Knowledge, Attitudes and Practices Relating to HIV among Motorbike Taxi Drivers in Parakou, Benin, in 2021

Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect@ applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

OPTIMIZATION OF AIRPORT TAXIING PLANNING DURING CONGESTED HOURS BASED ON IMMUNE CLONAL SELECTION ALGORITHM

In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.

Green Aircraft Taxiing Strategy Based on Multi-scenario Joint Optimization

The issue of green aircraft taxiing under various taxi scenarios is studied to improve the efficiency of aircraft surface operations and reduce environmental pollution around the airport from aircraft emissions.A green aircraft taxi programming model based on multi-scenario joint optimization is built according to airport surface network topology modeling by analyzing the characteristics of aircraft operations under three different taxiing scenarios:all-engine taxi,single-engine taxi,and electronic taxi.A genetic algorithm is also used in the model to minimize fuel consumption and pollutant emissions.The Shanghai Pudong International Airport is selected as a typical example to conduct a verification analysis.Compared with actual operational data,the amount of aircraft fuel consumption and gas emissions after optimization are reduced significantly through applying the model.Under an electronic taxiing scenario,fuel consumption can be lowered by 45.3%,and hydrocarbon(HC)and carbon dioxide(CO)emissions are decreased by 80%.The results show that a green aircraft taxiing strategy that integrates taxiway optimization and electronic taxiing can effectively improve the efficiency of airport operations and reduce aircraft pollution levels in an airport′s peripheral environment.

4-D Trajectory Prediction and Dynamic Planning of Aircraft Taxiing Considering Time and Fuel

Most of the traditional taxi path planning studies assume that the aircraft is in uniform speed,and the optimization goal is the shortest taxi time.Although it is easy to solve,it does not consider the changes in the speed profile of the aircraft when turning,and the shortest taxi time does not necessarily bring the best taxi fuel consumption.In this paper,the number of turns is considered,and the improved A*algorithm is used to obtain the P static paths with the shortest sum of the straight-line distance and the turning distance of the aircraft as the feasible taxi paths.By balancing taxi time and fuel consumption,a set of Pareto optimal speed profiles are generated for each preselected path to predict the 4-D trajectory of the aircraft.Based on the 4-D trajectory prediction results,the conflict by the occupied time window in the taxiing area is detected.For the conflict aircraft,based on the priority comparison,the waiting or changing path is selected to solve the taxiing conflict.Finally,the conflict free aircraft taxiing path is generated and the area occupation time window on the path is updated.The experimental results show that the total taxi distance and turn time of the aircraft are reduced,and the fuel consumption is reduced.The proposed method has high practical application value and is expected to be applied in real-time air traffic control decision-making in the future.

Control Research for a Small Fixed-Wing UAV DuringGround Taxiing

Ground taxiing is the key process of take-off and landing for a tricycle-undercarriage unmanned aerial vehicle( UAV). Nonlinear model of a sample UAV is established based on stiffness and damping model of landing gears and tires taken into account. Then lateral nonlinear model is linearized and state space equations are deduced by using nose wheel and ruder as inputs and lateral states as outputs. Adaptive internal model control( AIMC) is proposed and applied to lateral control based on decoupled and linearized dynamic model during ground taxiing process. Different control strategies are analyzed and compared by simulations,and then a combined control strategy of nose wheel steering with holding and rudder control is given. Hardware in loop simulations( HILS) proves the validity of the controller designed.

Taxi origin and destination demand prediction based on deep learning:a review

Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.

网约车平台算法个性化定价、乘客履约率及其监管对策

网约车平台算法个性化定价产生了复杂的市场影响,相比传统出租车服务,网约车乘客违约率达30%左右,因此算法个性化定价对乘客违约率的影响机制以及乘客是否履约的关键特征值得探索。文中尝试运用矩形Hotelling模型建立算法个性化定价与乘客违约率的因果关联机制,以两个网约车平台之间的Stackelberg博弈模型揭示歧视性定价、乘客违约率与平台之间竞争强度的关系。进一步运用网约车平台订单大数据,以Bhattacharyya距离、提升决策树及改进拉斯维加斯方法(包裹法)等归纳学习工具对网约车平台百万量级订单进行数据挖掘,找出决定乘客是否履约的关键特征。分析结果表明,平台进行个性化定价时乘客的最终消费选择主要取决于价格因素;而改进车辆匹配、派单策略及减少乘客候车时间可显著提高订单履约率。研究结果对网约车平台完善定价及运营策略以维持双边市场用户数量、保证平台持续成功运营具有重要参考价值,也为反垄断部门干预平台个性化定价提供了理论依据。

