Search

Due to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. In addition, the proposed method also maintains the battery state of charge (SOC) and state of hydrogen charge (SOHC) within a proper range to guarantee the stable operation of the system. Finally, the results including power results, cost analysis and degradation rate analysis of the MATLAB/Simulink show that the proposed method is more economically beneficial than the non‐considering degradation cost strategy. [ABSTRACT FROM AUTHOR]

The cascaded H‐bridge multi‐level converter is the main topology of high‐voltage and high‐capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H‐bridge multi‐level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H‐bridge multi‐level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H‐bridge multi‐level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result. [ABSTRACT FROM AUTHOR]

In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is currently under active development as a method for remote sensing, environmental monitoring and biomedical diagnostics. The development of hyperspectral sensors is aimed at their miniaturization and reducing the cost of components for the purpose of the widespread use of such devices on unmanned aerial vehicles and satellites. In this review, we present a broad overview of recent work on the development of hyperspectral devices' configurations, studies aimed at modifying sensors and the possibility of reducing the cost of components of such devices. In addition, we will present the main trends in the development of hyperspectral device configurations for ubiquitous applications. [ABSTRACT FROM AUTHOR]

As vehicular technology advances, intelligent vehicles generate numerous computation‐intensive tasks, challenging the computational resources of both the vehicles and the Internet of Vehicles (IoV). Traditional IoV struggles with fixed network structures and limited scalability, unable to meet the growing computational demands and next‐generation mobile communication technologies. In congested areas, near‐end Mobile Edge Computing (MEC) resources are often overtaxed, while far‐end MEC servers are underused, resulting in poor service quality. A novel network framework utilizing sixth‐generation mobile communication (6G) and digital twin technologies, combined with task migration, promises to alleviate these inefficiencies. To address these challenges, a task migration and re‐offloading model based on task attribute classification is introduced, employing a hybrid deep reinforcement learning (DRL) algorithm—Dueling Double Q Network DDPG (QDPG). This algorithm merges the strengths of the Deep Deterministic Policy Gradient (DDPG) and the Dueling Double Deep Q‐Network (D3QN), effectively handling continuous and discrete action domains to optimize task migration and re‐offloading in IoV. The inclusion of the Mini Batch K‐Means algorithm enhances learning efficiency and optimization in the DRL algorithm. Experimental results show that QDPG significantly boosts task efficiency and computational performance, providing a robust solution for resource allocation in IoV. [ABSTRACT FROM AUTHOR]

How bridge refurbishment projects are performed requires a trade-off between the speed and cost of the project and the amount of traffic disturbances during the project. A possible way to help reach a better balance between these two extremes is the ASTRA Bridge developed in Switzerland. The ASTRA Bridge is a 236-meter long steel ramp system on wheels, which is placed on top of the bridge deck undergoing refurbishment to enable vehicles to continue to pass over the bridge while construction work progresses underneath. This study illustrates new refurbishment processes by using the ASTRA Bridge and presents the first quantitative analysis of the effects of using the ASTRA Bridge on the time, costs and traffic disturbances associated with bridge refurbishment. The bridge investigated is a short-span (50 m long) highway bridge requiring refurbishment of its superstructure. The analysis indicates that the use of the ASTRA Bridge resulted in reductions in duration and costs (14% and 3% for the example), and a substantial reduction in user costs (51% for the example). Although more analysis is required for different types of refurbishment projects, the initial results indicate that the ASTRA Bridge may become an integral part of future highway bridge refurbishment projects. [ABSTRACT FROM AUTHOR]

