of DeFi protocols depend to some extent on the performance measures of these protocols under study, although the magnitude of the relationships and their directions differ for the different variables. The Granger causality test could not confirm that future DeFi protocol valuations can be effectively predicted by the TVLs of these protocols, while other directions of causality (one-way and two-way) were confirmed, e.g., a two-way causal relationship between DeFi protocol valuations and gross merchandise volume, which turned out to be the only variable that Granger-causes future DeFi protocol valuations.

Transfer Learning-Assisted Evolutionary Dynamic Optimisation for Dynamic Human-Robot Collaborative Disassembly Line Balancing

In a human-robot collaborative disassembly line, multiple people and robots collaboratively perform disassembly operations at each workstation. Due to dynamic factors, such as end-of-life product quality and human capabilities, the line balancing problem for the human-robot collaborative disassembly line is a dynamic optimisation problem. Therefore, this paper investigates this problem in detail and commits to finding the evolutionary dynamic optimisation. First, a task-based dynamic disassembly process model is proposed. The model can characterise all feasible task sequences of disassembly operations and the dynamic characteristics of tasks affected by uncertain product quality and human capabilities. Second, a multiobjective optimisation model and a feature-based transfer learning-assisted evolutionary dynamic optimisation algorithm for the dynamic human-robot collaborative disassembly line balancing problem are developed. Third, the proposed algorithm uses the balanced distribution adaptation method to transfer the knowledge of the optimal solutions between related problems in time series to track and respond to changes in the dynamic disassembly environment. Then, it obtains the optimal solution sets in a time-varying environment in time. Finally, based on a set of problem instances generated in this study, the proposed algorithm and several competitors are compared and analysed in terms of performance indicators, such as the mean inverted generational distance and the mean hypervolume, verifying the effectiveness of the proposed algorithm on dynamic human-robot collaborative disassembly line balancing.

Forecasting of production and scrap amounts using artificial neural networks

The increase in consumer needs and the scarcity of production resources cause the concept of ‘productivity’ to be essential for companies. Reducing costs is an essential factor for increasing competitiveness, and therefore, businesses are taking action to reduce scrap costs and increase efficiency. Since the increase in scrap will reduce productivity, it may cause production delays and thus customer dissatisfaction. In this study, the slitting line of one of the essential Japanese supplier companies operating in the automotive sector in Turkey is discussed. The proposed model aims to predict the amount of production and scrap that may occur to increase productivity in the slitting line by using an artificial neural network (ANN) and increasing the efficiency of the slitting line with the measures to be taken. In this context, different ANN designs were made for production and scrap. During the execution of the ANN models, the production and scrap amounts were forecasted at 99 and 85%, respectively. While measuring the successful performance of the ANN models, root mean squared error, mean absolute percent error and R 2 indicators were used; the forecasted values produced by the ANNs that were successful in terms of performance indicators were compared with the actual values, and the reliability of the study was increased.

EFFECTS OF WORKING ENVIRONMENT ON VILLAGE EXECUTIVE OFFICERS PERFORMANCE IN RURAL AREAS IN TANZANIA

The paper examined the effects of working environment on the performance of Village Executive Officers in rural areas in Tanzania specifically at Kalambo District Council in Rukwa Region in Tanzania, in terms of performance indicators which are availability, creativity, productivity, commitment and participation. The specific objectives were to assess VEOs working environment in rural areas, to determine the relationship between working environment and VEOs performance level and to identify measures for improving working environment for VEOs better performance. A cross-sectional survey study was conducted in four wards in the council, namely Kalalela, Katete, Mkowe and Katapuro. 39 Village Executive Officers were involved as respondents in the study. Data was collected from the primary source through interview guided by questionnaires and secondary sources through documentary review. The main variables in the questionnaire were socio- demographic characteristics (sex, age, marital status, education level and working experience), office building space, presence of privacy, availability of working tools. Analysis of data was done by using SPSS version 20 and presented in narrative and statistical ways where in narrative, sentences and paragraphs were used while in statistical form tables were used. The result revealed that the working environment elements have a significant effect on the performance of Village Executive Officers. These elements are presence of good quality office building, presence of standard houses to live and availability of working tools. The study concluded and that, the working environment has effects on the performance level of employees whereby absence of good quality office building, absence of houses, absence of transport facilities and absence of working equipment affect the performance of the VEO’s, so Kalambo District Council authority should provide good working environment to its employees, so as to enable them to perform better. KEY WORDS: Working Environment, Village Executive Officers, Performance

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

Weeds compete with crops for water, nutrients, and light consequently, have adverse effects on the crop yield and overall productivity. Mechanical weeding is the most common non-chemical method for weed control, which is applied in organic farming, and the weed cultivator is the most common implement in mechanical weeding. This study aimed to design and develop an innovative active tool to optimize the cultivation depth, which can avoid damage to crop roots and improve the key performance indicators of an inter-row cultivator. A quasi-Newton optimization method and a hybrid of the non-dominated sorting genetic algorithm (NSGA-II) and goal attainment method were separately applied to synthesize and develop a four-bar mechanism for weeding requirements. The transmission angle of the mechanism and the desired path of the weeding blade were simultaneously optimized using these multi-objective optimization techniques. The performance of the developed four-bar cultivator based on the optimization techniques was compared with the ones developed based on the classic methods and also with several conventional tools evaluated in other studies. The results showed that applying the quasi-Newton optimization method and hybrid genetic algorithm can propose a more effective weed cultivator in terms of performance indicators, namely weeding performance, mechanical damage to crop plants and cultivation depth. In addition, the optimization of the transmission angle guaranteed the smooth rotations in the mechanism’s joints.

