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Purpose: This study aims to investigate how to identify and address production levelling problems in assembly lines utilising an intensive manual workforce when higher productivity levels are urgently requested to meet market demands. Design/methodology/approach: A mixed-methods approach was used in the research design, integrating case study analysis, interviews and qualitative/quantitative data collection and analysis. The methodology implemented also introduces to the literature on operational performance a novel combination of data analysis methods by introducing the use of the Natural Language Understanding (NLU) methods. Findings: First, the findings unveil the impacts on operational performance that transportation, limited documentation and waiting times play in assembly lines composed of an intensive workforce. Second, the paper unveils the understanding of the role that a limited understanding of how the assembly line functions play in productivity. Finally, the authors provide actionable insights into the levelling problems in manual assembly lines. Practical implications: This research supports industries operating assembly lines with intensive utilisation of manual workforce to improve operational performance. The paper also proposed a novel conceptual model prescriptively guiding quick and long-term improvements in intensive manual workforce assembly lines. The article assists industrial decision-makers with subsequent turnaround strategies to ensure higher efficiency levels requested by the market. Originality/value: The paper offers actionable findings relevant to other manual assembly lines utilising an intensive workforce looking to improve operational performance. Some of the methods and strategies examined in this study to improve productivity require minimal capital investments. Lastly, the study contributes to the empirical literature by identifying production levelling problems in a real context. [ABSTRACT FROM AUTHOR]

Feasibility checking is a step in manufacturing system engineering for verifying the normalization and effectiveness of a manufacturing system associated with a specific configuration of resources and processes. It enables factory operators to predict problems before operational time, thus preventing equipment and machinery accidents and reducing labor waste in physically organizing the shop floor. In Industry 4.0, feasibility checking becomes even more critical since emerging challenges, such as mass personalization, require reconfiguring work cells quickly and flexibly on demand. Regarding this need, digital twin technologies have emerged as an ideal candidate for practicing feasibility checking. Indeed, they are tools used to implement digital representations of manufacturing entities that can constitute a digital environment and context. Factory operators can test a manufacturing process within a digital environment in different contexts before the execution with physical resources. This approach currently receives significant attention from the manufacturing community; however, there is still a lack of sharing experiences to implement it. Thus, this paper contributes a methodology to engineer a digital environment and context for a manufacturing work cell using AAS digital twins and physics-based 3D digital twins technologies. Technically, this methodology is a specific case of N-DTs, a general methodology for engineering heterogeneous digital twins. The product assembly line case study, also presented in this paper, is a successful experiment applying the above contributions. The two methodologies and the case study can be helpful references for both public and private sectors to deploy their feasibility-checking frameworks and deal with heterogeneous digital twins in general. [ABSTRACT FROM AUTHOR]

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness. Design/methodology/approach: This indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials. Findings: An Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings. Originality/value: The paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures. [ABSTRACT FROM AUTHOR]

The solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity. [ABSTRACT FROM AUTHOR]

In an era where countries worldwide are emphasizing the process of educational informatization, traditional experimental teaching methods encounter inherent limitations, such as constraints related to the availability of experimental sites and the amount of experimental equipment. Consequently, it becomes a challenge to provide students with an unconstrained, open, and collaborative experimental environment. The digital twin, as a pivotal technology for achieving interactive mapping of the physical world and the information world, possesses attributes characterized by real time interactivity and the seamless fusion of virtual and tangible elements. As a result, various virtual simulation experimental teaching platforms grounded in digital twin technology are now extensively integrated into talent development initiatives. In the context of the "Production Planning and Control" course at Harbin Institute of Technology (HIT), the virtual simulation experimental teaching method is examined, leading to the creation of a virtual simulation instructional platform that blends the virtual and tangible realms, thanks to the foundation of digital twin technology. The virtual simulation experimental teaching platform detailed in this paper, specifically designed for optimizing scheduling within assembly line workshops, represents a pioneering model practice in the integration of digital twin technology into workshop-based experimental instruction and assumes an influential role in enhancing students' grasp of theoretical knowledge and their capacity for practical innovation. [ABSTRACT FROM AUTHOR]

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

As the take-off of China's macro economy, as well as the rapid development of infrastructure construction, real estate industry, and highway logistics transportation industry, the demand for heavy vehicles is increasing rapidly, the competition is becoming increasingly fierce, and the digital transformation of the production line is imminent. As one of themost important components of heavy vehicles, the transmission front andmiddle case assembly lines have a high degree of automation, which can be used as a pilot for the digital transformation of production. To ensure the visualization of digital twins (DT), consistent control logic, and real-time data interaction, this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line. Firstly, theDT-based system architecture is designed, and theDTmodel is created by constructing the visualization model, logic model, and data model of the assembly line. Then, a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line. Eventually, some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment. [ABSTRACT FROM AUTHOR]