Higher Plasma Potassium Level Reduces 10-Year Cardiovascular Disease Risk Predicted by the Framingham Risk Score among Taxi-Motorbike Drivers Residing and Working in Cotonou, Benin

Hypertension, obesity, smoking, dyslipidemia, and type 2 diabetes (T2D) are the major risk factors for developing cardiovascular diseases (CVD). Recent studies revealed that taxi-motorbike drivers (TMDs) in Cotonou had higher rates of CVD risk factors, but their impacts on cardiovascular events have rarely been studied. The Framingham risk score (FRS) is an algorithm that considers CVD risk factors and estimates the risk of developing CVD in the next 10 years. Our objectives were to assess the 10-year CVD risk predicted by the FRS, and to examine the relationships of 10-year CVD risk with plasma iron and potassium levels among TMDs. We included 134 TMDs (22 - 59 years old) who had no prior diagnosis of CVD or T2D, and not taking medications affecting iron and potassium homeostasis. Conventional cardiovascular risk factors were used to calculate the 10-year CVD risk, which was categorized as low (20%). FRS > 2%, which corresponded to the 75th percentile of FRS distribution in our study population, was used as a cut-off value to classify participants into two groups. Plasma iron and potassium levels were segregated into tertiles and their associations with 10-year CVD risk were quantified by multivariate-adjusted logistic regression to calculate the odd ratios (ORs) to being above the 75th percentile of 10-year CVD risk with the corresponding 95% confidence intervals (CIs). We found that 62.0% of participants had at least one of cardiovascular risk factors. Approximately 97.8% of TMDs had 10-year CVD risk 4.8 mmol/L led to an 83% risk reduction of having 10-year CVD risk > 2% (OR = 0.17, 95% CI: 0.04 - 0.82, P = 0.027). In conclusion, our findings showed that high plasma potassium levels associate with reduced 10-year CVD risk among TMDs. Interventions focused on monitoring of plasma potassium, particularly in those with existing cardiovascular risk factors, may help prevent CVD.

时频电磁法在巴什布拉克-乌泊尔构造带深部结构勘探中的应用

利用时频电磁数据资料,并结合测井资料约束提高电磁反演结果的分辨率,探讨塔西南西部地区巴什布拉克构造带和乌泊尔构造带深层电性结构及其油气分布区域。该区域地形、浅地表形态以及深层地质结构都较为复杂,无法得到有效的地震资料,严重制约了目标层的油气勘探。时频电磁具有勘探深度大,信噪比高,不受高阻屏蔽的优点。通过对巴什布拉克-乌泊尔区域时频电磁资料进行处理解释,有效识别了该区域的主要断裂及褶皱。南北向和西北向的反演电阻率剖面和极化率剖面,展示了塔西南凹陷同时受南天山和西昆仑山双向挤压的电性结构特征,两侧分别形成逆冲推覆体,呈对冲构造形式,覆盖于塔西南凹陷之上,两侧推覆体发育多排逆冲断层,呈叠瓦状分布,其南侧为乌泊尔构造带,北侧为巴什布拉克构造带,与盆地呈现明显的断层接触关系,南北两侧为区域性大断裂。

考虑乘客感知效用的出租车停靠站选址模型

为了解决城市出租车停靠站布局的合理性问题,对出租车GPS数据进行处理并将乘客出行需求点进行聚类,得到出租车停靠站备选点以及乘客出行需求点。基于出租车停靠站服务能力和距离影响因素,结合竞争选址理论构建出租车停靠站对乘客的感知效用函数。在此基础上,以覆盖乘客出行需求量最大为目标,建立考虑乘客感知效用的出租车停靠站选址模型,以确定出租车停靠站的最佳位置。根据问题特征设计近似算法A进行求解,在求解大规模算例时,与遗传算法、萤火虫算法相比,近似算法求解精度提高0.91%,求解时间降低约97%。以西安市莲湖区局部区域为例进行实例分析,算法求解近似比为1.64,说明算法的实际使用效果较好。

