Performance Criteria Research Articles

A hybrid framework for assessing Pakistani commercial bank performance using multi-criteria decision-making

Overall bank performance in a particular year or period is important to all banking industry stakeholders, as it indicates their success or failure relative to predetermined targets. Due to conflicting criteria and uncertainties, assessing bank performance is a complicated decision-making problem. The current paper proposes the Fuzzy Level Based Weight Assessment (F-LBWA), the Fuzzy Logarithm Methodology of Additive Weights (F-LMAW), and the Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) combination as a practical and robust decision-making tool to cope with many complex ambiguities. In the first phase, the suggested hybrid Multi-Criteria Decision-Making (MCDM) approach estimates the weight coefficients of the performance criteria with the aid of a combined version of the F-LBWA and F-LMAW methods. In the second phase, the MARCOS method determines the ranking performance of the decision alternatives. The introduced model is tested and validated on a case study assessing publicly traded bank performance in Pakistan. The findings obtained from the sensitivity analysis revealed that the presented F-LBWA-F-LMAW-MARCOS approach produces consistent solutions and is a reliable and effective procedure in rational decision-making.

DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator

Internet of things (IoT) has incredibly transformed the whole domain of communication process. The extensive dependency on these devices leads to various advanced cyber security threats. IoT devices fall easily into the ambit of malicious threats and are susceptible to vast range of attacks due to their limited computation capabilities and memory constraints. Intrusion Detection Systems (IDSs) are dedicated outstanding frameworks to protect these devices from cyber threats. In this study, a comprehensive review of different AI based IDS applied on IoTs is done. It has been observed that machine learning and deep learning has widely influenced the domain of IoT security. The focus of the research carried out is to earmark the techniques that are performing best on a given data set. Features selection, type of attacks, proposed solutions in solving security menaces are taken into consideration. Further, we have presented DLIIoT, a deep learning based intelligent attack detection in IoT networks by generating precise IoT datasets in Cooja Simulator. Four Deep learning algorithms are utilized and analysed under standard performance criteria metrics such as Precision, Recall, Accuracy and F1-score. It was found that deep learning algorithms have remarkable potential in detecting and recognizing malicious data patterns in IoT networks.

Leveraging supported liquid extraction and automated workflow for high efficiency and high quality clinical LC-MS/MS method for quantification of urinary 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid (VMA) and homovanillic acid (HVA).

5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid (VMA) and homovanillic acid (HVA) are neurotransmitter metabolites used for the diagnosis and monitoring of neuroendocrine tumors. This study's objective was to apply automation to improve efficiency and quality of a liquid chromatography tandem mass spectrometry (LC-MS/MS) procedure for quantification of 5-HIAA, VMA and HVA in human urine. 5-HIAA, VMA and HVA in urine samples were extracted with automated supported liquid extraction (SLE) and quantified by LC-MS/MS utilizing scheduled multiple reaction monitoring (MRM). An in-house developed middleware was used to automate data analysis and reporting. The average recovery for 5-HIAA, VMA and HVA in different types of urine matrix varied between 92.5% and 99.0%, while the average matrix factor varied between 98.6% and 103.4%. The results of other validation studies: stability, sensitivity, analytical measurement range, selectivity, accuracy, precision, dilution verification, carryover, and method comparison all met the performance criteria. Automated SLE is a suitable sample preparation technique for quantification of 5-HIAA, VMA and HVA in human urine. Workflow automation described in this study resulted in 54% of overall time savings and 80% of hands-on time savings compared to all manual approach.

Comparative Study on the Spray Characteristics of Bottom-Up Sprinkler Heads

Bottom-up sprinkler heads are typically installed in parking lots of buildings and in places without a semi-finished ceiling. However, bottom-up sprinkler heads that comply with domestic fire codes can exhibit water spray performances depending on the manufacturer. In this study, we compare the spray characteristics of bottom-up sprinkler heads manufactured by five different vendors in Korea by conducting a waterproof amount test, spray distribution test, and spray pattern analysis. The results of the waterproof amount test indicated that all sprinkler head samples showed minimum and maximum deviations of 0.02 and 1.11, respectively, from the reference K-factor of 80; these deviations were within the allowable range specified in the relevant regulations. Further, the spray distribution test results indicated that all sprinkler head samples satisfied the three performance criteria specified in the relevant regulations, the radial spray distribution showed differences, and two distinct spray patterns (one that wets the ceiling and another that does not) were observed.