In a modular multilevel converter (MMC) system, the sub‐module (SM) capacitors account for a large proportion of cost, volume and weight. Increasing modulation index (m) is an effective method to reducing the capacitance. Traditional MMC based on half‐bridge SM (HBSM) cannot boosting m while the hybrid MMC composed of HBSMs and full‐bridge sub‐modules (FBSM) can increase the m. The negative voltage is required when hybrid MMC works at the boosted m, which is only provided by the FBSMs because the HBSMs cannot output negative voltage. Therefore, appropriate control strategies are needed to achieve the normal operation of hybrid MMC under boosted m. This paper presents a control method suitable for hybrid MMC with high m based on multi‐harmonic voltage injection. The m is increased to 1.41 with the proposed multi‐harmonic voltage injection. Compared with the HBSM‐MMC, the proposed method reduces the total capacitance by more than 32%. Boosting m also reduces the RMS and peak value of the arm current by 18%. A comprehensive comparison was presented to demonstrate the cost and effect. Simulation and experiment verified the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Every day, health centers receive a significant number of patients seeking care and hospitalization, leading to long waiting times. Simulation modeling techniques are considered an effective solution to this issue. However, some research efforts had limited success mainly due to the neglect of systematic and straightforward approaches to identify and eliminate time waste in the healthcare system. To address this limitation, this study developed an LSS approach by collecting arriving patients and staff in a health center and using it as a case study. Then, a simulation model of the current system was created using the SIMPROCESS software. Finally, alternative solutions to shorten the queue time were proposed and supported by statistical graphs and tables. This is expected to improve overall health outcomes and enhance the delivery of healthcare services. A scenario is presented to evaluate the impact of the proposed improvements, which includes adding a general practitioner, a pediatrician, a dentist, and another pharmacy counter. This scenario improves system performance by up to 21%, reduces patient waiting times by approximately 50%, and ensures a fair distribution of workload among staff. [ABSTRACT FROM AUTHOR]

This article delves into the implementation and benefits of collaborative logistics, a strategic approach wherein logistics stakeholders engage in close cooperation to optimize resources and processes. The significance of a robust technological infrastructure is underscored, enabling real-time information sharing and integrated planning, which are pivotal for enhancing supply chain efficiency, reducing costs, and fostering sustainability. The discussion includes various collaborative logistics models, such as shared transportation routes and warehouses, which are instrumental in minimizing waste and mitigating environmental impact. The article further emphasizes the critical role of trust, transparency, and comprehensive legal frameworks in ensuring the success of such collaborations. By incorporating sustainability aspects into inventory management practices, companies can not only achieve substantial economic and environmental benefits but also secure a long-term competitive advantage. This integrated approach aligns with contemporary demands for sustainable operations, ultimately contributing to reduced global environmental footprint and compliance with stringent regulatory standards. [ABSTRACT FROM AUTHOR]

This article examines the impact of integration between Lean Accounting (LA) tools and Just-in-Time (JIT) techniques on Cost Reduction (CR) in a modern sustainable manufacturing environment in Saudi companies, Ethical Standards (ESs) mediate these two variables. A quantitative methodology was used for realizing the study goals and answering study questions. The main tool used for producing the primary data was a fully structured questionnaire with closed-ended questions which was designed and distributed online through a chosen sample of 109 managers and accountants in Saudi industrialization companies. Descriptive and deductive statistics were adopted to deduce the findings. The gathered responses analyzed by SPSS and AMOS and the results pointed out that there is a direct and indirect impact of LA tools and JIT techniques on CR in Saudi industrial companies especially when using ESs. Moreover, these findings could provide decision-makers in Saudi manufacturing companies with the importance of adopting both methods in an integrated way to maximize the profits of these firms. The practical implications of this study are that the implementation of these methods has a positive impact on the promotion of sustainable manufacturing and consequently the achievement of distinct sustainable performance. [ABSTRACT FROM AUTHOR]

Recently, firms like Toyota and social planners like the Government of India have invested in various initiatives leading to the firm's cost reduction. Further, such cost reduction collaborations also benefit the social planner because of higher industry output, and therefore, higher employment opportunities. In this paper, we investigate different scenarios in which the firm, or the social planner, or both exert efforts to reduce the firm's manufacturing cost. Interestingly, we find that under certain conditions, the firm may exert lower effort than the social planner. Our analysis reveals that when both players' cost reduction effectiveness is low, then collaboration results in higher payoffs for both the players. However, when the effectiveness of any player is high, then both players are better off if only one of them exerts effort. Our study further reveals that collaboration between the social planner and the firm always leads to a win–win situation. [ABSTRACT FROM AUTHOR]

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions. [ABSTRACT FROM AUTHOR]

Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable energy supplies. This paper proposes a novel hybrid metaheuristic algorithm for optimal energy planning in microgrids using an Improved Salp Swarm Algorithm with Harris Hawk Foraging (ISSAHF). This technique is based on an improved multi-leader Salp Swarm Algorithm with an elite leader following strategy combined with Harris Hawks foraging. A simulation study is conducted on a low-voltage microgrid in off-grid and grid-connected modes. The optimization algorithm resulted in a daily average cost of 28.3370€ in off-grid mode compared to 19.2676€ in grid-connected one. Furthermore, the statistical study shows that the proposed algorithm outperforms well-established metaheuristic techniques regarding search capability and robustness. It yields mean optimal cost of 623.5248€ in off-grid and 404.7475€ for the grid-connected one, compared to other optimization techniques that vary from 667.2141€ to 959.5747€ in off-grid mode, and from 424.5841€ to 813.932€ in grid-connected mode. For robustness, the proposed technique performs well with a standard deviation of 20.765€ compared to the best (17.024€) and the worst (47.2423€) cases in off-grid mode, while in grid-connected mode, it is 28.8771€ compared to the best (21.6316€) and the worst (45.3774€) values. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article describes elements of the know-how of using carbon electrodes produced using the technology of molding around a rod when smelting silicon metal. Application of our know-how will dramatically increase the competitiveness of silicon metal production. Experts' concerns regarding the use of such electrodes were that such electrodes have a through axial hole. This significantly reduces the mechanical strength of such electrodes, which can presumably lead to problems associated with the breakage of the working side of the electrode, which is immersed in the smelting space of the furnace under the charge layer. Industrial testing of such electrodes was carried out in a 30 MVA furnace of "Tau-Ken Temir" LLP. During testing, we used an approach previously developed by our team for working with a furnace in the process of smelting silicon metal. In particular, we used an interval between top treatments of about 30 min and adhered to the principles of balanced smelting, i.e., provided a balance between the intensity of the uniform supply of the charge into the furnace and the current active electrical power. Industrial testing carried out over four weeks confirmed the stability of the operation of cheaper carbon electrodes with a through axial hole. The recovery of silicon into finished products was also improved to 88–89% and the specific energy consumption was reduced to 11.2–12.1 MWh/t of silicon metal from the initial value 14,752 MWh/t. Thus, we received additional evidence for the effectiveness of our approach in furnace operating compared to an approach based on the ultimate provision of gas and permeability of the furnace top due to excessively intense processing of the top and an uncontrolled, uneven supply of charge to the furnace. [ABSTRACT FROM AUTHOR]

In recent years, green credit has significantly supported the development of the sustainable economy. However, the existing literature presents differing views on the impact of green credit on bank performance, which is crucial for the sustainability of green credit business. Meanwhile, FinTech is comprehensively empowering green credit business. This paper investigates whether FinTech influences the effect of green credit on bank performance. Based on an analysis of data from 127 Chinese commercial banks from 2007 to 2022, we find that green credit significantly enhances bank performance, and FinTech further amplifies this positive effect. This finding partially explains the conflicting views in the existing literature, as the impact of green credit on bank performance varies under different levels of FinTech. We believe that FinTech exerts its influence through three mechanisms: cost reduction, reputation enhancement, and risk mitigation. Heterogeneity analysis reveals that the impact of FinTech is more pronounced in city commercial banks, in samples with better green credit development, and during banking industry downturns. Finally, we recommend that banks actively develop FinTech and apply it to green credit businesses to maximize the positive effects of green credit. Simultaneously, regulators and governments should provide necessary support for banks. [ABSTRACT FROM AUTHOR]

Aiming at the power supply scheme (PSS) of the on‐board supercapacitor‐powered tram, considering the cost and margin of the PSS, a two‐stage method is designed to optimize the layout of the charging stations and the configuration of the supercapacitor (SC). First, the SC‐powered tram model and stable cycle operation model are established, and a two‐stage optimization problem model with the lowest PSS cost and the largest SC margin is established. Then, an improved dual‐population differential evolution algorithm is designed, and the layout of charging stations and the configuration of SC are co‐optimized in the first stage, and then the layout of charging stations is optimized again in the second stage. The simulation results show that co‐optimization can obtain a lower cost of PSS, and furthermore, the layout of charging stations can be optimized again to effectively improve the margin of SC, thereby improving the matching degree between the layout of charging stations and the connection scheme of SC. [ABSTRACT FROM AUTHOR]

Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array. [ABSTRACT FROM AUTHOR]

The cost landscape in advanced technology systems is shifting dramatically. Traditionally, hardware costs took the spotlight, but now, programming and debugging complexities are gaining prominence. This paper explores this shift and its implications, focusing on reducing the cost of programming complex robot behaviors, using the latest innovations from the Generative AI field, such as large language models (LLMs). We leverage finite state machines (FSMs) and LLMs to streamline robot programming while ensuring functionality. The paper addresses LLM challenges related to content quality, emphasizing a two-fold approach using predefined software blocks and a Supervisory LLM. [ABSTRACT FROM AUTHOR]