基于AM-LSTM的飞行区航空器滑行轨迹预测与冲突识别

为解决航空器点源定位难以有效预测而引发冲突风险愈来愈多的问题,构建基于注意力机制(AM)和长短期记忆网络(LSTM)的时间序列轨迹预测模型AM-LSTM,预测未来短时间内飞行区航空器的瞬时点源位置;在此基础上,根据航空器型号和滑行航向对其进行轮廓扩展,以航空器速度作为安全距离权重,通过射线法实现轮廓冲突的判定;并以乌鲁木齐地窝堡机场为例进行验证,利用训练完成的轨迹预测模型预测飞行区航空器滑行轨迹,以识别航空器轮廓间的滑行冲突。结果表明:AM-LSTM预测模型能够准确预测飞行区航空器运动轨迹。未来3 s内轨迹位置预测的平均位移误差为1.05 m,轨迹点位置预测精准性可达94.37%,故能在轨迹预测的基础上精确识别滑行冲突风险,有利于保障飞行区的安全运行。

飞机牵引滑行工况下前起落架疲劳寿命仿真分析

为使机场运行更加高效经济,提出一种由牵引车将飞机高速牵引滑行至跑道起飞端的新模式。在牵引滑行工况下,前起落架要承受长时间长距离的高速重载荷牵引,同时受牵引车的加速和制动特性的影响,可能导致前起落架疲劳寿命降低。于是,针对飞机前起落架在该工况下的载荷分析以及疲劳寿命验证十分重要。通过Adams软件建立多体动力学模型,模拟真实工况,对不同牵引滑行速度下的牵引车-飞机系统仿真,获取到前起落架的载荷谱信息。结合前起落架有限元模型静力学分析和载荷谱,通过频域寿命分析法,在nCode软件中计算得到前起落架的疲劳寿命。结果表明,高速牵引滑行工况产生的大载荷加剧前起落架疲劳损伤,疲劳损伤从减震支柱凸耳向下阻力臂延展,疲劳寿命缩短3个数量级,需要对前起落架下阻力臂进行结构优化。本文中的研究可以为新牵引滑行模式下前起落架的疲劳寿命结构优化提供参考。

进港航班滑入时间预测

准确预测进港航班滑入时间对合理调配航班保障资源和提高机场场面运行效率具有重要意义,可有效克服各大机场粗放式预测航班进港时刻的不足,为此提出一种基于机器学习模型的滑入时间预测方法。以首都机场为具体研究对象,分析进港航班滑入时间的影响因素并构建特征集;将线性回归、K-最近邻、支持向量机、决策树、随机森林和梯度提升回归树6种在滑出时间预测方面得到广泛应用的机器学习模型用于进港航班滑入时间预测。研究结果表明:在误差范围±3 min内6种机器学习模型的预测精度均超过90%,表明特征集的构建和模型的选择是有效的;综合预测性能与模型拟合评估结果,梯度提升回归树模型的预测效果最好;在梯度提升回归树模型上场面流量特征的贡献度最大,新引入的跨区特征对预测模型的贡献度超过了大部分传统特征。

基于Q-Learning的航空器滑行路径规划研究

针对传统算法规划航空器滑行路径准确度低、不能根据整体场面运行情况进行路径规划的问题,提出一种基于Q-Learning的路径规划方法。通过对机场飞行区网络结构模型和强化学习的仿真环境分析,设置了状态空间和动作空间,并根据路径的合规性和合理性设定了奖励函数,将路径合理性评价值设置为滑行路径长度与飞行区平均滑行时间乘积的倒数。最后,分析了动作选择策略参数对路径规划模型的影响。结果表明,与A*算法和Floyd算法相比,基于Q-Learning的路径规划在滑行距离最短的同时,避开了相对繁忙的区域,路径合理性评价值高。