Experimental Analysis on Optimum Use of Filler Materials of River Bed Sub Base Sample for Pavement

Composition of different layers to resist the traffic load as per design on the flexible pavement, among them subbase is one. This research carried out to optimization of filler sample on riverbed sub-base material whose plastic index is nil. The sub-base material plays a pivotal role in supporting the overlying pavement structure, and optimizing its properties is essential for enhancing overall pavement performance The observations through laboratory testing and trials ensure the modified sub-base material meets the necessary performance criteria. The study aims to contribute valuable insights into sustainable and effective pavement construction practices and presents an experimental analysis focused on improving riverbed sub-base material through the optimal utilization of filler materials. During laboratory trials all necessary requirement, like gradation, plastic index, specific gravity, maximum dry density and California bearing ration are observed. From laboratory trials 10% threshold appears to be a critical point where the material satisfies the specified requirements, define by Government of Nepal, Department of Road, Standard Specification for Road and Bridges-2073 (with second abdmanent-2078). Regular quality control and testing during construction are essential to confirm that the material consistently meets the desired specifications. Additionally, collaboration with geotechnical engineers and adherence to local standards and guidelines are crucial in ensuring the longterm performance of road constructions.

Estimated Tardiness-Based Reinforcement Learning Solution to Repeatable Job-Shop Scheduling Problems

Intelligent manufacturing promises to revolutionize production processes, and it is expected to enhance efficiency and productivity. In this paper, we study the job-shop scheduling problem, which is a key enabler to the realization of intelligent manufacturing. Compared to previous studies, the proposed solution tackles generalized configurations by allowing the repetition of jobs and by assuming the limited capacity of machines and a sequence of operations constituting a job. The proposed solution is fully controlled by the trained reinforcement learning agent that finds the optimal match between the job and machine to schedule. In addition, by introducing the expected tardiness (ETD) metric, the agent can enhance the scheduling performance while effectively handling dynamic action space changes with the action masking technique. To effectively adapt to the particular manufacturing site, we propose a novel training approach that utilizes the average order arrival distribution learned from the historical logs. Such data-driven optimization can train an agent that effectively captures the general and site-specific characteristics of job arrivals, leading to improved generalization performance and a finely tuned model, respectively. To validate the proposed approach, we implement a custom environment with which extensive performance evaluation and comparison are carried out. The evaluation results show that the proposed approach can outperform the conventional heuristic priority dispatching rules under the desired performance criteria such as total tardiness and the total manufacturing cost. To be specific, in terms of the total cost metric, the proposed approach outperforms the considered approaches by 31.69% on average. In addition, the use of ETD can enhance the performance of the conventional approaches by 27.74% on average.

Application of a Multicriteria Genetic Algorithm for Structural Parametric Synthesis of Convolutional Neural Networks

The paper identifies and describes promising architectural solutions for convolutional neural networks, along with key parameters for further structural and parametric synthesis. The advantages and disadvantages of different blocks are outlined, and their relevance in structural synthesis is substantiated. The use of a genetic evolutionary algorithm for structural-parametric synthesis is proposed, along with a review of contemporary approaches. The configuration process of the evolutionary algorithm is shown and described. Based on optimization criteria, the fitness function, selection, mutation, and crossover methods are defined. The results of the experimental evolutionary process are presented and analyzed. An example model created by evolutionary algorithms is considered, which is based on functional blocks aggregated from various architectural approaches in convolutional neural networks. For each model, performance criteria, including average reduction in training time, benefits, and architectural integration details, were calculated during the synthesis process. Experimental results demonstrate that using complex structural blocks instead of traditional layers with a flexible fitness function configuration according to quality and performance criteria yields significant improvements for the final model

Analysis of Material selection for automotive fender using the weighted sum method

