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The increasing development of the competitive market has forced organisations to make great efforts in the processes of supply, production, and distribution to meet customer demand in the shortest time and at the lowest cost. A cross-docking (CD) system is one of the successful and practical strategies in this field considered by researchers in various fields. Also, business process management plays a key role in continuous improvement and increased productivity. In today's digital age, due to the ability to record all activities, process mining is an important method to identify the current situation and improve productivity. In this research, a newly established CD belonging to a chain store is studied to improve the current situation, in which different goods enter and then exit after different processes. The purpose of this study is to obtain the optimal number of doors and loaders as sources. First, helping an RFID system, all activities are recorded, and the current situation of the processes is monitored, and then, the real process model is identified using heuristic and inductive miner algorithms. After adapting to the event log by using the simulation process in Arena software, different scenarios are examined, and the best possible case is presented. [ABSTRACT FROM AUTHOR]

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Critics opine that full expensing of research and development (R&D) depresses near-term profits and incentivizes myopic managers to under-invest in R&D, compromising firm efficiency. Advocates of the expensing rule argue that little rigorous research evidence supports the claimed adverse consequences. We examine the impact of the R&D expensing rule on firm efficiency by exploiting an exogenous shock: a shift in the accounting regime in Germany from full expensing to partial capitalization of R&D when it mandated International Financial Reporting Standard (IFRS) adoption in 2005. We employ Stochastic Frontier Analysis and Data Envelopment Analysis to estimate efficiency for the same German firms before and after the IFRS adoption. We find robust evidence of efficiency improvement in the post-period relative to the pre-period for German R&D firms that report R&D expenditures, and for both early adopters and timely adopters. We also document that financially constrained firms and firms experiencing rapid R&D growth prior to the IFRS adoption show greater efficiency improvement. Moreover, we conduct three falsification tests to make sure our results are not attributable to other accounting changes associated with the IFRS adoption, and find no efficiency improvement for the three control groups (German "no-R&D" sample, U.K. firms, and Australian firms), respectively. We conclude that the change in the R&D reporting rule is the likely catalyst for improvements in efficiency of German R&D firms. [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.)

Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish. Synopsis: Evolution of ten thousand yeast populations from many strains for eight desired wine fermentation related traits broadened the evolutionary search spectrum and lead to wine yeasts with improved metabolic characteristics. Expansions of both replicates and lineage numbers led to better performing wine yeasts that were unencumbered by unwanted side-effects. Evolutionary gains coincided with distinct side effect syndromes that were characteristic of each selection niche. High-performing strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures. Evolution of ten thousand yeast populations from many strains for eight desired wine fermentation related traits broadened the evolutionary search spectrum and lead to wine yeasts with improved metabolic characteristics. [ABSTRACT FROM AUTHOR]

In the United States, the annual revenue attributable to tomato production is $1 billion. However, tomato production can cause negative environmental impacts, such as water pollution, often in the form of eutrophication-causing nutrient pollution. Hydroponic production can decrease excess nutrient leaching; however, optimization of nutrient management and cultivar choices could further decrease excess nutrient discharges. The objectives of this study were as follows: to evaluate and compare the responses of tomato growth characteristics, yield, and yield components to two nutrient management regimes (varying nutrient solution concentrations by growth stage and the use of a constant nutrient solution concentration from transplant to termination), and to analyze the effects of growth habits among six cultivars (Big Beef, Cherokee Purple, Heatmaster, Legend, Mountain Fresh Plus, and Tropic) on tomato yield and yield-correlated morphological characteristics. The nutrient management strategies were applied to tomato plants, and data regarding yield and related morphological characteristics were obtained. Data were analyzed using SAS PROC GLM. An analysis revealed no significant difference in the total fruit weight/plant between nutrient management regimes (P 5 0.05); however, the mean fruit weight (164.26 g) and diameter (71.70 mm) were significantly greater (P < 0.0001) for plants that received the constant concentration nutrient regime. Indeterminate plants had a significantly greater (P < 0.0001) mean fruit weight (192.76 g) and mean fruit diameter (76.42 mm). Among cultivars, Big Beef had a significantly greater (P < 0.05) total fruit weight/plant (9.25 kg). Applying a constant nutrient concentration to indeterminate cultivars, particularly Big Beef and Cherokee Purple, improved the factors analyzed and could decrease negative environmental impacts while increasing profits of the producers. [ABSTRACT FROM AUTHOR]

The various analytical indices of coal are important criteria for evaluating the quality of commercial coal. Coals of different qualities exhibit different physical and chemical characteristics in their utilization. In the case of multiple coal types, the spectral characteristics of different coals may overlap within certain wavelength ranges, or be affected by interference or noise from other coal types, leading to low accuracy in coal quality prediction. Rapid and accurate coal quality testing is of great significance for improving industrial production efficiency and enhancing corporate profitability. This study employs near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) combined techniques to explore the accuracy and feasibility of predicting coal quality based on coal type classification models. In terms of classification algorithms, coal samples are identified and classified using Support Vector Machine (SVM) based on fusion spectra. Regarding the modeling approach, Partial Least Squares (PLS) is utilized to establish both an overall model for all coal samples and individual classification models corresponding to each coal type. The results show that the precision, accuracy, recall, and F1 score of this classification algorithm reached 96.49%, 97.50%, 95.83%, and 96.41%, respectively. The determination coefficients (R2) for the classification model's predictions of ash, volatile matter, and sulfur in coal quality indicators reached 0.992, which represents improvements of 1.85%, 5.31%, and 10.10% over the overall model. The root mean square errors of prediction (RMSEP) for these indicators were 0.062, 0.080, and 0.008, showing reductions of 0.24%, 0.68%, and 0.05% compared to the overall model. It indicates that the method of first identifying the coal type and then predicting coal quality indicators using the corresponding classification model can significantly improve the accuracy of coal quality detection in complex coal type scenarios. [ABSTRACT FROM AUTHOR]

