Global,regional and national burdens of bipolar disorders in adolescents and young adults:a trend analysis from 1990 to 2019

Background Bipolar disorder is identified as a cause of severe damage to the physical,psychological and social functioning of adolescents and young adults.Aims The aim of this study is to ascertain the trends in the burden of bipolar disorder among individuals aged 10-24 years at global,regional and national levels from 1990 to 2019.Methods The data analysed in this study were from the Global Burden of Diseases 2019.The numbers,rates per 100000 population,average annual percentage changes(AAPCs)of incidence,prevalence and years lived with disability(YLDs)of bipolar disorder are reported at the global,regional and national levels among individuals aged 10-24 years.Global trends by age,sex and Social Development Index(SDI)were further analysed.Results Globally,the incidence of bipolar disorder among adolescents and young adults increased from 79.21 per 100000 population(95%uncertainty interval(Ul):58.13 to 105.15)in 1990 to 84.97 per 100000 population(95%Ul:61.73 to 113.46)in 2019,AAPC 0.24(95%confidence interval(Cl):0.22 to 0.26).In the past three decades,there has been an increase in incidence,prevalence and YLDs in both males and females.The largest increase in incidence between 1990 and 2019 was observed in those aged 20-24 years old from 51.76 per 100000 population(95%Ul:26.81 to 87.20)in 1990 to 58.37 per 100000 population(95%UI:30.39 to 98.55)in 2019;AAPC 0.42(95%Cl:0.38 to 0.47).By the SDI quintile,the largest increase in incidence was observed in the middle SDl;however,the high SDI countries had the highest incidence.Regionally,the largest increase in incidence was observed in southern Latin America.At the national level,the most pronounced increase in the incidence was in Greenland.Conclusions The global increase in incidence among adolescents and young adults between 1990 and 2019 indicates that strategies to improve their mental health still need to be emphasised.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

中国式现代化语境下主流节目IP创设的询唤机制解析——以“家国young貌”系列节目为例

在Web3.0时代,数字信息技术为主流节目IP的交互共享提供了技术支持,但节目内容的泛娱乐化倾向导致部分主流节目叙事传播不畅。文章以中央广播电视总台2023年系列节目“家国young貌”为例,剖析当代主流节目的破圈实践,并结合询唤理论,从话语下沉、仪式沉浸、知行规训三方面提出主流节目IP创设对培植文化自信、建构精神谱系的作用机制。

Mortality from Stroke in Young People in Brazzaville

Introduction: Cerebrovascular accidents (CVA) are an absolute neurovascular emergency and the main cause of mortality and acquired disability in adults. In the Congo, stroke is the leading cause of mortality and the leading cardiovascular emergency, with a hospital frequency of between 49.74% and 56.2%. The aim of the study was to identify the mortality factors associated with stroke in young people in Brazzaville. Patients and Methods: This is a longitudinal analytical study conducted from February to period from February to September 2019 in the neurology, general intensive care and medical emergency departments of the Brazzaville University Hospital (CHUB). All subjects aged 18 - 55 years of completed age, admitted for arterial stroke confirmed by brain imaging, were included. Study variables were: age, gender, socioeconomic level, laterality, time to admission and CT scan, vascular risk factors, history of cardiomyopathy, atrial fibrillation, TIA (transient ischemic attack) or stroke, NIHSS (National Institutes of Health Stroke Scale) score, Glasgow score, blood pressure, temperature, heart rate, occurrence or non-occurrence of complications, blood glucose, creatinine, lipid profile and blood count. Data were analyzed using SPSS 21 software. Descriptive analyses were performed using SPSS 21 software. Results: 103 patients were included in the study, of whom 45 (43.7%) had ischemic stroke and 58 (56.3%) with hemorrhagic stroke. Mortality was high at 29.1% in our study, and mainly concerned hemorrhagic strokes (73.7%). Two-week mortality in our study accounted for 63.33% of total lethality. After simple logistic regression, the factors associated with death within two weeks were age between 40 - 44 years (OR (odds ratio) = 2.95;p = 0.01), hemorrhagic stroke (OR = 1.41;p = 0.07), mass effect (OR = 3.26;p < 0.01), ventricular flooding (OR = 2.86;p < 0.001), Glasgow score (OR = 2.95 (0.92 - 9.43);p = 0.06), NIHSS score on admission > 15 (OR = 5.89 (2.90 - 11.95);p < 0.001) and bronchopulmonary infection (OR = 30, 95 (4.04 - 236.88), p < 0.001). From multivariate logistic regression, only NIHSS score on admission > 15 emerged as a predictor of death within two weeks (OR = 5.89 (2.90 - 11.95);p Conclusion: This study confirms the basic data of the African literature concerning stroke, as several factors were identified as independent factors associated with mortality.

