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Data collection using Mobile Sink (MS) is one of the best approaches to address the hot spot issue resulting from multihop data collection and extend the lifetime of Wireless Sensor Networks wherein the MS tours a few specific locations called sojourn locations that serve as data collecting points (DCPs). The best choice of these locations is an NP-hard problem, and the optimum or nearly optimum results can be achieved by applying meta-heuristic optimization methods. It is challenging to create an effective algorithm that allows MS for data collection irrespective of the network topology changes caused by node failures since these changes affect node coverage, data transmission, and network lifespan. Hence, an effort must be made to ensure a trade-off between the MS trajectory and the number of hops. Different MS-based techniques have been proposed; however, most of them fell short of addressing the above goals. With this inspiration, we propose iCapS-MS, which is an integrated approach that utilizes an improved Capuchin Search Algorithm (iCapSA) to determine the best set of DCPs and enhanced Ant Colony Optimization (e-ACO)-based MS trajectory design. Using iCapSA, the best DCPs are selected such that almost every node is served in one-hop communication with the shortest feasible hop distance and minimum coverage intersection between DCPs. The best trajectory for MS is established using e-ACO method. The results demonstrate that iCapS-MS outperforms existing methods based on several performance metrics. [ABSTRACT FROM AUTHOR]

This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers' use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants' informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants' demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets. [ABSTRACT FROM AUTHOR]

The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol's main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland's busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols. [ABSTRACT FROM AUTHOR]

Introduction and Objective: The ConcePTION project aims to improve the way medication use during pregnancy is studied. This includes exploring the possibility of developing a distributed data processing and analysis infrastructure using a common data model that could form a foundational platform for future surveillance and research. A prerequisite would be that data from various data access providers (DAPs) can be harmonised according to an agreed set of standard rules concerning the structure and content of the data. To do so, a reference framework of core data elements (CDEs) recommended for primary data studies on drug safety during pregnancy was previously developed. The aim of this study was to assess the ability of several public and private DAPs using different primary data sources focusing on multiple sclerosis, as a pilot, to map their respective data variables and definitions with the CDE recommendations framework. Methods: Four pregnancy registries (Gilenya, Novartis; Aubagio, Sanofi; the Organization of Teratology Information Specialists [OTIS]; Aubagio, Sanofi; the Dutch Pregnancy Drug Register, Lareb), two enhanced pharmacovigilance programmes (Gilenya PRIM, Novartis; MAPLE-MS, Merck Healthcare KGaA) and four Teratology Information Services (UK TIS, Jerusalem TIS, Zerifin TIS, Swiss TIS) participated in the study. The ConcePTION primary data source CDE includes 51 items covering administrative functions, the description of pregnancy, maternal medical history, maternal illnesses arising in pregnancy, delivery details, and pregnancy and infant outcomes. For each variable in the CDE, the DAPs identified whether their variables were: identical to the one mentioned in the CDE; derived; similar but with a divergent definition; or not available. Results: The majority of the DAP data variables were either directly taken (85%, n = 305/357, range 73–94% between DAPs) or derived by combining different variables (12%, n = 42/357, range 0–24% between DAPs) to conform to the CDE variables and definitions. For very few of the DAP variables, alignment with the CDE items was not possible, either because of divergent definitions (1%, n = 3/357, range 0–2% between DAPs) or because the variables were not available (2%, n = 7/357, range 0–4% between DAPs). Conclusions: Data access providers participating in this study presented a very high proportion of variables matching the CDE items, indicating that alignment of definitions and harmonisation of data analysis by different stakeholders to accelerate and strengthen pregnancy pharmacovigilance safety data analyses could be feasible. [ABSTRACT FROM AUTHOR]

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study's outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas. [ABSTRACT FROM AUTHOR]

Tourism management plays an important role in the context of Smart Cities. In this work, we have used thermal cameras for the development of an Object Detection solution in pedestrian areas. The solution can classify people, bikes, strollers, and count people in Real-Time by using telephoto and wide-angle thermal cameras, in hot squares where there is a relevant number of people passing by. This work has improved FASTER-R-CNN and YOLOv5 architectures with new data sets and fine-tuning approaches to enhance mean average precision and flexibility whether compared to state of the art solutions. Both top-down and bottom-up training adaptation approaches have been assessed in order to demonstrate that the proposed bottom-up approach can provide better results. Results have overcome the state-of-the-art in terms of mean Average Precision in counting (i) for relevant number of people in the scene (removing the limitation of previous state-of-the-art solutions that were set to provide good precision up to 10 people) and (ii) in terms of flexibility with respect to different kinds of camera and resolutions. The resulting model can produce results also when executed on thermal camera and in Real-Time on industrial PC of mid-level. The proposed solution has been developed and validated in the framework of the Herit-Data EC project and it has exploited the Snap4City platform for the final collection of data results, monitoring and their publication on real time dashboards. [ABSTRACT FROM AUTHOR]