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines. [ABSTRACT FROM AUTHOR]

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation. [ABSTRACT FROM AUTHOR]

In the context of assembly lines, the term 'rest time' refers to those time of inactivity allowed to a worker after the completion of his/her tasks on a product. Rest times are often assigned to workers that perform ergonomically hazardous tasks, to prevent their occupational diseases. When rest times are assigned after balancing the line, the line becomes unbalanced and the ergonomic load of workers is reduced at the price of deteriorated line performance. In this paper, an approach able to assign rest times during the balancing procedure is presented. The approach consists of a genetic algorithm coupled with a discrete event simulator to evaluate the fitness function of individuals, which considers the performance, the ergonomic, and the cost aspects. The OCRA index has been utilised to assess the ergonomic risk of workers. Results show that the presented approach can significantly support firms in designing manual assembly lines: the possibility to assign rest times during the balancing procedure allows for increasing the quality of solutions in 103 out of 135 benchmark instances. The greatest improvements are obtained when the ergonomic load of tasks is relevant, by achieving good performances of the line with limited costs, thus defending companies' profitability. [ABSTRACT FROM AUTHOR]

In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling approach. [ABSTRACT FROM AUTHOR]

Voltage standards are widely used to transfer volts from Josephson voltage standards (JVSs) at national metrology institutes (NMIs) into calibration labs to maintain the volts and to transfer them to test equipment at production lines. Therefore, commercial voltage standards based on Zener diodes are used. Analog Devices Inc. (San Jose, CA, USA), namely, Eric Modica, introduced the ADR1000KHZ, a successor to the legendary LTZ1000, at the Metrology Meeting 2021. The first production samples were already available prior to this event. In this article, this new temperature-stabilized Zener diode is compared to several others as per datasheet specifications. Motivated by the superior parameters, a 10 V transfer standard prototype for laboratory use with commercial off-the-shelf components such as resistor networks and chopper amplifiers was built. How much effort it takes to reach the given parameters was investigated. This paper describes how the reference was set up to operate it at its zero-temperature coefficient (z.t.c.) temperature and to lower the requirements for the oven stability. Furthermore, it is shown how the overall temperature coefficient (t.c.) of the circuit was reduced. For the buffered Zener voltage, a t.c. of almost zero, and with amplification to 10 V, a t.c. of <0.01 µV/V/K was achieved in a temperature span of 15 to 31 °C. For the buffered Zener voltage, a noise of ~584 nVp-p and for the 10 V output, ~805 nVp-p were obtained. Finally, 850 days of drift data were taken by comparing the transfer standard prototype to two Fluke 7000 voltage standards according to the method described in NBS Technical Note 430. The drift specification was, however, not met. [ABSTRACT FROM AUTHOR]

In recent years, the automotive industry has witnessed the increasing adoption of mixed-model assembly lines (MMALs) to meet the demands of mass customization. However, the diverse components of various end products are posing significant challenges to the material feeding processes in MMALs. To address this issue and enhance the material feeding efficiency, this paper proposes an innovative automatic monorail shuttle system (AMSS). The AMSS is designed based on the layout of line-integrated supermarkets and incorporates the load-exchangeable shuttles and crossovers. Considering the importance of cost control in manufacturing enterprises, the objective of this paper is to minimize the total costs associated with the material feeding system, which includes the installation costs of crossovers, as well as the input and operation costs of load-exchangeable shuttles. A mathematical model is thereby established to describe the problem, and its accuracy is validated through the Gurobi solver. Recognizing the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper-heuristic (SBHH) algorithm is developed to allocate and schedule the shuttles and crossovers, which adopts the shuffled frog leaping algorithm as the high-level heuristic for selecting low-level heuristics. To further improve the performance, dynamic decision unit is introduced to raise the solution accuracy and the convergence speed. Simulation results verify the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other benchmark optimization algorithms. Managerial applications indicate the effectiveness and practicality of the proposed approach, offering implications for practitioners in the automotive industry. [ABSTRACT FROM AUTHOR]

