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BACKGROUND Hidradenocarcinoma (HAC) is a rare adnexal carcinoma. To the best of the authors' knowledge, there are no published systematic reviews on HAC. OBJECTIVE To incorporate a case series from the authors' institution and systematically integrate reported information to provide a reference tool for optimization of diagnosis and management. METHODS A comprehensive MEDLINE search was conducted from database inception to 2021 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This yielded 225 studies with 165 cases of HAC. References of included articles were also searched. In addition, 9 patients with HAC were identified from the authors' institution over the past 10 years. RESULTS The mean age of HAC presentation is 60 years with a slight male predilection (60%). The head and neck is the most commonly affected region. Over 36% of cases either presented with metastatic disease or went on to metastasize. The most common treatment type was wide local excision, followed by Mohs micrographic surgery. CONCLUSION Early detection with accurate histologic interpretation is prudent in all cases of HAC. Wide local excision is the current first-line treatment. However, Mohs micrographic surgery offers complete marginal analysis with evidence of reduced risk of metastasis and better outcomes compared with wide local excision. Currently, there are no National Comprehensive Cancer Network guidelines for the treatment of HAC, and consensus guidelines are limited to tumor and nodal metastasis staging provided by the American Joint Committee on Cancer, eighth edition. Thus, this case series and systematic review integrates important aspects of diagnosis, workup, and management of HAC. [ABSTRACT FROM AUTHOR]

Efficient and effective handling of loading and unloading processes at ports is key to achieving set volume targets. In this context, intelligence strategy plays a crucial role in strengthening loading and unloading activities. This study aims to analyze how the implementation of intelligence strategy can enhance loading and unloading activities at Teluk Lamong Port. Through a case study approach, we explore the implementation of intelligence strategy by port authorities and its impact on achieving volume targets. The research findings indicate that well-integrated intelligence strategies can provide timely and relevant information to stakeholders, enabling more efficient resource management and enhancing coordination among various parties involved. The practical implications of these findings underscore the importance of employing intelligence strategies to enhance productivity and operational efficiency at ports, thereby contributing to the attainment of set volume targets. [ABSTRACT FROM AUTHOR]

The presented article deals with the issue of inventory management optimization through modelling and simulation in the software Tecnomatix Plant Simulation. Based on an in-depth analysis of the supply chain with an emphasis on inventory management, a simulation model was developed. It was optimized using the inventory theory methodology in combination with a discrete-event simulation approach. Dynamic simulation made it possible to perform experiments and optimize the existing state. The solution to the problem is elaborated on a case study to achieve the efficiency of the supply process of a specific operation of a clothing retail chain, which is struggling with the problems of excessive and inaccurate supply. [ABSTRACT FROM AUTHOR]

The modeling of blind people's behavior is an important topic in pedestrian evacuation dynamics. In order to study movement characteristics of blind people in evacuation process and the effect of indoor obstacles on the evacuation efficiency of blind people, an extended cellular automata model is proposed to simulate blind people evacuation by considering the influence of obstacles. The control variable method is adopted to study the effect of indoor obstacles on the blind evacuation efficiency rule. The numerical simulation is applied to analyze the evacuation process of blind people under different setting (e.g. the location, size and other factors) of obstacles and different evacuation scenarios. The shunt effect of obstacles is discussed and the layout strategy of obstacles is optimized. The results show that the longitudinal obstacles in front of the exit can effectively play the diversion role of obstacles when the obstacle is at a certain distance from the exit, which will improve the evacuation efficiency of the blind. Furthermore, a horizontal obstacle of appropriate length can constrain the trajectory of the blind people and improve the efficiency of evacuation, but when the horizontal obstacle is too long, it will induce extra jam on both sides of the obstacle and hinder the evacuation of the blind. The finding can provide the theoretical basis for the blind people's organization, management and the optimization of evacuation strategies during an emergency. [ABSTRACT FROM AUTHOR]

Automated inspection is vital in modern industrial manufacturing, optimizing production processes and ensuring product quality. Welding, a widely used joining technique, is susceptible to defects like porosity and cracks, compromising product reliability. Traditional nondestructive testing (NDT) methods suffer from inefficiency and limited accuracy. Many researchers have tried to apply deep learning for defect detection to address these limitations. This study compares traditional algorithms with deep learning methods, specifically evaluating the SwinUNet model for weld segmentation. The model achieves an impressive F1 score of 96.31, surpassing traditional algorithms. Feature analysis utilizing class activation maps confirms the model's robust recognition and generalization capabilities. Additionally, segmentation results for different welding defects were compared among various models, further substantiating the recognition capabilities of SwinUNet. The findings contribute to the automation of weld identification and segmentation, driving industrial production efficiency and enhancing defect detection. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMS) are considered to be crucial tools for agencies in managing their pavement networks and using accessible budgets in an appropriate technique. In this paper, a newly developed condition rating index [modified pavement condition rating (MPCR)] was used to assess the current and future pavement performance. The pavement future performance in terms of MPCR was predicted using two models: (1) a stochastic Markov chain model, and (2) an artificial neural network (ANN) model. In Markov chain modeling, the Long-Term Pavement Performance (LTPP) database was used to develop the transition probability matrix (TPM). An ANN model for future pavement performance prediction was developed based on the same LTPP data. For both models, 245 data points from the LTPP data were used for deterioration modeling. Both models were verified using LTPP data and data obtained from the General Authority of Roads, Bridges and Land Transport (GARBLT), Egypt. The Markov chain and ANN results were compared. The Markov chain model performed better than the ANN model, with a coefficient of determination, R2 , of 0.9 and RMS Error (RMSE) of 0.1268. the ANN model yielded an R2 of only 0.53 and a RMSE of 0.2205. Increasing the number data points did not lead to a significant improvement in the ANN model accuracy. A multiobjective particle swarm optimization (PSO) model is used for fund allocation to maximize the average pavement condition and minimize the maintenance cost. The developed optimization model was tested on two benchmark optimization problems to ensure its validity for real-life applications. The developed optimization model was used on a network of roads and was found to be capable of generating optimal or near-optimal solutions for the maintenance decisions to keep the pavement in good workable condition. As such, the current study presents a comprehensive development tackling different modules of a pavement management system. [ABSTRACT FROM AUTHOR]

