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Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem.

In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment.

The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace.

The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace.

This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.

The purpose of this paper is to examine empirically how managers establish ethical guanxi (interpersonal relationships) with their business partners to prevent potential ethical incidences in Chinese and Western business contexts.

The present study is guided by a qualitative, abductive approach and draws on in-depth interviews with ten senior managers in five urban New Zealand organisations.

The results point out that guanxi (interpersonal relationships) purely working through renqing (reciprocity) is not sustainable, because it perpetuates a never-ending cycle of favours, once exchanging favours stopped or disappeared, then business relationships dwindled. To establish an ethical guanxi model, the authors found that xinyong (trust) is the foundation, and its enlargement stimulates lijie (empathy) that transfers pure business relationships to a genuine friendship which enhances ethical decision-making. They also posit that once managers are embodied with lijie, then they will have the virtue of ren to behave like junzi (ideal Confucian ethical person) whose business actions tend to be intrinsically guided by a sense of obligation to do something right that will work for diverse stakeholders’ interests, for the prosperity of organisations and society.

This study suggests that managers should take Confucian virtues of xinyong (trust) and lijie (empathy), because they will trigger ren (humanity) as an intrinsic value. In this way, it is more likely for them to become junzi (ideal Confucian ethical person) whose business actions are intrinsically guided by a sense of obligation to do something right that benefits various stakeholders, organisations and society.

This study contributes to the extant literature on preventing ethical incidents of guanxi (interpersonal relationships) by drawing a framework of ethical guanxi, which is built on Confucian virtues of xinyong (trust), lijie (empathy) and ren (humanity). Further, this paper strongly suggests that companies should educate their staff to become more humane to make moral decisions in daily management practice.

Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.

This study is to reconfigure a hierarchical supply chain model utilizing databases and text files to understand future pathways due to COVID-19 pandemic has had a bullwhip effect, disrupting the global supply chain, and a mechanism is needed to address this disruptive event under pandemic uncertainties.

To address this mechanism, this study employs bibliometric analysis and text mining to reconfigure a hierarchical supply chain model under pandemic conditions and associates it with social media to conduct an intuitive visual analysis.

The current academic concerns are related to an overconcentration on risk management and a data-driven approach, generating an enormous gap between the concerns of academics and those of the public. The evidence shows that for both countries with outstanding performance and those that need improvement, the efficiency in terms of preventing the spread of the pandemic should be promoted.

This study contributes to (1) reconfiguring a hierarchical supply chain model under pandemic uncertainties and (2) bridging theory and practice by offering comparable interrelated attributes to guide post-COVID-19 strategies in the supply chain. The findings are that the supply management approach and big data are attributes that involve the concerns of world public and academics under pandemic uncertainties.

This study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.

Amidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.

We find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.

In the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

The purpose of this study is to explore mitigation measures for cash flow interruption during the epidemic and provide decision support to ensure the regular operation and robustness of the supply chain (SC).

Considering the scenarios of production capacity and demand disruption during the epidemic, the authors adopt system dynamics (SD) to construct a three-echelon SC financial system consisting of a core manufacturer, a capital-constrained retailer and the customer. In different interruption scenarios, through the decision adjustments of stakeholders, the differences in performance are compared to explore solutions for SC robust optimization.

The results show that partial credit guarantee (PCG) could solve cash flow interruption and maintain the regular operation of the SC. During epidemic, with the product price increases, the revenue of stakeholders and the robustness are generally negatively correlated. But when the manufacturer's production capacity is fully interrupted, increasing product price is the right decision for the retailer and could simultaneously promote performance and robustness.

This paper primarily focuses on the PCG under the cash flow interruption caused by epidemics. The authors adopt the supply chain finance (SCF) theory and SD method to supplement and expand existing research on interruption management of SC. It is a pioneering study to explore the robustness of the SC financial system under disruptions.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.