基于网格划分的常发性拥堵区域识别及演化模式分析

为准确识别常发性拥堵区域,分析其拥堵传播方向,疏通拥堵源头和阻断拥堵传播路径,提出一种基于网格模型和Fuzzy Core DBSCAN算法的常发性拥堵区域识别及演化模式分析方法。首先,对城市路网进行网格化处理,结合出租车GPS数据综合分析网格内车辆轨迹数量和轨迹加权平均速度,构建网格内部交通拥堵状态判别模型。然后,利用结合模糊集合论与DBSCAN算法扩展得到的Fuzzy Core DBSCAN算法识别常发性拥堵区域,选取拥堵规模、拥堵传播方向(即两个相邻网格之间的传播次数和传播概率)和关键网格3个指标来分析常发性拥堵的演化模式。最后,以黑龙江省哈尔滨市二环路范围内网格区域为例进行实证分析,结果表明:所识别出的常发性拥堵区域关键网格G(14,13)一周内拥堵传播到相邻网格共85次,其中27次(以31.8%的概率)沿西大直街向北传播至网格G(15,14);与百度地图开放平台数据相比,基于网格模型的拥堵判别结果准确率达85%;基于Fuzzy Core DBSCAN算法识别分析出的常发性拥堵区域拥堵演化模式与百度地图开放平台路况时变过程吻合。这表明融合Fuzzy Core DBSCAN算法与网格模型可有效识别常发性拥堵区域中关键网格的拥堵传播方向,能为制定科学合理的缓堵方案提供支撑。

基于图卷积神经网络的滑行时间预测研究

为准确预测滑行时间,提出一种基于机场场面运行态势演变的图卷积神经网络预测方法。首先,根据机场场面航空器时空分布情况,从路段流量、路段密度、路段速度等多角度构建交通态势指标体系;其次,利用主成分分析法对指标进行降维处理并利用K-means算法实现对机场场面路段的态势等级划分,绘制机场场面时空分布热力图;最后,利用图卷积神经网络(GCN)结合门控循环单元(GRU)来获取场面路段特征数据的时空特征,将GRU作为解码器预测输出滑行时间。以深圳宝安国际机场AirTOP仿真数据为例,对所提出的方法进行了分析和验证,并获得了符合预期的预测结果。实验结果表明,该方法在预测滑行时间方面具有有效性。

基于轨迹相似性的GPS数据快速地图匹配方法研究

提出1种基于网格划分的轨迹相似性匹配技术,能快速高效地应用于海量车辆GPS数据与路网的匹配。首先以实际交叉口为节点构建泰森多边形,将路网划分为子区域;其次,在子区域,按照拓扑关系计算出路网节点之间的所有有效路径轨迹,以欧式距离作为相似度指标,对有效路径轨迹和车辆GPS轨迹进行相似度测算。该方法大大简化最短路径的重复搜索和计算,同时按照拓扑规则增加了轨迹匹配的方向性约束。本文已应用在某市连续3个月的出租车GPS匹配当中,为城市交通改善提供支撑。

木聚糖酶TAXI型抑制蛋白的研究进展

木聚糖酶是一种重要的半纤维素酶,广泛用于饲料工业。近年来在小麦中发现一类能抑制木聚糖酶活性的蛋白,这类抑制蛋白可能会对木聚糖酶的应用效果产生影响。目前发现在不同的谷物中共存在2种类型的木聚糖酶抑制蛋白,根据其结构的不同,分为TAXI型(triticum aestivum xylanase inhibitor)和XIP型(xylanase inhibi-tor protein)。本文主要简述了TAXI型抑制蛋白的分子结构、抑制特性和活性影响因素等方面的研究进展,对于深入研究木聚糖酶及其活性影响因子,充分开发利用我国饲料资源和促进畜牧业生产可持续发展具有重要现实意义。

Research on development of urban taxi supply based on influence factors classification

In order to determine the regulations of the development of taxi supply under entry regulations in Chinese cities, an improved neural network model is applied to find the particular years when the government artificially puts new taxis into the market, and then extract the political influence from the taxi supply. The model is also utilized to study the relationships between the adjusted taxi supply and non-policy factors. A case study of Nanjing city is conducted. The results show that 2001 and 2007 are the particular years that the Nanjing government artificially put new taxis into its taxi market, which is in accordance with the five-year plan of China and the local development plans. The results also show that the improved neural network model has a good performance in expositing the evolution of adjusted taxi supply related to non-policy factors.