Droplet formation via liquid–liquid phase separation is thought to be involved in the regulation of various biological processes, including enzymatic reactions. We investigated a glycolytic enzymatic reaction, the conversion of glucose‐6‐phosphate to 6‐phospho‐D‐glucono‐1,5‐lactone with concomitant reduction of NADP+ to NADPH both in the absence and presence of dynamically controlled liquid droplet formation. Here, the nucleotide serves as substrate as well as the scaffold required for the formation of liquid droplets. To further expand the process parameter space, temperature and pressure dependent measurements were performed. Incorporation of the reactants in the liquid droplet phase led to a boost in enzymatic activity, which was most pronounced at medium‐high pressures. The crowded environment of the droplet phase induced a marked increase of the affinity of the enzyme and substrate. An increase in turnover number in the droplet phase at high pressure contributed to a further strong increase in catalytic efficiency. Enzyme systems that are dynamically coupled to liquid condensate formation may be the key to deciphering many biochemical reactions. Expanding the process parameter space by adjusting temperature and pressure conditions can be a means to further increase the efficiency of industrial enzyme utilization and help uncover regulatory mechanisms adopted by extremophiles. [ABSTRACT FROM AUTHOR]

In order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion. [ABSTRACT FROM AUTHOR]

When considering deficit irrigation, the most resistant period to water stress in olive crops is during the pit hardening. However, the determination of this period and the impact that water stress has on its onset or duration have not been clearly established. The aim of this work is to describe the impact of water stress on three processes that determine fruit development: fruit growth, endocarp hardening, and oil accumulation. This experiment was conducted between 2021 and 2022, in a plot with olive trees that were 3 years old at the beginning of the experiment. The young trees were of cv Manzanilla de Sevilla and were planted in a 4 × 1.5 m frame. The treatments were: Control, irrigated close to what is considered the optimum level in which the water status of the crop ensures the absence of water stress; RDI, with a moderate stress during the pit hardening stage; Rainfed and Traditional Rainfed. This latter was included only in 2022 using a 7 × 5 m frame. Traditional Rainfed was included to compare with Rainfed, these were similar in the maximum level of water stress but not in the duration. The fruit growth, oil accumulation, and endocarp hardness were measured for a period of 2 years. The hardness curves fit accurately established Period II which was the one with the fast hardness increases. The water potential was measured periodically on leaves at midday, whereby the minimum and average potential and the stress integral were obtained for each of the periods indicated before using pit hardness measurements. The results suggest that endocarp hardening was the process most resistant to water stress, as it showed very little variation from the seasonal pattern, despite the high levels of stress achieved. The fruit growth process was the most sensitive to water stress, but the recovery was fast in conditions of moderate water stress level. The oil accumulation started slowly during Period II, but its rate increased at the end of the period. This later process was moderately resistant to water stress. However, although the accumulation rate was faster during Period III, it was influenced by the water status during Period II. Cumulative water stress was related with fruit growth and oil accumulation processes. Endocarp development could be useful for determining a/the deficit irrigation period because of its almost constant pattern. These periods would permit water management optimization according to final use of the fruit (oil or table). However, continuous monitoring of water stress would be needed. [ABSTRACT FROM AUTHOR]

Conduits are plastic tubes extensively used to safeguard electrical cables, traditionally made from PVC. Recent safety guidelines seek alternatives due to PVC's emission of thick smoke and toxic gases upon fire incidents. Polypropylene (PP) is emerging as a viable alternative but requires modification with suitable halogen-free additives to attain flame retardancy (FR) while maintaining high mechanical strength and weathering resistance, especially for outdoor applications. The objective of this study was to develop two FR systems for PP: one comprising a cyclic phosphonate ester and a monomeric N-alkoxy hindered amine adjuvant achieving V0, and another with hypophosphite and bromine moieties, along with a NOR-HAS adjuvant achieving V2. FR performance along with mechanical properties, physicochemical characterization, and dielectric behavior were evaluated prior to and after 2000 h of UV weathering or heat ageing. The developed FR systems set the basis for the production of industrial-scale masterbatches, from which further optimization to minimize FR content was performed via melt mixing with PP towards industrialization of a low-cost FR formulation. Accordingly, two types of corrugated conduits (ø20 mm) were manufactured. Their performance in terms of flame propagation, impact resistance, smoke density, and accelerated UV weathering stability classified them as Halogen Free Low Smoke (HFLS) conduits; meanwhile, they meet EU conduit standards without significantly impacting conduit properties or industrial processing efficiency. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