Personal protective equipment (PPE) plays a crucial role in safeguarding the well-being of workers on construction sites by mitigating the risk of accidents and incidents. Nevertheless, incidents stemming from a lack of awareness regarding PPE usage persist. To enhance the oversight of construction laborers for accident prevention, most current approaches involve overseeing the availability and correct utilization of PPE using intricate data processing techniques. However, these methods encounter challenges related to their applicability and capacity for universal implementation. This study presents the hybridization of seahorse optimization (SHO) algorithm with the YOLOv5 model to ultimate detection accuracy. First, an improved YOLO network is constructed through scale and loss function enhancement. Second, the SHO is utilized to tune the parameters of the improved YOLOv5. A data set with 4535 construction work images was collected for training, validation and testing the hybrid SHO-YOLOv5 model. Experimental results supported by statistical tests show that the SHO-YOLOv5 is a capable method for detecting automatically the PPE with a precision value of 0.74, recall value of 0.65 and F1 score of 0.68. The SHO-YOLOv5 surpasses the benchmarked models like YOLOv8, YOLOv5, Faster-R-CNN MobilenetV3, and Faster-R-CNN Resnet50. Therefore, the proposed hybrid approach is a promising tool to support the project managers in the task of safety inspection. [ABSTRACT FROM AUTHOR]

Innovation resource allocation performance is an important driver of global competitiveness of nations. Due to the complexity of innovation production process, the structure of the innovation resource allocation is no longer single, but most of the existing studies on the innovation resource allocation performance are based on series cases overlooking parallel cases. We evaluate the innovation resource allocation efficiency of Chinese industrial enterprises from the research and development (R&D) stage and the commercialisation stage, where the commercialisation stage consists of two parallel sub-stages (technology commercialisation stage and product commercialisation stage). As a result, we develop a cross two-stage data envelopment analysis model (TS-DEA model) with nested parallel structure. The result shows that traditional DEA model overestimates the efficiency score due to pursuing the efficiency optimisation for decision-making unit (DMU). Compared with western China, central China was more efficient in allocating innovation resource for industrial enterprises. [ABSTRACT FROM AUTHOR]

We explore the effect of overlapping green policies on investment efficiency of listed firms in China, by regarding the dual policies of green credit and low-carbon city pilot in 2012 as a quasi-natural experiment. The results show that the superposition of green policies can more significantly improve investment efficiency of high-polluting firms than the green policy alone. In addition to limiting credit availability and increasing financial constraints, green policies also influence firms' ESG ratings, encourage green upgrading and transformation, and increase green investment to promote firm investment efficiency. Further research finds that green policies significantly contribute to investment efficiency improvements for state-owned firms, large-scale firms, firms with higher long-term debt, and capital-intensive firms. Therefore, coordinated green policies provide an opportunity to achieve a win-win situation of "pollution control" and "efficiency improvement". [ABSTRACT FROM AUTHOR]

Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into control loops. Additionally, Denial-of-Service attacks can be executed by inundating the network with valueless noise. Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models. Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow. Standard models, with their limited understanding of mobile networks, can achieve precision rates no higher than 75%. Therefore, more general and transversal protection mechanisms are needed to detect malicious behaviors transparently. This paper introduces a probabilistic trust model and control algorithm designed to address this gap. The model determines the probability of any node to be trustworthy. Communication channels are pruned for those nodes whose probability is below a given threshold. The trust control algorithm comprises three primary phases, which feed the model with three different probabilities, which are weighted and combined. Initially, anomalous nodes are identified using Gaussian mixture models and clustering technologies. Next, traffic patterns are studied using digital Bessel functions and the functional scalar product. Finally, the information coherence and content are analyzed. The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters. An experimental validation based on simulation tools and environments was carried out. Results show the proposed solution can successfully detect up to 92% of malicious data injection attacks. [ABSTRACT FROM AUTHOR]

Using a sample of Chinese listed enterprises over the 2010–2020 period, this study examines and empirically tests the impact of board diversity on inefficient investment. Drawing on principal-agent theory and resource dependence theory, the analysis is conducted within a comprehensive framework. The study provides evidence that board diversity plays a crucial role in mitigating inefficient investment in enterprises by effectively restraining both overinvestment and underinvestment. Mechanism tests indicate that under conditions of high agency costs and high financing constraints, board diversity exerts a stronger inhibitory effect on inefficient investment. Cross-sectional tests show that the impact of board diversity on inefficient investment varies under different ownership properties, firm sizes and levels of marketization. By comprehensively considering multiple characteristics of board diversity, this research fills the research gap and expands the perspectives on inefficient investment in enterprises. It provides valuable insights for shareholders and enterprises on effectively structuring their boards of directors. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasingly intensified conflicts between the development of heavily polluting enterprises and the resource environment, Confucian culture, which advocates environmental protection, has gradually penetrated into management decisions and has become an important tool for promoting green investment and transformation of enterprises. This study investigates the impact of Confucian culture on green investment efficiency using the SBM-DEA model and a multidimensional fixed-effects model (time, year, and region) with Chinese heavy polluting enterprises as a sample from 2016 to 2020. The findings of the study indicate that Confucian culture can enhance the efficiency of firms’ green investments. This is achieved by fostering human capital and managerial capabilities. This relationship is more pronounced among firms in regions with fewer environmental regulations and among state-owned enterprises. It is therefore recommended that heavily polluting enterprises, with the support of the government, should give play to the guiding role of Confucian culture and integrate the spirit of Confucianism into their operation and management. The results of this paper verify the effectiveness of Confucian culture, which is very important for accelerating the green development of heavy polluting enterprises and enriching and expanding related research. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a well-organizing network that provides efficient communication services. However, high energy usage and overloaded data sometimes become the key challenges that degrade the entire network's performance. Energy management and load balancing are critical processes for increasing the network lifespan. Therefore, in the present research, a novel Aquila-based Radial Basis Model (AbRBM) is created for vehicular network communication to reduce energy usage and balance the load with the CH selection. Initially, the required nodes are deployed in the network. Based on Aquila's optimal functions, the higher energy nodes are monitored and removed; optimal CH is selected for the data transfer. Finally, the load is balanced by sharing the data between the overhead and the rest of the nodes. The created framework was tested in the NS2 environment, and the network efficiency parameters were calculated. The model achieved a 98.3% Packet delivery rate, 1.10 mJ of average energy usage rate, 0.025-s delay, 3.8Mbps average throughput, and lifetime as 6210 rounds for the created 100 nodes in the WSN. Also, the control overhead rate is reduced to 0.42 KB. To validate the improvement, the results are related to the existing models. The higher Performance of the AbRBM satisfied the network's efficient energy management and load-balancing process. The model provided an efficient and reliable WSN for various applications. [ABSTRACT FROM AUTHOR]