Introduction: Material selection for automotive fenders involves the process of choosing the most suitable materials to be used in the construction of fenders based on various performance criteria, including mechanical properties, durability, weight, cost, safety, and environmental considerations. It involves assessing different materials and their characteristics to determine the optimal choice that meets the specific requirements of fenders. Research Significance: We will be able to choose a material with the right qualities thanks to research on the best materials for car fenders. Research on material choice for vehicle fenders was conducted for several reasons, including weight reduction and fuel efficiency, impact resistance including safety, resisting corrosion and durability, manufacturing procedure and cost optimisation, design flexibility and style, and environmental sustainability. It promotes innovation, aids in the creation of innovative materials, and aids automakers in meeting the changing demands of the market and customers. Method: This research employs the weighted sum approach to analyse the choice of material for an automobile fender. Alternate Parameters: High Strength Steels (Docol600DP and Docol1000DP), Aluminium alloys (AA2036T4 and AA6010T4), Thermoplastic polymers (PPE/PA/989Resin, PPO/PA66, NY66/40CF, PPS/40CF, AR/PC, PC/PBT resin) Evaluation Parameters: Ultimate Tensile Strength (UTS), Yield Strength (YS), Young's Modulus (YM), Linear Coefficient of Thermal Expansion (CTE), Material Cost (MC). Results: From the result it is seen that D1000DP is got the first rank where as is the PPE/PA/989 is having the lowest rank. Conclusion: the first ranking D1000DP is obtained with the lowest quality of PPE/PA/989.

Machine Learning Based Throughput Enhancement in Fifth Generation Networks

The 5G Enhanced Mobile Broadband (eMBB) category offers faster data rates, network capacity, and user experiences than prior generations. This research aims to boost the 5G uplink User Equipment (UE) user data transfer rate. We use Python to build frameworks and analyze data. A 250-m-radius center-excited Picocell Base Station (PBS) is investigated to support 15 clients. Cell-range Poisson distribution determines user position. All UEs send Channel State Information (CSI) to the PBS, which evaluates signal transmission channel conditions. The study uses Rayleigh, Rician, free space path, and longdistance route loss models. This inquiry produces a channel state dataset and then it is formulated dataset is dynamic. For servicespecific requirements, UEs use K-means clustering. Clustering concatenates bandwidth, enhancing system efficiency and UE sum rate. Observations from simulation findings. UEs are grouped by channel gain, achievable data rate, and minimum servicerequired data rate. Users in cluster 3 achieve the highest cumulative rate of 9.09 Mbps after clustering with an average of 7.16 Mbps. Bandwidth concatenation increased system capacity, meeting each UEs service needs. After evaluating different clustering models performance criteria, K-means remains the best algorithm for the framework. The methodology was carefully designed to satisfy study goals. This research will investigate beamforming and dynamic clustering to improve user fairness and performance.

Strategic model for integrating biogas a framework for sustainable energy integration in agro-industries

The framework of the methodology presented in this study is an effort to integrate and optimize the agro-industry sector, especially energy in biogas. In this study, the technique of the system in functional analysis is shown systematically to translate various energy requirements in the factory as criteria for performance and functional design to be integrated, optimized, and energy efficient. The case study results indicated that biogas power plants, with a capacity of 1.5 MW, can produce around 13,140 MWh per year. The annual return on investment (ROI) is around 37.13%. With this ROI value, the payback period is 31 months. The overall reduction of greenhouse gases is approximately 77,826 tons CO2 eq/year. The potential value of carbon trading is about USD 3,113,040 per year. This strategic model presents a novel approach by integrating biogas energy production with a customized wastewater treatment system adapted to biodigesters’ effluent characteristics. It offers a sustainable, economically feasible, and scalable solution, combining resource recovery, waste minimization, and potential for carbon trading into a unified system. The novelty of this research lies in maximizing the utility of biogas plants by efficiently treating and reusing wastewater, creating a closed-loop, zero-waste process. Future research on hybrid systems integrating Biogas power plants by focusing on efficiency optimization, economic feasibility, environmental impacts, and innovative approaches like AI and blockchain could make the hybrid system a more robust, scalable, and sustainable solution. Thus, the framework based on the results of this study finds tools that can maximize and integrate energy sources, especially biogas, in the agro-industrial sector.