An assembly cell line (ACL) is one type of cell production practice, derived from the Toyota Production System in the electronics industry and rapidly spread to other fields. In this mode, the conveyor line is divided into assembly cells (ACs) where various parts and tools are placed closer to the workers, enabling them to perform multiple tasks throughout an entire product assembly from start to finish. In this way, ACL allows manufacturers to rapidly configure an appropriate heterogeneous capacity to match heterogeneous demands with diversified customer orders in the high-mix, low-volume (HMLV) environment, which is the spread of the Just-In-Time (JIT) philosophy from the material level to the organization level. However, due to the lack of real-time information sharing in the ACL workshop, especially the up-to-date individual capacity and asynchronous production processes within and between ACs, it is hard to coordinate the heterogeneous capacities of ACs to meet the HMLV demands in a complex manufacturing environment with uncertainties. In this context, this paper proposes a heterogeneous demand–capacity synchronization (HDCS) for smart ACL by using artificial intelligence-enabled IIoT (AIoT) technologies, in which computer vision (CV) is applied for up-to-date capacity analysis of ACs. Based on these, an AIoT-enabled Graduation Intelligent Manufacturing System (GiMS) with feedback loops is developed to support real-time information sharing for the synchronous coordination of the ACL operation, which also provides the basis for the implementation of the HDCS mechanism through a rolling scheduling approach. Finally, a real-life industrial case is carried out by a proof-of-concept prototype to verify the proposed approach, and the results show that the measures on shipment punctuality and production efficiency are both significantly improved. [ABSTRACT FROM AUTHOR]

With the rapid advancement of manufacturing science and technology, the associated measurement technology has also been transformed. Traditional manual and single-instrument detection modes are unable to meet the requirements of fast beat, high precision, and online measurement in intelligent manufacturing. As connecting rods are typical and key components for automobile engines, their precision performance highly impacts the ultimate quality of engine assemblies. The online measurement of connecting rods was studied in this paper. According to the structural characteristics and parameter requirements of the connecting rods, an online multi-station measurement platform was designed and developed to measure, mark, and classify the measurements of the connecting rods in an automatic assembly line. Among these measurements, two significant parameters, the weight and the barycenter, were focused on in our work. A multi-point balance method was developed and applied to obtain the weights of the big and small ends as well as the position of the barycenter. An approach for weight sensor calibration and measurement data processing was also proposed. Finally, automatic and online measurement platforms were built for experimental verification. The results show that the measurement beats of the weight and the barycenter can reach 3 s/piece. The measured data were compared with a high-precision balance, and the average error of the connecting rod's weight was 0.27%. The on-site application verification further proved the effectiveness and efficiency of our proposed measurement methods and machine, which enables fast and high-precision online measurement of connecting rods. [ABSTRACT FROM AUTHOR]

In manufacturing, causal relations between components have become crucial to automate assembly lines. Identifying these relations permits error tracing and correction in the absence of domain experts, in addition to advancing our knowledge about the operating characteristics of a complex system. This paper is motivated by a case study focusing on deciphering the causal structure of a wafer manufacturing system using data from sensors and abnormality monitors deployed within the assembly line. In response to the distinctive characteristics of the wafer manufacturing data, such as multimodality, high-dimensionality, imbalanced classes, and irregular missing patterns, we propose a hierarchical ensemble approach. This method leverages the temporal and domain constraints inherent in the assembly line and provides a measure of uncertainty in causal discovery. We extensively examine its operating characteristics via simulations and validate its effectiveness through simulation experiments and a practical application involving data obtained from Seagate Technology. Domain engineers have cross-validated the learned structures and corroborated the identified causal relationships. [ABSTRACT FROM AUTHOR]

AbstractThe automation and assembly lines of factories have faced the need for reliable handling of porous, fragile, and soft objects. Current soft grippers have difficulty robustly grasping flat, flexible surfaces without distorting them. This paper proposes a soft gripper based on annulus microwedge adhesive for grasping flat objects using negative pressure actuation mechanisms, which can achieve controllable adhesives to reliably grasp and easily release smooth flat objects. This is achieved through negative pressure actuation, which provides a centripetal load to the annular microwedge adhesives for strong attachment, by returning to normal pressure, the centripetal load is rapidly removed, allowing for easy detachment from the adhesive surface. Additionally, the relationship between the geometrical parameters of the deformation chamber and the contact area of annulus microwedges is investigated by using finite-element method models and experiments. The results show that the wall thickness is a key parameter in designing the deformation chamber. The results of the payload test have determined the geometrical and pressure parameters of the deformation chamber with the best adhesion performance. Finally, a demonstration shows that the gripper can handle various objects for potential applications, such as in clean environmental industries. [ABSTRACT FROM AUTHOR]