The work focuses on the optimization of management strategies of energy supply chain in oil–gas enterprises to find out the optimal management strategy and overcome the fragility of the traditional energy supply system. Back propagation neural network (BPNN) is applied to energy supply predicting and energy security early warning in oil–gas enterprises, and the security early warning system is designed based on the auxiliary cognitive system. The security early warning system is used to predict the peak value of oil and gas supply, and then the detection performances and the data security transmission performances of different models are compared and analysed. Finally, a comprehensive evaluation model for security management of oil–gas supply chain based on the neural network is established. It is found that compared with convolutional neural network (CNNs), the improved BPNN model algorithm proposed shows increased accuracy by 5.4%, reduced model training time and test time, and improved accuracy and recall rate. The accuracy, recall rate, and F1 value of the proposed model are 88.48%, 75.88%, and 96.21%, respectively, which were obviously higher than those of the CNN. It suggests that the improved security management model of oil–gas supply chain shows better recognition and prediction accuracy. Based on the quantitative and qualitative analysis results, the optimization suggestions for management strategies in oil–gas enterprises are put forward, which is of strategic significance for improving the security of oil–gas supply. [ABSTRACT FROM AUTHOR]

The material developed focuses on the analysis of energy efficiency trends in Poland, utilising ODEX indicators for the sectors of industry, transport, households and in general, from 2011 to 2021. The objective of the study is to assess the progress made in energy efficiency and to forecast future trends in these sectors. The methods employed are based on statistical modelling of time series, taking into account sector-specific energy consumption dynamics. The following techniques were employed: linear regression, cluster analysis to identify patterns of change, statistical hypothesis testing for energy efficiency and simplified autoregressive models. The results demonstrated significant improvements in energy efficiency in the industrial sector, stability of the ODEX indicator in the transport sector and gradual improvements in households and overall. The prediction indicates an upward trend in the ODEX indicator in the short term, suggesting an increase in energy demand. However, it also predicts a decline in the long term, which may indicate the effectiveness of future energy efficiency strategies and investments. Consequently, the necessity for continued efforts to increase energy efficiency and further research into the factors influencing energy efficiency in different economic sectors is emphasised. [ABSTRACT FROM AUTHOR]

Agricultural and industrial production activities generate GDP and greatly improve the quality of life of residents. At the same time, these activities also lead to increased energy consumption and emit large amounts of pollutants, which affect public health. However, there has been little past research focused on all the above associations. This study innovatively proposes a parallel two-stage EBM-DEA model to re-measure health production efficiency. The objective is to conduct a joint analysis of the economic development, pollutant emissions and human health of agriculture and industry in 30 provinces in China from 2016 to 2020, and calculate the health production efficiency, stage efficiency (agricultural production efficiency, industrial production efficiency and health efficiency) and environmental pollution efficiency. The research shows that the health production efficiency in other provinces has not reached the efficiency frontier except in Beijing, Fujian, Zhejiang, and Ningxia. In addition, there is room for improvement to varying degrees. In terms of geographical disparity, agricultural production efficiency and health efficiency are distributed in the trend of east > west > central, whereas industrial production efficiency is distributed in a ladder shape from east > central > west. Health efficiency contributes to overall efficiency, whereas agricultural and industrial production efficiencies drag down overall efficiency. The discharge efficiency of various pollutants shows that China's environmental control policies have achieved good results, but environmental problems in some provinces are still serious. Moreover, areas with higher pollutant discharge efficiency values have higher tuberculosis incidence efficiency values. Furthermore, provinces should pay attention to improving the efficiency of medical and health fund utilization. [ABSTRACT FROM AUTHOR]

The behaviors of local governments can affect the enforcement of environmental regulations. Environmental regulation can influence the behaviour of energy-intensive enterprises. This paper examines the relationship between local government competition, environmental regulation and the investment efficiency of high energy consuming firms using a basic panel regression and moderating effects model based on data from 2011-2020 for high energy consuming firms in China. The paper found that local government competition and environmental regulation can promote increased investment efficiency in energy-intensive firms in the short term. However, environmental regulation is not beneficial to investment efficiency in the long run. Local government competition plays a positive moderating role in the impact of environmental regulation on the investment efficiency of high energy consuming firms. There is a threshold effect on the role of local government competition. When the level of government competition exceeds 2.097, it has a negative impact on the investment efficiency. We further test for heterogeneity and we find that this promotion effect is also more obvious in areas with high environmental taxes and low marketability. Finally, we provide constructive suggestions for the development of government and the improvement of the investment efficiency of high energyconsuming firms. [ABSTRACT FROM AUTHOR]