Many schools face barriers that hinder access to services, including limited funding and staff trained in evidence-based interventions. In particular, rural schools must provide targeted intervention to promote academic growth and narrow achievement gaps despite these barriers. One seldom-considered solution to ease the resource costs in the provision of intervention services is to provide high school students with the training and supervision necessary to carry them out. Thirty elementary students participated in an explicit timing intervention administered by either a high school student or graduate student in school psychology. A 2 × 2 mixed factorial ANOVA found significant growth from pre- to post-test with no effects of interventionist education level. These findings imply that school-based interventionists may be able to expand the reach of their practice by recruiting and training older students to carry out interventions. [ABSTRACT FROM AUTHOR]

The increasing demand for machined components of superior quality at competitive costs has driven machine and cutting tools manufacturers to innovate in machine tool technologies and cutting tools materials. Consequently, optimizing the machining process becomes an important subject of research. Particularly, optimizing machining parameters like cutting speed, depth of cut and feed rate stands out. Literature review highlights that optimization considers various "objective functions" such as minimum production cost, maximum production rate, and minimum energy consumption. However, one aspect not objectively considered is the customer's demand within a specific timeframe, known as takt time. The relevance, originality and contribution of this work is the development of a method that, considering the current machining parameters and the takt time for a given machining stage, defines the optimized cutting speed for each cutting tool to meet the takt time at the lowest production cost. An algorithm based on classical equations for calculating machining times and costs for machining processes with multiple cutting tools was developed. A software in Excel receives process data, "x and K" coefficients from Taylor's life equation and calculates the optimized cutting speeds. The present method was applied in a heavy machining industrial environment using actual production data and machining parameters. Taylor coefficients x and K were obtained through cutting tool life tests carried out on the part under analysis. Results showed potential machining cost savings of up to 6.2% compared to traditional methods for obtaining takt time on shop floor. [ABSTRACT FROM AUTHOR]

A reduced-cost explicitly correlated second-order Møller–Plesset (MP2-F12) approach is presented. Based on our recent reduced-cost explicitly correlated coupled-cluster method [J. Chem. Theory Comput., 19, 174 (2023)], the frozen natural orbital, the natural auxiliary basis, and the natural auxiliary function approximations are utilised to decrease the size of the molecular orbital basis and the auxiliary basis sets required for the density fitting and for the expansion of the explicitly correlated geminals. The necessary modifications of the above approximations for MP2-F12 are discussed. The performance of the new method is tested for atomisation and reaction energies. Our results demonstrate that speedups of 1.5–4 can be achieved in the calculation of MP2-F12 correlation energies with a moderate loss of accuracy. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The newly developed computational fluid dynamics, transport, and chemical kinetics-based monolith catalyst dimensioning methodology consists of the following steps: (i) initial calculations, which generate some of the data, e.g., average inlet fluid velocity used in the (ii) computational fluid dynamics (CFD) modelling, which uses the laminar flow interface and the transport of diluted species interface while the user has to provide the kinetics of the reactions; (iii) the model order reduction uses a modified version of the plug flow reactor model and the linear pressure variation model; and (iv) the dimensioning optimization algorithm extracts the optimal monolith catalyst's channel geometry, which satisfies the user's performance constraints and reduces material consumption. Therefore, the methodology enables chemical engineers to quickly and efficiently design and dimension monolith catalysts for many different applications in an environmentally friendly way, which enables them to reduce both the material and operating costs while maintaining sufficient catalyst performance and, therefore, achieve its cost-effective performance. [ABSTRACT FROM AUTHOR]

Feature selection is a preprocessing step in machine learning that aims to reduce dimensionality and improve performance. The approaches for feature selection are often classified according to the evaluation of a subset of features as filter, wrapper, and embedded approaches. The high performance of wrapper approaches for feature selection is associated at the same time with the disadvantage of high computational cost. Cost-reduction mechanisms for feature selection have been proposed in the literature, where competitive performance is achieved more efficiently. This work applies the simple and effective resource-saving mechanisms of the fixed and incremental sampling fraction strategies with memory to avoid repeated evaluations in multi-objective permutational-based differential evolution for feature selection. The selected multi-objective approach is an extension of the DE-FSPM algorithm with the selection mechanism of the GDE3 algorithm. The results showed high resource savings, especially in computational time and the number of evaluations required for the search process. Nonetheless, it was also detected that the algorithm's performance was diminished. Therefore, the results reported in the literature on the effectiveness of the strategies for cost reduction in single-objective feature selection were only partially sustained in multi-objective feature selection. [ABSTRACT FROM AUTHOR]

