TL-FCM: A hierarchical prediction model based on two-level fuzzy c-means clustering for bike-sharing system

In recent years, shared bikes have gradually emerged into public life as a new way to travel and helped solve the last-mile problem of residents’ travel. While this development has brought convenient travel to users, a series of problems exist, a prominent one is the uneven distribution of bikes at each shared bike station. Accurately predicting bike usage in a bike-sharing system can help solve this problem. In this paper, we investigate how to improve the accuracy of predicting the usage of bikes in bike-sharing system. First, considering both geographic location information of shared bike stations and the migration trend of bikes between stations, we design a two-level fuzzy c-means clustering algorithm to cluster shared bicycle stations into groups, which can better capture the connection between shared bicycle stations and improve the clustering accuracy of shared bicycle sites, then, we combine the two-level fuzzy c-means clustering algorithm with the multi-similarity reference model to predict the usage of bikes, which can significantly improve the accuracy of the forecast. To evaluate the performance of our model, we validate our model in the New York Bike-Sharing System. The results shows that our model obtained significantly better results than other models.

     

A system for head-neck rehabilitation exercises based on serious gaming and virtual reality

Acute and chronic neck pain are common medical conditions, and the treatment typically includes physical therapy involving daily exercises. Insufficient motivation of people afflicted with neck pain to adhere to the prescribed exercise regimen may delay their recovery. Accordingly, in this work, we propose a system that motivates the users to perform neck exercises by engaging them in a serious exergame within virtual reality (VR) environment. The system measures the users’ neck movements via a few static and dynamic kinematic tests and a novel VR serious game, tailored to the neck range of motion of each individual user. The game is designed to make the users perform rehabilitative neck movements according to the prescribed exercise regimen while playing. The analysis of acquired data from VR hardware provides insight into flexibility of the neck during head movements and overall neck kinematics, which is valuable for assessment of pain-related stiffness, as well as for progress monitoring. In a user study performed with the proposed system and the Oculus Rift DK2 VR headset, we show that the users find exercising more interesting and engaging when using the proposed system, and that introducing visually rich VR environments makes the users more motivated to continue exercising.     

The effects of shoulder posture on neck and shoulder musculoskeletal loading and discomfort during smartphone usage

While using their smartphone, users tend to adopt awkward neck and shoulder postures for an extended duration. Such postures impose the risk of MSDs on those body parts. Numerous studies have been undertaken to examine neck posture; however, few studies have investigated shoulder postures. This study examined various shoulder postures during smartphone use and their effect on neck and shoulder kinematics, muscle loading, and neck/shoulder discomfort. Thirty-two asymptomatic young adult smartphone users randomly performed texting tasks for 3 min at four different shoulder flexion angles (15°, 30°, 45°, and 60°), while maintained a neck posture in the neutral position (0° neck flexion angle). Measures were taken of neck and shoulder muscle activity of the cervical erector spinae (CES), anterior deltoid (AD), upper trapezius (UT) and lower trapezius (LT), and kinematic data (angle, distance and gravitational moment). Results showed AD and LT muscle activity significantly increased when the shoulder flexion angle increased with an opposite effect on CES and UT. A recommended shoulder posture was identified as 30° flexion, as this yielded the best compromise between activation levels of the four muscles studied. This angle also induced the lowest neck/shoulder discomfort score. The findings suggest smartphone users hold their device at approximately 30° shoulder flexion angle with their neck in a neutral posture to reduce the risk of shoulder and neck musculoskeletal disorders when smartphone texting.Relevance to industrySmartphone use in the manufacturing and service industries is an integral part of work and useful means of communication tool. Awkward postures during extensive smartphone use impose an increased risk of both neck and shoulder musculoskeletal disorders. Shoulder flexion angles need consideration when making recommendations about safe work postures during smartphone use.     

