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This study examines the relationship between financial reporting quality (FRQ) and investment efficiency (IE) in Vietnamese manufacturing firms and analyses the moderating role of various firm characteristics, including debt maturity (Stdebt), financial strength (Z), firm size and tax upon such relationship. The study uses a sample of manufacturing firms listed on the Vietnamese stock market from 2008 to 2021 to test and analyse overinvestment and underinvestment situations. Results indicate a positive relationship between FRQ and IE among manufacturing firms in Vietnam, and such a relationship is moderated by firm characteristics, including Stdebt, Z, firm size and tax. Moreover, debt maturity, TobinQ, audit and tax positively affect IE, whereas financial strength and firm size tend to affect IE negatively. Similarly, when firms are underinvested, TobinQ and leverage positively affect IE, whereas financial strength has a negative relationship with IE. Furthermore, the studying findings demonstrate that firm characteristics moderate the relationship between FRQ and IE. The results enhance the understanding of firm performance for CEOs and managers and provide short- and long-term internal strategies for sustainable development and goal achievement. Additionally, these findings assist investors in analysing and making informed investment decisions, helping firms minimise risks and enhance their competitiveness. Given the limited empirical research regarding the relationship between FRQ and IE in an emerging market, this study contributes to the existing literature by adding original value to this area. Furthermore, the findings confirm the agency and asymmetry information theories' view. [ABSTRACT FROM AUTHOR]

We study the matching of workers to firms in which workers choose an observable and contractable effort after the match. If there are complementarities between a worker's ability and a firm's technology, positive assortative matching (PAM) is the only matching in any equilibrium and is the unique efficient matching. We investigate the effect of a policy that changes the matching of firms to workers from any matching to PAM, such as implementing a centralized clearing house. We characterize two sets of sufficient conditions on the production and cost functions under which the total output and welfare both increase. Under the first set of conditions, the increase in total output is an upper bound for the efficiency gain. In contrast, under the second set of conditions, the increase in total output is a lower bound for the efficiency gain. We identify a third set of conditions under which the total output decreases while welfare increases. [ABSTRACT FROM AUTHOR]

The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss functions within the eXtreme Gradient Boosting (XGBoost) algorithm, which is a critical step in enhancing the algorithm's performance for specific applications. Our research is motivated by the need for precision and efficiency in the industrial domain, where the implications of misclassification can be substantial. We focus on the drill-wear analysis of melamine-faced chipboard, a common material in furniture production, to demonstrate the impact of custom loss functions. The paper explores several variants of Weighted Softmax Loss Functions, including Edge Penalty and Adaptive Weighted Softmax Loss, to address the challenges of class imbalance and the heightened importance of accurately classifying edge classes. Our findings reveal that these custom loss functions significantly reduce critical errors in classification without compromising the overall accuracy of the model. This research not only contributes to the field of industrial machine learning by providing a nuanced approach to loss function customization but also underscores the importance of context-specific adaptations in machine learning algorithms. The results showcase the potential of tailored loss functions in balancing precision and efficiency, ensuring reliable and effective machine learning solutions in industrial settings. [ABSTRACT FROM AUTHOR]

Innovation and obsolescence describe dynamics of ever-churning and adapting social and biological systems, concepts that encompass field-specific formulations. We formalize the connection with a reduced model of the dynamics of the “space of the possible” (e.g., technologies, mutations, theories) to which agents (e.g., firms, organisms, scientists) couple as they grow, die, and replicate. We predict three regimes: The space is finite, ever growing, or a Schumpeterian dystopia in which obsolescence drives the system to collapse. We reveal a critical boundary at which the space of the possible fluctuates dramatically in size, displaying recurrent periods of minimal and of veritable diversity. When the space is finite, corresponding to physically realizable systems, we find surprising structure. This structure predicts a taxonomy for the density of agents near and away from the innovative frontier that we compare with distributions of firm productivity, COVID diversity, and citation rates for scientific publications. Our minimal model derived from first principles aligns with empirical examples, implying a follow-the-leader dynamic in firm cost efficiency and biological evolution, whereas scientific progress reflects consensus that waits on old ideas to go obsolete. Our theory introduces a fresh and empirically testable framework for unifying innovation and obsolescence across fields. [ABSTRACT FROM AUTHOR]

• A review of waste management optimization models (WM-OMs). 2. • A novel characterization method for examining and benchmarking the features WM-OMs. 3. • A pioneering guide for the selection of proper uncertainty analysis approaches. 4. • A compilation of characteristics of the papers reviewed and their data (Appendix). 5. • A perspective on the design of WM-OMs for different applications and stakeholders. 6. The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders. [ABSTRACT FROM AUTHOR]

This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model's loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency. [ABSTRACT FROM AUTHOR]

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company's profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios. [ABSTRACT FROM AUTHOR]