Comment on: Observation of peripheral refraction in myopic anisometropia in young adults

Dear Editor,We read with great interest the recently published article titled“Observation of peripheral refraction in myopic anisometropia in young adults”by Du et al[1].The study conducted at the InEye Hospital of Chengdu University of TCM provides valuable insights into the relationship between anisometropia and peripheral refraction in myopic young adults.We commend the authors for their thorough investigation and adherence to ethical guidelines.While the study contributes significantly to our understanding of myopic anisometropia,we would like to draw attention to some limitations that merit consideration for a comprehensive interpretation of the findings.

Comment on:Recurrence after spontaneous separation of epiretinal membrane in a young woman:a case report

Dear Editor,Herein,we provide a commentary on the recently published article by Zeng QZ and Yu WZ[1].This case report provides interesting novel insights into the recurrence of epiretinal membrane(ERM)following self-separation in a young patient.In addition to the study,we have been investigating spontaneous ERM release for many years and have recently published a related paper[2].

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Acute Coronary Syndrome of Young Subjects at the Luxembourg Mother-Child University Hospital in Bamako

Introduction: Acute coronary syndrome is often the first event of coronary disease of young subjects. Objective: To study sociodemographic, clinical, paraclinical, therapeutic and evolutionary aspects of acute coronary syndrome of young subjects. Patients and Methods: Descriptive cross-sectional study with prospective recruitment from October 01, 2020 to March 31, 2022. Were included all patients admitted for acute coronary syndrome whose age was less or equal to 45 years and who had undergone coronary angiography at the Mother-Child University Hospital on Luxembourg from Bamako. Results: During the study period, we collected 60 patient files out of 198. These 60 patient files met our inclusion criteria. Hospital frequency was 30.30%. Average age of patients was 40.43 ± 3.9 years. Sex ratio M/F was 5.3. Main cardiovascular risk factor was smoking tobacco (23.42%), followed by dyslipidemia (13.92%). Functional signs were dominated by angina 62% followed by dyspnea 25.3%. Persistent ST-segment elevation on electrocardiogram was present in 76.4%. Left ventricular systolic dysfunction was present in 26.5%. Time to first medical contact was more than 12 hours in 62.7% of cases. Radial approach was adopted in 94% of cases. Coronary angiography was pathological in 85% (n = 51) of cases. Lesions were single-vessel disease in 47.1% and culprit artery was anterior interventricular in 51% of cases. Dual anti-platelet aggregation was aspirin and ticagrelor in 91.8% of cases. Angioplasty was performed in all patients who had significant abnormalities at coronary angiography. In-hospital mortality was 3.9%. Conclusion: Acute coronary syndromes exist in young Africans with a male predominance. Main cardiovascular risk factor is smoking tobacco. Coronary lesions are single vessel disease in the majority of case. Most of patients meet medical team after 12 hours. Time to first medical contact is a main challenge in our country.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Alcohol and illicit drugs:prevalence of alcohol and illicit drug use and their predictors in young people from Argentina,Bulgaria,Chile and Romania

Background:Alcohol and illicit drugs(AID)continue to be a major global health concern.Although preventable,AID is linked to millions of deaths annually worldwide.The situation is particularly grave for young people,with AID being a major direct risk factor for disability-adjusted youth life-years lost and death.It further contributes to assaults,road crashes,accidental poisoning,and suicide,leading to long-term issues and public health concerns.Objective:This study aimed at disclosing current AID prevalence data for Argentinian,Bulgarian,Chilean and Romanian youth.It shed light on the predictors of AID in young people from those countries.Method:The study used an online survey to gather data from people aged 18 to 25(n=1,297).The survey was underpinned by the theory of planned behaviour(TPB).Predictors were investigated separately for drinking alcohol and using illicit drugs.Results:Our data revealed that across the four target countries,49%to 90%of the participants drank alcohol,and 8%to 35%used illicit drugs in the past three months.Between 20%and 91%of them intended to drink,and between 8%and 31%intended to use illicit drugs in the following three months.Our TPB model predicted statistically significant(P<0.001)amounts of variance in drinking alcohol(between 61%and 72%)and using illicit drugs(between 20.3%and 74.4%).Intention was consistent in significantly predicting both behaviours.Evidence around the predictive validity of self-efficacy,age and gender was mixed across the investigated countries.Conclusion:This research provided an update on the scarce AID epidemiological data.It also supplied evidence about what theoretically-informed measures might be useful targets of interventions in the case of Argentina,Bulgaria,Chile and Romania.This new knowledge of understanding substance abuse determinants and prevalence may help researchers and practitioners better meet young people's health prevention needs.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Postpartum Family Planning at the Timbuktu Hospital in Mali

