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Based on the disaster characteristics and the geo-conditions at the scene, in this study, the occurrence mechanism of a serious rock burst accident that occurred in the Tangshan Coal Mine, China, was analysed. Ground stress measurements showed that the mine is in a high ground stress area dominated by horizontal tectonic stress around 33 MPa. Laboratory testing revealed that the coal was a hard seam of 8.3 MPa over bedded by a thick and hard roof stratum with uniaxial compressive strength of 66 MPa. The calculation results indicated that the accident occurred in the roof rebounding area. It is proposed that the hard roof and the hard coal seam formed a seesaw structure around the working face. The vertical pressure relief caused the rib coal mass to lose its clamping forces from the roof and floor and rush into the roadway, resulting in a rock burst accident. Based on the causality mechanism of the rock burst disaster developed in this study, pertinent coal bump prevention measures have been undertaken in practice. Large-diameter boreholes were drilled to eliminate the pivot effect of the seam. Roof blasting was undertaken to prevent the roof from forming a seesaw plank. To summarize, a new causality mechanism for rock burst in coal mines under hard roof and hard seam geo-conditions was developed. [ABSTRACT FROM AUTHOR]

Entry retaining via roof cutting is a new longwall mining method that has emerged in recent years, and is characterized by high resource utilization and environmental friendliness. Due to the complexity of this method, a field study is commonly employed for process optimization. Roof blasting is a key operation for retaining the entry, and the current practice involves dynamically adjusting blasting parameters through on-site testing and postblasting monitoring. However, the existing literature lacks detailed descriptions of blasting operations, making it difficult for field engineers to replicate the results. In this study, based on a roof cutting project for entry retaining, a preliminary design of blasting parameters is made based on theories and on-site geological conditions. The on-site test methods and equipment for roof-cutting blasting are described in detail, and the fractural patterns under different blasting parameters are analyzed. After the retreat of the working face, the state of roof caving in the goaf is analyzed based on monitoring data, and the effectiveness of top cutting is evaluated through reverse analysis, leading to dynamic adjustments of the blasting parameters. This research provides a reproducible construction method for roof-cutting operations and establishes the relationship between blasting parameters and post-mining monitoring data. It contributes to the development of fundamental theories and systematic technical systems for entry retaining via roof cutting, offering high-quality case studies for similar geological engineering projects. [ABSTRACT FROM AUTHOR]

In multi-manned assembly lines, more than one operator is allowed on the same workstation. Limited research has been carried out on sequence-dependent set-up times. Therefore, this article optimizes the multi-manned assembly line balancing problem with sequence-dependent set-up times using an enhanced migrating birds optimization (EMBO) algorithm. Sequence-dependent set-up times are embedded in two ways: forward and backward set-ups. EMBO designs a mixed neighbourhood search and acceptance criteria to help it escape from local optima, and a diversification mechanism to enhance the global exploration ability. Three types of experiment and two scenarios, with low and high set-up times, are conducted to test the performance of the mixed-integer linear programming (MILP) model and EMBO. The first experiment compares three MILP models (no, low and high set-up times) to illustrate the significance of set-up times. The final two experiments demonstrate the effectiveness of EMBO by comparison with the MILP model and six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Coal contains cracks and has strong heterogeneity, so the data dispersion is large in laboratory tests. In this study, 3D printing technology is used to simulate hard rock and coal, and the rock mechanics test method is used to carry out the coal–rock combination experiment. The deformation characteristics and failure modes of the combination are analyzed and compared with the relevant parameters of the single body. The results show that the uniaxial compressive strength of the composite sample is inversely proportional to the thickness of the weak body and directly proportional to the thickness of the strong body. The Protodyakonov model or ASTM model can be used as a verification method for the results of a uniaxial compressive strength test of coal–rock combination. The elastic modulus of the combination is the equivalent elastic modulus, and the elastic modulus of the combination is between the elastic modulus of the two constituent monomers, which can be analyzed using the Reuss model. The failure of the composite sample occurs in the low-strength material, while the high-strength section is rebounding as an extra load on the low-strength body, which may cause a sharp increase in the strain rate of the weak body. The main failure mode of the sample with a small height–diameter ratio is splitting, and the failure mode of the sample with a large height–diameter ratio is shear fracturing. When the height–diameter ratio is not greater than 1, it shows pure splitting, and when the height–diameter ratio is 1~2, it shows a mixed mode of splitting and shear fracture. The shape has a significant effect on the uniaxial compressive strength of the composite specimen. For the impact propensity, it can be determined that the uniaxial compressive strength of the combination is higher than that of the single body, and the dynamic failure time is lower than that of the single body. It can hardly determine the elastic energy and the impact energy of the composite with the relationship to the weak body. The proposed methodology provides new cutting-edge test technologies in the study of coal and coal-like materials, with an exploration of their mechanical properties under compression. [ABSTRACT FROM AUTHOR]