Lead recycling is very important for reducing environmental pollution risks and damages. Liquid lead is recovered from exhaust batteries inside stirred batch reactors; the process requires melting to be cleaned. Nevertheless, it is necessary to establish parameters for evaluating mixing to improve the efficiency of the industrial practices. Computational fluid dynamics (CFD) has become a powerful tool to analyze industrial processes for reducing operating costs, avoiding potential damages, and improving the equipment's performance. Thus, the present work is focused on simulating the fluid hydrodynamics inside a lead-stirred reactor monitoring the distribution of an injected tracer in order to find the best injection point. Then, different injected points are placed on a control plane for evaluation; these are evaluated one by one by monitoring the tracer concentration at a group of points inside the batch. The analyzed reactor is a symmetrical, vertical batch reactor with two geometrical sections: one cylindrical body and a semi-spherical bottom. Here, one impeller with four flat blades in a shaft is used for lead stirring. The tracer concentration on the monitoring points is measured and averaged for evaluating the efficiency inside the tank reactor. Hydrodynamics theory and a comparison between the concentration profiles and distribution of tracer curves are used to demonstrate both methods' similarities. Then, the invariability of the tracer concentration on the monitoring points is adopted as the main parameter to evaluate the mixing, and the best injection point is found as a function of the shortest mixing time. Additionally, the influence of the impeller rotation speed is analyzed as an additional control parameter to improve industrial practices. [ABSTRACT FROM AUTHOR]

Headache occurs commonly in individuals diagnosed with cerebral neoplasm. Though the features of a brain tumor-associated headache may vary, a progressive nature of headache and a change in headache phenotype from a prior primary headache disorder often are identified. Pathophysiologic mechanisms proposed for headache associated with brain tumor include headache related to traction on pain-sensitive structures, activation of central and peripheral pain processes, and complications from surgical, chemotherapeutic and/or radiotherapy treatment(s). Optimization of headache management is important for an individual's quality of life. Treatments are based upon patient-specific goals of care and may include tumor-targeted medical and surgical interventions, as well as a multimodal headache treatment approach incorporating acute and preventive medications, nutraceuticals, neuromodulation devices, behavioral interventions, anesthetic nerve blocks, and lifestyles changes. [ABSTRACT FROM AUTHOR]

The need of translating increasing outlays into better outcomes on various public programmes has attracted the attention of researchers worldwide to focus more on the quality of public spending, often assessed in terms of its efficiency. The present article is a contribution to the existing literature on the subject in that it assesses the efficiency of government expenditure of Indian states for two most basic social services—elementary education and nutrition during 2014–2015 and 2018–2019 using data envelopment analysis and Malmquist productivity index techniques, in both input‐oriented as well as output‐oriented settings under variable returns to scale assumption; it constructs the average performance index for children at elementary education and malnutrition index for women and children to utilise them as output indicators; and it adopts a robust bootstrap truncated regression procedure to avoid various econometric issues while estimating the impact of selected environmental factors on bias‐corrected inefficiency scores. The major findings of this article seem to suggest that, while states should be provided greater flexibility in their fiscal operations, they have to complement their fiscal autonomy with good governance to improve the efficiency of their spending on basic social services. Towards this end, states like Bihar, Jharkhand and those belonging to North‐Eastern and Himalayan regions need special attention with a different set of development policies. [ABSTRACT FROM AUTHOR]

With the growth of global population and economic development, people are facing the problem of increasing scarcity of renewable energy and unsustainable energy use. To achieve the sustainable development goals (SDGs) proposed by the United Nations, research on energy consumption efficiency has become particularly important. This research evaluates the energy consumption efficiency of 270 cities in China through an improved EBM model and finds a common phenomenon of low energy consumption efficiency in the cities, with the highest efficiency in northeast China and the lowest efficiency in eastern China. In addition, the efficiency of industrial exhaust emissions most significantly positively correlates with the efficiency of employed population and total energy consumption efficiency, while the efficiency of regional GDP does not significantly correlate with the efficiency of the two input variables. Using the LMDI method to decompose the driving factors of energy consumption efficiency in the cities, we find that the most important factor affecting energy consumption efficiency is their own energy endowment. Therefore, to improve the energy consumption efficiency of its cities, the China government should comprehensively consider factors such as regional economic development level, industrial structure, and technological level differences, formulate relevant energy-saving and emission-reduction policies, focus on optimizing the energy consumption structure, encourage technological progress and innovation, and help increase investment in science and technology. [ABSTRACT FROM AUTHOR]

The synthesis of imides holds significant importance in various scientific disciplines and industrial applications due to their extensive use in biomolecules, inorganic compounds, and organic compounds. Traditional methods for imide synthesis often rely on the use of toxic or expensive oxidizing agents, limiting their sustainability and practicality. Herein, we present a green and environmentally friendly approach for the synthesis of imides from N-alkylamides using electrocatalytic oxidation with H2O as the green oxygen source. This sustainable and atom-efficient approach outperforms traditional methods by eliminating the need for toxic or expensive oxidants while simultaneously achieving high yields under mild reaction conditions. The protocol exhibits broad substrate compatibility, enabling the transformation of a wide range of N-alkyl (methyl, ethyl and cyclopropyl) amides into imides. The resulting imide products can serve as valuable building blocks for the synthesis of biologically relevant 1,2,4-triazole compounds. Furthermore, the practicality of this method is demonstrated through a gram-scale reaction, affirming its efficiency and potential for industrial applications. Our work presents a sustainable and versatile strategy for imide synthesis, aligning with green chemistry principles and sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

