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Androgenetic alopecia (AGA) is a common type of hair loss in men and efficacy and safety of current medical treatment remain limited. Therefore, the present study aimed to investigate the efficacy and safety of botulinum toxin type A (BTA) combined with Minoxidil in patients with AGA. 60 male patients were included in this study and control group received topical 5% Minoxidil and the treatment group received BTA combined with topical 5% Minoxidil. BTA injections (60–70 U) were administered at 30–35 scalp sites. Head photographs were taken at baseline, 2nd, 4th, and 6th months. Clinical descriptions recorded scalp conditions, and patient satisfaction along with Dermatology Life Quality Index scores were documented. The treatment group (TG) showed significant hair growth differences compared to the control group (CG) at the 4th month (P < 0.001) and 6th month (P = 0.0046) post-treatment. TG had improved Investigator Global Assessment (IGA) scores in the 4th month (P = 0.0001) and 6th month (P = 0.0259) compared to CG. Patient satisfaction in TG for hair growth and scalp improvement was higher than CG (all P < 0.05). TG exhibited substantial quality of life improvement at the 4-month (P = 0.0009) and 6-month (P = 0.0099). No adverse reactions were observed post-botulinum toxin injection. BTA combined with Minoxidil effectively promotes hair growth, enhances the quality of life, and alleviates scalp symptoms in male AGA patients at 4th and 6th months, with no adverse effects compared to Minoxidil alone. Trial registration number: Ethics Committee of Shanghai Tongji Hospital (ID: K-2018-026). [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]

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]

In the mixed-model assembly line balancing and sequencing problem (MALBSP), workstations are assumed to be constantly available. The failure of any workstation will make the entire assembly line stop working. Preventive maintenance (PM) is a way to maintain the workstation before its failure, reduce unexpected downtime, and prolong its useful life. Previous studies have considered PM scenarios (PMS) in the simple and U-shaped assembly line to improve production efficiency and smoothness effectively, but not in the mixed-model assembly line. This paper fills this research gap, and the MALBSP considering PMS (MALBSP_PMS) is studied in this paper. A mixed-integer linear programming model is proposed to minimize makespan and task alteration. A migrating birds optimization algorithm is improved (IMBO) to obtain well-distributed Pareto frontier solutions. This algorithm designs a restart mechanism and an intra-population crossover operator to avoid falling into the local optimal and enhance its searchability. Experimental results demonstrate the effectiveness of two improvements and the IMBO algorithm. In addition, a real-world case study is introduced to illustrate the importance of considering PM scenarios in MALBSP. [ABSTRACT FROM AUTHOR]

While solving the shunting scheduling of EMU first-level maintenance (SSEFM), most existing literature assumed a single maintenance route for all trains and considered only a through depot. It neglects the problem-specific characteristics in terms of varied maintenance routes and a stub-end depot, causing the infeasibility of the generated schedule in such particular circumstances. Therefore, the SSEFM problem with flexible maintenance routes in a stub-end depot with a transversal yard configuration is considered in this work. First, a multi-objective mixed-integer linear programming (MILP) model is formulated to maximize the reservation time in the storage area, and minimize the overstay time in the cleaning and inspecting areas. The relationship between constraints including flexible maintenance routes, train shunting conflicts, track occupation conflicts, and train arrival/departure times, are coordinated. Subsequently, a heuristic-based enhanced particle swarm optimization algorithm (EPSO) with two improvements is proposed to tackle this NP-hard problem. Specifically, three heuristic rules about the depth-first operation track allocation, the conflict-free bottleneck track allocation, and the right-shift track occupancy repair are designed to ensure the feasibility of the shunting schedule. Accordingly, a three-level decoding mechanism is designed to achieve a near-optimal shunting schedule with great train and route sequences. Two improvements on crossover and mutation operators are developed to enhance the exploration and exploitation ability. Finally, a real-world instance in China is solved to verify the effectiveness and efficiency of the proposed model and algorithm. Experimental results show that EPSO is relatively more effective than all the compared algorithms. [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]

Disassembly is the first and vital step in recycling and remanufacturing end-of-life products. Disassembly lines are utilized frequently due to high productivity and suitability. This research studies the disassembly line balancing problem on the U-shaped disassembly lines, which have higher flexibility than the traditional straight disassembly lines. A mixed-integer linear programming (MILP) model is developed to formulate the AND/OR precedence relationships with the objective of minimizing the number of stations. This model is also extended to a mixed-integer nonlinear programming model to optimize four objectives. To tackle this NP-hard problem effectively, a two-phase artificial bee colony algorithm and a bee algorithm are proposed and improved. In these algorithms, the first phase selects the stations with less loads on the last two stations for the purpose of achieving the optimal number of stations. The second phase hierarchically optimizes multiple objectives to achieve better line balances. Case studies show that the proposed MILP model obtains optimal solutions in terms of station number for the small-size instances, and the U-shaped disassembly lines obtain better fitness values than the straight disassembly lines. The comparative study demonstrates that the proposed methodologies perform competing performances in comparison with other 13 re-implemented algorithms, including tabu search algorithm, iterated local search algorithm, genetic algorithm, particle swarm optimization, three artificial bee colony algorithms and the original bee algorithm. [ABSTRACT FROM AUTHOR]