The longwall mining method with gob-side entry retaining via roof cutting is a new underground coal mining method which has the characteristics of a high resource recovery ratio and environmental friendliness. Due to the complexity of this method, the research method of case-based dynamic on-site monitoring, analysis, adjustment, and optimization is usually adopted. Based on a roadway retaining via roof cutting project, in addition to the traditional indirect monitoring method of hydraulic support pressure, this study innovatively establishes a direct monitoring method for roof caving by monitoring the gangue pressure in the goaf, which provides data for the roof cutting effect and offers a new method for studying the overlying strata movement. In the project, a comprehensive monitoring and analysis system was established, including gangue pressure, cable bolt stress, bracket pressure, roadway deformation, and roof separation, which was used to dynamically analyze the effect of roof cutting and optimize the support design. The results show that the pressure of the hydraulic support close to the roof cutting is low, indicating that roof cutting is favorable in the roadway retaining mining method. The roadway deformation in the advanced abutment pressure area of the working face is small. The mining-induced stress caused by the collapse and compaction of the overlying strata in the goaf is the dominant factor affecting the effect of roadway retaining, especially in the 50–100 m range behind the working face, where the dynamic load causes high bearing capacity of the support elements, large roadway convergence, and roof separation. Temporary support and supplementary reinforcement should be added when necessary. The monitoring system presented in this study is highly comprehensive, simple, reliable, and low in cost, providing a reference for roof cutting roadway retaining projects and roof caving-related studies. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer is one of the leading cancer-related death causes. Enormous efforts have been focused on this field in these years. However, clinical trial failure is becoming a massive obstacle for researchers to apply their research results for clinical use. This study aimed to analyze the reasons behind clinical failures and identify potential risk factors of clinical trial failures.Methods: On December, 1, 2021, we queried ClinicalTrials.gov for gastric cancer listed in phase II/III. We included trials specifying their interests in "stomach cancer", "Stomach Neoplasms", "Gastric Cancer", "Gastric Neoplasms", "Gastric Carcinoma", "Stomach Carcinoma", "Gastroesophageal Junction Cancer". Exclude criteria are: (1) Trials that start prior to 01/01/2007 and start after 12/01/2020; (2) Trials with "not yet recruiting", "suspended", "withdrawn", or "unknown" status; (3) Trials do not provide an anticipated accrual number or a start date.Results: A total of 567 trials are included. 10.2% of these trials are failed. 16 (2.82%) were terminated for good reasons, and 42 (7.41%) were terminated for bad reasons. Multi-centre (P-value = 0.088) and anticipated accrual (P-value = 0.099) are potential risk factors for clinical failures in the simple logistic regression model. After considering the interaction between multi-centre and anticipated accrual, the odds ratio of anticipated accrual is 0.60 (P-value = 0.009) in single centre trials. In multi-centre trials, the odds ratio of anticipated accrual is 0.72 (P-value = 0.025). The primary reason for gastric cancer trial terminations is recruitment failure.Conclusion: The rate that trials terminated in gastric cancer has decreased compared to previous studies. Comparing to other types of oncology trials, poor accrual continues to be the predominant reason, followed by business or sponsor reasons. Single-center trials with smaller anticipated accrual number are more likely to be terminated which may resulted by limited resources invested to the trial. Single-center design exacerbated the difficulty of participant recruitment. Future studies need to continue tracking the rate of trial termination across oncology and whether the reasons behind them have changed. [ABSTRACT FROM AUTHOR]

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed-integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub-populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses. • Define the MALBP-MW by a MILP model to minimize the energy and cost requirements. • Design a new decoding with idle time reduction and a collaborative initialization. • Propose four problem-specific evolutionary operators for MOCEA. • Develop MOCEA with a self-learning selection and dual-cooperation. [ABSTRACT FROM AUTHOR]