This paper extends the literature on economic consequences of corruption. We simultaneously consider corruption's impacts on output, production efficiency, and environmental performance in an eco-efficiency framework. Pollution is considered as undesirable output in the production process. We construct an eco-efficiency measure from a non-oriented data envelopment analysis model that incorporates a non-convex metafrontier, super-efficiency, and undesirable outputs into a directional distance function. By using panel data from 30 Chinese provinces from 1998 to 2016 in a spatial analysis, we find that corruption, measured by filed-cases of corruption-related crimes, has a significantly negative direct impact on eco-efficiency. The regional spillover effect is not significant for provinces in the same geographical region, but is marginally significant for provinces sharing a common border. [ABSTRACT FROM AUTHOR]

This study examines the impact of CEO overseas experience and innovation efficiency on firm value. Using the propensity score matching technique with 645 observations from 129 Chinese chip companies listed from 2015 to 2019, we found that CEOs with overseas experience significantly increase firm value. Innovation efficiency has a significantly positive effect on firm value. Importantly, CEO overseas experience moderates the impact of innovation efficiency on firm value, indicating that these CEOs are better able to leverage innovation for value creation. The results underscore the importance of attracting and retaining CEOs with overseas experience and enhancing innovation efficiency to stay competitive. The study has theoretical and policy implications for companies, policymakers, and industry stakeholders. [ABSTRACT FROM AUTHOR]

Water and nitrogen are the two main limiting factors on wheat growth. How to optimize the water and fertilizer management practices of irrigated spring wheat, so as to retain high and stable yield, maintain highly use efficiency of agricultural resources and to be environmentally friendly, is an urgent production problem need to be solved. The promotion of controlled experiment results is limited by experiment sites, experiment duration or the number of experiment designs, while the process-based crop models can effectively solve this difficulty. In recent years, the crop model has become a powerful and technical tool for exploring better crop planting management practices and for evaluating how agriculture production adapts to climate change. In this study authors collected experimental observation data (meteorological, crop, soil, and management data) of spring wheat (Yongliang 4) from 2010 to 2018 at the agricultural meteorological experimental station in Linhe district, Bayannur city, Inner Mongolia Autonomous Region to determine the key genetic parameters of wheat growth and development in the APSIM-wheat model. Based on the calibrated model and meteorological data from1986 to 2020, we designed multiple intelligent management scenarios under different drought levels, and then explored the final optimal water and nitrogen management modes of spring wheat based on several key selection indicators (yield, water/fertilizer application rate, and water/fertilizer utilization efficiency). The results showed that: (1) the APSIM-wheat model can effectively reproduce the spring wheat development and growth with the RMSE of 1.96d to 3.21d for the spring wheat developmental stages (emergence, jointing, flowering and maturity). As for the wheat growth process (leaf area index (LAI), aboveground dry matter mass, and yield), the RMSE values were 1.65, 292.44g·m-2, and 588.96 kg·ha-1, respectively. (2) Based on the key selection indicators (yield, irrigation amount, water utilization efficiency (WUE), nitrogen fertilizer application amount, and nitrogen utilization efficiency (NUE)), the research concluded that the two management modes, that auto irrigation be applied when soil root layer (60cm) water deficit reached 40% (S3P40) and when soil root layer water deficit reached 50% (S3P50), were superior to other management modes. (3) Compared with the conventional management mode, the S3P40 management mode was preferred with the production goal to significantly improve yield, in which lead to a 14.4% increase in yield production. With the production goal to stabilize yield and reduce water and fertilizer application amount simultaneously, the S3P50 management mode was favored, which resulted in a 23.2% reduction in irrigation amount and 32.4% reduction in nitrogen application rate compared with traditional management mode. [ABSTRACT FROM AUTHOR]

The tiered-memory system can effectively expand the memory capacity for virtual machines (VMs). However, virtualization introduces new challenges specifically in enforcing performance isolation, minimizing context switching, and providing resource overcommit. None of the state-of-the-art designs consider virtualization and address these challenges; we observe that a VM with tiered memory incurs up to a 2× slowdown compared to a DRAM-only VM. We propose vTMM, a hardware-software collaborative tiered-memory management framework for virtualization. A key insight in vTMM is to leverage the unique system features in virtualization to meet the above challenges. vTMM automatically determines page hotness and migrates pages between fast and slow memory to achieve better performance. Specially, vTMM optimizes page tracking and migration based on page-modification logging (PML), a hardware-assisted virtualization mechanism, and adaptively distinguishes hot/cold pages through the page "temperature" sorting. vTMM also dynamically adjusts fast memory among multi-VMs on demand by using a memory pool. Further, vTMM tracks huge pages at regular-page granularity in hardware and splits/merges pages in software, realizing hybrid-grained page management and optimization. We implement and evaluate vTMM with single-grained page management on an Intel processor, and the hybrid-grained page management on a Sunway processor with hardware mode supporting hardware/software co-designs. Experiments show that vTMM outperforms existing tiered-memory management designs in virtualization. [ABSTRACT FROM AUTHOR]