In this paper, flexible preventive maintenance (PM) activities are incorporated into two-stage assembly flow shop scheduling where m dedicated machines in the fabrication stage and one machine in the assembly stage. The operational status of each machine is described by a continuous variable, maintenance level. The maintenance level value is inversely proportional to the processing time. Once a PM activity is performed, this value will return to the initial value. Different from the PM at fixed predefined time intervals, flexible PM can be carried out at any time point, but the maintenance levels are not less than 0. Hence, a MILP model with maintenance level constraints is formulated to minimize the total completion time and maintenance time. Regarding the methods, a latest PM decision strategy is proposed to determine the execution time of PM activities. This new strategy is embedded into 15 constructive heuristics and 7 meta-heuristics (three variants of iterated local search, three variants of Q-learning-based ant colony system with local search and a Q-learning-based hyper-heuristics) to address this new problem. The final experimental analysis demonstrates the significance of the integrated model and the effectiveness of the proposed constructive heuristics and meta-heuristics. [ABSTRACT FROM AUTHOR]

Adipose-derived stem cells (ADSCs) play an important role in cell therapy and regenerative medicine. However, local nutritional deficiency often limits therapeutical effect of the transplanted cells. Acetyl l-carnitine (ALC) is a common energy metabolism regulator and free radical scavenger. This study investigated the effect of ALC on ADSCs exposed to severe serum-deprivation and explored the relative machanisms. Treating with 1 mM ALC improved proliferation and alleviated senescence of starved cells, accompanied with reduced reactive oxygen species (ROS) and increased protein expression of SOD1 and catalase. In addition, ALC inhibited apoptosis but increased starvation-induced autophagy, which might be related to the regulation of phases of dissociation of Bcl-2-Beclin1 and Bcl-2-Bax complexes. Evidence obtained by replacing ALC with N-acetylcysteine (N-AC) suggested that ROS might be the central inducer of autophagy, apoptosis and senescence. There was a difference between ALC and N-AC in the protection mechanism, that was, compared with N-AC, ALC maintained autophagy well at the same time as anti-oxidation. Inhibition of autophagy by 3-methyladenine (3-MA) partially offset the protective effect of ALC. However, despite low-level ROS and enhanced autophagy, ALC with high concentration (10 mM) markedly aggravated cell apoptosis and senescence, thus losing cytoprotection and even causing damage. [ABSTRACT FROM AUTHOR]

Adipose-derived stem cells (ADSCs) are easily available and play an important role in regenerative medicine. In recent years, Cell spheroid models have been in the spotlight because of their various advantages and physiological proximity. Promoting the spreading of ADSCs spheroids may improve the therapeutic effect the transplanted ADSCs. In this study, we prepared autologous decellularized extracellular matrix (d-ECM) and ADSCs spheroids, and investigated in vitro spreading of the spheroids on the d-ECM-coated substrate. In addition, the effect of d-ECM powder (ECM-P) on the aggregation of ADSCs was analyzed in a three-dimensional (3D) culture system. The results showed that d-ECM accelerated the spreading of spheroids, and promoted the migration and proliferation of the surrounding monolayer cells, accompanied by ERK1/2 activation and an increase in the expression of MMP2 and MMP9. In addition, ECM-P facilitated the aggregation of free cells in 3D culture in a concentration-dependent way. The spheroid spreading and cell aggregation were both prevented by ERK1/2 selective inhibitor PD98059. Our data suggest that the d-ECM substrate and its derivant may regulate the transformation between ADSCs spheroids and the monolayer or free cells, and ERK1/2 signalling pathway may be involved in these processes. [ABSTRACT FROM AUTHOR]

Existing algorithms of dish recognition mainly focus on accuracy with predefined classes, thus limiting their application scope. In this paper, we propose a practical two-stage dish recognition framework (DRNet) that yields a tradeoff between speed and accuracy while adapting to the variation in class numbers. In the first stage, we build an arbitrary-oriented dish detector (AODD) to localize dish position, which can effectively alleviate the impact of background noise and pose variations. In the second stage, we propose a dish reidentifier (DReID) to recognize the registered dishes to handle uncertain categories. To further improve the accuracy of DRNet, we design an attribute recognition (AR) module to predict the attributes of dishes. The attributes are used as auxiliary information to enhance the discriminative ability of DRNet. Moreover, pruning and quantization are processed on our model to be deployed in embedded environments. Finally, to facilitate the study of dish recognition, a well-annotated dataset is established. Our AODD, DReID, AR, and DRNet run at about 14, 25, 16, and 5 fps on the hardware RKNN 3399 pro, respectively. [ABSTRACT FROM AUTHOR]

The fast and accurate estimation of makespan is essential for the determination of the delivery date and the sustainable development of the enterprise. In this paper, a high-quality training dataset is constructed and an adaptive ensemble model is proposed to achieve fast and accurate makespan estimation. First, both the logistics features extracted by the Pearson correlation coefficient and the new meaningful nonlinear combination features dug out by gene expression programming are first involved in this paper for constructing a high-quality dataset. Secondly, an improved clustering with elbow criterion and a resampling operation are applied simultaneously to generate representative subsets; and correspondingly, several back propagation neural network (BPNN) with the architecture optimized by genetic algorithm are trained by these subsets respectively to generate effective diverse learners; and then, a K-nearest neighbor based dynamic weight combination strategy which is sensitive to current testing sample is proposed to make full use of the learner's positive effects and avoid its negative effects. Finally, the results of effective experiments prove that both the newly involved features and the improvements in the proposed ensemble are effective. In addition, comparison experiments confirm that the proposed enhanced ensemble of BPNNs outperforms significantly the prevailing approaches, including single, ensemble and hybrid models. And hence, the proposed model can be utilized as a convenient and reliable tool to support customer order acceptance. [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]