There are complex coupling relationships between flight motion and energy management for hybrid electric fixed wing UAVs with photovoltaic/fuel cell/battery. Aiming at realizing high-energy-efficiency long endurance autonomous flight, this paper proposes a coordinated optimization method of trajectory optimization and energy management for hybrid electric UAVs. Numerical simulation and experiment are carried out. Separated by surplus demand energy except that produced by PV, this method includes an optimal energy flight trajectory optimization layer and an energy management layer. For the former, fuzzy neural network sequential convex optimization (FNNSCP) is proposed to handle the flight trajectory optimization problem for maximizing utilization solar energy. For the latter, an online energy management strategy based on convex quadratic programming MPC (CQPMPC) is proposed to realize the power allocation. Numerical simulation results show that FNNSCP reduced 2% energy requirement compared with Radau pseudo-spectral method (RPM) from flight trajectory optimization level. From energy management level, CQPMPC saves hydrogen consumption by 3.1% and 16.3% compared with nonlinear model predictive control (NMPC) and fuzzy logic state machine (FLSM), respectively. In the experiment, less hydrogen fuel is consumed by proposed method by 5.2% and 15.6% compared with NMPC and FLSM, respectively. Experimental results show that the proposed method has better energy-saving performance and excellent real time performance. It indicates that optimizing flight trajectory and improving energy management efficiency can realize the high-energy-efficiency flight of hybrid electric UAVs. This method is conducive to promoting the application of hydrogen energy in the field of UAVs. [Display omitted] • A coordinated optimization method is proposed for the high-energy-efficiency flight. • Fuzzy neural network adaptive sequential convex optimization method is proposed. • Online energy management strategy with convex quadratic programming MPC is proposed. • The proposed method is verified by simulation and energy management experiment. [ABSTRACT FROM AUTHOR]

This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model's sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility. [ABSTRACT FROM AUTHOR]

This study explores the determinants of the export performance of Indonesia's low-, medium-, and high-technology manufacturing industries by focusing on the role of raw-material imports and technical efficiency. Micro firm-level data from 2010–2015 were utilized for the analysis in this study. The stochastic frontier analysis was employed to measure technical inefficiency and to determine its effect on export performance. Our findings indicate that in all categories of industry technical efficiency, raw materials imports, foreign direct investment (FDI), location, firm size, labour productivity, and concentration of industries were significant determinants of export performance. While high efficiency increases exports in low- and medium-technology firms, exports decrease in firms with high efficiency accompanied by high imports, FDI, size, and labour productivity. Furthermore, in high-technology industries, efficiency reduces exports and again increases them when mediated by a concentration of industries and location. The empirical strategy also supports the positive effect of imports on export performance in both industries, which also aligns with decreased exports in firms with high imports accompanied by high FDI, efficiency, labour productivity, the concentration of industries, and size. To this end, the study has implications for low-, medium-, and high-technology manufacturing that are mainly concerned with increasing exports. [ABSTRACT FROM AUTHOR]

At the critical window of comprehensively promoting Chinese-style modernization, the high-quality development of the feed industry has become the only way to accelerate the construction of an agricultural power and realize the modernization of agriculture and rural areas. Facing the Chinese-style modernization, the digital economy has achieved remarkable results in the accelerated iterative innovation of digital technology, the rapid development of digital finance, and the rapid rise of e-commerce platforms, providing a strong driving force for the high-quality development of the feed industry. From the perspective of internal mechanism, the digital economy can not only effectively improve the internal management efficiency of feed enterprises, but also help to build a good corporate brand image, accelerate the reconstruction of traditional marketing channels, and drive the high-quality development of the feed industry. From the perspective of the realization path, feed enterprises should focus on promoting the development of digital trade and expanding the export scale of feed products, take promoting the orderly flow of data as a foothold to reduce the transaction costs of the industrial chain, take strengthening the construction of online platforms as a starting point to expand the space for industrial value-added and efficiency enhancement, thereby promoting the high-quality development of the feed industry. [ABSTRACT FROM AUTHOR]

Left ventricular assist devices (LVAD) are surgically implanted mechanical support devices utilized with increasing frequency as a bridge to myocardial recovery, destination therapy, and heart transplantation. While the use of such devices in patients with advanced heart failure has shown significant survival benefits and improved quality of life, they bear their own risks and complications.1 Bleeding, infection, pump thrombosis, and stroke are just some of the serious complications associated with LVADs.2 LVAD-associated pseudoaneurysms are rare, with prior reports of occurrence at the left ventricular apex and at the anastomosis site of the outflow graft to the ascending aorta.3,4 Typically, this device-related complication requires surgical repair and, if at all feasible, heart transplantation. However, in cases of difficult anatomy, unfavorable position, and significant comorbidities, surgery may be contraindicated due to high surgical risk. This case portrays a patient suffering from a left ventricular pseudoaneurysm after HeartMateIII implantation that was not amenable to surgical repair due to heightened surgical risk. We document the first pseudoaneurysm associated with the HeartMate-III in available literature and describe a novel management strategy of documented nonoperative course of LVAD-associated pseudoaneurysm, with the patient surviving 56+ months with medical optimization and management. [ABSTRACT FROM AUTHOR]