STRENGTH AND STABILITY OF MOVEMENT OF ROTOR SYSTEMS OF TANK ENGINE AIR SUPERCHARGER

The work researches the processes and states that occur in the impeller of the air supercharger of tank engine. The influence of the tension in the connection of the impeller with the shaft and the yield strength of its material on the stress-strain state and critical speeds of movement is studied. For this purpose, appropriate mathematical and numerical models were built. Parametric models of the studied processes and states have also been developed. A number of varied parameters are considered. This makes it possible to study the influence of these parameters on the performance and strength of the rotor part of the air compressor. On this basis, a specialized database with research results can be built. The analysis of these results is the basis for establishing certain regularities, as well as for substantiating progressive technical solutions of the rotor parts of the air blowers of tank engines according to the criteria of strength and performance. The impeller of the air compressor of the tank engine limits its strength and durability. As determined in the course of research, a direct increase in the mechanical properties of the aluminum alloy material from which it is made is not productive. Among the significant factors, it is necessary to take into account the acting loads from centrifugal forces and tension in the coupling "shaft - impeller", and in their combination. It is also important to take into account the elastic-plastic deformation of the material of the impeller. Recommended are not separate measures and solutions regarding the rotary parts of the air compressor, but their combination for the purposeful improvement of the characteristics of strength and durability.

Optimizing foam concrete performance using mixed foaming method: impact of mixing speed, mixing duration, and foam dosage

Foamed concrete has gained significant attention, especially in the field of thermal insulation and acoustic insulation. However, all production methods are based on the pre-foaming method, while the mixed foaming method is an infrequent approach that should be considered and could be challenging. For this reason, this paper attempt to highlight this method and valuate it on par with the pre-foaming method in the production of foamed concrete, both in terms of structure and performance. These performances are directly dependent on the pore structure of this material (pore size, porosity rate, and pore distribution). Therefore, a process has been developed for sample preparation to achieve a final product with a well-controlled size and distribution of porosity, meeting the desired performance criteria. This process involves varying the following parameters: mixing speed (from 400 to 1000 rpm), mixing time (from 2 to 12 minutes), and the dosage of foaming agent (from 0.05 to 0.2%). The effect of mixing speed, mixing duration and the dosage of the foaming agent on the generated foam rate, density, structure at the millimeter scale, structure at the micrometer scale, and thermal conductivity was demonstrated. The obtained results show that with a generated foam rate extending to 79%, a density reaching 428 kg/m³, and a thermal conductivity achieving 0.181 w/k.m, the mixed foaming method becomes an important and competitive approach to the pre-foaming method in the production of foamed concrete.

Can Public Sector Service Delivery Be Improved?

AbstractNational surveys have documented a major decline in Canadians' satisfaction with public sector service delivery since 2020. Service satisfaction levels for federal services are back to levels not seen for two decades. Potential reasons for the recent declines in public sector service delivery performance are examined, as well as lessons from public organizations which have maintained high levels of service satisfaction in recent years despite the challenges. The article suggests the building blocks of a more robust approach and proposes a citizen‐centred, results‐based model for public sector service delivery in Canada, using service satisfaction as the primary results measure. Regular, measurable improvement in service satisfaction should be both the objective and the criterion of high performance in public sector service delivery and will require a sustained, multi‐channel approach to service improvement planning and implementation based on measurement of, and focus on, key drivers of service satisfaction.

Audio Watermarking System in Real-Time Applications

Watermarking is widely employed to protect audio files. Previous research has focused on developing systems that balance performance criteria, including robustness, imperceptibility, and capacity. Most existing systems are designed to work with pre-recorded audio signals, where the characteristics of the host signal are known in advance. In such cases, processing time is not a critical factor, as these systems generally do not account for real-time signal acquisition or report tests for real-time signal acquisition nor report the elapsed time between signal acquisition and watermarking output, known as latency. However, the increasing prevalence of audio sharing through real-time streams or video calls is a pressing issue requiring low-latency systems. This work introduces a low-latency watermarking system that utilizes a spread spectrum technique, a method that spreads the signal energy across a wide frequency band while embedding the watermark additively in the time domain to minimize latency. The system’s performance was evaluated by simulating real-time audio streams using two distinct methods. The results demonstrate that the proposed system achieves minimal latency during embedding, addressing the urgent need for such systems.

