Risk factors and digital interventions for anxiety disorders in college students:Stakeholder perspectives

The worldwide prevalence of anxiety disorders among college students is high,which negatively affects countries,schools,families,and individual students to varying degrees.This paper reviews the relevant literature regarding risk factors and digital interventions for anxiety disorders among college students from the perspectives of different stakeholders.Risk factors at the national and societal levels include class differences and the coronavirus disease 2019 pandemic.College-level risk factors include the indoor environment design of the college environment,peer relationships,student satisfaction with college culture,and school functional levels.Family-level risk factors include parenting style,family relationship,and parental level of education.Individual-level risk factors include biological factors,lifestyle,and personality.Among the intervention options for college students'anxiety disorders,in addition to traditional cognitive behavioral therapy,mindfulness-based interventions,psychological counseling,and group counseling,digital mental health interventions are increasingly popular due to their low cost,positive effect,and convenient diagnostics and treatment.To better apply digital intervention to the prevention and treatment of college students'anxiety,this paper suggests that the different stakeholders form a synergy among themselves.The nation and society should provide necessary policy guarantees,financial support,and moral and ethical supervision for the prevention and treatment of college students'anxiety disorders.Colleges should actively participate in the screening and intervention of college students'anxiety disorders.Families should increase their awareness of college students'anxiety disorders and take the initiative to study and understand various digital intervention methods.College students with anxiety disorders should actively seek psychological assistance and actively accept and participate in digital intervention projects and services.We believe that in the future,the application of methods such as big data and artificial intelligence to improve digital interventions and provide individualized treatment plans will become the primary means of preventing and treating anxiety disorders among college students.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

联合物理层与MAC层的multi-TRP上行重叠传输处理机制

针对非理想回程下现有协议难以有效处理多传输接收节点(multi-TRP)场景中多定时提前(multi-TA)导致的严重上行链路(UL)重叠传输问题,联合改进物理层复用技术和介质访问控制(MAC)令牌桶技术,提出了一种新型的UL重叠传输处理机制。该新型机制通过改进物理层重叠信道识别流程、复用要求及复用规则,将物理层复用信息与重叠信息反馈至MAC层,并对MAC层令牌桶技术进行优化。通过仿真实验对所提机制与现有协议机制进行对比,结果表明,在逻辑时隙不可重叠和可重叠2种情形下,物理上行控制信道(PUCCH)实际复用数量性能平均提升了57.58%和49.40%,物理上行共享信道(PUSCH)实际可用资源数量性能平均提升了12.09%和26.03%;优先级最高逻辑信道实际占用资源数量性能平均提升了33.33%和45.48%。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

Lagos State Hospital Waste Management Practices: A Descriptive Overview with Stakeholders’ Role and Key Recommendations

Background: Waste generation and its disposal is an essential issue in the sustainability of the environment and the planet’s future. Waste management is essential across sectors, likewise the health sector. Therefore, there is a need to employ extra care and attention to handling waste generated from healthcare facilities to avoid the dangers of poor biomedical waste management. We carried out this study to examine the waste management practice in healthcare facilities in Lagos State. Methods: The study was a descriptive survey carried out in one-thousand two hundred and fifty-six (1256) healthcare facilities in Lagos State. Nine hundred sixty-nine (969) of these facilities are located in urban areas, while two hundred and eighty-seven (287) are rural. The facilities studied are government/public health facilities (15.45%), private-for-profit facilities (82.88%), NGOs, Mission/Faith-Based medical facilities (1.67%). The data collected were analyzed using descriptive statistics. Specifically, we utilized bar charts, frequency, and percentage. Result: The result shows that 98.4% (1236) of the studied facilities are registered with the Lagos State Waste Management Authority (LAWMA), while 1.6% (20) are not registered. 98.5% (191) of the 194 government-owned facilities, 98.5% (1025) of the 1041 private-for-profit facilities, and 98.2% (20) of the 21 NGOs/faith-based health facilities are registered with Lagos State Waste Management Authority. The result also shows that 94% of the healthcare facilities studied in Lagos State use color-coded waste bags to segregate waste at the point of origin. 58.7% of the facilities use red-colored bags, 33.3% use yellow-colored bags, 10.7% use black-colored bags, and 1.3% use brown biohazard bags for segregating Infectious waste. Also, 34.2% of the health facilities in Lagos state use red-colored bags, 36.9% use yellow-colored bags, 11% use black-colored bags, and 4.1% use brown-colored bags to segregate their hazardous waste. Conclusion: Some healthcare facilities in Lagos State do not follow the recommended guidelines for medical waste segregation. Waste generated is not appropriately segregated at the point of origin into the recommended colored bags/bins in some facilities. Thus, a policy and procedure regulating healthcare waste are mandatory. It is important to regularly train healthcare workers on proper waste management practices and encourage staff to read and apply WHO rules in managing healthcare waste. Healthcare personnel should realize that hazardous material is a potential cause of a public disaster.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