Performance and usability tradeoff in a cluster display wall

Cluster-based display walls provide cost-effective and scalable display infrastructures with high resolution and large display area, making them suitable for a wide range of high-resolution applications. As a consequence, a wide offer of new cluster display-wall platforms together with their software frameworks have been proposed. Their performance and the satisfaction of their users have aroused the interest of some researchers. This work is focused on the Liquid Galaxy cluster display wall originally built to run Google Earth to create an immersive experience for the users. In this paper, the Liquid Galaxy is benchmarked running Google Earth, as a representative interactive application with high performance requirements, in different configurations and environments, to test the satisfaction, effectiveness and efficiency. Thus, we wish to know how users react to the system performance. In order to do so, we use a performance metric defined in previous research to relate the performance of the system with the user’s perception. Taking into account the trend of this metric in the experimentation, we model the behavior of the system in a way that the performance for any given visualization cluster running Google Earth could be predicted by using a reference system.     

基于用户激励的共享单车调度策略

针对共享单车的调度问题,在考虑预算限制、用户最大步行距离限制、用户时空需求以及共享单车分布动态变化的情况下,提出一种用户激励下的共享单车调度策略,以达到提高共享单车平台长期用户服务率的目的。该调度策略包含任务生成算法、预算分配算法和任务分配算法。在任务生成算法中,使用长短期记忆（LSTM）网络预测用户未来的单车需求量;在预算分配算法中,采用深度策略梯度（DDPG）算法来设计预算分配策略;任务分配完预算后,需要将任务分配给用户执行,因此在任务分配算法中使用贪心匹配策略来进行任务分配。基于摩拜单车的数据集进行实验,并把所提策略分别与无预算限制的调度策略（即平台不受预算限制,可以使用任意金钱激励用户将车骑行至目标区域）、贪心的调度策略、卡车拖运下的调度策略以及未进行调度的情况进行对比。实验结果表明,与贪心调度策略和卡车托运下的调度策略相比,用户激励下的共享单车调度策略能有效提高共享单车系统中的用户服务率。     

Shoulder and neck muscle activities during typing with articulating forearm support at different heights

Use of forearm support is known to reduce physical stress of computer users, but research about how to properly position the forearm support is insufficient. This study was aimed to determine whether the height of forearm support influences muscular loads during typing. Twenty four subjects performed a typing task with a pair of articulating forearm support at three different heights as well as without any support, while shoulder, neck and forearm muscle activities and posture data were recorded. Typing with the support at resting elbow height produced significantly (p < 0.05) lower shoulder and neck muscle activities than that of no support condition. Typing with the support at heights higher than the resting elbow height produced significantly greater shoulder and neck muscle activities compared to the no support condition. Results suggest that forearm support can help computer users lessen physical stress in typing, but only when the supports are positioned at resting elbow height.

Practitioner Summary: Use of forearm support is known to alleviate physical stress of PC users in computer works such as typing. This experimental study addressed the importance of proper positioning of forearm support by comparing neck and upper extremity muscle activities between conditions with varying heights of forearm support in keyboard typing.     

Neck/shoulder pain,habitual spinal posture and computer use in adolescents: the importance of gender

Neck/shoulder pain is a common complaint, with evidence suggesting rates in adolescence have increased in line with increased computer use. The study aimed to examine the influence of gender on relationships between computer use, habitual posture and neck/shoulder pain. Adolescents (n = 1483) participating in the 14 year follow-up of the Raine Study cohort were surveyed for computer use, habitual sitting posture and neck/shoulder pain. Females used computers less than males (52% vs. 45% used for up to 7 h per week). Females sat much more upright than males with greater anterior pelvic tilt (9.4° vs. 0.4°). Females reported a higher 1 month prevalence of neck/shoulder pain (34.7%) than males (23.1%). A multivariate model showed neck/shoulder pain risk was increased in females (OR 2.61, 95% CI 1.70–4.00) and with computer use (OR 1.19, CI 1.01–1.40). Computer use is related to neck/shoulder pain and posture in adolescents but this relationship is different in boys and girls.

Statement of Relevance: This study showed the confounding effect of gender on the relationships among computer use, posture and neck/shoulder pain and thus the need to consider genders separately in research and practice aiming to optimise young people's use of computers.     