This paper deals with a scheduling problem arising at the tactical decision level in aeronautical assembly line. It has the structure of a challenging multi-mode resource-constrained project scheduling problem with incompatibility constraints, a resource leveling objective and also a large number of tasks. We first present a new event-based mixed-integer linear programming formulation and a standard constraint programming formulation of the problem. A large-neighborhood search approach based on the constraint programming model and tailored to the resource leveling objective is proposed. The approaches are tested and compared using industrial instances, yielding significant improvement compared to the heuristic currently used by the company. Moreover, the large-neighborhood search method significantly improves the method proposed in the literature on a related multi-mode resource investment problem when short CPU times are required. [ABSTRACT FROM AUTHOR]

In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. [ABSTRACT FROM AUTHOR]

In the weighted fair sequences problem (WFSP), one aims to schedule a set of tasks or activities sthat the maximum product between the largest temporal distance between two consecutive executions of a task and its priority is minimized. The WFSP covers a large number of applications in different areas, ranging from automobile production on a mixed‐model assembly line to the sequencing of interactive applications to be aired in a digital TV environment. This paper proposes an iterative heuristic method for the WFSP centered on an efficient implementation of a variable neighborhood search heuristic. Computational experiments on benchmark instances show that the proposed metaheuristic outperforms the state‐of‐the‐art method proposed to the problem, obtaining comparable solution values in much less computational time. [ABSTRACT FROM AUTHOR]

With the growth of industrialization, the global manufacturing industry is continually evolving and reforming in the direction of intelligence and green production. Industrial robots have replaced human workers because of the benefit of production efficiency. However, the large-scale application of robots requires a large amount of energy consumption and generates a large amount of CO2, which will lead to energy waste and environmental pollution. In addition, in term of performing some particular tasks, current robot technology cannot achieve the same level of intelligence as human. Therefore, the design trend of assembly lines in industry has shifted from traditional configuration to human-robot collaboration to achieve higher productivity and flexibility. This paper investigates the human-robot collaboration (HRC) assembly line balancing problem, taking cycle time and carbon emission as primary and secondary objectives. A new mixed-integer programming model that features a cross-station design is formulated. A particle swarm algorithm (PSO) with two improvement rules is designed to solve the problems. The comparative experiments on ten benchmark datasets are conducted to assess the performance of the proposed algorithm. The experimental results indicate that the improved particle swarm algorithm is superior to the other two heuristics: simulated annealing (SA) and the late acceptance hill-climbing heuristic (LAHC). [ABSTRACT FROM AUTHOR]

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors' research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown. [ABSTRACT FROM AUTHOR]

Purpose: This paper's purpose is to deepen our understanding of what drives bottom-up operations strategy formation -- that is, continuous improvement activities at the front line -- with a particular focus on operations strategy understanding. That way, it aims to contribute to the awareness of management quality in manufacturing -- a cornerstone of national competitiveness. Methodology: We examine the antecedents of individual Kaizen generation by frontline employees, drawing on the well-established Motivation-Opportunity-Ability framework and focusing on the dimension of ability -- that is, understanding operations strategy. Survey data on 217 frontline employees, working in 17 teams on 11 different production lines, were "triangulated" with their team leader assessments and the plant's archival records. We tested the hypothesized relationships via analyses that incorporate both structural equation modeling and multiple regression techniques. Findings: Our results suggest that employees typically overestimate their understanding of the plant's operations strategy and that productivity is driven more by an objective than a subjective understanding of that strategy. We also find that incremental innovation is facilitated by supervisor support, employee engagement, and an employee suggestion scheme; in contrast, neither autonomy nor selected control variables (e.g., age or seniority) has a significant effect. Originality: Our findings and the unique metrics we developed for better management of strategy understanding should help managers increase the productivity of their operations and thus the competitiveness of their respective firms. [ABSTRACT FROM AUTHOR]

Learning from demonstration (LfD) has been widely studied as a convenient method for robot learning. In the LfD paradigm for redundant manipulators, the reproduced trajectories ought to be similar to human demonstrations in both task and joint space. Despite many advancements achieved in the context of learning in task space, the solutions for generating similar reproductions in joint space are still largely open. In this paper, a novel non-parametric LfD framework for 7-DOF anthropomorphic manipulators with high computational efficiency is proposed. The proposed method leverages redundancy resolution and kernel-based approaches to formulate an efficient model characterized by a limited set of open parameters. Experiments were conducted to evaluate the performance of the proposed method and compare it with the commonly used 'LfD+IK' solution. The results indicated that the proposed method behaves much better in terms of the similarity between the demonstration and reproduction with high computing efficiency. As the proposed method can learn from human demonstrations effectively in both task and joint space, it has the potential to significantly enhance human–robot collaboration, streamline assembly line processes, or improving robot learning. An important future challenge will be extending the proposed method for general-purpose redundant manipulators and considering task constraints to perform complex tasks. [ABSTRACT FROM AUTHOR]