Taking the large-size shipbuilding block as the research object, this article describes the general phenomenon of the long blasting operation time and long painting operation time, and analyzes its impact and reasons. Through a series of refined management practices such as fine production preparation, strengthened process control and improved equipment efficiency, a new process of "block blasting completion in the morning" is put forward to achieve good results such as production efficiency improvement and cost reduction as well as social benefits. [ABSTRACT FROM AUTHOR]

The assessment of barriers to the application of cost-reduction techniques in delivering educational buildings in Nigeria is essential in addressing the infrastructural shortage, building performance, delay, cost, time overrun, and abandonment in the delivery of higher educational buildings (HEBs). This study examines barriers to applying cost-reduction techniques in educational buildings in southwestern Nigeria. Using a survey design, the questionnaire was distributed to stakeholders who participated in delivering the government intervention, private donors, and internally generated revenue educational buildings in public tertiary institutions in southwestern Nigeria from 2012 to 2022. A total of 150 copies of the questionnaire were administered, while 133 responses were obtained and analyzed. To begin with, data reliability and validity were examined using Bartlett's sphericity, Cronbach's alpha, and Kaiser–Meyer–Olkin (KMO) tests, accordingly, followed by descriptive, Kruskal–Wallis H test, and exploratory factor analysis. The six components obtained from exploratory factor analysis for explaining the barriers to applying cost-reduction techniques in educational buildings were as follows: ambiguity in HEB contracts awards and project executions, lack of control from the HEIs management over HEB project delivery, perceived political influence in HEB procurement, unrealistic contract requirements and change orders, non-prioritization of automation integration in HEB delivery, and deficiencies in contract documents and costing. This study recommends establishing a project-monitoring team involving independent consultants from project inception to reduce excessive errors, practices of assigning contracts to the lowest bidder, and excessive claims for variation orders that escalate the project's final sum. [ABSTRACT FROM AUTHOR]

Biological denitrification is a generally economically viable and reliable technology for nitrate (NO3−-N) and nitrite (NO2−-N) removal from wastewater. Nitrate/nitrite reduction requires an electron donor, which can be supplied from influent wastewater organic compounds, added as an external carbon source, or by endogenous respiration. Decisions regarding the selection of an external carbon source should take into consideration its cost, sludge generation quantity, the rate and efficiency of denitrification, and chemical storage safety. The expense of waste management and external carbon sources accounts for up to 50% of the overall cost of wastewater treatment. The carbon source characteristics required for biological denitrification affect the microbial community structure, denitrification rate, and intermediate products. This review is based on a bibliometric analysis and a systematic literature review providing information and insight into the topic of the denitrification process using different carbon sources. In this review, the main focus was on discussing alternative carbon sources vs. traditional carbon sources in terms of the carbon source price, C/N ratio, denitrification efficiency and rate, operational parameters, and advantages and disadvantages, as well as the limitations in the denitrification process. Future perspectives may include the operating parameters influencing the stability of the removal performance; the maintenance and improvement of nitrate removal; a study of the diversity of the microbial community; research on the application of new alternative carbon sources in denitrification; and N2O emission detection and minimisation. [ABSTRACT FROM AUTHOR]

As a result of global efforts to combat the rise in global climate change and carbon dioxide emissions, there has been a substantial increase in renewable energy investment for both residential and utility power generation. Solar power facilities are estimated to be among the major contributors to global decarbonization in terms of capacity by 2050. Consequently, the majority of economically significant countries are progressively implementing utility-scale photovoltaic (U-PV) systems. Nevertheless, a major obstacle to the expansion of U-PV generation is the identification and assessment of direct current (DC) faults in the extensive array of PV panels. In order to address this obstacle, it is imperative to provide an evaluation method that can accurately and cost-effectively identify and locate potential DC faults in PV arrays. Therefore, many studies attempted to utilize thermal cameras, voltage and current sensors, power databases, and other detecting elements; however, some of these technologies provide extra hurdles in terms of the quantity and expense of the utilized hardware equipment. This work presents a sophisticated system that aims to diagnose and locate various types of PV faults, such as line-to-ground, line-to-line, inter-string, open-circuit, and partial shading events, within a PV array strings down to a module level. This study primarily depends on three crucial indicators: precise calculation of the PV array output power and current, optimal placement of a limited number of voltage sensors, and execution of specifically specified tests. The estimation of PV array power, along with selectively placed voltage sensors, minimizes the time and equipment required for fault detection and diagnosis. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate that the proposed approach can accurately diagnose and localize faults with only NS/2 voltage sensors, where NS is the number of PV array parallel strings. [ABSTRACT FROM AUTHOR]