With the requirements of just-in-time and make-to-order, it is necessary to consider the batch delivery in distributed heterogeneous assembly flow shop scheduling as well as the minimization of inventory and tardiness costs. Hence, this work proposes a new mixed integer linear programming model and a self-learning knowledge-based multi-objective evolutionary algorithm based on decomposition. In this algorithm, 4 problem-specific properties are extracted and embedded in the designed solution representation, forward-reverse objective calculation and local search operators. A new hybrid initialization with six knowledge-based NEH heuristics and a random generation is developed to generate better initial solutions. A self-learning selection strategy is developed to coordinate the above knowledge-based local search operators. The experimental study, based on 900 small and 540 large instances, demonstrates that the designed components such as hybrid initialization and self-learning selection strategy are effective, and the proposed algorithm is superior to 6 existing algorithms in the literature in solving this new problem. • Formulate distributed heterogeneous assembly permutation flow shops with batch delivery. • Design a property-based solution representation and forward-reverse objective calculation. • Propose a hybrid initialization, self-learning selection strategy in MOEA/D. • Propose some knowledge-based local search operators in MOEA/D. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is a crucial and challenging task for intelligent transportation systems. The prediction task can be accomplished via graph neural networks with structured data, but accurate traffic speed prediction is challenging due to the complexity of traffic systems and the constantly dynamic changing nature. To address these issues, a novel evolution temporal graph convolutional network (ETGCN) model is proposed in this paper. The ETGCN model first fuses multiple graph structures, and utilizes graph convolutional network (GCN) to model spatial correlation. Then, the spatial–temporal dependence and their dynamical changes are learned simultaneously to predict traffic speed on a road network graph. Especially, a similarity-based attention method is proposed to fuse multiple graph adjacency matrices. Then, the gated recurrent unit is combined with GCN to capture spatial–temporal correlations and their changing status, simultaneously. Extensive experiments on two large-scale datasets show that our methods provide more accurate prediction results than the existing state-of-the-art methods in every prediction horizon. [ABSTRACT FROM AUTHOR]

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms. [ABSTRACT FROM AUTHOR]

The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station's storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods. [ABSTRACT FROM AUTHOR]

The field of image classification faces significant challenges due to the scarcity of target samples, leading to model overfitting and difficult training. To address these issues, few-shot learning has emerged as a promising approach. However, current methods do not fully utilize the correlations among samples and external semantic information, resulting in poor recognition accuracy. To overcome these limitations, we propose a new few-shot classification method that incorporates both attributes and attention guided approach. The method leverages the attention mechanism to extract discriminative features from the images. By exploring regional correlations among samples, it assists in generating visual representations by utilizing predicted attribute features. As a result, accurate prototypes are generated. Extensive experiments were conducted on two attribute-labeled datasets, namely Caltech-UCSD Birds-200–2011(CUB) and SUN Attribute Database (SUN) Attribute Dataset. With the Resnet12 backbone, the method achieves remarkable accuracies of 79.95% and 89.34% for 1-shot and 5-shot, respectively, on the CUB dataset. Similarly, with the Conv4 backbone, the method achieves notable accuracies of 67.21% and 80.87% for 1-shot and 5-shot, respectively, on the SUN Attribute dataset. The achieved accuracies highlight the robustness and generalizability of our method, and show the capability of our method to accurately classify samples with limited training data, which is a significant advantage in real-world scenarios where labeled data are often scarce. [ABSTRACT FROM AUTHOR]

Abstract Since the industrial robots are incrementally utilized in U-shaped assembly lines to replace operators, the focus of these lines is not only on productivity improvement, but also more on the carbon and noise emissions. Hence, this paper proposes a novel multi-objective mixed integer non-linear model to minimize carbon emission, noise emission and cycle time concurrently. In this model, quantifying carbon emission and noise emission is achieved respectively via presentations connected with processing time of tasks and robots. Existing general constraints of precedence relationship are readjusted into a novel integrated formula so as to remove worthless equations and improve computational efficiency. Besides, Hybrid Pareto Grey Wolf Optimization (HPGWO) is designed to solve the multi-objective balancing problems. This algorithm proposes a novel code to initialize the wolves and designs two new searching methods to update the position of wolf. Simultaneously, two crossover operators are designed to enhance the communication between the low-grade wolves. Finally, the proposed algorithm is compared with five other well-known multi-objective algorithms and the results indicate that the proposed algorithm outperforms these compared algorithms in the evaluation metrics of the inverse generation convergence (IGD), maximum spread (MS) and hypervolume ratio (HVR). That is, our proposed algorithm can achieve the trade-off in reducing carbon and noise emission and minimizing cycle time. Highlights • Carbon and noise emission are minimized in U-shaped robotic assembly line. • A mathematical model is developed and optimized within a multi-objective optimization framework. • The mathematical model utilizes binary variable to describe discrete decisions effectively. • A hybrid Pareto grey wolf optimization is employed to solve the multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