This research introduces a pioneering Energy Management System (EMS) for microgrids, integrating fuzzy neural networks and a modified particle swarm optimization (MPSO) algorithm. The key contribution lies in minimizing production costs while optimizing the use of renewable sources like photovoltaic (PV), wind turbines (WT), and energy storage. The novel approach considers time-dependent constraints, ensuring adaptability and superior system performance. Additionally, the study introduces an innovative demand response (DR) analysis using a neural-fuzzy network, enhancing customer response and energy cost dynamics. The MPSO algorithm addresses economic load distribution challenges, demonstrating superior performance in comparative analysis. This integrated approach offers a groundbreaking solution for sustainable and efficient energy planning in microgrids. The analysis demonstrates that the proposed method achieves higher energy savings (83%) compared to baseline levels of 72%, showcasing its superior efficiency. Comparative analysis with genetic and particle swarm optimization algorithms reveals consistently lower average expenses and increased cost-effectiveness with the proposed approach. • FNN with modified PSO algorithm is proposed for analyzing the EMS in MG. • PV, WT, MT, FC and Battery are considered as the MG. • Demand response (DR) is evaluated using FNN method. • The objective function is to minimize the cost functions and maximum profits. • Consider various operating conditions and evaluate them with statital indecs. [ABSTRACT FROM AUTHOR]

Objectives: Cuproptosis represents an innovative type of cell death, distinct from apoptosis, driven by copper dependency, yet the involvement of copper apoptosis-associated long non-coding RNAs (CRLncRNAs) in hepatocellular carcinoma (HCC) remains unclear. This study is dedicated to unveiling the role and significance of these copper apoptosis-related lncRNAs within the context of HCC, focusing on their impact on both the development of the disease and its prognosis. Methods: We conducted an analysis of gene transcriptomic and clinical data for HCC cases by sourcing information from The Cancer Genome Atlas database. By incorporating cuproptosis-related genes, we established prognostic features associated with cuproptosis-related lncRNAs. Furthermore, we elucidated the mechanism of cuproptosis-related lncRNAs in the prognosis and treatment of HCC through comprehensive approaches, including Lasso and Cox regression analyses, survival analyses of samples, as well as examinations of tumor mutation burden and immune function. Results: We developed a prognostic model featuring six cuproptosis-related lncRNAs: AC026412.3, AC125437.1, AL353572.4, MKLN1-AS, TMCC1-AS1, and SLC6A1-AS1. This model demonstrated exceptional prognostic accuracy in both training and validation cohorts for patients with tumors, showing significantly longer survival times for those categorized in the low-risk group compared to the high-risk group. Additionally, our analyses, including tumor mutation burden, immune function, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and drug sensitivity, further elucidated the potential mechanisms through which cuproptosis-associated lncRNAs may influence disease outcome. Conclusions: The model developed using cuproptosis-related long non-coding RNAs (lncRNAs) demonstrates promising predictive capabilities for both the prognosis and immunotherapy outcomes of tumor patients. This could play a crucial role in patient management and the optimization of immunotherapeutic strategies, offering valuable insights for future research. [ABSTRACT FROM AUTHOR]

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision. [ABSTRACT FROM AUTHOR]

The authors investigate 275-nm AlGaN-based deep-ultraviolet (DUV) light-emitting diodes (LEDs) of varied dimensions (100, 150, 200, and 300 μm) with or without SiO2 passivation. The results indicate SiO2 passivation significantly enhances the emission intensity and external quantum efficiency in smaller LEDs (100 μm) by mitigating sidewall defects and non-radiative recombination. Conversely, SiO2 passivation adversely affects larger LEDs (150, 200, and 300 μm) due to increased current crowding and heat accumulation. The study emphasizes the tradeoffs between sidewall optimization and heat management in micro-sized DUV LEDs. These insights are critical for the strategic development of efficient DUV LEDs tailored for practical applications. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises lack a scientific and effective energy efficiency assessment framework and methodology. This lack leads to an inaccurate understanding of energy usage and its benefits. As a result, there is energy wastage and loss. This wastage and loss negatively affect product costs. They also present a challenge to effective energy management. To address these issues, this paper introduces a novel, comprehensive energy efficiency evaluation system. This system integrates both qualitative and quantitative measures. It proposes an evaluation model based on the Particle Swarm Optimization (PSO) combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), wherein PSO is employed to optimize the weights determined by AHP, ensuring that the significance attributed to various indicators is scientific, objective, and rational. The FCE method is utilized to convert diverse factors affecting corporate energy efficiency, across different types and scales, into standardized 0–1 values, enabling a comparative analysis of the impact of each process and indicator on energy efficiency. Furthermore, the paper introduces an energy efficiency prediction model employing a multivariate linear regression algorithm, which demonstrates a good fit, facilitating the transition from retrospective energy efficiency evaluation to proactive improvements. Utilizing data on actual consumption of water, electricity, and steam from an enterprise, along with expert assessments on the implementation levels of new processes, technologies, equipment, personnel scheduling proficiency, steam recovery rates, and adherence to policies and assessments, a simulation experiment of the proposed model was conducted using Python. The evaluation yielded an energy efficiency score of 0.68; this is consistent with the real-world scenario of the studied enterprise. The predicted mean square error of 9.035416039503998 ×   10 − 9 indicates a high model accuracy, validating the practical applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency. [ABSTRACT FROM AUTHOR]