Arginine deiminase (ADI) has been widely employed for the biological production of nutraceutical citrulline. However, sub-optimal enzyme properties, operational instability, and difficulty in reuse impede its industrial usage as well as increase the production cost. The present study describes a simple, cost-effective carrier-free immobilization technology that can circumvent these limitations. ADI was precipitated and cross-linked with glutaraldehyde to obtain novel cross-linked arginine deiminase aggregates (ADI-CLEA). A neural network-based multiobjective genetic algorithm (MOGA-NN) was employed to optimize the immobilization of ADI. MOGA-NN optimization yielded ADI-CLEA with 0.30 IU/mL activity and 61 % recovery. Structural characterization using TEM, SEM, FTIR, DLS/ZETA revealed that the developed ADI-CLEA were monodisperse, ultra-porous, and highly stable. ADI-CLEA showed an improved pH /temperature profile and resistivity against denaturants than free enzyme. The efficiency of ADI-CLEA in the industrial production of nutraceutical citrulline was also studied. ADI-CLEA produced 215.4 g/L citrulline in first cycle of usage. Reusability and storage stability analysis further showed that ADI-CLEA could be reused for more than seven consecutive cycles and stable for up to 50 days. In conclusion, this novel formulation increases the stability/robustness of ADI, eases the product separation process and could be reused for economically viable bioproduction of nutraceutical citrulline in industries. [Display omitted] • Novel cross-linked enzyme aggregates of arginine deiminase (ADI-CLEA) were reported. • Immobilization conditions were optimized by network-based multiobjective genetic algorithm. • ADI-CLEA exhibited enhanced catalytic efficiency, stability and robustness. • Improved citrulline yield (215 g/L) was obtained by ADI-CLEA. • ADI-CLEA could be recycled for seven cycles without losing its activity. [ABSTRACT FROM AUTHOR]

Technological advances in the management of diabetes, especially type 1 diabetes (T1D), have played a main role in significantly improving glycemic control of these patients in recent years. Undoubtedly, the most important advance has been the commercialization of hybrid closed-loop systems (HCL). Their effectiveness places them in the different guidelines from scientific societies as the gold standard for the treatment of people with T1D. However, obtaining the maximum performance from these systems requires a degree of expertise from the professionals who care for these patients. Specifically, the Tandem X2:slim with Control-IQ technology system, due to its features and configuration options and adjustments, allows T1D patients to better adapt the management of diabetes to multiple circumstances in their day-to-day life. It is necessary, however, to follow a systematic process to start the system and also for the subsequent follow-up, which allows its optimization in the shortest possible time. This expert recommendation reviews the main features of this HCL system, suggesting how to implement it and optimize its use after gaining experience treating many patients. [ABSTRACT FROM AUTHOR]

With the wide application of high proportion of distributed clean energy in regional microgrids, the issue of maximizing the utilization of renewable energy among multi-microgrids has become a research hotspot. Based on the proposed multi-microgrids' energy collaborative optimization and complementation model, a multi-microgrids' energy real-time optimization management and dispatch strategy is proposed that fully considers the real-time complementarity of renewable energy between multi-microgrids and achieves the best coordinated dispatch of energy. Two typical scenarios were set up in the IEEE 33-bus network model for verification, and the synergistic effects of different schemes were compared and set up. The data obtained demonstrate that the dispatch and management strategy proposed in this paper can achieve the maximum integration of renewable energy and the lowest operating cost among multi-microgrids, and it also validates the real-time, feasibility, and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research. [ABSTRACT FROM AUTHOR]

Video surveillance systems have been widely used in public security and urban management. Following the increasing demand for video surveillance in various countries worldwide, the number of outdoor surveillance cameras is rising. Their unified management and layout optimization have become a great challenge in the construction of video surveillance. Although there is a large number of outdoor surveillance cameras, there exists a lack of spatial location information, leading to difficulties regarding their quantification, space management, and construction status analysis. Hence, a method for estimating the location and orientation of outdoor surveillance cameras based on spatial segmentation and the use of a rapid survey device was proposed. Using the position information obtained from the movement of a designed marker and surveillance images simultaneously captured by each camera, we constructed an initial camera space and segmentation plane using binary space partitioning. By recursive segmentation, the real location of each camera is gradually approximated, and the azimuth angle of the camera orientation is calculated, achieving the rapid estimation of the spatial location and orientation of all surveillance cameras. A city area spanning 2.8 km2 was selected as an experimental area to verify the method proposed here, yielding an average error of 1.78 m between the estimated and actual locations of the cameras and a maximum error of 3.87 m. The average offset between the estimated and actual orientation was 3.13°, with a maximum offset of 6°. Thus, the target of the surveillance camera's estimated deviation is still in the visible range of the operator. The proposed method represents a new way for the rapid investigation of outdoor surveillance cameras and provides a basis for their unified management and layout optimization. [ABSTRACT FROM AUTHOR]