Introduction: Postpartum family planning is the prevention of pregnancies during the 12 months following childbirth. Few studies have been devoted to postpartum family planning in Mali. Our work will contribute to reducing unmet need for family planning. Objective: To study the use of contraceptive methods in the postpartum period in the obstetrics and gynecology unit of Timbuktu hospital. Materials and Methods: This was a descriptive and analytical cross-sectional study with prospective collection of data from January 1, 2022 to December 31, 2023. All women who gave birth having chosen and benefited from a contraceptive method were included. The statistical test used was the Fisher test with a significance threshold fixed at 5%. Results: The frequency of contraception in the postpartum period was 17.03%. The average age of clients was 26.14% with extremes of 14 and 45 years. They were paupiparous at 56.4% with an inter-birth interval of less than 12 months at 12.3%. More than half of the counseling (58.5%) was done during postnatal visits. The methods chosen were implants at 48.1%, injectable progestins at 21.3%, intrauterine device at 14.7%, miro-progestin pills at 13.5%, tubal ligation at 1 .4% and condoms at 1%. The regular follow-up rate was 51.1% of cases and 25.6% had no follow-up. Conclusion: The overall rate of postpartum family planning of 17.08% remains low. Improving FP staff skills will reduce unmet needs and contribute to increasing contraceptive prevalence in Timbuktu.

Heuristic Expanding Disconnected Graph:A Rapid Path Planning Method for Mobile Robots

Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Correlates of Mistimed Pregnancy and Unmet Need for Family Planning among Women of Reproductive Age in Sandema, Ghana

Background: Globally, an estimated 80 million unintended pregnancies comprising both mistimed and unwanted pregnancies are recorded yearly. Yet only half of the women at risk of mistimed pregnancy use contraceptives. In developing countries, over 100 million females have unmet need, and national surveys in Ghana indicate 23% unmet need rate. Methods: Using a cross-sectional community-based approach, a sample size of 300 women of reproductive age were selected using multi-step cluster sampling techniques. The study was quantitative, using structured interviewer-administered questionnaires. Results: Two-third (66%) of the women in reproductive age still had unmet need, 71% were currently pregnant, and more than a third (36%) confirmed ever having a mistimed pregnancy. Fifty-three percent (53%) of the women confirmed never communicating with their partners on family planning issues, a little below half (45%) took their own health care decisions. Seventy nine percent (79%) ever received family planning services from a health professional. Factors related to unmet needs included mistimed pregnancy, level of education, preferred birth/pregnancy interval, communication between partners and the autonomy to spend self-earnings. Conclusion: Considering that high rates of unmet need results in mistimed pregnancy, improved policies around the influence of unmet need on mistimed pregnancies are needed.

Trajectory planning for multi-robot coordinated towing system based on stability

Given the unconstrained characteristics of the multi-robot coordinated towing system,the rope can only provide a unidirectional constraint force to the suspended object,which leads to the weak ability of the system to resist external disturbances and makes it difficult to control the trajectory of the suspended object.Based on the kinematics and statics of the multi-robot coordinated towing system with fixed base,the dynamic model of the system is established by using the Newton-Euler equations and the Udwadia-Kalaba equations.To plan the trajectories with high stability and strong control,trajectory planning is performed by combining the dynamics and stability of the towing system.Based on the dynamic stability of the motion trajectory of the suspended object,the stability of the suspended object is effectively improved through online real-time planning and offline manual adjustment.The effectiveness of the proposed method is verified by comparing the motion stability of the suspended object before and after planning.The results provide a foundation for the motion planning and coordinated control of the towing system.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.