云模型在防止Google Earth地标泄密中的应用

Google Earth给使用者带来方便的同时,也对传统信息安全带来了冲击,应当引起高度注意。设计了Google Earth使用管理软件,其中采用云模型,设立中心服务器,预设了重要监控点的坐标和云参数;当用户点击某个位置,会在中心服务器上生产云图,靠云滴的密集程度来决定是否允许用户标注的地标信息出现在Google Earth上。实验表明,该方法很好地控制了GoogleEarth上重点地点的信息标注,解决了Google Earth的"地标"泄密问题。     

GRASP with path relinking for the selective pickup and delivery problem

Bike sharing systems are very popular nowadays. One of the characteristics is that bikes are picked up from some surplus bike stations and transported to all deficit bike stations by a repositioning vehicle with limited capacity to satisfy the demand of deficit bike stations. Motivated by this real world bicycle repositioning problem, we study the selective pickup and delivery problem, where demand at every delivery node has to be satisfied by the supply collected from a subset of pickup nodes. The objective is to minimize the total travel cost incurred from visiting the nodes. We present a GRASP with path-relinking for solving the described problem. Experimental results show that this simple heuristic improves the existing results in the literature with an average improvement of 5.72% using small computing times. The proposed heuristic can contribute to the development of effective and efficient algorithms for real world bicycle reposition operations.     

Large neighborhood search for the bike request scheduling problem

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.     

A national cross-sectional study in the Danish wood and furniture industry on working postures and manual materials handling

Musculoskeletal disorders constitute a major problem in the wood and furniture industry and identification of risk factors is needed urgently. Therefore, exposures to different work tasks and variation in the job were recorded based on an observation survey in combination with an interview among 281 employees working in wood working and painting departments. A questionnaire survey confirmed high frequencies of symptoms from the musculoskeletal system: The one-year prevalence of symptoms from the low back was 42% and symptoms from the neck/shoulder was 40%. The exposure was evaluated based on: (1) classification of work tasks, (2) work cycle time, (3) manual materials handling, (4) working postures, and (5) variation in die job. Among the employees 47% performed feeding or clearing of machines, 35% performed wood working or painting materials, and 18% performed various other operations. Among the employees 20% had no variation in their job while 44% had little variation. Manual materials handling of 375 different burdens was observed, which most often occurred during feeding or clearing of machines. The weight of burdens lifted was 0·5-87·0 kg, where 2% had a weight of more than 50 kg. Among the lifting conditions 30% were evaluated as implying a risk of injury. An additional risk factor was the high total tonnage lifted per day, which was estimated to range from 132 kg to 58 800 kg. Working postures implied a risk of injury due to prolonged forward and lateral flexions of the neck, which was seen most frequently during wood working or painting materials. These data substantiate the finding that work tasks mainly during feeding or clearing of machines imply a risk of injury to the low back and a risk of injury to the neck and shoulder area mainly during wood working or painting materials. Optimal strategies for job redesign may be worked out by using these data in order to prevent occupational musculoskeletal disorders.     

Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals

We develop an autonomous system to detect and evaluate physical therapy exercises using wearable motion sensors. We propose the multi-template multi-match dynamic time warping (MTMM-DTW) algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session. While allowing some distortion (warping) in time, the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates, based on DTW distance. It can detect and classify the exercise types, and count and evaluate the exercises as correctly/incorrectly performed, identifying the error type, if any. To evaluate the algorithm's performance, we record a data set consisting of one reference template and 10 test executions of three execution types of eight exercises performed by five subjects. We thus record a total of 120 and 1200 exercise executions in the reference and test sets, respectively. The test sequences also contain idle time intervals. The accuracy of the proposed algorithm is 93.46% for exercise classification only and 88.65% for simultaneous exercise and execution type classification. The algorithm misses 8.58% of the exercise executions and demonstrates a false alarm rate of 4.91%, caused by some idle time intervals being incorrectly recognized as exercise executions. To test the robustness of the system to unknown exercises, we employ leave-one-exercise-out cross validation. This results in a false alarm rate lower than 1%, demonstrating the robustness of the system to unknown movements. The proposed system can be used for assessing the effectiveness of a physical therapy session and for providing feedback to the patient.     