Effective vendor-buyer collaboration is a successful supply chain management (SCM) cornerstone. By working together, both parties can achieve significant benefits. This collaboration approach fosters open communication, which allows for better communication, improved efficiency, service levels, and reduced costs, i.e., a win-win situation. Ultimately, this partnership strengthens the entire supply chain, increasing efficiency and profitability for vendors and buyers. This study aims to investigate the dynamics of collaboration between supply chain partners. It focuses on a scenario where a single vendor supplies a single product to multiple buyers through a vendor-managed inventory (VMI) and consignment stock (CS) agreement. Managing inventory and product quality is a key challenge, as the components may be imperfect. Buyers are responsible for inspecting these items, but the process is susceptible to misclassification. To address these issues, the research aims to develop an optimization model that incorporates costs associated with collaboration, quality, inspection, and potential misclassification. In addition, developing a methodology to identify the optimal solution minimizes overall costs and maximizes benefits for both vendors and buyers. Furthermore, a numerical example is presented for tangible illustration, offering practical insights into the benefits of partner collaboration in SCM. The findings underscore the efficacy of implementing a collaborative approach between vendors and buyers to minimize supply chain costs. This collaborative policy necessitates a willingness from both parties to forgo their individual cost-minimization strategies in favor of optimizing the overall system cost through cooperation. Through this sacrifice, synergistic benefits emerge, resulting in enhanced efficiency, reduced expenses, and improved overall performance within the supply chain ecosystem. [ABSTRACT FROM AUTHOR]

The paper investigates the impact of AI on the support for logistics in a practical case study for Aramex, a leading Jordanian logistic company. Applying AI-driven technologies of automation, predictive analytics, and route optimization significantly improved the operations of Aramex while reducing costs and enhancing customer satisfaction. It points to the key performance indications: operational cost reduction by 25%, increase in shipping volume by 30%, and a cut in carbon emission by 29.17%. On the other hand, some of the challenges observed included high initial investment costs and also skill gaps regarding how to manage the AI system. The paper demonstrated the ways AI can enable logistics to be scalable, flexible, and sustainable; usage needs to overcome both financial and technical barriers. [ABSTRACT FROM AUTHOR]

Downy mildew is a production limiting factor for Brazil's main onion producing region. In the present study we tested two management strategies, namely the utilization of a resistant cultivar and the implementation of a disease alert system. Our objective was to mitigate the frequency of systemic fungicide applications while concurrently sustaining optimal yield levels. By carrying out fungicide sprays only when the alert system indicated a severe risk and using the cultivar "SCS379 Robusta", it was possible to reduce the number of sprays from 11 (calendar) to 5 (alert system), maintaining statistically similar yields (32.58 tonnes/ha – calendar; and 30.76 tonnes/ha – alert system). The reduction in spray applications using an integrated management strategy reduced the cost of spraying by more than half from BRL 0.069/Kg to BRL 0.033/Kg. This study demonstrates that implementation of an integrated management strategy can reduce chemical use and cost without compromising the protection of onion using alert system and more susceptible varieties. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Small Business & Entrepreneurship is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We study maintenance service operations in large facilities portfolios with a focus on modeling logistics of associated spare parts and their storage locations serviced by personnel from several trades who move around the buildings in their vehicles. The key challenge is to reduce the cost of spare parts inventory, storage, and vehicles while providing responsive customer service. To address these challenges, we propose (i) inventory models to determine stock levels and (ii) storage space in personnel vehicles to allocate the mix and location of spare parts by trade. The two models can be readily integrated, and conceptually, technician vehicles serve as “forward-reserve storage” that improves service responsiveness. The proposed model is applied to a large facility of a non-profit organization with 330 buildings, 17,000 spare parts, and 254 vehicles. Compared to prevailing practice, the proposed model can potentially reduce $1.27 million worth of inventory costs, a reduction of ~77%. [ABSTRACT FROM AUTHOR]