Simple Summary: Comparisons across equine social behaviour studies are currently impeded by the lack of a universally accepted ethogram. Therefore, this ethogram introduces standardised definitions for thirty-seven distinct equine social behaviours, drawing from existing ethograms and refining definitions through meticulous video observations. The definitions consider contextual cues, such as ear position and facial expressions, and communicative nuances. Video examples enhance clarity by capturing the dynamic flow and sequence of social interactions. This approach allows researchers to observe temporal aspects like sequence, duration, and rhythm, providing a detailed representation of equine social behaviours. Standardized definitions, along with video illustrations, promote clear communication among researchers and enable consistent cross-study comparisons, which, in turn, can contribute to a better understanding of how husbandry practices and health conditions impact equine social behaviour, aiding in the assessment and optimization of management practices to enhance equine welfare. Equine social behaviour studies face challenges stemming from the absence of a comprehensive ethogram with unequivocal standardised definitions and the resulting limits to data comparison across studies. To address these constraints, this ethogram offers researchers a standardised framework, defining thirty-seven distinct equine social behaviours supplemented by video examples for enhanced clarity. These definitions amalgamate insights from existing ethograms and are fine-tuned through meticulous video observations, encompassing contextual cues such as distinguishing between aggressive and playful circling based on ear position and facial expressions and communicative nuances to provide a detailed representation of equine social behaviours. Video recordings complement the standardised definitions by capturing the dynamic flow and sequence of social interactions. By providing a dynamic and detailed representation, videos allow researchers to observe the temporal aspects of behaviour, including the sequence, duration, and rhythm of interactions. These detailed data are crucial for interpreting social behaviours and unravelling the complexities of equine societies. Standardized and video-illustrated definitions of equine social behaviour facilitate clear and consistent communication between researchers, enabling cross-study comparisons regarding the impact of husbandry practices and health conditions on equine social behaviour, which, in turn, can facilitate the assessment and optimisation of management practices and equine welfare. [ABSTRACT FROM AUTHOR]

This study investigates the effect of economic integration on the environment in a sample of 36 African countries from 1990 to 2018. We employ the fixed-effect D&K estimator and the mediation analysis to examine direct and indirect effects, respectively. The findings reveal that economic integration improves the environment only when its negative indirect effects are neutralized. Precisely, economic integration has a direct negative effect on carbon dioxide (CO2) emissions and an indirect positive effect through urbanization, renewable energy consumption, financial development, and industrialization. The net effect is positive, and therefore, economic integration undermines the environmental quality in Africa. This result is robust when economic integration is split into trade and financial integration. The main implication of this study is that integration policies in Africa should also target greening the financial sector, sustainable urban planning, energy efficiency in the industrial sector, and renewable energy use. [ABSTRACT FROM AUTHOR]

Several technological challenges delay the adoption of electrified powertrains in the heavy-duty transport sector. For fuel-cell hybrid electric trucks, key issues include slow cold start, reduced cooling power during high ambient temperatures, and uncertainties regarding durability. In addition, the engineers must handle the complexity of the system. In this article, a Matlab/Simulink library is introduced, which has been developed to aid engineers in the design and optimization of energy management systems and strategies of this complex system that consider mechanical, electrochemical, and thermal energy flows. The library is introduced through five example vehicle models, and through case studies that highlight the various kinds of analysis that can be performed using the provided models. All library code is open source, open for commercial use, and runs in Matlab/Simulink without any need for external libraries. [ABSTRACT FROM AUTHOR]

In recent years, energy management (EM) has become a vital tool for the planning of various energy sources in the microgrid. The EM scheme helps in the effective planning of various energy sources so that a proper balance between generation and load demand can be maintained. In this aspect, different management schemes have been developed to effectively plan various sources. In this work an interactive class topper optimization (I-CTO) based energy management scheme for an interconnected microgrid considering renewable energy sources, battery storage systems, demand side management, etc. is presented. The proposed scheme aims to optimally plan different energy sources in the microgrid so that the generation and emission cost may be minimized. To achieve this objective of having an optimal distribution of generation among different energy sourest, an objective function is formulated. This developed objective function is minimized using the proposed I-CTO scheme while maintaining some of the operational constraints encountered in real-time scenario. The reduced objective corresponds to the optimal distribution of generation among different energy sources which would help to fulfill the load demand with less generation and emission cost. To test the effectiveness of the proposed schemes and show their supremacy over some existing methods, a comparative study is presented using a numerical test example and CEC-2020 benchmark function. [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]