Reference 1 Zhang Z. Assessing clinical trial failure risk factors and reasons in gastric cancer. The original article can be found online at https://doi.org/10.1186/s12876-022-02592-4. [Extracted from the article]

The original article can be found online at https://doi.org/10.1186/s12876-022-02592-4. Reference 1 Zhang Z. Assessing clinical trial failure risk factors and reasons in gastric cancer. [Extracted from the article]

Disassembly lines are widely utilized to disassemble end-of-life products. Most of the research focuses on the complete disassembly of obsolete products. However, there is a lack of studies on profit and on carbon emission saved. Hence, this study considers the multi-objective partial disassembly line balancing problem with AND/OR precedence relations to optimize profit, saved carbon emission and line balance simultaneously. Firstly, a multi-objective mixed-integer programming model is formulated, which could optimally solve the small number of instances with a single objective. Meanwhile, an improved multi-objective artificial bee colony algorithm is developed to generate a set of high-quality Pareto solutions. This algorithm utilizes two-layer encoding of the task permutation vector and the number of selected parts, and develops two-phase decoding to handle the precedence relation constraint and cycle time constraint. In addition, the modified employed bee phase utilizes the neighborhood operation, and the onlooker phase utilizes the crossover operator to achieve a diverse population. The modified scout phase selects a solution from the Pareto front to replace the abandoned individual to obtain a new high-quality solution. To test the performance of the proposed algorithm, the algorithm is compared with the multi-objective simulated annealing algorithm, the original multi-objective artificial bee colony algorithm and the well-known fast non-dominated genetic algorithm. The comparative study demonstrates that the proposed improvements enhance the performance of the method presented, and the proposed methodology outperforms all the compared algorithms. [ABSTRACT FROM AUTHOR]

Objective To investigate whether shikonin can regulate autophagy and promote wound healing in diabetic rats by upregulating C-X-C chemokine receptor type 4 (CXCR4). Methods A total of 24 Sprague-Dawley (SD) rats were assigned to control, diabetic, diabetic+ Shikonin, and diabetic+Shikonin+sh-CXCR4 groups (6 rats in each group) using random number table method. A diabetic rat model was established by intraperitoneal injection of streptozotocin, and a 1 cm-diameter skin punch was used to create an ulcer model on the back of the rat. Wound healing rates were calculated on days 0, 7, 14, and 21 after wounding. On day 21 after wounding, skin tissues were collected for hematoxylin & eosin and Masson trichrome staining to analyze the pathological changes of the wound. CD31 immunohistochemistry was performed to analyze the microvascular density of the wound and Western blotting was used to detect the expression of CXCR4 and cell autophagy-related proteins microtubule-related proteins light chain 3 (LC3) II/ LC3 I, Beclin1, and soluble p62 in the wound tissue. Autophagosomes were observed by transmission electron microscopy. One-way analysis of variance was used for comparison between groups. Results Compared with the control group, the diabetic group had reduced wound healing rate (on day 21: 72.44%±6.21% vs. 93.71%±6.76%), neovascularization, collagen deposition, microvascular density, levels of CXCR4, LC3 II /LC3 I, and Beclin, and increased p62 levels in the wound tissue, as well as decreased autophagosomes (P<0.05). Compared with the diabetic group, wound healing rate (on day 21: 86.74%±4.86% vs. 72.44%±6.21%), neovascularization, collagen deposition, microvascular density, levels of CXCR4, LC3 II/LC3 I, and Beclin, and autophagosomes were increased, but p62 levels were decreased in the diabetic+Shikonin group (P<0.05). Compared with the diabetic+Shikonin group, the diabetic+Shikonin group had decreased wound healing rate (on day 21: 76.15%±3.85% vs. 86.74%±4.86%), neovascularization, collagen deposition, microvascular density, levels of CXCR4, LC3 II/LC3 I, and Beclin, and autophagosomes in the wound tissue, as well as increased p62 levels (P<0.05). Conclusion Shikonin promotes the healing of diabetic ulcer wounds by upregulating CXCR4 expression and activating cell autophagy. [ABSTRACT FROM AUTHOR]