This survey aims to provide insightful and objective perspectives on the research history of quantitative portfolio management strategies with suggestions for the future of research. The relevant literature can be clustered into four broad themes: portfolio optimization, risk-parity, style integration, and machine learning. Portfolio optimization attempts to find the optimal trade-off of future returns per unit of risk. Risk-parity attempts to match the exposure of various asset classes such that no single asset class dominates portfolio risk. Style integration combines risk factors on a security level such that rebalancing differences cancel out. Finally, machine learning utilizes large arrays of tunable parameters to predict future asset behavior and solve non-convex optimization problems. We conclude that machine learning will likely be the focus of future research. [ABSTRACT FROM AUTHOR]

This work aims to comprehensively analyze the cooperation of an electricity storage facility with an operating photovoltaic installation in a manufacturing company regarding the efficiency and effectiveness of the device and the economic profitability of the investment. This work aims to check the benefits that can be brought by expanding the PV system with an electricity storage facility. Based on the real energy balance and the characteristics of electricity distribution in the company, profitability calculations were carried out reflecting the expected savings generated by using individual solutions. These methods allowed the authors to calculate the market value of the investment with the assumed boundary criteria and to determine the economic effectiveness of the investment. Additionally, the practical process of selecting an electricity storage facility was presented and key moments in the company's energy report were analyzed, in which the use of a battery could bring results. Calculations showed that supplementing the described PV installation with an energy storage facility will increase the current level of self-consumption of PV energy by over 14%. The benefits translate into the final effect of energy storage operation, which brings additional annual savings for the company of approximately EUR 23,000 in the case of a weaker device and roughly EUR 40,000 in the case of a more powerful energy storage device. The proposed research could improve the planning of new industrial plants for photovoltaic installations, as well as the redesign of existing ones. [ABSTRACT FROM AUTHOR]

Operational resilience is crucial for navigating the increasing transportation disruption challenges, but building this capability can be expensive and sometimes result in inefficiencies. Meanwhile, firms must prioritize efficiency to remain competitive and profitable. However, it is unclear how and when firms' pursuit of efficiency priority hinders or helps their resilience to specific disruptions. This research uses the theory of constraints to propose that while efficiency priority limits opportunities for improving operational resilience, buffering and bridging strategies lessen this constraint by enabling firms to align efficiency priority with operational resilience objectives. The study hypothesizes that these strategies positively moderate the negative effect of efficiency priority on operational resilience to transportation disruptions. These arguments are tested on primary data from a sample of 199 firms in Ghana using moderated regression analysis and the Johnson-Neyman technique. The results reveal that efficiency priority is negatively related to the disruption absorption dimension of operational resilience but unrelated to its recoverability dimension. Additionally, the study finds that under low conditions of buffering and bridging strategies, efficiency priority has stronger negative associations with both dimensions of operational resilience. In contrast, these relationships are positive under the high conditions of either strategy. These findings contribute to resolving existing debates on the efficiency-resilience link and have important implications for supply chain and business executives, as discussed in this article. [ABSTRACT FROM AUTHOR]

By clustering multiple microgrids (MGs), a multi-microgrid (MMG) system plays a significant role in integrating a large amount of renewable generation. However, the large-scale utilization of renewable energy also brings uncertainties to MMG energy management. In this work, a novel comprehensive bi-level MMG energy management model considering uncertainties of renewable energy is first developed which includes unit commitment (UC) problem and other models in the lower MG level. Then considering the non-convexity of the bi-level problem, a decomposition method called analytical target cascading (ATC) is employed to deal with it by decentralization. With regard to the energy management of each individual MG, a two-stage distributionally robust model is developed which describes the uncertainties from probability distribution of renewable generation with a metric-based ambiguity set. Moreover, an efficient solution scheme based on column and constraint generation (CCG) algorithm and a decomposition method without duality is designed to handle the MG energy management problem. Finally, we implement extensive experiments to corroborate the effectiveness of the proposed approach. Particularly, the optimal solution from the proposed method can attain 2.98% less cost compared with that from robust optimization (RO) method and achieve 86.17% less energy trading amount than that in independent mode. [ABSTRACT FROM AUTHOR]

The clean transformation of building energy consumption is a critical pathway for achieving deep decarbonization. In light of the diversity of building energy needs, this paper proposes an integrated zero-carbon energy supply building architecture that involves the coupling of electrical, hydrogen, heat, and cooling energy sources. To address challenges in real-time supply-demand balancing and reliability of supply under extreme or boundary conditions due to complex energy couplings, the paper introduces an energy management framework with two control layers comprising mixed-integer linear programming (MILP) and model predictive control (MPC). This framework conducts hierarchical optimization to synergize economic and technical objectives. At the economic optimization layer, optimal economic parameters are formulated based on MILP; while at the MPC optimization layer, these parameters serve as reference states. By incorporating state relaxation method, the framework accommodates the uncertainties associated with system operation. Simulation analyses of typical weeks during summer and winter seasons demonstrate that the energy optimization management approach proposed in this paper can achieve highly reliable and economically stable operation of the building energy supply system. • A zero-carbon energy supply building energy structure is constructed. • Dynamic evolution model of electricity-hydrogen-heat-cooling multi-energy coupling system is established. • A double-layer energy management method including economic optimization and MPC optimization is proposed. [ABSTRACT FROM AUTHOR]