A Searchable Encryption Scheme with Biometric Authentication and Authorization for Cloud Environments

Cloud computing offers the possibility of providing suitable access within a network for a set of resources. Many users use different services for outsourcing their data within the cloud, saving and mitigating the local storage and other resources involved. One of the biggest concerns is represented by storing sensitive data on remote servers, which can be found to be extremely challenging within different situations related to privacy. Searchable Encryption (SE) represents a particular case of Fully Homomorphic Encryption (FHE) and at the same time represents a method composed from a set of algorithms meant to offer protection for users’ sensitive data, while it preserves the searching functionality on the server-side. There are two main types of SE: Searchable Symmetric Encryption (SSE), where the ciphertexts and trapdoors for searching are performed using private key holders, and Public Key Searchable Encryption (PKSE), in which a specific number of users have the public key based on which are capable of outputting ciphertexts and giving the possibility of producing the trapdoors by using the private key from the holder. In this article, we propose a searchable encryption system that uses biometric authentication. Additionally, biometric data are used in the trapdoor generation process, such that an unauthorized user cannot submit search queries. The proposed system contains three components: classic user authentication (based on username, password, and a message with a code using short message service (SMS), biometric authentication, and the searchable encryption scheme. The first two components can be seen as two-factor authentication (2FA), and the second component represents the initialization step of the searchable encryption scheme. In the end, we show and demonstrate that the proposed scheme can be implemented with success for medium to complex network infrastructures. We have granted special attention to the trapdoor function, which generates a value that can be used to perform the search process and search function that is based on the trapdoor pair for searching within the index structure. We provide the correctness and security proof of the operations, which gives us the guarantee that the cloud servers return the correct documents. Additionally, we discuss measuring the performance of the authentication scheme in terms of performance indicators, introducing two indicators for measuring purposes—namely, cloud average number of non-legitim the user actions for cloud purposes (CANNL) and cloud average number of legitim user actionsCANLU.

Analysis of performance indicators in terms of healthy city in Khuzestan province city by using data envelopment analysis (DEA)

Analysis of performance indicators in terms of healthy city in Khuzestan province city by using data envelopment analysis (DEA)

STUDI EVALUASI PENGENDALIAN BIAYA (COST CONTROL) DAN JADWAL PELAKSANAAN (SCHEDULING) PADA PROYEK PEMBANGUNAN GEDUNG RAWAT JALAN DAN BASSEMENT RSUD MAJALENGKA MENGGUNAKAN METODE EARNED VALUE DAN SOFTWARE MS. PROJECT

Planning and controlling costs and time is part of the overall construction project management. In addition to the assessment in terms of quality, the achievement of a project can also be assessed in terms of cost and time. The costs that have been incurred and the time spent in completing a job must be measured continuously for deviations from the plan. The presence of significant cost and time deviations indicates poor project management. With the existence of project performance indicators in terms of cost and time this allows preventive actions so that project implementation goes according to plan. The concept of "Earned Value" is one of the tools used in project management that integrates cost and time and by using Ms. Projects. Ms. Project is a project management software product and is developed and sold by Microsoft. It is designed to assist a project manager in developing plans, assigning resources and tasks, tracking progress, managing budgets and analyzing workloads. From the results of the evaluation of cost control and project schedules on outpatient and basement building construction projects using the earned value method, it can be concluded that the CV (Cost Variant) in this project results in 0 which means the Zero Number on the Cost Variant indicates that the cost is within the budget, SV (Schedule). Variant ) the value shows a negative number, this means that the completion of the work is delayed, the CPI (Cost Performance Index) is 1, meaning that the expenditure is in accordance with the project budget

An attempt for sustainability evaluation considering implementation of rapid transit system

With the rapid urbanization and related increase in urban vehicle population the transportation related issues are increasing day by day. Therefore sustainability and its evaluation has become the point of concern for everyone. There are number of limitations for evaluation of sustainability related to transportation system and it is required to develop the framework for assessment of sustainability in terms of performance indicators considering the prefixed goals for the purpose of planning. The Multi Attribute Utility Theory based methodology is adopted for sustainability evaluation based on combination of individual performance indicators for emerging metropolitan city of Nagpur. The assessment was carried out for with and without mass rapid transit system scenario. The results are represented in the form of scaled values for some performance indicators under consideration in this paper. The improvement in terms of scaled values for travel time index (TTI) was observed as 16% and 73% respectively for years 2021 and 2036. For program time index (PTI) improvement of 18% and 78% was observed. Thus the results obtained due to implementation of mass rapid transit system can be considered to be favourable as far as attainment of sustainability is concerned.