Muscle Activity and Comfort Perception on Neck,Shoulder, and Forearm While Using a Tablet Computer at Various Tilt Angles

Tablet computers have become ubiquitous. There is a serious risk that using tablets may lead to musculoskeletal disorders. This research aims to investigate, for tablet computer users, the musculature load and comfort perception of the engaged upper extremity for three angles of viewing and common task types performed at a computer workstation. Thirty healthy adults were recruited. A 3 × 2 repeated experimental design with tilt angle (22.5°, 45°, and 67.5° from horizontal) and task type (movie watching vs. game playing) was employed. The muscular activity of the upper extremity was assessed by electromyography measurement. Subjective comfort ratings were collected using the visual analogue scale. The results showed that when tablets were mounted at a high tilt angle (67.5°), neck muscle activity was low; however, when the tablet computer was mounted at a low tilt angle (22.5°), shoulder forward flexion activity was low, particularly during the game-playing task. This article suggests that users who feel musculoskeletal discomfort in the neck area increase the angle of their tablet computers to decrease neck stress and that users who have musculoskeletal discomfort in the shoulder area position the tablet computer at a lower tilt angle to decrease shoulder stress.     

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Nowadays, clusters of interconnected workstations have become a common solution for powering large composite displays, or “cluster display walls”, to visualize high resolution images. Our paper is focused on analyzing a specific cluster display wall developed by Google, named Liquid Galaxy, made up of heterogeneous commodity hardware with different degrees of heterogeneity, running master-slave (Google Earth) and client-server (Quake III Arena) multimedia applications. With this in mind, we define and test different scenarios, representing the behavior of many kinds of users. Our results show that the CPU, memory and network are good enough to execute the client-server application, while, depending on the user behavior, the external network constitutes the bottleneck of the system in Google Earth. So, the master-slave application has focused our attention. Likewise, in order to analyze the users’ point of view when interacting with Google Earth in the Liquid Galaxy, we define a new metric, named Visualization Rate (VR), which enables a relationship to be established between the user experience and the platform performance. In order to set the minimum acceptable value of the VR parameter according to users perception, we carried out different tests with real users. Then, this minimum threshold was compared with the VR value obtained from the automated benchmarking performed afterwards on clusters with different heterogeneity degrees. Finally, we analyzed the VR trend when the Liquid Galaxy is scaled from 3 up to 8 nodes in both the homogeneous and heterogeneous architectures to study the scalability of the system.     

基于数据场聚类的共享单车需求预测模型

共享单车系统日益普及,积累了海量的出行轨迹数据.在共享单车系统中,用户的借车和还车行为是随机的,且受天气、时间等动态因素影响,使得共享单车调度不平衡,影响单车用户体验,并给运营商造成巨大经济损失.提出了新型基于站点聚类的共享单车需求预测算法,通过构建单车转移网络计算站点活跃度,充分考虑站点地理位置和单车转移模式因素,基...     

Perceived musculoskeletal symptoms among dental students in the clinic work environment

Musculoskeletal symptoms are a major concern among dental practitioners. Dental students perform the same clinical tasks as dentists in private practice, yet only recently has scientific evidence suggested a relationship between the tasks and musculoskeletal symptoms. This study investigates the clinical tasks that place students at physical risk and the relationship between the tasks and musculoskeletal symptoms. Student perceptions of physical symptoms were established with a questionnaire identifying variables on general health, tasks, physical demands, workload and environment. A total of 61% (358/590) reported that during the past year they experienced musculoskeletal symptoms related to work at dental school (second year (n = 212), third year (n = 201) and fourth year (n = 177)). Of those students, the neck represented 48%, shoulder 31%, back 44% and hands 20% (p < 0.001). Third year students reported the highest occurrence of symptoms in every body area (p < 0.001). Third and fourth year students reported significant relationships between the occurrence of symptoms and equipment utilisation, work efficiency and general health. Further studies are recommended to evaluate types and levels of potential risk factors. A total of 75% of dental practitioners are at risk for developing work-related musculoskeletal disorders. Results from this study have found that dental students were no exception. Third year dental students reported the highest level of musculoskeletal symptoms with the prevalence of pain in the neck, shoulder and lower back.     

Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees

Conducting quantitative studies on the carbon balance or productivity of oil palm is important for understanding the role of this ecosystem in global climate change. The MOD17 algorithm is used for processing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate the values of gross primary productivity (GPP) and net primary productivity for input to global carbon cycle modelling. In view of the increasing importance of data on carbon sequestration at regional and national levels, we have studied one important factor affecting the accuracy of the implementation of MOD17 at the sub-global level, namely the database of MODIS land cover (MOD12Q1) used by MOD17. By using a study area of approximately 7 km × 7 km (49 MODIS pixels) in semi-rural Johor in Peninsular Malaysia and using Google Earth 0.75 m resolution images as ground data, we found that the land-cover type for only 16 of these 49 MODIS pixels was correctly identified by MOD12Q1 using its 1 km resolution land-cover database. This leads to errors of 24% to 50% in the maximum light use efficiency, leading to corresponding errors of 24% to 50% in the GPP. We show that by using the Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) land-cover database developed by Gong et al., this particular error can be essentially eliminated, but at the cost of using extra computing resources.     

Incidence of self-reported reduced productivity owing to musculoskeletal symptoms: association with workplace and individual factors among computer users

The aim of the present study was to assess the incidence and identify possible risk factors for self-reported reduced productivity owing to musculoskeletal symptoms among computer users. DESIGN: a cohort study with a baseline questionnaire and monthly follow ups during 10 months. METHODS: the study base consisted of 1,283 computer users, 636 men and 896 women. Ergonomists observed workstation characteristics before entering the cohort. Cases were defined as subjects reporting reduced productivity or reporting being on sick leave owing to musculoskeletal symptoms. RESULTS: women had approximately two times the incidence of self-reported reduced productivity owing to symptoms in the neck, shoulder and in the forearm/hand than men. There was no difference in gender for the incidence of self-reported reduced productivity owing to back symptoms. Working overtime and job demands were risk factors for self-reported reduced productivity owing to neck and back symptoms. Physical exercise fewer than 8 times the last month was a risk factor for self-reported reduced productivity owing to neck, shoulder and forearm/hand symptoms. Computer mouse use for more than 0.5 h/day was a risk factor for self-reported reduced productivity owing to shoulder and forearm/hand symptoms. CONCLUSIONS: risk factors for self-reported reduced productivity owing to musculoskeletal symptoms included life style factors, such as overweight and low physical exercise, occupational factors, such as overtime, job demands and computer mouse operating time.     

Balancing bike sharing systems with constraint programming

Bike sharing systems need to be properly rebalanced to meet the demand of users and to operate successfully. However, the problem of Balancing Bike Sharing Systems (BBSS) is a demanding task: it requires the design of optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS problem by means of Constraint Programming (CP). First, we introduce two different CP models for the BBSS problem including two custom branching strategies that focus on the most promising routes. Second, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform an experimental evaluation of our approaches on three different benchmark sets of instances derived from real-world bike sharing systems. We show that our CP models can be easily adapted to the different benchmark problem setups, demonstrating the benefit of using Constraint Programming to address the BBSS problem. Furthermore, in our experimental evaluation, we see that the pure CP (branch & bound) approach outperforms the state-of-the-art MILP on large instances and that the LNS approach is competitive with other existing approaches.     

Google Earth在森林植被营养元素循环研究中的应用

森林植被营养元素循环是森林生态系统分析中的重要参数.将Google Earth应用在其研究过程中具有重要作用.阐述了Google Earth的主要原理和关键技术,重点分析了KML和Google EarthAPI;设计了Google Earth应用的数据处理流程和系统架构;最后实现了基于Google Earth的贵州龙里森林植被营养元素循环研究系统.