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP. • The problem considering both cycle time and trailer fuel consumption is formulated. • Several properties of the energy-efficient JRALB-FP are analyzed and provided. • A specific sampling mechanism and two local search operators are designed. [ABSTRACT FROM AUTHOR]

Background: Within the manufacturing sector, assembly processes relying on mechanical fasteners such as nuts, washers, and bolts hold critical importance. Presently, these fasteners undergo manual inspection or are identified by human operators, a practice susceptible to errors that can adversely affect product efficiency and safety. Given considerations such as time constraints, escalating facility and labor expenses, and the imperative of seamless integration, the integration of machine vision into assembly operations has become imperative. Results: This study endeavors to construct a robust system grounded in deep learning algorithms to autonomously identify commonly used fasteners and delineate their attributes (e.g., thread type, head type) with acceptable precision. A dataset comprising 6084 images featuring 150 distinct fasteners across various classes was assembled. The dataset was partitioned into training, validation, and testing sets at a ratio of 7.5:2:0.5, respectively. Two prominent object detection algorithms, Mask-RCNN (regional-based convolutional neural network) and You Look Only Once-v5 (YOLO v5), were evaluated for efficiency and accuracy in fastener identification. The findings revealed that YOLO v5 surpassed Mask-RCNN in processing speed and attained an mean average precision (MAP) of 99%. Additionally, YOLO v5 showcased superior performance conducive to real-time deployment. Conclusions: The development of a resilient system employing deep learning algorithms for fastener identification within assembly processes signifies a significant stride in manufacturing technology. This study underscores the efficacy of YOLO v5 in achieving exceptional accuracy and efficiency, thereby augmenting the automation and dependability of assembly operations in manufacturing environments. Such advancements hold promise for streamlining production processes, mitigating errors, and enhancing overall productivity in the manufacturing sector. [ABSTRACT FROM AUTHOR]

In this article, we propose a distributed synchronization solution to achieve decentralized coordination in a system of collaborative robots. This is done by leveraging cloud-based computing and 5G technology to exchange causal ordering messages between the robots, eliminating the need for centralized control entities or programmable logic controllers in the system. The proposed solution is described, mathematically formulated, implemented in software, and validated over realistic network conditions. Further, the performance of the decentralized solution via 5G technology is compared to that achieved with traditional coordinated/uncoordinated cabled control systems. The results indicate that the proposed decentralized solution leveraging cloud-based 5G wireless is scalable to systems of up to 10 collaborative robots with comparable efficiency to that from standard cabled systems. The proposed solution has direct application in the control of producer–consumer and automated assembly line robotic applications. [ABSTRACT FROM AUTHOR]

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human–machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems. Design/methodology/approach: The mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences. Findings: The developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line. Originality/value: This study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software. [ABSTRACT FROM AUTHOR]

• A sophisticated 3D FEM model for segmental linings with different assembly types. • Crack distribution and development of cross-jointed and stagger-jointed linings. • Failure mechanism of segmental linings illustrated by damaged plasticity point. • Analysis on the structural effect of staggered-jointed linings. The assembly type of segmental linings has a great impact on mechanical behaviors and failure mechanism of the linings. In this paper, a sophisticated 3D FEM model for segmental linings with different assembly types (single ring, three rings cross-jointed and stagger-jointed) was established to investigate the mechanical behaviors and failure mechanism of the linings under loading condition, with particular focus on the structural effect of the staggered-jointed linings. The effectiveness of the numerical model was verified by the data from the on-site measurements. The results show that the segmental linings turn into horizontal ellipse under the overloading condition. For the stagger-jointed linings, the initial cracks are emerged on the segment body and some oblique cracks and circumferential cracks are generated due to the interaction among rings. The failure process of the stagger-jointed linings was illustrated by the damaged plasticity point which are mainly staggered distributed on segment body and the time sequence of the points reaching certain structural performance state are progressive and discrete. The stagger effect is captured on the internal force of stagger-jointed linings and the major reasons causing the bending moment transmission are analyzed. Above analysis will contribute to the structural safety evaluation of the tunnels. [ABSTRACT FROM AUTHOR]