Are conventional methods sufficient to calculate growth parameters of Pontastacus leptodactylus (Eschscholtz, 1823)? A case study of artificial intelligence from Keban Dam Lake

In this study, the length–weight relationships of Pontastacus leptodactylus, a freshwater crayfish species found in the Keban Dam Lake, were assessed using both conventional methods and artificial intelligence techniques. Throughout the research process, all biometric measurements of the crayfish were meticulously recorded, including TL, TW, and other biometric data. These measurements were analyzed using both the conventional length–weight relationship method and artificial neural networks. The results obtained using artificial neural networks and conventional methods were compared, and the analysis was based on MAPE and R2 performance criteria. The study showed that the ANNs method outperformed the conventional LWR method, showing more accurate results. The models employed to predict the length–weight relationships of the crayfish demonstrated high accuracy, and the Artificial Neural Networks method was identified as the most effective model. These results provide strong evidence that the ANNs method performs significantly better in predicting the LWRs of freshwater crayfish.

Energy Efficiency in Subtropical Homes: Replacing Asbestos–Cement Roofs with Sustainable Alternatives

This study evaluates the impact of building orientation, typology, and envelope characteristics on energy efficiency and CO2 emissions in urban dwellings in subtropical climate, with a focus on Cartagena, Colombia. North-facing dwellings consistently demonstrate superior energy performance, achieving an average efficiency increase of 4.27 ± 1.77% compared to south-facing counterparts. This trend is less pronounced near the equator due to the sun’s high zenith angle. Semi-detached homes exhibit 23.17 ± 9.83% greater energy efficiency than corner houses, attributed to reduced exterior wall exposure, which lowers energy demand and CO2 emissions by 2.16 ± 0.74 kg CO2/m2 annually. Significant disparities in emissions are observed across socioeconomic strata; homes in strata 3 and 4 show the lowest emissions (6.69 ± 1.42 kg CO2/m2 per year), while strata 5 and 6 have the highest (10.48 ± 1.42 kg CO2/m2 per year), due to differences in construction quality and glazing ratios. Roofing materials also play a key role, with thermoacoustic (TAC) roofs reducing emissions by up to 5.80% in lower strata compared to asbestos–cement roofs. Furthermore, sandwich panels demonstrate substantial potential, achieving CO2 emissions reductions of up to 51.6% in strata 1 and 2 south-facing median homes and a minimum saving of 9.4% in strata 5 and 6. These findings underscore the importance of integrating energy performance criteria into public housing policies, promoting construction practices that enhance sustainability and reduce greenhouse gas emissions while improving occupant comfort and property value.

Nonlinear Adaptive Optimal Control Design and Implementation for Trajectory Tracking of Four-Wheeled Mecanum Mobile Robots

This study proposes a nonlinear adaptive optimal control method, the adaptive H2 control method, applied to the trajectory tracking problem of the wheeled mobile robot (WMR) with four-wheel mecanum wheels. From the perspective of solving mathematical problems, finding an analytical adaptive control solution that satisfies the adaptive H2 performance criterion for the trajectory tracking problem of the WMR with four-wheel mecanum wheels is an extremely challenging task due to the high complexity of the dynamic system. To analytically derive the control law and adaptive control law for this trajectory tracking problem, a proportional-derivative (PD) type transformation is employed to formalize the trajectory tracking error dynamics between the WMR and the desired trajectory (DT). Based on an in-depth analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from the highly complex nonlinear dynamic system equations. This control law provides a solution to the trajectory tracking problem of the WMR while satisfying the adaptive H2 performance criterion. The proposed adaptive nonlinear control method offers a simple control structure and advantages such as improved energy efficiency. Finally, simulations and experimental implementations were conducted to verify the performance of the proposed adaptive H2 control method and the H2 control method in tracking the DT. The results demonstrate that, compared to the H2 control method, the adaptive H2 control method exhibits superior trajectory tracking performance, particularly in the presence of significant model uncertainties.