In this paper, a new model predictive control termed as intelligent model predictive control (IMPC) combined with an improved new competitive swarm optimizer (CSO) is designed. The analytical predictive model is not necessarily established a priori in the proposed IMPC algorithm, and the control plant can be used directly as the predictive model to reduce the complexity of the algorithm. In addition, two new techniques, dynamic initialization and back steps methods, are proposed and utilized to improve the traditional CSO to realize constraints management during the optimization process. An application to aeroengine transient-state control is studied to verify the effectiveness of the presented IMPC algorithm. It is shown that, benefitting from the IMPC algorithm, the control task is well completed and all the constraints are satisfied. Practical Applications: This paper proposes a new optimization algorithm to solve some complex optimization problems with constraints. The designed new optimization algorithm is simple and easy to implement and it is more suitable for applications with black-box models or complex optimization objective functions compared with some existing optimization algorithms. This paper may provide some help to researchers who are interested in model predictive control or metaheuristic algorithms and to people whose work contains some complex optimization problems. This paper also provides a design method of aeroengine transient-state control plans. The simulation results showed that compared with some existing control methods or optimization algorithms, the aeroengine could accelerate to the desired steady-state point with shorter transient time and all constraints are satisfied when utilizing the proposed new optimization algorithm. [ABSTRACT FROM AUTHOR]

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, we describe a solution that has been developed to create an intelligent software platform for the optimal management of energy trade (specifically a P2P trade) in microgrids. Furthermore, the choice of a solution based on the architecture of two-level hierarchical systems using the restrictions and recommendations of the control center is substantiated. A general functional design of the platform is proposed using blockchain technology and cyber security solutions. The proposed solution includes the technical specification of the presented platform using the game theory model as a special case without loss of generality. The enhanced learning method was used to simulate customer behavior. Simulation of the platform operation was carried out using the example of optimizing the peak-to-average network load by users making individual decisions using game theory algorithms. The simulation results of the implementation of scenarios with and without recommendations confirmed that the proposed model provides a better peak-to-average ratio (PAR) in the network. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and the development of society, there are also problems of resource shortage and environmental pollution. People are eager to change this situation. How to achieve sustainable development has become an important problem to be solved. The development of society is inseparable from the supply chain, but the traditional supply chain cannot meet the requirements of sustainable development. The research manifests that the Closed-Loop supply chain (CLSC) can protect the environment while economic development, and achieve harmonious coexistence between man and nature. CLSC is studied initially, and the connotation and characteristics of CLSC are introduced. A management optimization of CLSC based on deep learning technology is proposed. Meanwhile, the genetic simulated annealing algorithm is used for simulation, and the obtained solution is the optimal solution. And this algorithm has good convergence, which provides new ideas for the future development of Chinese enterprises. [ABSTRACT FROM AUTHOR]

With the development of mobile Internet, more and more readers search for book information through mobile phones and other devices, so how to optimize the efficiency and accuracy of book information search has become an important issue of intelligent library system management. This paper presents a book information search algorithm based on cellular network, which can effectively improve the efficiency and accuracy of book information search. Based on the existing problems of book information search in the intelligent library system, this paper proposes a book information search algorithm based on cellular network. By assigning the book information to multiple search nodes, the algorithm makes use of the advantages of cellular network to realize efficient search and transmission. At the same time, the improved text similarity algorithm is introduced, which can improve the search efficiency and accuracy by analyzing and processing the keywords searched by users, and can match the readers' search requests and book information records more accurately. The experimental results show that this scheme can improve the search efficiency and data transmission rate of intelligent library system, and provide a new idea and method for the management optimization of intelligent library system. [ABSTRACT FROM AUTHOR]

The pandemic, followed by the Russian aggression against Ukraine, caused rapid changes in the economy. European countries experienced unprecedented price increases, which resulted in a significant increase in the cost of capital. This resulted primarily in limited access to capital and a significant reduction in investments in the real estate market. In addition, investors began to withdraw capital from investments in the real estate market to other assets, encouraged by their rising rates of return. The article presents how the indicated circumstances translated into the financial efficiency of companies from the Real Estate sector. Listed companies of the European Economic Area in the years 2019-2022 were analyzed. Changes in the main accounting measures and market measures for individual countries as well as the characteristics of real estate market participants were analyzed. [ABSTRACT FROM AUTHOR]

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study. [ABSTRACT FROM AUTHOR]

Objective To analyze the spatial distribution characteristics, efficiency, and influencing factors of pension institutions in Dongguan, so as to provide reference for optimizing the resource allocation of pension institutions in Dongguan. Methods The resource allocation data of pension institutions in Dongguan were obtained by questionnaire survey, and the spatial distribution characteristics and efficiency of pension institutions were analyzed and evaluated based on GIS and three-stage DEA model. Results There was a good spatial coupling between the resources of pension institutions in Dongguan and the elderly population over 60 years old; the lowest occupancy rate was 12.50%, the highest was 100.005%, and the phenomenon of "difficulty in finding a bed" and "half of the beds were vacant" coexisted. In the third stage, the average comprehensive efficiency decreased from 0.679 to 0.635, and environmental factors had a great influence on the efficiency of resource allocation in Dongguan. There were great differences in the efficiency of pension institutions in different areas. The comprehensive efficiency of urban areas was 0.784, which exceeded the average level of the whole city (0.635). The comprehensive efficiency of the eastern industrial park was only 0.439, which did not reach the average level of the whole city. The per capita regional GDP and the level of aging had a negative impact on efficiency. Conclusion It is suggested to rationally allocate the resources of pension institutions, optimize regional development planning, promote the upgrading of institutional pension services, improve the efficiency of resource allocation, and provide improved reference scheme for improving the service level of pension institutions in Dongguan. [ABSTRACT FROM AUTHOR]

