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Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.

This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.

This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.

The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.

This article examines the impact of banks’ corporate social responsibility communication through social media (CSR-S), electronic word of mouth (eWOM), and brand reputation on consumer behavior during the COVID-19 crisis, with a focus on purchase intention.

The study employed a quantitative approach to analyze data from a survey of 621 Egyptian bank customers who followed the banks’ social media pages and interacted with CSR-S initiatives. A genetic algorithm selected the most relevant variables affecting purchase intention. A Bayesian regression model was used to analyze the impact of CSR-S communication, eWOM, and brand reputation on purchase intention.

CSR-S initiatives, eWOM, and brand reputation were found to influence customer purchase intention. CSR-S initiatives can boost purchase intention by encouraging brand reputation and initiative sharing with friends and other customers. However, CSR-S negatively moderates the positive impact of eWOM and brand reputation on the predisposition to contract products and services with the bank.

This study addresses critical research gaps in CSR literature. Firstly, it examines the impact of CSR-S actions on customer behavior, a perspective less explored in previous research. Secondly, it investigates the intricate relationships between CSR-S, eWOM, brand reputation, and purchase intention, shedding light on their interplay, particularly during the COVID-19 pandemic. Additionally, this research extends CSR-S investigations to the competitive banking industry and focuses on a developing country context, enhancing the applicability of findings for Egyptian banks. Lastly, the study employs advanced methodologies to improve the accuracy of results.

本文擬探討於2019冠狀病毒病危機期間、銀行透過社交媒體而進行關於企業社會責任的溝通 (以下簡稱社媒企社責溝通) 、電子口碑和品牌聲譽,如何影響消費行為; 研究會聚焦於客戶的購買意向上。

研究以定量方法、去分析來自涵蓋621名埃及銀行客戶的調查的數據; 這些客戶均有追隨銀行的社交媒體頁面,並曾與銀行就企業社會責任提出的倡議進行互動交流。研究人員以基因演算法挑選了與購買意向相關性最密切的變量,並以貝葉斯回歸模型,去分析探討社媒企社責溝通、電子口碑和品牌聲譽、如何影響客戶的購買意向。

研究結果顯示,透過社交媒體傳達的企業社會責任倡議、電子口碑和品牌聲譽,均會影響客戶的購買意向。這類倡議會透過促進品牌聲譽和朋友或客戶間的互相共享而令購買意向提昇。唯社媒企社責溝通會減弱電子口碑和品牌聲譽給客戶購買意向帶來的正面影響,使他們與銀行訂立商品或服務契約的意欲降低。

本研究致力回應企業社會責任文獻內重要的研究空白。首先,研究人員探討社媒企社責溝通對客戶行為帶來的影響,這研究角度從來沒有被充分利用。其次,本研究探討社媒企社責溝通、電子口碑、品牌聲譽和購買意向之間錯綜複雜的關係,這幫助闡明各元素的相互作用,尤以2019冠狀病毒病肆虐期間為甚。再者,本研究把關於社媒企社責溝通的研究擴展至競爭性銀行業,並聚焦於涉及一個發展中國家的背景,這都使研究結果更能應用於分析埃及銀行上。最後,研究人員為了提高研究結果的準確性,採用了先進的方法進行研究。

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.

This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.

In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.

From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.

This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.

An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.

Design a novel optimal integrated control algorithm for the automotive electric steering system to improve the stability and adaptation of the system.

Simulation and calculation.

The output signals follow the reference signal with high accuracy.

The optimal integrated algorithm is established based on the combination of PID and SMC. The parameters of the PID controller are adjusted using a fuzzy algorithm. The optimal range of adjustment values is determined using a genetic algorithm.

With shorter product cycles and a growing number of knowledge-intensive business processes, time consumption is a highly relevant target factor in measuring the performance of contemporary business processes. This research aims to extend prior research on the effects of knowledge transfer velocity at the individual level by considering the effect of complexity, stickiness, competencies, and further demographic factors on knowledge-intensive business processes at the conversion-specific levels.

We empirically assess the impact of situation-dependent knowledge transfer velocities on time consumption in teams and individuals. Further, we issue the demographic effect on this relationship. We study a sample of 178 experiments of project teams and individuals applying ordinary least squares (OLS) for regression analysis-based modeling.

The authors find that time consumed at knowledge transfers is negatively associated with the complexity of tasks. Moreover, competence among team members has a complementary effect on this relationship and stickiness retards knowledge transfers. Thus, while demographic factors urgently need to be considered for effective and speedy knowledge transfers, these influencing factors should be addressed on a conversion-specific basis so that some tasks are realized in teams best while others are not. Guidelines and interventions are derived to identify best task realization variants, so that process performance is improved by a new kind of process improvement method.

This study establishes empirically the importance of conversion-specific influence factors and demographic factors as drivers of high knowledge transfer velocities in teams and among individuals. The contribution connects the field of knowledge management to important streams in the wider business literature: process improvement, management of knowledge resources, design of information systems, etc. Whereas the model is highly bound to the experiment tasks, it has high explanatory power and high generalizability to other contexts.