This study examines a firm's strategic choices of self-development or cooperation with (wholesaling from) its stronger rival. Both choices can improve the efficiency and competitive strength. We develop a benchmark of a full competition model, coopetition strategy, self-development strategy and hybrid strategy to investigate the effect of coopetition and self-development on firms who sell substitutive products in the end market. We show that both coopetition and self-development can benefit the high-cost firm. While the low-cost firm prefers the coopetition strategy. And in the hybrid strategy, the high-cost firm just utilises the self-development ability as a threat to enjoy a lower wholesale price. Interestingly, our research indicates that the mixed cooperation and competition relationship varies low-cost firm's attitudes towards product differentiation. On the one hand, the low-cost firm wants to produce homogeneous products to compete with rivals. On the other hand, he also wants to provide heterogenous products to alleviate competition in the coopetition strategy. The result provides company managers with a certain meaning in the complicated cooperative and competitive market environment. [ABSTRACT FROM AUTHOR]

This research focuses on the dynamic landscape of today’s competitive market, where production management must meet clients’ expectations for high-quality products and shorter lead times while managing internal disruptions due to inevitable defects and unpredictable equipment failures. Achieving these operational goals without compromising product quality, missing deadlines, or experiencing production disruptions is essential for minimizing operational expenses. The study examines the dynamics of the system’s operating costs through the development of models, mathematical formulations, optimization techniques, and algorithm proposals. It demonstrates the system’s convexity and establishes the optimal batch time for implementing the proposed methodologies. The research results show relevant failure costs of 3.51 %, overtime added costs of 4.57 %, outsourcing setup costs of 0.73 %, outsourcing variable costs of 41.82 %, quality-related costs of 2.98 %, in-house variable costs of 40.42 %, and in-house holding costs of 3.55 %. The study develops strategies for optimal overtime use to meet production targets without excessive labor costs and provides a structured framework for making informed outsourcing decisions that balance cost savings with quality and reliability considerations. Overall, the research provides a robust framework for reducing operational costs while maintaining or improving the quality and reliability of manufacturing processes [ABSTRACT FROM AUTHOR]

This study investigates the potential benefits of integrating coarser particle size distributions (PSDs) of 45–106 µm into laser-based powder bed fusion of metals (PBF-LB/M), aiming to reduce costs while maintaining quality standards. Despite the considerable advantages of PBF-LB/M for producing intricate geometries with high precision, the high cost of metal powders remains a barrier to its widespread adoption. By exploring the use of coarser PSDs, particularly from electron beam-based powder bed fusion of metals (PBF-EB/M), significant cost-saving opportunities are identified. Through a comprehensive powder characterization, process analysis, and mechanical property evaluation, this study demonstrates that PBF-LB/M can effectively utilize coarser powders while achieving comparable mechanical properties as those produced with a 20–53 µm PSD. Adaptations to the process parameters enable the successful processing of coarser powders, maintaining high relative density components with minimal porosity. Additionally, market surveys reveal substantial cost differentials between PBF-LB/M and PBF-EB/M powders, indicating a 40% cost reduction potential for the feedstock material by integrating coarser PSDs into PBF-LB/M. Overall, this study provides valuable insights into the economic and technical feasibility of printing with coarser powders in PBF-LB/M, offering promising avenues for cost reduction without compromising quality, thus enhancing competitiveness and the adoption of the technology in manufacturing applications. [ABSTRACT FROM AUTHOR]

Fruit juice concentration gains popularity due to the reduced shipping costs for concentrated products. However, the main drawback of the widely used thermal concentration process is the degradation of product's quality parameters. Lyophilization, or the freeze‐drying process, is applied to concentrate coconut water to demonstrate that the quality of the coconut water can improve even when a significant fraction of pure water is removed from it. The measurements of antioxidant activities, total phenolic content (TPC), pH, and titratable acidity are investigated to understand process efficiency without compromising the original quality of coconut water. Study applies first order kinetics to assess the coconut water's shelf‐life, using pH and Brix as critical parameters. Results suggest that Brix, TPC, antioxidant activity (AAO), total dissolved solids (TDS), and potassium increase with increase in lyophilization time, where pH remains the same. The shelf‐life of concentrated product increases to double that of raw coconut water. Practical applications: Coconut water has very large worldwide demand as health drink across all ages and all societies, particularly has great demand in sport/health sectors. The raw coconut is inconvenient to transport long distances because of its bulky volume. Further, coconut grows in a very limited area, it needs to travel long distance to reach customers across globe. So business with packed concentrated coconut water would be profitable for food industries. There is no study that has proven that concentrated coconut water through non‐thermal processes can actually improve its shelf‐life drastically. This study suggests that Brix, TPC, AAO, total dissolved solids, and potassium ions were concentrated with the increase in lyophilization time, where pH remains same due to an undisturbed natural buffering system. Non‐thermal freeze‐drying techniques proved that non‐thermal treatment like polymeric/ceramic membrane treatment would be beneficial for industry to sell packed coconut water without adding preservatives. [ABSTRACT FROM AUTHOR]

