Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions.

This paper proposes a novel genetic algorithm (GA) approach that utilizes a multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, the former is represented by a string of categorical values, whereas the latter forms a sequence of operations. Consequently, the chromosome of conventional GAs for solving FJSP consists of a categorical part and a sequential part. Since these two parts are different from each other, different kinds of genetic operators are required to solve the FJSP using conventional GAs. In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. In order to implement the unified approach, the sequential part is evolved by applying a candidate order-based GA (COGA), which can use traditional crossover strategies such as one-point or two-point crossovers. Such crossover strategies can also be used to evolve the categorical part. Thus, we can handle the categorical and sequential parts in an identical manner if identical crossover points are used for both. In this study, the unified approach was used to extend the existing COGA to a unified COGA (u-COGA), which can be used to solve FJSPs. Numerical experiments reveal that the u-COGA is useful for solving FJSPs with complex structures.

A vibration atomic layer deposition reactor was developed for fabricating a conformal thin-film coating on nanosize particles. In this study, atomic layer deposition of 10–15-nm-thick Al2O3 films was conducted on a high-surface-area acetylene black powder with particle diameters of 200–250 nm. Intense vibration during the deposition resulted in the effective separation of particles, overcoming the interparticle agglomeration force and enabling effective diffusion of the precursor into the powder chunk; this phenomenon led to the formation of a conformal film coating on the nanopowder particles. It was also confirmed that the atomic layer deposition Al2O3 films initially grew on the high-surface-area acetylene black powder particles as discrete islands, presumably because chemisorption of the precursor and water occurred only on a few sites on the high-surface-area acetylene black powder surface. Relatively sluggish growth of the films during the initial atomic layer deposition cycles was identified from composition analysis.

Data analytics provides important tools and methods for processing the data generated during legal services. This paper aims to provide a systematic survey of the research papers on the application of quantitative data analytics algorithms in the legal domain. To this end, relevant research papers were collected and used to analyze topics and trends of research on data analytics-based Legal Tech. The key findings of this paper are as follows. Firstly, the number of research papers about Legal Tech has increased dramatically recently. Secondly, the application of supervised learning techniques to legal judgment data is a very popular approach in this research area. Thirdly, preprocessing legal documents is a very important procedure as many legal documents exist in text form. Fourthly, artificial neural networks and their variations are widely used in research on data analytics-based Legal Tech. Fifthly, data analytics-based Legal Tech is a multidisciplinary research topic related to computer science and social science, etc.

This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.