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Abstract Background There are few reported studies on stigma in young and middle-aged stroke patients during the rehabilitation period, however, the rehabilitation period plays a key role in the patients’ disease regression. Exploring the level of stigma and the influencing factors in young and middle-aged stroke patients during the rehabilitation period is crucial for determining how to reduce the level of stigma and improve the patients’ motivation for rehabilitation treatment. Therefore, this study investigated the level of stigma in young and middle-aged stroke patients and analyzed the factors influencing stigma in order to provide a reference or basis for healthcare professionals to develop effective and targeted stigma intervention programs. Methods Using a convenience sampling method, 285 young and middle-aged stroke patients admitted to the rehabilitation medicine department of a tertiary care hospital in Shenzhen, China, from November 2021 to September 2022 were selected and surveyed using a general information questionnaire, the Stroke Stigma Scale(SSS), the Barthel Index(BI), and the Positive and Negative Emotions Scale(PANAS), and multiple linear regression and smoothed curve fitting were used to analyze the factors influencing the stigma of young and middle-aged stroke patients during the rehabilitation period. Results SSS score of 45.08 ± 11.06, univariate analysis of age, occupation, education level, pre-stroke monthly income, insurance type, comorbid chronic disease status, primary caregiver, BI, positive and negative emotion as factors influencing stigma. Multiple linear regression showed that age, pre-stroke monthly income, BI, positive and negative emotions were independent influences on stigma in young and middle-aged stroke patients, explaining 58.0% of the total variance in stigma. A smoothed curve fit revealed a curvilinear relationship between the above influences and stigma. Conclusion Young and middle-aged stroke patients have a moderate level of stigma. Medical staff should focus on young patients aged 18–44 years, those with high monthly income before the stroke, those with poor self-care ability, and those with low positive and high negative emotion scores, and conduct early assessments and adopt targeted intervention programs according to the influencing factors to reduce the stigma of young and middle-aged stroke patients, improve their motivation for rehabilitation, and help them return to their families and society as soon as possible. Trial Registration Registration number of China Clinical Trials Registration Center: 20,220,328,004-FS01.

As chloride (Cl−) is a commonly found anion in natural water, it has a significant impact on electrocatalytic oxidation processes; yet, the mechanism of radical transformation on different types of anodes remains unexplored. Therefore, this study aims to investigate the influence of chlorine-containing environments on the electrocatalytic degradation performance of levofloxacin using BDD, Ti4O7, and Ru-Ti electrodes. The comparative analysis of the electrode performance demonstrated that the presence of Cl− improved the removal and mineralization efficiency of levofloxacin on all the electrodes. The enhancement was the most pronounced on the Ti4O7 electrode and the least significant on the Ru-Ti electrode. The evaluation experiments and EPR characterization revealed that the increased generation of hydroxyl radicals and active chlorine played a major role in the degradation process, particularly on the Ti4O7 anode. The electrochemical performance tests indicated that the concentration of Cl− affected the oxygen evolution potentials of the electrode and consequently influenced the formation of hydroxyl radicals. This study elucidates the mechanism of Cl− participation in the electrocatalytic degradation of chlorine-containing organic wastewater. Therefore, the highly chlorine-resistant electrocatalytic anode materials hold great potential for the promotion of the practical application of the electrocatalytic treatment of antibiotic wastewater.

We synthesized a series of Ru-Ti-Ni/CNT electrodes to degrade levofloxacin in wastewater. The unique structure of Ni single-atom-kernelled Ru nanoparticles regulates the reaction process, producing a notable synergistic effect. The reaction kinetics constant of Ru-Ti-Ni/CNT with a Ru: Ti ratio of 10:0 outperforms Ni/CNT by 3.7 times and CNT alone by 16.4 times. Comprehensive characterizations revealed its morphology and structure nature. Ru nanoparticles can shield Ni, while Ni single-atom facilitates electron transfer in Ru nanoparticles, generating abundant reactive oxygen radicals (ROS) that degrade pollutants.

Train timetables are usually established far in advance of operations and are based on forecasted demands. However, due to changes in actual freight traffic, railways need to determine the actual operation of trains arising in daily operations through train path selection, i.e., selecting a portion of the timetable for these trains to execute. To improve current freight train scheduling in daily operations, this paper suggests taking into account the car flow transfer between consecutive trains and shipment delivery time requirements. A train path selection optimization model is developed to minimize the total travel time of freight trains while seeking minimum penalties for shipment delivery delays. A tabu search algorithm is designed to solve this problem. The effectiveness of the proposed model and algorithm is demonstrated by numerical experiments on instances built on real data from the Menghua railway, a rail freight corridor. The results show that, compared to current practice, this optimization method can achieve a reduction in total train travel time and ensure the punctual delivery of shipments.