The objectives of the research are 1) To find out how the Pamong Praja Police Unit (Satpol PP) behaves as a Public Servant in curbing illegal traders in the Pedesterian Area of Airmadidi Market, North Minahasa Regency; 2) To find out what is hindering the Pamong Praja Police Unit in controlling illegal traders in the pedestrian area of Airmadidi Market, North Minahasa Regency. Researchers used qualitative research methods with snowball sampling techniques. The results of the research show that 1) activities to control Street Vendors (PKL) on the roads and sidewalks (Pedestrian) of the Airmadidi market by the Pamong Praja Police Unit of North Minahasa Regency have been carried out but are not yet optimal due to a lack of knowledge, street vendors, lack of understanding of the function of roads and sidewalk. Not all street vendors understand the regulations of the North Minahasa regional government, even though they have been socialized; 2) Understanding of the community, street vendors and members of the Pamong Praja Police Unit regarding their respective roles as obligations which has resulted in the implementation of street vendor control activities not being optimal; 3) The control of street vendors has not been achieved and is not yet optimal due to inhibiting factors, including a lack of qualified human resources, inadequate infrastructure and limited operational costs; 4) Satpol PP's behavior in taking action against street vendors who violate regional regulations always starts with direction and guidance through good communication; 5) Action against street vendors who violate regional regulations selling in the Airmadidi Market district is carried out through warnings in accordance with the established SOP. [ABSTRACT FROM AUTHOR]

This paper proposes a new metaheuristic optimization algorithm, namely thermal exchange optimization (TEO), to solve the optimal power flow (OPF) problems. Various conflict objective functions, such as the total fuel cost (TFC), the total power loss, total emission gas (TEG), and the total voltage deviation have been optimized individually and simultaneously. The proposed TEO is validated on the electric test system Institute of Electrical and Electronics Engineers 30-Bus. The optimization results achieved by the proposed method in solving single-objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results clearly show the superiority of the proposed method in the majority of the case studies, with a better solution and competitive computational time. In contrast, the proposed multi-objective TEO (MOTEO) based OPF is investigated to solve the multi-objective OPF. It can be noticed from the results obtained that the proposed MOTEO achieved the better optimum compromise solution with a TFC value of 822.4796 $/h and a TEG value of 0.26939 ton/h, which yields a competitive total cost (970.8219 $/h) compared to those obtained by other algorithms. Moreover, the statistical analysis proves that the proposed MOTEO needs a lower number of trials to locate the best solution, also the standard deviation required to solve the single-objective problems is 0.03361, which is better compared to other techniques. The simulation results achieved by this method compared with other competitive algorithms proved the superiority of MOTEO in finding better solutions while also producing a high-quality Pareto front with appropriate precision. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of the maintenance management problem regarding airport pavements is addressed by developing a series of fast computational procedures whose rationales pursue both practical and economical objectives. Particularly, the study involves the design of five new heuristic algorithms replicating different management strategies that can be undertaken by the airport managers in a certain time horizon for reducing the pavement maintenance costs while considering the impact on the service level of the airport. Each algorithm works on the basis of a preliminary computational framework that has a twofold scope: (i) selecting the pavement portions to be maintained during the provided time horizon. (ii) using a K-means method based on three well-known performance indicators, which, namely, are Residual Life (RL), International Roughness Index (IRI) and Pavement Condition Index (PCI), to group the selected portions into homogenous clusters named work-zones. To evaluate the effectiveness and the efficiency of the proposed optimization algorithms in coping with the maintenance programming problem under investigation, an extended design of experiments based on international airport regulations has been arranged. The obtained numerical results revealed that no single strategy can be selected as the most performing in terms of cost and quality conditions of the pavements. To make robust the numerical results, a sensitivity analysis is conducted to evaluate the influence of the total cost of maintenance on runway length and number of sections. However, the results obtained in this study provide a series of managerial implications, further expanding the research contribution. [ABSTRACT FROM AUTHOR]

Batteries are classically used as backup in case of power outages in telecommunications networks to keep the services always active. Recently, network operators use the batteries as a demand response lever, so as to reduce the energy costs and to generate revenues in the energy market. In this work, we study how the telecommunications operator can optimize the use of a battery over a given horizon to reduce energy costs and to perform load curtailments efficiently, as long as the safety usage rules are respected. First, we formulate the related optimization problem as a mixed integer program taking into account the telecommunication engineering and battery operation constraints, as well as the market rules. Second, we give two practical variants of the problem that can be solved to optimality in polynomial time thanks to a graph-oriented algorithm we propose. We also use this algorithm as a heuristic to solve efficiently the main problem. Finally, simulations based on real data from the French telecommunications operator Orange show the relevance of the model and of the graph-oriented algorithm: these prove to be computationally efficient in solving large scale instances, and significant savings and revenues can be generated through our optimized battery management policies. [ABSTRACT FROM AUTHOR]

Purpose: Slotting is one of the main operations in warehouse management. It is based on the efficient allocation of slots for stock-keeping units (SKUs). Order picking and slotting represent a high percentage of total logistics costs; therefore, improving these activities leads to significant savings in overall performance. This paper aims to develop an allocation model integrating SKUs physical variables, warehousing design and operation (dimensions, layout, material handling equipment), and heterogeneous product demand. Design/methodology/approach: The modeling methodology considers two phases. First, an integer linear programming model for warehousing spaces assignment for SKUs considering priority and required orders is developed. Then, the total operation times using different strategies are calculated. Findings: The effectiveness of the model was verified through simulation using historical data. The results showed that the best performance in the total time of the slotting operation is achieved by using the ABC as a criterion for the classification of the SKUs and by sequentially assigning the row, level, column, and section. Practical implications: This approach can be adapted to different industrial sectors and serves as a basis for more robust models regarding the number of constraints or the incorporation of additional warehouse operating parameters. Originality/value: The most important contribution of this work is the development of an adaptable methodology to changes in the operation to improve the efficiency of storage management through slotting. Future work includes other objective functions of sustainable operations and uncertainty treatment techniques. [ABSTRACT FROM AUTHOR]