The collaborative robots (cobots) are increasingly being utilized in industries due to the advancement in the field of robotic technology and also due to the increase in labor costs. The cobots on the assembly line can be utilized to complete the tasks independently or assist the workers to complete the tasks. This study considers the U-shaped assembly line balancing problem with cobots, where several cobots with different purchasing costs are selected under the budget constraint. Three mixed-integer programming models are formulated to optimize the cycle time, and the built models are capable of solving the small-sized instances optimally. Two algorithms, artificial bee colony algorithm and migrating bird optimization algorithm, are developed and improved to tackle the large-sized instances, where new encoding scheme and decoding procedure are developed for this new problem. The computational tests demonstrate that the utilization of collaborative robots reduces the cycle time effectively in the assembly line. The comparative study on a set of instances shows that the proposed methodologies obtain competing performance in comparison with other 12 implemented algorithms. [ABSTRACT FROM AUTHOR]

Androgenetic alopecia (AGA) affects the quality of life (QoL) and has a negative psychological impact on patients. We sought to investigate the frequency of anxiety and depression in AGA patients and explore their relationship with patients' QoL. This cross-sectional survey enrolled 192 participants with AGA. Hairdex, World Health Organization Quality of life Brief Version (WHOQOL-BREF), and dermatology life quality index (DLQI) instruments were used to assess the QoL of patients, and hospital anxiety and depression scale (HADS) was used to evaluate patients' anxiety symptom and depression symptom. HADS, WHOQOL-BREF, and DLQI scores and domains of the Hairdex score were analyzed using linear regression and Pearson correlation. Apart from self-confidence, all four domains of Hairdex were significantly correlated with depression scores and anxiety scores. All four domains of WHOQOL-BREF were significantly correlated with depression and anxiety scores. While all six domains of DLQI were also significantly correlated with anxiety scores only. This study demonstrates a significant correlation between psychiatric morbidity and QoL in individuals with AGA. Hence, psychiatric treatment evaluations should be considered. [ABSTRACT FROM AUTHOR]

Considering preventive maintenance (PM) scenarios in the assembly line balancing problem (ALBP) have been proved effective and can be used to increase production efficiency. All these studies assumed that the processing time of tasks was fixed. However, many factors can affect the processing time in the actual production process and make the original task allocation plans infeasible. The robust method is introduced to solve the uncertain processing time, and the robust ALBP considering PM scenarios (RALBP_PMs) is studied in this paper. In this problem, three objectives are optimized: the cycle time under the regular scenario, the maximum cycle time, and the maximum task alteration under all PM scenarios. A robust mixed-integer optimization model is proposed to hedge against uncertainty, and then it is linearized by duality to make it solvable. A Q-learning-based variable neighborhood search (QVNS) algorithm with several improvements is designed to solve this problem. Several heuristic rules are designed to improve the performance of the initial population. Roulette wheel selection based on crowding distance is designed to select a solution with more potential. Eleven problem-specific neighborhood structures are designed to improve search efficiency. These structures are sorted by the Q-learning-based sorting method to find a good solution quickly. Experiment results demonstrate that considering PM scenarios in RALBP is necessary. The proposed mathematical model can obtain the Pareto solutions of small-scale instances, and the proposed QVNS algorithm is more suitable for solving large-scale instances. • RALBP considering PM scenarios promotes productivity and smoothness. • A mathematical model is designed to generate allocation plans for scenarios. • Eleven neighborhood structures are designed to generate a new solution. • Q-learning-based sorting method is proposed to sort neighborhood structures. • Results indicate that QVNS outperforms the other six algorithms. [ABSTRACT FROM AUTHOR]

Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO. [ABSTRACT FROM AUTHOR]

For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers' preferences should be concerned and hence the derived Pareto-optimal solution set (POS) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar -dominance and three modifications is proposed to obtain a preferred POS. Specifically, ar -dominance is integrated into it to guide the advancement direction of the preferred POS. Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way. • A novel ALBP-PM concerning production managers' preferences is tackled. • The ar -dominance advances Pareto-optimal solutions towards the region-of-interest. • Enhanced neighborhood structures generate neighbor solutions with smaller cycle time. • Problem-specific restart mechanism sets threshold adaptively to escape local optimum. • Statistical results indicate IVNS outperforms all other comparison algorithms. [ABSTRACT FROM AUTHOR]