World Trading Signals for Indian Agriculture during WTO Regime: Multi-Stakeholders Decision Making

Globally, any country in the world either exporting or importing country need to look at international market signals. Agriculture is one of the most contorted sectors in international trade. The study is basically based on estimation and identification of various international trading signals to advocate their usefulness in decision making to multi-stake holders. Study period is 1990-91 to 2015-16 and the study employed is the Foreign Trade Philosophy to analyze the international market signals, trends, growth rates, elasticity’s, instability index, AOI, meta-analysis and the vision. It was observed that the export and import price elasticity’s for all the crops shown are positive except the wheat export price elasticity (-0.3%) and import price elasticity of soybean (-0.45%). Among cereals, pulses, oilseeds and fiber crops, rice (1.24%), peas (2.36%), mustard (0.97%) and cotton (0.75%) have high export elasticity’s respectively. These trade price elasticity’s are the important signals for the policy makers to layout the future trade. Study observed that the domestic support offered in the agricultural sector in Russia, India, China and New Zealand is more compared to other WTO member countries. Technical Barriers to Trade, Sanitary and Phytosanitary and Anti-dumping were found to be the most prominent in world and the highest imposed in Asia, Europe and North America. Study concluded, India has a comparative advantage in pulses, oilseeds and wheat and terms of trade of India’s cereals (except rice, maize), pulses (except pigeon pea, peas), cotton and jute which were found to be increased. The poor treatment towards the agriculture sector by the governments and World Bank Funding was observed. India’s import basket majorly consists of oilseeds and rice is the major exported product. Present study adds to the research directed at the impacts of domestic support and measures policies for WTO negotiations.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Multi-scale data joint inversion of minerals and porosity in altered igneous reservoirs—A case study in the South China Sea

There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.However,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably difficult.At the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations.To overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks.Specifically,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric model.Then we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity.However,it is always difficult to determine the elemental eigenvalues of different minerals in inversion.This paper uses multiple linear regression methods to solve this problem.Finally,an integrated inversion technique for altered igneous formations was developed.The results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction.The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Contribution of Imagery in the Diagnosis of Multisystemic Sarcoidosis in the Service Radiology Department of the Mother and Child Hospital “Le Luxembourg” in Bamako: A Case Report

The aim of this study was to report a case of multi-visceral sarcoidosis at the Mother-Child Hospital Center (CHME) “Le Luxembourg” in Bamako, Mali. Observation: This is a patient aged 62 at the time of consultation, a housewife, residing in the Banconi district, who was referred to us for thoracic-abdominopelvic imaging for chronic liver disease. After several diagnostic errors, the thoracic-abdominopelvic CT scan and liver MRI performed in our center showed, at the thoracoabdominal level, bilateral diffuse pulmonary micronodules and bilateral mediastinal-hilar lymphadenopathy;on the abdominal level, a dysmorphic liver with plaques of steatosis and a granular appearance of the liver parenchyma without periportal fibrosis. These imaging data combined with those from the liver nodule biopsy and biology confirmed the diagnosis of sarcoidosis type II. Treatment with corticosteroids gave satisfactory results and the patient recovered after 18 months. Clinical and CT monitoring 2 years from the start of the disease and 2 months from the end of treatment showed complete resolution of the lesions. Conclusion: The multi-visceral location of sarcoidosis is an entity whose diagnosis remains difficult;diagnostic and interventional imaging has an important place in its management.

Incidence, risk factors and clinical outcome of multidrug-resistant organisms after heart transplantation