People make future plans with the aim of simplifying their lives, and these plans are essential for preparing for forthcoming challenges. Forecasting methodologies take precedence in order to anticipate and plan for future events. Time series data stands out as a pivotal information type employed for predicting the future. This research introduces a framework for selecting the optimal model among classical artificial neural networks in time series forecasting. The classical artificial neural networks considered encompass the LSTM, CNN, and DNN models. The framework employs various parameters -- including the dataset, model depth, loss functions, minimal success rate in model performance, epochs, and optimization algorithms -- to determine the best-fitting model. Users have the flexibility to adjust these parameters to address specific issues. By default, the framework incorporates seven distinct loss functions and five optimization algorithms to facilitate model selection. The mean average error loss function is used as the metric for evaluating model performance. To validate the framework, Brent oil prices were utilized as the dataset in a series of tests, encompassing a total of 9000 daily price data points. The dataset was partitioned into 80\% for training and 20\% for testing purposes. The training iterations within the framework were 50 epochs. In the test scenarios, the price for the eighth day was predicted using price data from the preceding seven days. Consequently, a mean average error score of 1.1239657 was achieved. The results showed that the LSTM model, comprising two layers, the Adadelta optimization algorithm, and the mean square error loss function, emerged as the most successful configuration. [ABSTRACT FROM AUTHOR]

The current nursing situation in Ecuador is characterized by complex challenges that affect health care throughout the country. The uneven geographic distribution of nurses, quality of training, and staff retention are critical areas requiring attention. Therefore, this study focused on analyzing the optimization of the nursing workflow in Ecuador from a neutrosophic perspective, which considers the indeterminacy of the existing elements. The results revealed the need for an automated system for optimizing nursing workflow based on the diversity of local conditions and needs. In conclusion, it was seen that patient retention policies and the training of health personnel are fundamental, but their impact varies depending on the region analyzed. This neutrosophic approach highlights the complexity and need for adaptation in the management and optimization of nursing workflow in Ecuador. [ABSTRACT FROM AUTHOR]

This paper focuses on a systematic assessment of the impact of development projects on different performance areas, with an emphasis on social. In the context of the ever-evolving digital era, development projects are becoming a key element for achieving competitiveness and sustainability in all sectors. The aim of this paper is to analyze how development (housing) projects affect economic growth, firm efficiency, social interaction and sustainable development from a theoretical knowledge perspective. [ABSTRACT FROM AUTHOR]

To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOCUC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOCB was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. [ABSTRACT FROM AUTHOR]

This paper forecasts the energy efficiency coefficients at the Polish province level (NUT-2), based on imperfect and contradictory knowledge. On the one hand, we have information on the aggregated national energy efficiency coefficients in the industrial, transport, household, and service sectors. On the other hand, we also have information on the energy intensity at the Polish province level. Since the two samples are of different natures and known with uncertainty, we are obviously dealing with an inverse stochastic problem whose solution requires particular statistical devices. The applied technique of non-extensive cross-entropy econometrics generalizes the Shannon-Kullback-Leibler approach based on the Gaussian assumptions. Its justification is explained throughout this paper from both methodological and empirical points of view. The model forecasts lead to the high-value energy efficiency estimates from quasi-unstructured sets of information. This constitutes the main contribution of this research. These outputs should provide energy policy units with valuable new devices for the optimization of the energy management processes on a disaggregated local level where, by contrast, different agents and households act decisively. On a global level, the proposed technique can be applied in different EU countries and elsewhere, in the context of experimental official statistics. [ABSTRACT FROM AUTHOR]

We have developed, manufactured, and tested a new feed design for interferometric radio telescopes with "large-N, small-D" designs. Such arrays require low-cost and low-complexity feeds for mass production on reasonable timescales and budgets, and also require those feeds to be compact to minimize obstruction of the dishes, along with having ultra-wide frequency bands of operation for most current and future science goals. The feed presented in this paper modifies the exponentially tapered slot antenna (Vivaldi) and quad-ridged flared horn antenna designs by having an oversized backshort, a novel method of maintaining a small size that is well-suited for deeper dishes (f ∕ D ≤ 0. 2 5). It is made of laser cut aluminum and printed circuit boards, such that it is inexpensive (≲ 7 5 USD per feed in large-scale production) and quick to build; it has a 5:1 frequency ratio, and its size is approximately a third of its longest operating wavelength. We present the science and engineering constraints that went into design decisions, the development and optimization process, and the simulated performance. A version of this feed design was optimized and fabricated for the Canadian Hydrogen Observatory and Radio-transient Detector (CHORD) prototypes. When simulated on CHORD's very deep dishes (f ∕ D = 0. 2 1) and with CHORD's custom first-stage amplifiers, the on-sky system temperature T sys of the complete receiving system from dish to digitizer remains below 30 K over most of the 0.3–1.5 GHz band, and maintains an aperture efficiency η A between 0.4 and 0.6. The entire receiving chain operates at ambient temperature. The feed is designed to slightly under-illuminate the CHORD dishes, in order to minimize coupling between array elements and spillover. [ABSTRACT FROM AUTHOR]