Background: This study aimed to synthesize and quantitatively examine Health State Utility Values (HSUVs) for Type 2 Diabetes Mellitus (T2DM) and its complications, providing a robust meta-regression framework for selecting appropriate HSUV estimates. Method: We conducted a systematic review to extract HSUVs for T2DM and its complications, encompassing various influencing factors. Relevant literature was sourced from a review spanning 2000-2020, supplemented by literature from PubMed, Embase, and the Web of Science (up to March 2024). Multivariate meta-regression was performed to evaluate the impact of measurement tools, tariffs, health status, and clinical and demographic variables on HSUVs. Results: Our search yielded 118 studies, contributing 1044 HSUVs. The HSUVs for T2DM with complications varied, from 0.65 for cerebrovascular disease to 0.77 for neuropathy. The EQ-5D-3L emerged as the most frequently employed valuation method. HSUV differences across instruments were observed; 15-D had the highest (0.89), while HUI-3 had the lowest (0.70) values. Regression analysis elucidated the significant effects of instrument and tariff choice on HSUVs. Complication-related utility decrement, especially in diabetic foot, was quantified. Age <70 was linked to increased HSUVs, while longer illness duration, hypertension, overweight and obesity correlated with reduced HSUVs. Conclusion: Accurate HSUVs are vital for the optimization of T2DM management strategies. This study provided a comprehensive data pool for HSUVs selection, and quantified the influence of various factors on HSUVs, informing analysts and policymakers in understanding the utility variations associated with T2DM and its complications. [ABSTRACT FROM AUTHOR]

PurposeMethodsConclusionPractical implicationsThis paper investigates the evolutionary trajectory of the traditional Business-to-Business (B2B) market across various states and the changing proportion of intermediary network transformation. It offers insights and decision support for the advancement of B2B e-commerce, facilitates enterprise transformation, realization of optimal supply chain management, and the creation of novel service models and business paradigms.By transforming traditional intermediaries into online counterparts and employing optimal decision-making techniques alongside simulation, this study delves into enhancing transaction efficiency in B2B e-commerce through supply chain management technology.The selection patterns of traditional intermediaries regarding B2B e-commerce platforms exhibit diverse evolutionary trajectories under different external circumstances. Increased interconnectedness among traditional intermediaries augments their capacity to influence one another toward opting for online B2B platforms for sales, indicating that collaborative efforts among intermediaries can directly impact their strategic decisions.Collaborative endeavors between network intermediaries and B2B platforms reshape the transactional dynamics between buyers and suppliers. The advent of platform network intermediaries enables enterprises to access bespoke customization services, data analytics solutions, and supply chain financial services. Thus, embracing the platformization of transactional structures within the supply chain offers discernible service value for both buyers and suppliers. [ABSTRACT FROM AUTHOR]

We introduce a systematic framework centered on superstructure optimization to identify the most efficient economic and environmentally friendly approach for managing plastic waste. Applying the proposed framework to low-density-polyethylene (LDPE) plastic waste, we determine that pyrolysis is the most profitable technology followed by hydroformylation to C4-C8 olefins, and the oligomerization of higher carbon olefins. Coupling the results with geographical information, the selected superstructure has the potential to improve the economics of plastic waste management by approximately $3 per kgLDPE in countries like the United States. On the other hand, the lowest CO2 emission plastic waste management uses solvent-based recycling only when there is significant degradation during mechanical recycling. When plastic waste can be recycled mechanically more than five times, the emissions in mechanical recycling are lower. These technologies collectively contribute to emissions reductions ranging from 1.5 and 3 kgCO2eq. per kgLDPE, for mechanical and solvent-based recycling, respectively. [ABSTRACT FROM AUTHOR]

The level of digitalization and automation, including robotic systems, is increasing in modern operating theaters. However, the integration of such systems within an already complex and limited environment creates challenges, such as collaboration with dynamic robots and thus potential collisions. Yet, the placements of robotic systems largely rely on empirical guidelines provided by manufacturers, which do not account for the specific requirements of operating theater layouts or individual surgical procedures. In this work, we propose a framework for optimization of robotic set-ups in the operating theater to address these limitations and to enhance the overall workflow efficiency. By utilizing a simulation-based approach, we aim to determine the optimal placement of each surgical equipment within the constrained operating theater environment. In this study, we set the stage for future efforts and demonstrate the effectiveness of our approach by determining the optimal placement of a LBR iiwa 7 R800 as a surgical assistant for an abdominal surgery. Overall this work lays the groundwork for future research that will accommodate more complex scenarios and is a crucial first step towards a holistic workflow optimization in the operating theater. [ABSTRACT FROM AUTHOR]

In patients who underwent surgery for pancreatic cancer and who can start treatment within three months of surgery, adjuvant chemotherapy is currently recommended to limit the risk of disease recurrence, but questions remain for the clinician. Recently, modified FOLFIRINOX has become the standard in the non-Asian population, but there is still a risk of toxicity and its administration may be limited in heavily pretreated patients or those who have not enough recovered from surgery. The gemcitabine-nab-paclitaxel combination, gemcitabine or 5-FU single agents are alternatives to discuss for patients not eligible for FOLFIRINOX. In Asia, chemotherapy based on the compound S1 is the reference. The objective of this review is to summarize the adjuvant management after resection of pancreatic cancer and to highlight current and future questions, in particular the need for biomarkers and the optimization of the management of patients. [ABSTRACT FROM AUTHOR]

China is the largest producer of jujubes in the world, with the Xinjiang region accounting for about 50 % of the total production. However, local jujube processing still relies on traditional methods, leading to reduced industrial efficiency. Research shows that jujube polysaccharides possess various biological properties, such as antioxidant, anti-tumor, and anti-inflammatory activities. Enhancing the extraction of jujube polysaccharides to boost their food, medicinal, and economic value has become an important development direction of the jujube industry. This paper reviews the current research on the active functions of grey jujube polysaccharides, extraction technologies, and recent advancements in their production and application. It systematically introduces both traditional and emerging extraction technologies and summarizes the current state of production and application of grey jujube polysaccharides. The aim is to provide references for other researchers interested in jujube polysaccharides and to extend the value chain of the grey jujube industry. [ABSTRACT FROM AUTHOR]