BACKGROUND Transplant recipients commonly harbor multidrug-resistant organisms(MDROs),as a result of frequent hospital admissions and increased exposure to antimi-crobials and invasive procedures.AIM To investigate the impact of patient demographic and clinical characteristics on MDRO acquisition,as well as the impact of MDRO acquisition on intensive care unit(ICU)and hospital length of stay,and on ICU mortality and 1-year mortality post heart transplantation.METHODS This retrospective cohort study analyzed 98 consecutive heart transplant patients over a ten-year period(2013-2022)in a single transplantation center.Data was collected regarding MDROs commonly encountered in critical care.RESULTS Among the 98 transplanted patients(70%male),about a third(32%)acquired or already harbored MDROs upon transplantation(MDRO group),while two thirds did not(MDRO-free group).The prevalent MDROs were Acinetobacter baumannii(14%),Pseudomonas aeruginosa(12%)and Klebsiella pneumoniae(11%).Compared to MDRO-free patients,the MDRO group was characterized by higher body mass index(P=0.002),higher rates of renal failure(P=0.017),primary graft dysfunction(10%vs 4.5%,P=0.001),surgical re-exploration(34%vs 14%,P=0.017),mechanical circulatory support(47%vs 26%P=0.037)and renal replacement therapy(28%vs 9%,P=0.014),as well as longer extracorporeal circulation time(median 210 vs 161 min,P=0.003).The median length of stay was longer in the MDRO group,namely ICU stay was 16 vs 9 d in the MDRO-free group(P=0.001),and hospital stay was 38 vs 28 d(P=0.006),while 1-year mortality was higher(28%vs 7.6%,log-rank-χ2:7.34).CONCLUSION Following heart transplantation,a predominance of Gram-negative MDROs was noted.MDRO acquisition was associated with higher complication rates,prolonged ICU and total hospital stay,and higher post-transplantation mortality.

Behaviours of Multi-Stakeholders under China’s Renewable Portfolio Standards:A Game Theory-Based Analysis

China has implemented both quantitative and policy incentives for renewable energy development since 2019 and is currently in the policy transition stage.The implementation of renewable portfolio standards(RPSs)is difficult due to the interests of multiple stakeholders,including power generation enterprises,power grid companies,power users,local governments,and the central government.Based on China’s RPS policy and power system reform documents,this research sorted out the core game decision problems of China’s renewable energy industry and established a conceptual game decision model of the renewable energy industry from the perspective of local governments,power generation enterprises and power grid companies.The results reveal that for local governments,the probability of meeting the earnings quota or punishments for not reaching quota completion are the major determinants for active participation in quota supervision.For power grid firms,the willingness to accept renewable electricity quotas depends on the additional cost of receiving renewable electricity and governmental incentives.It is reasonable,from the theoretical perspective,to implement the RPS policy on the power generation side.Electricity reform will help clarify the electricity price system and increase the transparency of the quota implementation process.Policy implications are suggested to achieve sustainable development of the renewable energy industry from price incentives and quantity delivery.

GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland

Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniques, the Gishari Agricultural Marshlands having steeped land with grassland is classified into five classes of flooding namely very low, low, moderate, high, and very high which include 430%, 361%, 292%, 223%, and 154%. Government of Rwanda and other implementing agencies and major key actors have to contribute on soil and water conservation strategies to reduce the runoff and soil erosion as major contributors of flooding.

基于Multi-WHFPN与SimAM注意力机制的版面分割

作为OCR的预处理工作,版面分割技术越来越受到学术界和工业界重视。针对版面分割中遇到的检测速度慢、目标区域边界不准确以及细小目标易遗漏等问题,提出了YOLOv7-MSY模型。此模型首先借鉴残差连接思想,提出了Multi-WHFPN网络结构。它采用可训练的权重参数,突出特征融合过程中特征重要性,并添加了小目标检测头,从而提升对小目标的检测性能;其次,引入SimAM注意力机制,可以在不增加额外参数的基础上在3D维度评估特征权重,以增强重要特征,抑制无效特征;最后,使用YEIOU来代替原模型中的定位损失函数,提升了模型的收敛速度与回归精度。在江苏省档案馆提供的数据集上进行实验对比,YOLOv7-MSY对目标区域边界检测更加敏感,对细小目标的检测效果更好。YOLOv7-MSY的mAP@.5达到了0.871,相较于原YOLOv7模型提高了7.84%。该模型的版面分割的效果优于其他类型的版面分割算法,具有良好的泛化性能,并且版面分割速度处于较高水平。

COMSOL Multiphysics在锂离子电池中的应用

作为一种具有前景的能量存储系统,锂离子电池需要进一步提高能量密度、功率密度、可靠性和循环稳定性,以满足不断增长的大型能源存储、电动汽车和便携式电子设备需求。当前对锂离子电池的实验研究仍然面临多个挑战,这些挑战包括电解液的导电性和安全性、高能量负极的沉积-剥离机制的优化、高能量正极的循环电压和容量维持、高电流条件下的界面极化和容量释放,以及在极端电流-温度-针刺条件下的热失控管理等问题。这些问题涉及到电-化-力-热等多个场的耦合作用,需要进行协同优化处理。COMSOL Multiphysics提供了一种可行的工具,通过求解多物理场耦合的连续方程,能够同时考虑载流子浓度、电流密度、电-化学势、温度、应力/应变和几何形态等综合信息的演化。本文概述了该工具在锂离子电池的电解液、负极和正极设计等方面的研究,并聚焦于多场耦合对电池性能的综合影响、多场耦合模拟方法以及理论模拟与实验表征的结合。最后,本文对理论与实验联合研究中的多场和多尺度问题进行了展望。