Mass personalized production in green manufacturing requires flexible adjustments in the assembly line, and the contradiction between the impact on resources and regaining production efficiency makes it difficult for decision-makers to balance. In order to quantify the cost of adjustment and provide a theoretical basis for rebalancing optimization, this paper introduces an adjustment complexity measurement method and a rebalancing optimization model. To solve the optimization problem, an adaptive rebalancing algorithm is designed based on the nondominated sorted genetic algorithm-II (NSGA-II). In this case, the algorithm is tailored to address the rebalancing problem by acquiring optimal operation distribution plans that minimize adjustment complexity. Finally, the effectiveness of the proposed rebalancing method is demonstrated through two case studies. Through analysis of algorithm performance and solutions, as well as comparison with existing algorithms, the results show that the proposed algorithm has good convergence and optimization performance. The proposed method can provide decision-makers with rebalancing solutions with different focuses. When adjusting the assembly line, the impact of the solution on resources and adjustment costs is fully considered. [ABSTRACT FROM AUTHOR]

The use of machine learning models for productivity prediction in complex manufacturing systems has garnered significant attention. However, the implementation of conventional black-box machine learning models is challenging due to the complexity and limited data availability in aircraft final assembly lines. These challenges stem from a lack of explainability, leading to compromised accuracy and diminished trustworthiness. To address this issue, this paper proposes an explainable neural network model, the Attention-SP-LSTM-FIG, specifically designed for productivity prediction in aircraft final assembly lines. First, the serial-parallel LSTM is utilized to map the process sequence within each assembly station, and a customized attention mechanism maps the requirements of workers and materials. This process results in independent sub-models corresponding to each station. Subsequently, the outputs from each sub-model are combined using a specially-designed final integrated gate to produce the final output. Finally, a post-hoc analysis of the weight data in the model is performed following the backpropagation order to identify the various production bottlenecks in the assembly line. The performance of the proposed model has been evaluated through an industrial case study. In terms of accuracy, the Attention-SP-LSTM-FIG surpasses other benchmark neural network models in terms of error, correlation, and precision metrics. Regarding explainability, the Attention-SP-LSTM-FIG can precisely identify bottlenecks for practitioners, thus facilitating a better understanding of the rationale behind the model's outputs. [ABSTRACT FROM AUTHOR]

Two hybrid flow shop scheduling lines must be coordinated to assemble batches of terminated products at their last stage. Each product is thus composed of two jobs, each produced in one of the lines. The set of jobs is to be processed in a series of stages to minimize the makespan of the scheduling, but jobs forming a product must arrive at the assembly line simultaneously. We propose a mixed integer linear programming model. Then, based on the model, we propose a pull-matheuristic algorithm. Finally, we present two metaheuristics, a greedy randomized adaptive search procedure and a biased random key genetic algorithm, and compare all the methodologies with real-based instances of a production scheduling problem in the automobile manufacturing industry. The greedy algorithm yields high-quality solutions, while the genetic one offers the best computational times. [ABSTRACT FROM AUTHOR]

Many industrial production lines today are initially constructed and outfitted with machinery that, despite its inherent capabilities, struggles to satisfy increasing demand over time. The goal of this research is to find solutions to bottlenecks and improve the efficiency of the production line to satisfy rising productivity demands. The study primarily looks at an existing bottle production line in the edible oil industry, where the product is witnessing a boom in demand. Data on processing times at various workstations and their capacities were thoroughly collected and analyzed. Simulation emerged as the preferred tool for investigating and addressing the production line's intrinsic difficulties. Using Microsoft Excel for statistical analysis, an existing simulation model was created using Flexsim simulation, a process-oriented simulation software, to detect bottlenecks in the production line using the Processor block in the simulation. As a result, a modified model was offered to reduce waiting times by 12%, increase productivity by 6%, and increase overall profitability while efficiently dealing with rising demand across all seasons. As a result, this research represents a complete effort to identify operational challenges and present viable solutions that correspond with the industry's changing objectives. The study aims to contribute to optimizing production lines by utilizing contemporary simulation tools and statistical analysis, guaranteeing that they stay adaptive and responsive in the face of rising productivity demands. [ABSTRACT FROM AUTHOR]

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data. [ABSTRACT FROM AUTHOR]