Since the employee representatives on the board of directors and board of supervisors were adopted by the Company Law in 1993, they have been powerless and gradually neglected by the policymakers and the enterprises. However, to realize the ambitious commitments regarding the labour protection in the agreement 'in principle' for a new the EU-China Comprehensive Agreement on Investment (CAI), reached by the EU and China on December 30, 2020, China must promote the companies to enforce the labour laws consciously by improving these employee governance mechanisms. Under the Company Law, both the employee directors and employee supervisors are mandatory for the SOEs, while the private companies can decide whether they install the employee directors. Due to the dramatic increment of the employees in the private enterprises brought out by the excessive pursuit of efficiency rather than equity in the reform of SOEs and the failure of the labour laws as the external regulations of companies to isolate the workers from the intolerable exploitation in the large private companies the Amendment of Company Law, released on December 24, 2021, has realized the significance of employees' protection in the large private enterprises. But it has not yet changed the shareholders' meeting centred model, which fundamentally restricts the functions of the employee directors and employee supervisors. In this regard, this article suggests that the new Company Law should eliminate the supreme power of shareholders' meeting and impose the definite duty of complying with the labour laws upon the decision-makers of the corporations. [ABSTRACT FROM AUTHOR]

• Efficiency of water companies was evaluated using alternative parametric methods. • Efficiency scores based on SFA, BSFA and StoNED were 0.623, 0.583 and 0.522, respectively. • Quality of service variables influence the performance of water companies. The evaluation of efficiency can be of great value to water companies and regulators to adopt policies and design incentives to enhance performance. This study delves into the implications of employing distinct methodologies, namely the classical Stochastic Frontier Analysis (SFA), Bayesian SFA, and Stochastic non-parametric Envelopment of Data (StoNED), to evaluate cost and quality of service efficiency within the water industry. Chilean water companies reported average efficiencies of 0.623, 0.583, and 0.522 using the SFA, BSFA, and StoNED approaches, respectively. Furthermore, the SFA analysis suggested that the performance of water companies experienced a decline of −0.59% per year from 2010 to 2018. In contrast, the BSFA and StoNED estimations indicated an opposite trend, with annual performance improvements of 0.51% and 0.17% respectively, over the same period. These findings underscore the critical role of selecting appropriate methodologies when interpreting and comparing efficiency results for making informed long-term decisions. [ABSTRACT FROM AUTHOR]

In this paper, Optimization of System Parameters of 2:3 Good Serial System controlled with the help of a controlling unit Using Deep Learning Optimization with packing unit in series with priority in repair and single server which never fails is carried out. There are three units of different capacities working in parallel in which if three/two units are working then the system is working at full/reduced capacity. Working of these online parallel and offline units is managed by the controlling unit, which also manages the preventive maintenance of all type of units, together with a 24/7 repair facility is modeled for reliability performance measurements. Taking exponential failure and repair rates of units and facilities a steady state transition diagram (depicting transition rates and states) is drawn using Markov process. The system parameters are modelled using Regenerative Point graphical Technique (RPGT) and optimized using Deep learning methods such as Adam, SGD, RMS prop. The results of the optimization may be used to validate and challenge existing models and assumptions about the systems. [ABSTRACT FROM AUTHOR]

In many industries and applications, downtime or failure can have serious consequences, such as financial losses, safety hazards, or reputational damage. A hot standby unit can help minimize the impact of such events by providing a backup that can quickly and seamlessly take over in the event of a failure. Further, the question of as to how many hot standby units should be used also needs to be addressed. So, an N+1-Unit-system is investigated wherein N units are on hot standby, whereas one unit is operational and the system is such that the hot standby units can take over seamlessly if the single operative unit fails. The system breaks down completely when all the units fail. It is assumed that the failure rates of all the operational units and the redundant units will vary exponentially. To get different performability measurements, the regenerative point technique has been applied to optimize the value of N. [ABSTRACT FROM AUTHOR]

The market for the halal food and beverage industry sector has experienced rapid growth in recent years, which indicate excellent investment opportunities. This paper examine the effect of Technical Efficiency (TE) on firm value in 5 selected influential countries in halal food and beverage sector based on Global Islamic Economy Report 2020. Two steps estimation was used to run the data, using the Stochastic Frontier Analysis (SFA) model to determine the company's TE and panel data to test the effect of TE through firm value. The results show that Indonesia has the highest score for TE (62%), followed by Pakistan (59%), South Africa (57%), Malaysia (55%), and Singapore (52%), which means, in general, there is inefficiency in allocating resources over 38% up to 48% and needs to be improved by halal food and beverage companies in. Regarding panel data, all countries sample except Pakistan highlight that TE significantly affect company value. It indicates that the crucial part of managing efficiency can be a sign in stock market performance. The result shows that company managers should set efficiency strategies to their business process for creating sustainability and increase their value in the capital market. As for investors, this TE can be used as an indicator before choosing company stocks; if the company is efficient, then it is worthy of being one of the portfolio assets. Form the government side, the finding can help them to set appropriate policy setting to boost halal food and beverages industry such as giving subsidy or incentive to increase the efficiency ability of halal food and beverage companies and identify the industry's strength by comparing the result of TE between 5 countries. [ABSTRACT FROM AUTHOR]