Highlights Silica is a renewable resource that has become the primary filler for the rubber used in eco‐friendly tires. However, silica tends to agglomerate in the rubber matrix, particularly, micro‐ and nano‐scale silica, which limits its application. When ordinary‐particle‐size silica with low cost and high stacking density is modified using appropriate methods, high dispersion in rubber and good performance can be achieved with common processing equipment, which has significant engineering application value. In this study, silica particles with different sizes (nano‐scale, 19 and 45 μm) were treated with a 3‐mercaptopropyltriethoxysilane coupling agent (KH580). Subsequently, the silica with the ordinary particle size of 300 mesh (45 μm) was coated with natural latex (NL), and the mechanical properties of the modified silica‐filled styrene‐butadiene rubber (SBR)/carbon black (CB) compounds were investigated. The results revealed that the mechanical properties of the NL‐coated 45 μm silica‐filled composite improved significantly, particularly, in the area of tear strength and elongation at break, the composite properties were improved by 17.1% and 118.2%, respectively. Excellent performance over composites filled with coupling agent‐treated micron‐ and nanoscale silica. Enhancing the surface modification of silica through latex coating provides a means to improve the efficiency of industrial production and reduce costs. The low‐fine silica treated with KH580 has good dispersibility. Improved mechanical compatibility of NL‐coated silica with rubber. Improvement in strain performance of composites after NL coated with silica. The effect of silica dispersion on composite properties is greater than the effect of particle size. Industrial silica can be successfully addressed, paving the way for extensive utilization. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) have gained significant attention in recent years as a promising solution for optimizing the operation of power systems. VPPs allow for the integration and coordination of Distributed Energy Resources (DERs), such as renewable energy sources, to provide grid support and stabilize the power supply. This paper presents a VPP that utilizes a three-phase power flow analysis for its modeling. This approach allows for an accurate representation of the power flow in the VPP, considering the dynamic nature of the power system. In addition to the three-phase power flow analysis, the authors propose a tabu continuous ant colony search (TCACS) to optimize the operation of the VPP, which combines tabu search and ant colony optimization to find near-optimal solutions for complex optimization problems. It uses a tabu list to guide the search towards promising solutions while avoiding getting stuck in local minima. The performance of the proposed VPP has been evaluated through simulation studies, and the results show that it is effective at minimizing power loss and improving the stability of the power system. The proposed VPP can be a valuable tool for utilities to manage their power generation and distribution, particularly in the context of increasing renewable energy integration. [ABSTRACT FROM AUTHOR]

Tree plantations are an important forest resource that substantively contributes to climate change mitigation and carbon sequestration. As the area and standing volume of tree plantations in China have increased, issues such as unreasonable structure, low productivity, limited ecological functionality and diminishing ecological stability have occurred, which hinder the ability of tree plantations to enhance carbon sequestration. This study outlined the trajectory of carbon sequestration and its associated benefits in tree plantations by examining the current state of tree plantation establishment and growth, elucidated the strategies for advantages of carbon sequestration and climate change mitigation in planted forests, and summarized the existing problems with tree plantations. This paper underscores the pressing need for concerted efforts to boost carbon sequestration within planted forests and proposes management and development strategies for Chinese tree plantations. In the future, it will be necessary to apply scientific theories to practice and develop multi-objective management optimization models for the high-quality development of tree plantations. This will involve establishing a cohesive national carbon trading market, improving the prediction of carbon sequestration, and identifying priority zones for afforestation and reforestation, to better serve China's national strategy for achieving peak carbon and carbon neutrality. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal planning for collection, sorting, and recycling operations. The problem arises in industrial waste management, where distinct actors manage the collection and the sorting operations. In a weekly or monthly plan horizon, they usually interact to find a suitable schedule for servicing customers but with a not well-defined scheme. We proposal an improved negotiation-based approach using an auction mechanism for optimizing these operations. Two interdependent models are presented: one for waste collection by a logistics operator and the other for sorting operations at a recycling plant. These models are formulated as mixed-integer linear programs where costs associated with sorting and collection are to be minimized, respectively. We describe the negotiation-based approach involving an auction where the logistics operator bids for collection time slots, and the recycling plant selects the optimal bid based on the integration of sorting and collection costs. This approach aims to achieve an optimization of the entire waste management process. Computational experiments are presented. [ABSTRACT FROM AUTHOR]

Litter size is an important reproductive trait of goats and is essential for their commercial production. Currently, there is a lack of feeding and breeding management and a disregard for fertility traits, such as litter size, during hybrid production. Therefore, in this study, we conducted whole-genome resequencing of the high- and low-fecundity hybrid white goats to determine the genes associated with fertility. In this study, we selected 6 ewes from more than 150 breeding populations and divided them into high- and low-fecundity groups (H group and L group, respectively; n = 3/group) and collected their blood and ovarian tissue samples for WGR analysis. The results of WGR analysis revealed the presence of several genetic variations, including single nucleotide polymorphisms, insertions or deletions, structural variations, and copy number variations, between the genomes of the two groups. Annotation and KEGG pathway analysis of the mutation data revealed that most of the genes were enriched in 19 biological pathways. Among these, we selected a few pathways associated with fertility and identified the genes that were specific to the H group. The results revealed that the SNPs in the genes LHCGR, JAK3, GDF9, GnRHR, and CIZ1 and the InDels in the genes PCDHAC1, RXRG, AGO2, CNOT10, TEAD4, and TAF10 were specific to the H group, suggesting that these genes may be associated with their high lambing performance. The results of our study provide insights into the optimization of management strategies for goats with different reproductive performances. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations. [ABSTRACT FROM AUTHOR]

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments. [ABSTRACT FROM AUTHOR]