Background: Ergonomic behaviors play a crucial role in preventing work-related musculoskeletal disorders (WMSDs). To measure these behaviors, this research aimed to develop and evaluate an ergonomic behaviors tool (EBET) based on the Social Cognitive Theory (SCT) among women workers on assembly lines (WwAL). Methods: The study was conducted from December 2022 to January 2023 with a focus on the psychometric assessment of EBET. Initially, a literature review and interviews were carried out to identify crucial concepts and primary items. The questionnaire's validity was evaluated using the Content Validity Ratio (CVR) and the Content Validity Index (CVI). To determine the domains of the tool, construct validity was examined by administering the items to 270 eligible women. The reliability of the tool was assessed using McDonald's Omega coefficient. Results: From a total of 67 primary items, 50 were confirmed. The study demonstrated good validity with CVR = 0.92 and CVI = 0.97, along with reliable results indicated by McDonald's Omega coefficient of 0.74. The exploratory factor analysis (EFA) revealed ten distinct dimensions: outcome expectations, outcome expectancies, normative beliefs, perceived barriers, social support, observational learning, reinforcement, behavioral skills, self-efficacy, and intention. Together, these dimensions accounted for 66.25% of the variance in the data. Additionally, the confirmatory factor analysis results supported the presence of these ten constructs and demonstrated a satisfactory fit. Conclusions: EBET is a dependable and valid instrument for evaluating the ergonomic behaviors of workers, utilizing the principles of SCT. Researchers can employ EBET to gather data and implement suitable training interventions to enhance ergonomic behavior among WwAL. However, it is crucial to recognize that EBET may not encompass all facets of ergonomic behaviors. Therefore, it is imperative for future research to prioritize the evaluation of EBET's suitability among diverse worker populations and to consider additional dimensions of ergonomics to ensure its wider applicability and effectiveness. [ABSTRACT FROM AUTHOR]

Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1's phase synchrony and H2's coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems. [ABSTRACT FROM AUTHOR]

These days, many assembly lines are becoming automated, leading to a trend of decreasing defect rates. However, in assembly lines that have opted for partial automation due to high cost of construction, defects still occur. The cause of defects are that the location of the work instructions and the work field are different, which is inefficient and some workers who are familiar with the process tend not to follow the work instructions. As a solution to establishing a system for object detection without disrupting the existing assembly lines, we decided to use wearable devices. As a result, it is possible to solve the problem of spatial constraints and save costs. We adopted the YOLO algorithm for object detection, an image recognition model that stands for "You Only Look Once". Unlike R-CNN or Fast R-CNN, YOLO predicts images with a single network, making it up to 1000 times faster. The detection point was determined based on whether the pin was fastened after the worker's hand appeared and disappeared. For the test, 1000 field data were used and the object-detection performance, mAP, was 35%. The trained model was analyzed using seven regression algorithms, among which Xgboost was the most excellent, with a result of 0.15. Distributing labeling and class-specific data equally is expected to enable the implementation of a better model. Based on this approach, the algorithm is considered to be an efficient algorithm that can be used in work fields. [ABSTRACT FROM AUTHOR]

Scholarly discussions of the consequences of the on-demand economy on work mainly focus on precarity. Using a case study of Didi Chuxing, this article moves beyond this conventional approach to highlight coercion as a striking feature of labor relations in China's ride-hailing industry. Drawing upon the conceptual tool of neo-bondage, this article foregrounds the central role played by forced labor in securing a cheap and docile work force during Didi's rapid market expansion. This article advances the existing literature in two ways. First, it highlights the need for a more robust analysis of the productive forces in the on-demand economy. Second, it argues that the on-demand economy not only represents an intensification of the ongoing trend toward precarity, but also an extension of forced labor regimes from electronic assembly lines to the service industry. [ABSTRACT FROM AUTHOR]

The use of machine vision methods has rapidly developed for detecting product defects, but it remains challenging to identify shape and color defects as well as the assembly relationship of parts due to occlusion between components and changes in assembly structures. This paper proposed a method for detecting saw chain assembly defects based on residual networks and knowledge coding to achieve parts segmentation and identification of assembly defects. Firstly, we design an adaptive segmentation algorithm for saw chain parts, which uses the Hough transform to locate rivets and completes the image segmentation based on the inherent assembly features between parts. Secondly, A trained ResNet (Residual Network) based deep learning model was used to identify the individual defects of saw chain parts, in which the transfer learning method was used to improve the detection speed of individual defects. Finally, a parallel inference method based on a knowledge encoding matrix is proposed for detecting part defects and constructing a sparse matrix to locate assembly defects. Evaluating the proposed method in building a saw chain testing platform. Results show that the proposed method can achieve real-time and high-precision detection requirements in different application scenarios. The detection accuracy on individual and assembly defects reaches 94.2% and 89.2%, respectively, which is better than the current state-of-the-art object detection models. Assembly defect detection methods are an important reference for inspecting other types of simple assemblies in the industry. [ABSTRACT FROM AUTHOR]