Despite many advantages of distributed generation (DG), it can have adverse effects on the network protection coordination by raising the short-circuit level of buses. This issue can often be resolved by incorporating fault current limiters (FCLs) that are properly located and sized in the network. We propose an efficient method to simultaneously attain the optimal location and size of DG and FCL units for protection coordination of overcurrent relays and cost reduction in a distribution network. The proposed method involves three stages. First, two separate objective functions are derived, representing the operation times of relays and the network costs associated with the installation of DGs and FCLs and system losses. The Strength Pareto Evolutionary Algorithm 2 (SPEA-2) is then used for finding a Pareto-optimal solution set for the interrelated multiobjective problem at hand. Finally, the k-means clustering method is utilized to group the best solutions. In contrast to the conventional weighted sum (WS) method, the proposed method is more computationally efficient while being capable of treating complex networks with non-convex Pareto front solutions. These features are demonstrated by implementing the proposed approach in the 14-bus IEEE test grid and comparing the results with those obtained using the conventional WS method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The acceleration of digital technology development is an important strategic choice to realize the transformation of green innovation. Prior research focuses on the mechanism by which artificial intelligence technology promotes economic growth but ignores the impact of digital technology on green innovation performance. Using panel data on 30 regions in China from 2003 to 2019, this paper examines the impact of digital technology on green innovation performance. The results are as follows: (1) Digital technology can significantly promote green innovation performance. After a series of robustness tests, the conclusion is still valid. (2) Digital technology improves green innovation performance through the green technology upgrading effect, cost reduction effect, and green resource allocation optimization effect. (3) Heterogeneity test results show that in regions with high human capital, high R&D intensity, and strong environmental regulation, the improvement effect of digital technology on green innovation performance is more obvious. These findings not only enrich the literature on digital technology and green innovation performance but also serve as a reference for governmental departments as they optimize their strategy for developing digital technology and achieving green economic development. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs. [ABSTRACT FROM AUTHOR]

It has been well documented that the usage of a textured cover layer reduces reflection from the air‐solar cell interface, which ultimately enhances the power conversion efficiency (PCE) of a solar cell. The most commonly used patterns, such as pyramids, micropillars, nanowires (NW), and nanoholes have been widely studied and optimized. Besides using such NWs to enhance light absorption, this work also additionally considers the concept of mimicking the antireflection coating of single or multiple layers in minimizing the reflectance and thus enhancing the total absorptance further. It is shown here that at least one order of magnitude shorter multilayer NW pattern of 268 nm total height can outperform a standard NW of 4270 nm height, which needs less material and can also be fabricated at a reduced cost. Furthermore, the proposed design with reduced height has a significantly lower surface‐to‐volume ratio, which also reduces surface recombination loss than the other textured surface patterns. The results presented in this work have been comprehensively analyzed by initially optimizing optical absorption and then completing the electrical simulations. The optimized design in conjunction with a back reflector offers an efficiency as high as 16.434%. [ABSTRACT FROM AUTHOR]

There is a need for increased renewable energy to meet net-zero targets and decarbonise the economy. Harnessing the predictable power of the tides with tidal stream turbines can contribute to this. Tidal energy is a nascent technology with higher costs at present. However, cost reductions have been observed with an increased deployment in other renewable energy technologies that have received financial support, and it is postulated that similar will happen with tidal energy. The first tidal stream projects have been awarded market support in the UK through the Contracts for Difference (CfD) scheme, with almost 100 MW expected to be commissioned by 2028. This work uses learning rates to investigate how much investment in ongoing market support might be needed to achieve cost reductions through subsidised deployment alongside research and innovation. Using a range of informed 'what if?' scenarios, it shows sensitivity to key inputs. The results show that the support needed is most sensitive to the learning rate, reducing it from 15% to 12.5% or 10% doubles or more than quadruples the investment required, respectively. The support is also highly dependent on the starting cost from which learning occurs, taken as the CfD Strike Price in 2025. Varying this between 156 and 220 GBP/MWh results in total investment of GBP 6.7 and 22.3 bn, respectively. Most importantly, a balance is needed between subsidising deployment to drive down costs through learning and funding innovation to maintain a high learning rate. [ABSTRACT FROM AUTHOR]

The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits. [ABSTRACT FROM AUTHOR]

Copyright of Accounting & Financial Studies Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Accounting & Financial Studies Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)