Riboflavin, an essential vitamin for humans, is extensively used in various industries, with its global demand being met through fermentative processes. Hyphopichia wangnamkhiaoensis is a novel dimorphic yeast species capable of producing riboflavin. However, the nutritional factors affecting riboflavin production in this yeast species remain unknown. Therefore, we conducted a kinetic study on the effects of various nutritional factors—carbon and energy sources, nitrogen sources, vitamins, and amino acids—on batch riboflavin production by H. wangnamkhiaoensis. Batch experiments were performed in a bubble column bioreactor to evaluate cell growth, substrate consumption, and riboflavin production. The highest riboflavin production was obtained when the yeast growth medium was supplemented with glucose, ammonium sulfate, biotin, and glycine. Using these chemical components, along with the mineral salts from Castañeda-Agullo's culture medium, we formulated a novel, low-cost, and effective culture medium (the RGE medium) for riboflavin production by H. wangnamkhiaoensis. This medium resulted in the highest levels of riboflavin production and volumetric productivity, reaching 16.68 mg/L and 0.713 mg/L·h, respectively, within 21 h of incubation. These findings suggest that H. wangnamkhiaoensis, with its shorter incubation time, could improve the efficiency and cost-effectiveness of industrial riboflavin production, paving the way for more sustainable production methods. [ABSTRACT FROM AUTHOR]

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system. [ABSTRACT FROM AUTHOR]

For the problem that massive data and cloud computing in the industrial Internet of Things cannot meet the requirements of low latency, a cloud hybrid network architecture is proposed. In the single user scenario, RS coding is used to realize data redundancy, and the security evaluation method is put forward on this basis. By using the ant colony algorithm, the data transmission delay and security objective function are established to obtain the best allocation scheme. In the multi-user scenario, the SD-CFIIoT architecture is constructed by combining SDN and fog computing technology. The optimal solution is found by applying the ant colony algorithm to solve the objective function of the data transmission delay. Simulation results show that when the security constraint is 0.9, the data transmission delay of RS is 5.04 s, which is 0.18 s less than LDPC; 0.19 s less than MBR. When the safety constraint is 0.9, the delay of the cloud mixed structure is 5.82 seconds; 1.53 seconds less than the cloud core structure and 1.20 seconds less than the fog core structure. When the data volume reached 80 MB, the delay of SD-CFIIoT was 2.20 s, 6.61 s lower than the fog structure and 15.80 s lower than the cloud structure. The simulation results prove the effectiveness of the proposed scheme, which can ensure data security while realizing low delay data transmission, improve the efficiency of the Internet of Things, and then improve the industrial production efficiency. This has a certain positive significance to the industrial modernization and intelligent development. [ABSTRACT FROM AUTHOR]

The smart factory leads to a strong digitalization of industrial processes and continuous communication between the systems integrated into the production, storage, and supply chains. One of the research areas in Industry 4.0 is the possibility of using autonomous and/or intelligent industrial vehicles. The optimization of the management of the tasks allocated to these vehicles with adaptive behaviours, as well as the increase in vehicle-to-everything communications (V2X) make it possible to develop collective and adaptive intelligence for these vehicles, often grouped in fleets. Task allocation and scheduling are often managed centrally. The requirements for flexibility, robustness, and scalability lead to the consideration of decentralized mechanisms to react to unexpected situations. However, before being definitively adopted, decentralization must first be modelled and then simulated. Thus, we use a multi-agent simulation to test the proposed dynamic task (re)allocation process. A set of problematic situations for the circulation of autonomous industrial vehicles in areas such as smart warehouses (obstacles, breakdowns, etc.) has been identified. These problematic situations could disrupt or harm the successful completion of the process of dynamic (re)allocation of tasks. We have therefore defined scenarios involving them in order to demonstrate through simulation that the process remains reliable. The simulation of new problematic situations also allows us to extend the potential of this process, which we discuss at the end of the article. [ABSTRACT FROM AUTHOR]

We propose a Competitive Leverage model, and apply it to analyze the impact of tariffs on the attitudes of domestic import-competing firms, especially regarding their disincentives to maintain a Competitive Stance against foreign competitors in the domestic market. We propose that tariffs are a disincentive to the pursuit and maintenance of production efficiency in firms and industries. When countries implement tariff protection for their domestic firms, the most common excuse sounds reasonable, except that it is also economically flawed. While the short-run impacts of such a measure might seem plausible, its long-term effects appear to be less than favorable, and may, in fact, turn out to be damaging to the economy. The paper applies the competitive leverage concept in a unique way to highlight how tariffs distort the output levels of domestic firms, and gives an empirical analysis of the model, using USA data to verify the predictions of the model. Particularly, we develop a Cournot-competition type Competitive Leverage model, and use it to examine the role played by tariffs as a disincentive to a typical firm's pursuit and maintenance of production efficiency. The competitive leverage model is formulated and applied to verify the effects of tariff protection from the typical domestic firm's strategic interaction standpoint. We further apply crosssectional data to carry out an empirical analysis of the model, to verify how tariffs impact the domestic firm's output and pricing decisions. Our findings would enable us to contribute to the debate about the need to remove protective tariffs, on the grounds that tariffs do not only enable domestic firms to operate inefficiently, but also tends to distort the output and pricing outcomes that would otherwise have been realized in the free market. The results from the study are used to propose some major policy implications of imposing tariff protection, among which are that there is need for implementation of more deliberately sustained Research and development program on the part of domestic firms in the economy, to enable them withstand vigorous competition in a free-trade world. Also, since protective tariffs significantly increase firms' profit rates, which in turn enables them to unduly earn and accumulate economic rents that amount to inefficiencies and distortions in free-market prices and quantities outcomes, governments should always resist any pressures from big businesses to impose protective tariffs against competition from international rivals. Yet a clear public policy implication from this paper is that, at the macro level, as the results suggest that a government policy that aims at protecting domestic firms through imposition of tariffs may have an adverse effect on the firms' competitive stance in the industry, a practical message is for the government to impose only a moderate tariff at the most, if it must, especially as it relates to agricultural products. [ABSTRACT FROM AUTHOR]