Process mining technology has been widely used to optimize the processes of various organizations, especially in enterprises. It facilitates cooperation between departments and prompts efficient process design and resource scheduling in the production workshop. However, as a data-driven approach, the lack of production logs hinders the development of enterprise process mining research. Therefore, we introduce a new benchmark dataset named TV-ALP to provide effective data support for the combination of process mining and workshop production. The dataset comes from the field survey experience and product operation instructions, which highly restores the operation of the production workshop and details the processing of television (TV) sets in the assembly line. We compare TV-ALP with other public datasets for detailed statistical analysis and provide benchmark performance for the remaining time prediction task. The experimental results show that TV-ALP can meet the requirements of process mining and analysis research in terms of both scale and quality. In addition, while maintaining the data commonality of public datasets, it also emphasizes the importance of role information and supports a series of role-based log studies. The complete dataset is accessible for download at https://github.com/Zzou-Sdust/TV-ALP-dataset. [ABSTRACT FROM AUTHOR]

Chinese creators and Douyin (a prominent Chinese short-video platform) are building a strong relationship in cultural production. Based on the social exchange theory, this study tries to explore the process and mechanism of monetization in Chinese platforms. Using the app walkthrough method and in-depth interviews with 19 full-time creators, we contend that Chinese creators and Douyin engage in a repetitive but unequal exchange with the common goal of earning income. Douyin works with creators to cultivate their thirst for revenue over time, while mastering the creative labor, before finally tying them to monetary gains. Douyin not only binds exposure and allocates Internet traffic to new creators, but also develops Dou+, a traffic marketing tool that leverages the affordances of creative work visibility. Douyin first enhances the creators' income expectations through this step. The platform then directs creators through the Star-Chart content trading system, and establishes the platform-advertiser-creator transaction chain. When the exchange between Douyin and creators becomes stable, the platform sets up an uncertain revenue mechanism. Douyin's commitment to creating a standardized assembly line of production and financial benefit for creators has established an exchange mode between the platform and creators at the expense of innovation and diversity. We argue that this standardized labor process eliminates the autonomy of cultural production. [ABSTRACT FROM AUTHOR]

The assembly line biosynthesis of the powerful anticancer‐antiviral didemnin cyclic peptides is proposed to follow a prodrug release mechanism in Tristella bacteria. This strategy commences with the formation of N‐terminal prodrug scaffolds and culminates in their cleavage during the cellular export of the mature products. In this study, a comprehensive exploration of the genetic and biochemical aspects of the enzymes responsible for both the assembly and cleavage of the acylated peptide prodrug scaffolds is provided. This process involves the assembly of N‐acyl‐polyglutamine moieties orchestrated by the nonribosomal peptide synthetase DidA and the cleavage of these components at the post‐assembly stage by DidK, a transmembrane CAAX hydrolase homolog. The findings not only shed light on the complex prodrug mechanism that underlies the synthesis and secretion of didemnin compounds but also offer novel insights into the expanded role of CAAX hydrolases in microbes. Furthermore, this knowledge can be leveraged for the strategic design of genome mining approaches aimed at discovering new bioactive natural products that employ similar prodrug biochemical strategies. [ABSTRACT FROM AUTHOR]

To improve the traditional energy production and consumption of resources, the acceleration of the development of a clean and green assembly line is highly important. Hydrogen is considered one of the most ideal options. The method of production of hydrogen through water splitting constitutes the most attractive research. We synthesized CoMoO4 nanofibers by electrospinning along with post-heat treatment at different temperatures. CoMoO4 nanofibers show a superior activity for hydrogen evolution reaction (HER) and only demand an overpotential of 80 mV to achieve a current density of 10 mA cm–2. In particular, the CoMoO4 catalyst also delivers excellent performances of oxygen evolution reaction (OER) in 1 M KOH, which is a more complicated process that needs extra energy to launch. The CoMoO4 nanofibers also showed a superior stability in multiple CV cycles and maintained a catalytic activity for up to 80 h through chronopotentiometry tests. This is attributed mainly to a synergistic interaction between the different metallic elements that caused the activity of CoMoO4 beyond single oxides. This approach proved that bimetallic oxides are promising for energy production. [ABSTRACT FROM AUTHOR]