Independent testing of PIT tags for fisheries research: a framework for standardization and performance evaluation

BackgroundPassive integrated transponder (PIT) tags are widely used to track animal movements and survival. Rigorous testing protocols are necessary to ensure reliability in PIT tag performance and resulting data across various environmental conditions. This study aimed to document a comprehensive testing framework for PIT tags as a model for the broader biotelemetry community and to showcase how independent evaluations can validate the performance of new PIT tag offerings against established regional performance criteria. Independent testing and adherence to regionally applied standards were key components of this effort.ResultsThe Voda IQ HQ12, HQ10, HQ9, and HQ8 PIT tags were evaluated through a series of independent tests, including assessment of physical dimensions, electrical parameter testing, and proximity evaluations. The HQ10 and HQ9 tags passed all performance criteria, while the HQ12 tag excelled in most areas but exceeded the region's maximum weight threshold by 0.0022g. Despite this, the HQ12 tag showed strong detection efficiency and read range, particularly in challenging environments like the Bonneville Corner Collector. The HQ8 tags, while showing a more limited read range, offer advantages in applications requiring minimal tag burden. Independent testing played a crucial role in validating the performance of these tags under established protocols.ConclusionsThis study underscores the importance of rigorous testing for PIT tags to ensure reliability across diverse environmental conditions. Independent evaluations like these not only inform stakeholders, but also encourage the adoption of new technologies and vendors. The methods and results presented here offer a valuable model for testing new biotelemetry technologies, applicable across different species, ecosystems, and monitoring programs worldwide.

Comparative Study on the Performance of Open Graded Friction Course using Hot and Warm Mix Technology

Conventional pavements and parking lots are constructed as impervious structures which leads to large volumes of stormwater runoff. This increases the load of stormwater drains besides the water carries the dirt and debris present on the surface and pollutes the water bodies. Surface detention basins are usually provided to collect the runoff and slow down its rate. But this requires additional land space. So, a solution in this regard is to provide a permeable pavement system that permits the infiltration of the stormwater. Open graded friction courses are provided as the surface course of such pavements. This research aims at assessing the feasibility of using warm mix technology in the production of open graded friction course mixes. The focus was on the benefits that could be acquired by the use of WMA technology in a porous asphalt mix. In this study a chemical additive namely sasobit was used to prepare warm mixes. A comparative assessment between hot and warm porous bituminous mixes was made. The fundamental parameters evaluated includes volumetric properties of the mix, permeability, drain down, Cantabro abrasion loss, and moisture susceptibility. It was found that the warm mix additive was capable of reducing the production temperature and the maximum temperature reduction was obtained in the case of 3% sasobit. About 100C reduction in mixing and 130C reduction in compaction temperature could be achieved. The results indicate that open graded mixes prepared with warm mix technology met the performance criteria and hence it is suitable for application in porous pavements. This reduction in temperature could be translated to reduced fuel consumption for heating the binder and reduced level of emissions. Thus, it leads to better working environment for those involved in road construction.

Evaluation of Occupational Health and Safety Management System Performance in Shipyard Businesses with FAHP and FPROMETHEE Methods

Shipyards are places where new ships are built, routine and mandatory maintenance and dismantling of ships are carried out and a wide range of work equipment and chemicals are used during these processes. The fact that most of the world is covered with water and that maritime transportation is advantageous compared to other types of transportation brings maritime transportation to the forefront. This situation also leads to an increase in work intensity in shipyards. Therefore, occupational health and safety (OHS) practices are also important in shipyards. In this context, this study aims to evaluate the OHS Management System (OHSMS) performances of shipyard enterprises and to rank the shipyard enterprises selected as alternatives according to their OHSMS performances in line with the determined criteria. In the first stage, five main and 16 sub-criteria as OHSMS performance criteria and five alternative shipyard enterprises were identified through literature research. In line with the expert opinions, the weights of the criteria were found with the Fuzzy Analytic Hierarchy Process (FAHP) method, one of the multi-criteria decision-making methods, and the alternative priority order for the criteria was determined with the Fuzzy PROMETHEE (FPROMETHEE) method using the results obtained from the FAHP method. As a result of the analysis, the main criterion "Management Review" was determined as the criterion with the highest weight ratio. As a result of the operations carried out with the criterion weights determined by FAHP, Shipyard 3 alternative was ranked first in terms of OHSMS performance in the priority ranking made by using FPROMETHEE method. In this study, OHSMS performance evaluation in shipyard enterprises has been carried out by using FAHP and FPROMETHEE methods, which are multi-criteria decision-making methods, and the findings have contributed to the shipyard sector and the literature.