The integration of the Industrial Internet of Things (IIoT) brings about a significant improvement in the efficiency and productivity of industrial processes. The speed and accuracy of various tasks have been greatly enhanced with the utilization of IIoT which allows smoother operations and cost-effective solutions, to protect uninterrupted intrusions and avoid potential threats. This article proposes a novel recurrent federated network-based enhanced local search grasshopper (RFN-ELG) algorithm. Datasets like UNSW-NB15 and MQTT-IoT-IDS2020 datasets are employed to determine the performances of IIoT via two diverse phases, namely the data preprocessing phase as well as attack detection phase. The recurrent neural networks (RNNs) are integrated with federated learning (FL) to overcome the gradient issues during the training process. In addition to this, the hyperparameters of RNN-FL are tuned via a grasshopper optimization algorithm with a local search strategy. Accuracy, precision, recall, F1-score, and false alarm rate (FAR) are the performance metrics taken for attack detection, where accuracy attains the value of 98.1%, precision attains the value of 97.4%, recall attains the value of 96.1%, F1-score attains the value of 97.2%, and FAR attains the value of 96.1%, respectively. Therefore, the proposed RFN-ELG algorithm attains high performance in detecting the attacks in IIoT. [ABSTRACT FROM AUTHOR]

The theory of compensation incentive suggests that matching management positions with appropriate compensation is conducive to improving the efficiency of corporate production and operations. However, within the backdrop of China's institutional framework, the pay restriction policy for executives of state-owned enterprises (SOEs) has led to a phenomenon known as pay-position upside down, which goes against the compensation incentive theory. This paper explores how this phenomenon affects financial investment from the standpoint of the profit-seeking incentive of pursuing political promotions and the rational economic man's motivation to flee accountability for performance evaluations. Our difference-in-differences estimations show that the CEO pay-position upside down has a positive effect on financial investment. Mechanistic analyses show that this link is strengthened by SOEs undergoing mixed reform, SOEs in the provinces inspected by the Central Inspection Team, and SOEs in situations where CEOs are younger and hold shorter tenures. Additionally, the central SOEs' inclination to pursue political promotions and handle performance reviews results in increased investment in financial assets. The findings of this study offer a new perspective on how to use compensation contracts for management incentives effectively and provide valuable insights into the rational arrangement of compensation and positions within state-owned enterprises. [ABSTRACT FROM AUTHOR]

The increasing number, frequency, and sophistication of phishing website-based attacks necessitate the development of robust solutions for detecting phishing websites to enhance the overall security of cyberspace. Drawing inspiration from natural processes, nature-inspired metaheuristic techniques have been proven to be efficient in solving complex optimization problems in diverse domains. Following these successes, this research paper aims to investigate the effectiveness of metaheuristic techniques, particularly Genetic Algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO), in optimizing the hyperparameters of machine learning (ML) algorithms for detecting phishing websites. Using multiple datasets, six ensemble classifiers were trained on each dataset and their hyperparameters were optimized using each metaheuristic technique. As a baseline for assessing performance improvement, the classifiers were also trained with the default hyperparameters. To validate the genuine impact of the techniques over the use of default hyperparameters, we conducted statistical tests on the accuracy scores of all the optimized classifiers. The results show that the GA is the most effective technique, by improving the accuracy scores of all the classifiers, followed by DE, which improved four of the six classifiers. PSO was the least effective, improving only one classifier. It was also found that GA-optimized Gradient Boosting, LGBM and XGBoost were the best classifiers across all the metrics in predicting phishing websites, achieving peak accuracy scores of 98.98%, 99.24%, and 99.47%, respectively. [ABSTRACT FROM AUTHOR]

High concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps emissions from oil and gas operations, where sustained photolysis of the precursors builds ozone to unhealthy concentrations. The basin's winter-ozone system is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language ("sufficient snowfall and high pressure leads to high ozone"), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more prescribed manner than more complex, black-box inference methods such as neural networks, increasing user trustworthiness of our model prototype before further optimization. Herein, we develop an ozone forecasting system, Clyfar, informed by an archive of meteorological and air-chemistry measurements. This prototype successfully demonstrates proof-of-concept despite rudimentary tuning. We describe our framework for predicting future ozone concentrations if input values are drawn from numerical weather prediction forecasts rather than observations as Clyfar initial conditions. We evaluate inferred values for one winter, finding our prototype demonstrates mixed performance but promise after optimization to deliver useful forecast guidance for decision-makers when forecast data are used as input. This early version model is the basis of ongoing optimization through machine learning. [ABSTRACT FROM AUTHOR]

Theoretical and empirical evidence point to the ability of political leaders to manipulate economic policies and leverage local firms to elevate their political careers. Despite this, there is limited understanding of how these career incentives impact the operational dynamics of the firms involved. This empirical study delves into this gap, revealing that city leaders with fewer promotional incentives are more inclined to mobilize state-owned enterprises (SOEs) within their jurisdiction to pursue sustainable development, as indicated by heightened corporate operational efficiency. Our analysis further indicates that the career prospects of city leaders significantly influence the operational efficiency of SOEs by driving a shift in focus from rapid growth to sustainable development and firms' adoption of disruptive technologies. We posit that this increase in operational efficiency not only benefits the SOEs but also generates unique value for stakeholders, resulting in elevated market capitalization and reduced stock price crash risk. Our findings carry direct relevance to the ongoing discourse on political incentives and contribute to operations management research, shedding light on the intricate ways in which the political environment can impact the operational performance of firms. [ABSTRACT FROM AUTHOR]