Intelligent connected vehicles have garnered significant attention from both academia and industry in recent years as they form the backbone of intelligent transportation and smart cities. Vehicular networks now exchange a range of mixed information types, including safety, sensing, and multimedia, due to advancements in communication and vehicle technology. Accordingly, performance requirements have also evolved, prioritizing higher spectral efficiencies while maintaining low latency and high communication reliability. To address the trade-off between communication spectral efficiency, delay, and reliability, the 3rd Generation Partnership Project (3GPP) recommends the 5G NR FR2 frequency band (24 GHz to 71 GHz) for vehicle-to-everything communications (V2X) in the Release 17 standard. However, wireless transmissions at such high frequencies pose challenges such as high path loss, signal processing complexity, long pre-access phase, unstable network structure, and fluctuating channel conditions. To overcome these issues, this paper proposes a deep reinforcement learning (DRL)-assisted intelligent beam management method for vehicle-to-vehicle (V2V) communication. By utilizing DRL, the optimal control of beam management (i.e., beam alignment and tracking) is achieved, enabling a trade-off among spectral efficiency, delay, and reliability in complex and fluctuating communication scenarios at the 5G NR FR2 band. Simulation results demonstrate the superiority of our method over the 5G standard-based beam management method in communication delay, and the extended Kalman Filter (EKF)-based beam management method in reliability and spectral efficiency. [ABSTRACT FROM AUTHOR]

Agricultural industry agglomeration is an important driving force for the green and high-quality development of agriculture. Based on the perspective of agricultural industry agglomeration, this paper discusses the impact of agricultural industry agglomeration on the level of green development of agriculture, which is of great significance for the realization of sustainable development of agriculture and the implementation of the “double carbon” goal. Based on the panel data of 30 provinces (municipalities) and autonomous regions in China from 2011 to 2020, the non-expected Super-SBM model and location entropy index method were used to measure the efficiency of agricultural green development and the level of agricultural industry agglomeration. The spatial Dubin model, tradition and spatial Markov chain were used to demonstrate the influence of agricultural industrial agglomeration on agricultural green development efficiency and its spatio-temporal dynamic evolution.The results showed that: (1) There was spatial autocorrelation in agricultural green development efficiency. Areas with higher agricultural green development efficiency will drive the improvement of agricultural green development efficiency in surrounding areas, with a positive spillover effect. (2) Industrial agglomeration can improve the efficiency of agricultural green development, and this enhancement has spatial spillover. (3) The influence of agricultural industrial agglomeration on agricultural green development efficiency and its spatial spillover have regional heterogeneity. The spillover effect of AIA to AGDE in the eastern region is greater than that in the central and western regions. Based on this, it is necessary to grasp the spatial correlation law of provincial agriculture, promote the sustainable development of regional agriculture, optimize the allocation of resources, and put forward policy implications for the improvement of agricultural green efficiency from the aspects of financial support, industrialization, human capital and urbanization. [ABSTRACT FROM AUTHOR]

This paper presents a machine vision system that performs the automatic positioning of optical components in LED modules of automotive headlamps. The automatic adjustment of the module is a process of great interest at the industrial level, as it allows us to reduce reworks, increasing the company profits. We propose a machine vision system with a flexible hardware–software structure that allows it to adapt to a wide range of LED modules. Its hardware is composed of image-capturing devices, which enable us to obtain the LED module light pattern, and mechanisms for manipulating and holding the module to be adjusted. Its software design follows a component-based approach which allows us to increase the reusage of the code, decreasing the time required for configuring any type of LED module. To assess the efficiency and robustness of the industrial system, a series of tests, using three commercial models of LED modules, have been performed. In all cases, the automatically adjusted LED modules followed the ECE R112 regulation for automotive lighting. [ABSTRACT FROM AUTHOR]

Since the 1980s, many electricity markets around the world have undergone restructuring aimed at improving efficiency. In 2002, the Chinese government also introduced a vertical unbundling reform to dismantle vertical monopolies and boost efficiency in the power sector. This study estimates the unified, operational, and environmental efficiency to comprehensively assess the impact of China's vertical unbundling on the production performance of thermal power firms. A non-radial data envelopment analysis (DEA) approach is employed to measure these efficiencies. Subsequently, a difference-in-difference model is used to assess the reform's effects. Empirical results show that the vertical unbundling improved the unified efficiency of firms. Further analysis indicates that the reform improved the operational efficiency of firms by increasing their capacity utilization and coal quality. Additionally, we find that the reform led to an increased utilization of source control technologies but resulted in a reduction in the use of end-of-pipe treatments. Due to the opposing effects on different pollution treatment stages, the reform did not have a significant impact on environmental efficiency. [ABSTRACT FROM AUTHOR]