Profile of Multidrug Resistant Bacteria in Bukavu Hospitals and Antimicrobial Susceptibility to Escherichia coli, Pseudomonas aeruginosa, Proteus mirabilis and Staphylococcus aureus

Objective: To evaluate the spread of Multidrug-Resistant (MDR) bacterial infections in Bukavu hospitals and test antimicrobial susceptibility patterns of some isolates to usual marketed antibiotics. Methods: The prevalence of MDR strains was determined by using general antimicrobial susceptibility data collected from 3 hospital laboratories. The susceptibility of some isolates to usual antibiotics was processed by agar diffusion method with standard E. coli ATCC8739 and standard antibiotics discs as controls. The tested antibiotics were ampicillin, ceftriaxone, gentamicin, chloramphenicol and ciprofloxacin. Results: At the 3 hospitals, 758 tests were realized in urine, pus, stool, FCV, blood, LCR, split and FU specimens;46 strains were unidentified and 712 strains were identified. Of 712 identified strains, 223 (31.4%) were MDR or XDR strains including Escherichia coli, Klebsiella pneumoniae, Enterobacter, Proteus mirabilis, Salmonella enterica, Pseudomonas aeruginosa, Citrobacter freundii, Morganella morganii, Enterococcus faecalis and E. faecium, Neisseria gonorrohoae, Staphylococcus aureus, coagulase-negative, staphylococci, Streptococcus pneumoniae and Streptococcus pyogenes. Of the infected patients, 36 (21.5%) children were under 16 years and 188 (78.5%) adults were predominately women (58.5%). The susceptibility test showed that all strains but S. aureus were resistant to ampicillin and amoxicillin and ciprofloxacin. Gentamicin, ceftriaxone, and chloramphenicol remain partially active (27% - 80%) against P. mirabilis, E. coli and P. aeruginosa. The resistance is more likely related to strain mutation than to pharmaceutical quality of the antibiotics prescribed. Conclusion: Both data from hospital laboratories and in vitro post-testing findings confirmed the ongoing elevated prevalence of MDR strains in Bukavu. The causes of antibiotic misuse and socio-economic determinants of the phenomenon of resistance should be scrutinized in order to take adequate strategies in the prospective of establishing an effective control system against this threat to overall health. The results of this work on MDR profiles have various implications for the management of infectious diseases. It provides indicators for the surveillance of antimicrobial resistance, practical guidelines for antibiotic susceptibility testing in biomedical laboratories, and guidance for antibiotic therapy.

基于Anubis的Multi-GNSS观测质量评估与可视化表达

为满足Multi-GNSS观测数据质量评估的需求,基于G-Nut/Anubis和MATLAB软件开发了多指标量化与可视化表达程序KMQC。该程序能够显著改善原生Anubis存在的交互性差和可视化性能低等问题,可便捷地输出BDS、GPS和GLONASS等系统的观测质量分析图表。本研究采集了KMCORS中3个基准站2022年7d的RNX3观测数据,对KMQC的性能进行测试。结果表明,该程序可高效输出CORS站的Multi-GNSS观测数据评估结果,并能够通过各型图表进行直观表达。因此,该软件能够显著降低多元GNSS观测数据的质量检核难度,可有效辅助质检部门及时监控CORS系统的运行健康度。

基于长短时记忆神经网络的Multi-GNSS卫星钟差建模预报

针对卫星钟差预报中二次多项式模型存在易受噪声干扰、预报精度不高的问题,构建一种基于长短时记忆神经网络的multi-GNSS卫星钟差预报模型,并分析不同卫星系统、不同钟类型基于不同建模方案的模型精度。为验证该模型的有效性和可行性,利用LSTM模型、QP模型、QP-LSTM模型分别基于12 h和24 h钟差序列进行建模,预报1 h、3 h、6 h、12 h钟差。结果表明,LSTM模型建模24 h、预报1 h精度最高。multi-GNSS卫星钟差LSTM预报模型中Galileo系统精度最高,其次为BDS-2系统和GPS系统,GLONASS系统精度最低,精度分别为0.018 ns、0.069 ns、0.133 ns、0.242 ns。不同原子钟预报精度不同,氢原子钟预报精度优于铷原子钟、铯原子钟。LSTM神经网络模型预报精度相较于QP-LSTM模型提升27%,相较于QP模型提升36%。