Formula Student (FS) competitions aim to prepare and encourage engineering students to participate in the progression of automotive and motorsport industries. The built racing cars adhere to strict regulations set by competition guidelines to ensure the safety of both teams and spectators. For electric racing cars, the high-voltage (HV) battery system usually operates within a voltage range between 100 V to 600 V to supply the motor and its controller with the required electrical power. It is essential to ensure that these components are operating effectively to minimize battery and motor current as well as to ensure efficient and reliable performance throughout the race. A low-voltage control system (LVCS), usually operating at 12 V, is used to coordinate a wide array of critical operational and safety functions to control the HV system. These functions include: (1) turning on/off procedures, (2) monitoring speed, voltage, and current, (3) interfacing with pedals, (4) controlling dashboard features, (5) managing lighting, (6) facilitating data communication, and (7) implementing safety protocols. The design and operation of the LVCS are crucial for compliance with safety regulations and enhancing the FS electric racing car (FSERC) performance. This details and discusses the design procedures of the LVCS, using the Lancaster E-Racing (LER) FSERC as a case study. The LER car employs a 400 V battery system to power a 68-kW permanent manet synchronous motor (PMSM) using a three-phase voltage source inverter. Using mathematical analysis, SIMULINK/MATLAB® computer simulations, and the experimental real-data results provided by the LER FSERC, this study seeks to offer valuable insights regarding the LVCS practical implementation and optimization. [ABSTRACT FROM AUTHOR]

Polycrystalline diamond compact (PDC) bit is one of the most widely used drill bits for improving the rate of penetration in deep oil and gas well and geothermal well. However, the dynamic rock fragmentation mechanics characteristics of PDC bits are still unclearly. A coupled fragmentation mechanics model of PDC cutter-rock interaction is established by combining the mixed fragmentation modes with dynamic strength. The coupling influence laws of cutter angle, cutting depth, dynamic strength ratio, breaking modes on the horizontal force coefficient (HFC), vertical force coefficient (VFC) and specific energy are analyzed. The model of this paper can optimize cutter inclination angle, cutting depth and minimum specific energy. With the increase of the cutter inclination angle, the dynamic VFC changes into two modes. The definition of the dynamic modes depends on the dynamic strength ratio. As the cutting angle increases, the cutting force increases. The cutting force increases nonlinearly with increasing cutting depth. The specific energy of rock fragmentation increases nonlinearly with increasing cutting depth. With the increase of dynamic strength, the specific energy of rock fragmentation increases nonlinearly. When the input-energy increases, the rate of penetration response is divided into three stages. The results have important guiding significance for the PDC bit design and drilling parameters optimization to increase the rate of penetration and the efficiency of exploration and development. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze the impact of intellectual capital (IC) and its components on firm financial performance using data from Chinese agricultural listed companies during 2015–2020. The moderating role of board diversity in the relationship between IC and firm financial performance is also tested. The modified value-added intellectual coefficient (MVAIC) model is used to measure IC, and board diversity is measured by several indicators, such as diversity in gender, experience, professional background, and educational background. The results suggest that the overall IC and only one element (human capital) positively influence firm financial performance. Diversity in gender, professional background, and educational background positively moderate the relationship between IC and financial performance, while experience diversity has a negative moderating effect. Among IC components, experience diversity, and educational background diversity negatively moderate the relationship between human capital and financial performance. In addition, gender diversity and experience diversity have a negative moderating effect on the relationship between physical capital and financial performance, while professional background diversity and educational background diversity have a positive moderating effect. This study can provide some new insights for managers to devise strategies to improve IC performance and strengthen corporate governance in order to achieve sustainable development of the agricultural industry. It also can guide policymakers in making policies to improve IC efficiency and firm performance. [ABSTRACT FROM AUTHOR]

Statistical methods must be developed and optimized in distributed systems due to the increasing amount of data and processing demands in modern applications. The application and optimization of mathematical and probabilistic statistical methods in distributed computing settings is the main topic of this study. Algorithms like these have the potential to improve performance, scalability, and parallel processing abilities when integrated into distributed systems. We commence our investigation by reviewing current mathematical and probabilistic statistical algorithms, determining their advantages and disadvantages, and evaluating their suitability for distributed architectures. We then suggest new approaches for their smooth incorporation into distributed computing structures, making use of distributed storage and parallel processing to effectively manage massive datasets. Improving these algorithms' performance in distributed environments is the focus of this research's refinement phase. We seek to optimize the use of distributed infrastructures by minimizing latency and maximizing computational resources by investigating efficient communication protocols, load balancing mechanisms, and parallelization approaches. The suggested algorithms are put into practice inside a distributed structure for empirical confirmation, and their effectiveness is evaluated in comparison to more conventional, non-distributed competitors. We test the scaling, precision, and effectiveness of the methods in practical scenarios using a variety of datasets and use cases. [ABSTRACT FROM AUTHOR]

A genetically optimized fuzzy control algorithm is used to realize the parallel hybrid system's real-time energy distribution between the engine and the electric motor. This paper first adopts fuzzy control to improve the robustness and real-time performance of the entire system. Then, a membership function optimization method based on a genetic algorithm is proposed by simulating the working state of CYCUDDS. Simulation experiments show that compared with unoptimized fuzzy control, the improved fuzzy controller can improve the vehicle's fuel economy and prevent continuous battery discharge, thereby improving battery endurance. [ABSTRACT FROM AUTHOR]

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With the widespread application of information technology in education, the real-time detection of student behavior in the classroom has become a key issue in improving teaching quality. This paper proposes a Student Behavior Detection (SBD) model that combines YOLOv5, the Contextual Attention (CA) mechanism and OpenPose, aiming to achieve efficient and accurate behavior recognition in complex classroom environments. By integrating YOLOv5 with the CA attention mechanism to enhance feature extraction capabilities, the model's recognition performance in complex backgrounds, such as those with occlusion, is significantly improved. In addition, the feature map generated by the improved YOLOv5 is used to replace VGG-19 in OpenPose, which effectively improves the accuracy of student posture recognition. The experimental results demonstrate that the proposed model achieves a maximum mAP of 82.1% in complex classroom environments, surpassing Faster R-CNN by 5.2 percentage points and YOLOv5 by 4.6 percentage points. Additionally, the F1 score and R value of this model exhibit clear advantages over the other two traditional methods. This model offers an effective solution for intelligent classroom behavior analysis and the optimization of educational management. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter 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.)