High viability and further adipogenic differentiation of adipose-derived stem cells (ADSCs) are fundamental for engraftment and growth of the transplanted adipose tissue. It has been demonstrated that extracellular matrix (ECM) regulates cell proliferation and differentiation by interacting with ERK1/2 signalling pathway. In this study, we prepared autologous decellularized extracellular matrix (d-ECM) and explored its effect on the proliferation and adipogenic ability of ADSCs in low serum culture. We found that 2% foetal bovine serum (FBS) in growth medium inhibited cell viability and DNA replication, and decreased mRNA and protein levels of PPARγ and C/EPBα compared with 10% FBS. Correspondingly, after 14-days adipogenic induction, cells cultured in 2% FBS possessed lower efficiency of adipogenesis and expressed less adipocyte differentiation markers ADIPOQ and aP2. On the contrary, the d-ECM-coated substrate continuously promoted the expression of PPARγ, and regulated the phosphorylation of ERK1/2 in different manners during differentiation. Pretreatment with ERK1/2 inhibitor PD98059 neutralized the effects of d-ECM, which suggested d-ECM might regulate the adipogenesis of ADSCs through ERK1/2-PPARγ pathway. In addition, d-ECM was revealed to regulate the transcription and expression of stemness-associated genes, such as OCT4, NANOG and SOX2, in the undifferentiated ADSCs, which might be related to the initiation of differentiation. [ABSTRACT FROM AUTHOR]

The current assembly line balancing studies ignore the preventive maintenance (PM) of machines in some workstations, implying that the already-known PM information has been completely missed. Moreover, PM may bring about a production stoppage for a considerable time. Hence, this paper considers PM scenarios into the assembly line balancing problem to improve the production efficiency and smoothness simultaneously. For this multi-objective problem, a heuristic rule relying on the tacit knowledge is dug up via gene expression programming to obtain an acceptable solution quickly. Then, an enhanced grey wolf optimizer algorithm with two improvements is proposed to achieve Pareto front solutions. Specifically, a variable step-size decoding mechanism accelerates the speed of the algorithm; the specially-designed neighbor operators prevent the algorithm from trapping in local optima. Experiment results demonstrate that the discovered heuristic rule outperforms other existing rules; the joint of improvements endows the proposed meta-heuristic with significant superiority over three variants and other six well-known algorithms. Besides, a real-world case study is conducted to validate the discovered rule and the proposed meta-heuristic. • ALBP considering PM scenarios promotes the productivity and smoothness. • The heuristic rule mined by GEP can obtain an acceptable solution quickly. • The variable step-size decoding method can speed up the solution procedure. • Specially-designed neighbor operators can balance exploration and exploitation. • Statistical results indicate EGWO with two improvements outperforms others. [ABSTRACT FROM AUTHOR]

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study. [ABSTRACT FROM AUTHOR]

Frequently occurring and unavoidable perturbations in railway systems cause a reduction in performance and even the infeasibility of the original schedule. In addition, existing related studies assume that the maintenance routes are fixed in a deterministic situation. Therefore, it is necessary to focus on robust scheduling of electric multiple units first-level maintenance with flexible maintenance routes to defend against stochastic uncertainties. Firstly, a mixed-integer linear programming model with uncertain parameters in objective functions and constraints is built for the first time. Multiple constraints are formulated to consider flexible maintenance routes, train shunting conflicts, and track occupation conflicts in a stub-end depot. A robust optimization approach is adopted to obtain a deterministic robust counterpart model with uncertainties in processing/arrival/departure times. In this model, uncertainty and reliability levels are introduced to describe and quantify the disturbance degree of processing/arrival/departure times and the allowable violation degree of resource constraints respectively. Moreover, an adaptive iterative local search is proposed by embedding heuristic rules for the initialization. The proposed algorithm includes problem-specific neighborhood structures to improve the evolutionary ability, a variable neighborhood descent method to guide and drive the search toward promising areas of the search space, and an adaptive perturbation mechanism to enable the algorithm to jump out of local optimum. Numerical results from China's railway system validate the proposed model and quantitatively demonstrate the merit of the proposed algorithm. Further analysis shows that our approach can achieve an appropriate trade-off between robustness and efficiency for various uncertainty and reliability levels. • The SSEFM problem under stochastic uncertainties is addressed. • A multi-objective MILP model with deterministic robust equivalent counterpart is defined. • An adaptive iterative local search is proposed to solve this problem. • The algorithm is enhanced with a variable neighborhood descent and adaptive perturbation. [ABSTRACT FROM AUTHOR]