The article examines the activity of the municipality from the point of view of the education management system in modern realities, namely in the conditions of digital transformation of society and its life, in particular, digitalization of educational activities. The main advantages and disadvantages of the digitalization of education are analyzed and presented. In the context of the research, the theoretical, normative and legal aspects of the current educational activity are presented, as a key link in solving problems of any nature for the benefit of the local population, in particular at the municipal level. In conclusion, the priority directions of the development of education management in the conditions of digitalization are given, which should effectively influence the optimization of organizational and management processes in the field of digital transformation, as well as the active penetration of digital technologies into the educational process of various educational organizations. The article reveals the effectiveness of information and communication technologies in municipal educational institutions training future specialists. In the context of the implementation of the research goal, the role of digitization as a factor in the implementation of educational activities in the process of training specialists in the economic field is studied. In the modern conditions of the development of the information society, there is a need for fast and high-quality calculation of economic indicators. It is this need that prompts the widespread use of software accounting methods. Digitalization has absorbed various fields of human activity. [ABSTRACT FROM AUTHOR]

This study empirically investigates the implications of corporate social responsibility (CSR) on the value-added intellectual capital efficiency of food and agribusiness firms in India. This study uses the firm-level data collected from the Prowess database of the Center for Monitoring Indian Economy for 362 food and agribusiness companies in 2019. The study contributes to the theoretical and managerial practices by assessing the causal relationship between the value-added intellectual capital (VAIC) and CSR for food and agribusiness firms in an emerging economy, i.e., India. The results suggest a significant mean difference in the key characteristics of firms with and without CSR expenditure. Further, there is a significant relationship between CSR and Value-Added Intellectual Capital Efficiency in the Indian food and agribusiness firms. Finally, the regression analysis indicates that CSR expenditure has a positive and significant implication on value-added intellectual capital (ß = 0.119, p < 0.01), including all the dimensions of firm's capital efficiency except structural capital; i.e., capital employed (ß = 0.095, p < 0.01), human capital (ß = 0.145, p < 0.01) and intellectual capital (ß = 0.124, p < 0.01). However, CSR does not seem to have significant implications on structural capital standalone (ß = 0.044, p > 0.10), implying that the structural capital efficiency of the firms does not get affected by CSR expenditure. The findings of the study provide a good reason for implementing mandatory CSR, which goes beyond the performance of firms, as there is a positive and significant impact of CSR on the Value-Added Intellectual Capital of the firms. This paper contributes to the existing literature by providing extended understanding on the relationship between CSR and VAIC among food and agribusiness firms. [ABSTRACT FROM AUTHOR]

This study proposes a novel heat sink for insulated-gate bipolar transistor modules utilizing flat miniature-heat-pipe arrays. The influence of heat load, cooled-air temperature, and distribution mode on the start-up and heat dissipation performance of the proposed heat sink with and without aluminum plate was investigated experimentally and numerically. As a result, the start-up time of the heat sink with aluminum plate was approximately 2–3 min, and maximum temperature for heat sink was 76.8 °C under cooled-air parallel flow conditions when heat flux density was 6.67 W cm−2 and cooled-air temperature was 40 °C, which was 4.6 °C lower than that obtained without aluminum plate. In addition, the study found that cooled-air parallel distribution mode was better for heat dissipation than opposite distribution mode, and that maximum temperature decreased more obviously as the heat load increased. The predicted models between two responses and four independent design factors were obtained by response surface method, and the interactive impacts of different factors on responses were studied. Finally, multi-objective joint optimization was developed to determine the optimal dimensions of the aluminum plate width, longitudinal fin height, spacing, and thickness, which were 82.02, 48.79, 1.5, and 0.58 mm, respectively, to minimize maximum temperature and materials cost. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals (SDGs). In 2020, the energy consumption of the world's ten major energy-consuming countries accounted for 66.8% of the global total. This paper applied data envelopment analysis (DEA) to calculate these ten major energy-consuming countries' total-factor energy efficiency (TFEE) at national and sectoral levels from 2001-2020, and explored the influencing factors of total-factor energy efficiency with the Tobit regression model. The results showed significant difference in the ten countries' energy efficiency. The United States and Germany topped the list for total-factor energy efficiency, while China and India were at the bottom. Meanwhile, the energy efficiency of the industrial subsector has increased significantly over the past two decades, while that of the other subsectors has been relatively flat. The industrial structure upgrading, per capita GDP, energy consumption structure, and foreign direct investment had significant impacts on energy efficiency with national heterogeneity. Energy consumption structure and GDP per capita were determinative factors of energy efficiency. [ABSTRACT FROM AUTHOR]

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]