Team managers should take care to allow the optimal knowledge transfer situation within the team. This is particularly important when knowledge sharing is central, e.g. in product development and consulting processes. If this is not possible, interventions should be applied to the individual knowledge transfer situation to improve knowledge transfers among team members.

Faster and more effective knowledge transfers improve the performance of both commercial and non-commercial organizations. As nowadays, the individual is faced with time pressure to finalize tasks, the deliberated increase of knowledge transfer velocity is a core capability to realize this goal. Quantitative knowledge transfer models result in more reliable predictions about the duration of knowledge transfers. These allow the target-oriented modification of knowledge transfer situations so that processes speed up, private firms are more competitive and public services are faster to citizens.

Time consumption is an increasingly relevant factor in contemporary business but so far not been explored in experiments at all. This study extends current knowledge by considering quantitative effects on knowledge velocity and improved knowledge transfers.

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

The manufacturing sector is witnessing the need to continuously improve overall performance by eliminating inefficiencies in the supply chain. The adoption of lean concepts to address wasteful or non-value-adding activities in the supply chain is crucial. This article determines key factors of lean supply chain management (LSCM) for continuous improvement in the manufacturing sector.

The methodology comprises three steps. The first step identifies critical factors of LSCM in manufacturing from prior research and a series of expert consultations. Critical factors are identified and validated that industries can leverage to attain their lean goals. The second step uses the decision-making and trial evaluation laboratory (DEMATEL) method to determine the causal relationship among the factors. DEMATEL analysis categorizes factors into cause and effect, which will assist industry personnel in decision-making. The third step involves further data analysis to visualize the importance of the most critical factors. It develops a machine learning (ML) model in the form of a decision tree that helps in assessing the factors into cause or effect groups via a threshold value of expert ratings.

IT tools, JIT manufacturing and material handling and logistics form the most critical factors for LSCM implementation.

The analysis from DEMATEL and ML together will be beneficial for manufacturing practitioners to improve the supply chain performance based on the identified factors and their criticality towards LSCM implementation.

This study aims to determine how the applications of blockchain technology (BT) can play a crucial role in managing financial flows in the humanitarian supply chain (HSC) and what benefits and challenges are associated with BT in a humanitarian setting.

The present study used a qualitative research approach, incorporating a systematic literature review and conducting semi-structured interviews with 12 experts in the fields of humanitarian operations, supply chain management, fintech and information technology.

The findings show that the humanitarian sector has the potential to reap significant benefits from BT, including secure data exchange, efficient SCM, streamlined donor financing, cost-effective financial transactions, smooth digital cash flow management and the facilitation of cash programs and crowdfunding. Despite the promising prospects, this study also illuminated various challenges associated with the application of BT in the HSC. Key challenges identified include scalability issues, high cost and resource requirements, lack of network reliability, data privacy, supply chain integration, knowledge and training gaps, regulatory frameworks and ethical considerations. Moreover, the study highlighted the importance of implementing mitigation strategies to address the challenges effectively.

The present study is confined to exploring the benefits, challenges and corresponding mitigation strategies. The research uses a semi-structured interview method as the primary research approach.

This study adds to the existing body of knowledge concerning BT and HSC by explaining the pivotal role of BT in improving the financial flow within HSC. Moreover, it addresses a notable research gap, as there is a scarcity of studies that holistically cover the expert perspectives on benefits, challenges and strategies related to blockchain applications for effective financial flows within humanitarian settings. Consequently, this study seeks to bridge this knowledge gap and provide valuable insights into this critical area.

Contemporary stochastic optimal control by synergy of the probability density evolution method (PDEM) and conventional optimal controller exhibits less capability to guarantee economical energy consumption versus control efficacy when non-stationary stochastic excitations drive hysteretic structures. In this regard, a novel multiscale stochastic optimal controller is invented based on the wavelet transform and the PDEM.

For a representative point, a conventional control law is decomposed into sub-control laws by deploying the multiresolution analysis. Then, the sub-control laws are classified into two generic control laws using resonant and non-resonant bands. Both frequency bands are established by employing actual natural frequency(ies) of structure, making computed efforts depend on actual structural properties and time-frequency effect of non-stationary stochastic excitations. Gain matrices in both bands are then acquired by a probabilistic criterion pertaining to system second-order statistics assessment. A multi-degree-of-freedom hysteretic structure driven by non-stationary and non-Gaussian stochastic ground accelerations is numerically studied, in which three distortion scenarios describing uncertainties in structural properties are considered.

Time-frequency-dependent gain matrices sophisticatedly address non-stationary stochastic excitations, providing efficient ways to independently suppress vibrations between resonant and non-resonant bands. Wavelet level, natural frequency(ies), and ratio of control forces in both bands influence the scheme’s outcomes. Presented approach outperforms existing approach in ensuring trade-off under uncertainty and randomness in system and excitations.

Presented control law generates control efforts relying upon resonant and non-resonant bands, and deploys actual structural properties. Cost-function weights and probabilistic criterion are promisingly developed, achieving cost-effectiveness of energy demand versus controlled structural performance.