In emergency rescue, the allocation of comprehensive transportation network emergency vehicles often affects the efficiency of the whole rescue process. In the context of disasters, this paper researches the one-to-many two-sided matching problem between the emergency vehicles and the materials to be transported. Firstly, based on the needs of both parties involved in the matching, the satisfaction evaluation systems are constructed; with the goal of maximizing the weighted satisfaction of the affected areas and vehicles, the optimization model of the materials and emergency vehicles matching is established; then, an improved National Intern Matching Program (NIMP) algorithm is designed to solve the model, which is based on the k: 1 experimental pairing and updating ideas, and can take into account the capacity and destination constraints of vehicles in the matching process. Finally, through the calculation of an example, the matching scheme can make the satisfaction of material transportation reach 0.7392, and the simulation analysis proves that the scheme keeps certain stability in risky conditions.

[This corrects the article DOI: 10.1371/journal.pone.0222365.].

Presently, in the context of the novel coronavirus pneumonia epidemic, several antibiotics are overused in hospitals, causing heavy pressure on the hospital’s wastewater treatment process. Therefore, developing stable, safe, and efficient hospital wastewater treatment equipment is crucial. Herein, a bench-scale electrooxidation equipment for hospital wastewater was used to evaluate the removal effect of the main antibiotic levofloxacin (LVX) in hospital wastewater using response surface methodology (RSM). During the degradation process, the influence of the following five factors on total organic carbon (TOC) removal was discussed and the best reaction condition was obtained: current density, initial pH, flow rate, chloride ion concentration, and reaction time of 39.6 A/m2, 6.5, 50 mL/min, 4‰, and 120 min, respectively. The TOC removal could reach 41% after a reaction time of 120 min, which was consistent with the result predicted by the response surface (40.48%). Moreover, the morphology and properties of the electrode were analyzed. The degradation pathway of LVX was analyzed using high-performance liquid chromatography–mass spectrometry (LC–MS). Subsequently, the bench-scale electrooxidation equipment was changed into onboard-scale electrooxidation equipment, and the onboard-scale equipment was promoted to several hospitals in Dalian.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Due to variations in freight traffic arising in daily operations, railways need to develop a distribution plan to determine the actual movements of empty cars every day. This paper investigates the daily empty car distribution problem in the Chinese railway system, which is a scheduled railway system but one where scheduled train services in the timetable are not fixed. To make current empty car distribution more market-oriented, this paper takes the timeliness requirement of empty car demands into account. A time-space network that divides the planning horizon into multiple decision-making stages is constructed to describe the process of empty car distribution. With consideration of car types and the network capacity shared by loaded and empty cars, an integer programming model is proposed to minimize total cost in the distribution process. A Lagrangian relaxation heuristic algorithm is designed to solve this problem. The effectiveness of the proposed model and algorithm is demonstrated by numerical experiments based on realistic data from the Haoji Railway, a rail freight corridor. Finally, sensitivity analyses indicate that unit shortage costs and delay penalties can be set relatively high and that many train paths should be scheduled in the timetable to ensure that stations receive empty cars on time. [ABSTRACT FROM AUTHOR]

Background: Chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family (CMTM) is a gene family involved in multiple malignancies. CMTM4 is a member of this family and is located at chromosome 16q22.1, a locus that harbours a number of tumour suppressor genes. It has been defined as a regulator of cell cycle and division in HeLa cells; however, its roles in tumourigenesis remain poorly studied. Methods: An integrated bioinformatics analysis based on the array data from the GEO database was conducted to view the differential expression of CMTM4 across multiple cancers and their corresponding control tissues. Primary clear cell renal cell carcinoma (ccRCC) and the paired adjacent non-tumour tissues were then collected to examine the expression of CMTM4 by western blotting, immunohistochemistry, and quantitative RT-PCR. The ccRCC cell lines A498 and 786-O and the normal renal tubular epithelial cell line HK-2 were also tested for CMTM4 expression by western blotting. Cell Counting Kit-8 (CCK-8) and viable cell counting assays were used to delineate the growth curves of 786-O cells after CMTM4 overexpression or knockdown. Wound healing and transwell assays were performed to assess the cells' ability to migrate. The effects of CMTM4 on cellular apoptosis and cell cycle progression were analysed by flow cytometry, and cell cycle hallmarks were detected by western blotting and RT-PCR. The xenograft model in nude mice was used to elucidate the function of CMTM4 in tumourigenesis ex vivo. Results: By omic data analysis, we found a substantial downregulation of CMTM4 in ccRCC. Western blotting then confirmed that CMTM4 was dramatically reduced in 86.9 % (53/61) of ccRCC tissues compared with the paired adjacent non-tumour tissues, as well as in the 786-O and A498 ccRCC cell lines. Restoration of CMTM4 significantly suppressed 786-O cell growth by inducing G2/M cell cycle arrest and p21 upregulation, and cell migration was also inhibited. However, knockdown of CMTM4 led to a completely opposite effect on these cell behaviours. Overexpression of CMTM4 also markedly inhibited the tumour xenograft growth in nude mice. Conclusions: CMTM4 is downregulated and exhibits tumour-suppressor activities in ccRCC, and could be exploited as a target for ccRCC treatment. [ABSTRACT FROM AUTHOR]