The traditional motion monitoring system can only provide limited information and cannot meet people's requirements for details and accuracy. Therefore, the purpose of this paper is to develop an optical detection system based on fuzzy decision algorithm, which aims to achieve accurate and comprehensive acquisition of motion performance data and provide reliable monitoring performance in practical applications. A fuzzy decision algorithm is used to process optical signals and extract motion performance data. By establishing a suitable fuzzy rule set, the optical signal is transformed into real motion data, and the accuracy and reliability of the algorithm are verified by experiments and tests. The experimental results show that the optical detection system based on fuzzy decision algorithm can accurately collect motion performance data and has good monitoring performance. The system can maintain stable work under different lighting conditions, accurately capture and analyze the details in motion, and the system can meet people's needs for accuracy and comprehensiveness, and show good performance in practical applications. [ABSTRACT FROM AUTHOR]

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs). [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

The detection of local extinctions is often hindered by the lack of long-term monitoring schemes, and thus relies on time series of presence data. Recently, citizen science has repeatedly shown its value in documenting species occurrences. We investigated the effectiveness of unstructured citizen science records in reducing the perception of local extinctions in butterfly populations across Italian National Parks. We addressed three research questions: (i) the ability of citizen science data to supplement existing knowledge to complete time series of occurrences, (ii) the impact on data collection of three species features (species size, distribution and length of flight period) determining their appearance, and (iii) the interplay between participant effort and species appearance in the amount of diversity recorded on the iNaturalist platform. Our analysis of 98,922 records of Italian butterflies (39,929 from literature and 58,993 from iNaturalist of which 7427 from National Parks) showed that the addition of iNaturalist data filled many recent gaps in time series, thus reducing the perception of potential local extinctions. Records from more engaged users encompassed a higher fraction of local biodiversity and were more likely to reduce the perception of local extinctions. User effort strongly interacted with species features in determining the frequency of records for individual species. In particular, more engaged users were less affected by species size. We provided updated butterfly checklists for Italian National Parks and a R package to calculate potential extinction upon time series. These results offer guidance for protected areas, conservationists, policymakers, and citizen scientists to optimize monitoring of local populations. [ABSTRACT FROM AUTHOR]

Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators, are challenging in design, data collection and interpretation. Indeed, inconsistent results across the resources are possible. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality, and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology. [ABSTRACT FROM AUTHOR]

Digital twin (DT) technology has become an effective means of industrial digital transformation due to its ability to realize virtual-real mapping, data fusion, etc. In recent years, the application of DT technology to the assembly process of complex mechanical products in multiple fields can effectively overcome the drawbacks of the current assembly method, which is a new strategy for achieving high-precision and high-efficiency intelligent assembly. However, there are few reviews of DT's intelligent assembly technology and application for complex mechanical products. Based on the composition of the DT assembly system, the paper systematically summarizes the critical technologies of DT assembly, including DT model representation of the assembly objects, DT model construction of the assembly process, and DT data collection/management. The application of the DT assembly model based on virtual and real fusion in online monitoring, quality prediction, process planning, real-time control, etc. is comprehensively reviewed. Finally, the challenges faced by DT intelligent assembly of complex mechanical products are described, and future research directions are predicted. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to add empirical data to the nascent field of metaverse learning and teaching by examining factors affecting student participation and their perceived experiences of different metaverse platforms. For data collection, 57 Korean undergraduates participated in a self-administered questionnaire and a short reflective essay regarding their experiences on three metaverse platforms (ifland, Gather Town, & Frame VR). For data analysis, exploratory factor analysis was first executed to derive the underlying factors that can explain student participation in metaverse platforms. The social and interactive learning as well as individualized and behavioral learning were identified as two main contributing factors. While the three platforms had no statistical difference in terms of social presence, students' sentimentally perceived differences among them. The sentiment analysis shows that 60.00% of ifland users were positive, followed by 53.66% of Frame VR users and 51.22% of Gather Town users. Furthermore, the additional keyword analysis shows why students expressed the perceived experiences of each platform in a different way. Given that the success of metaverse instruction can be dependent upon whether students regard it as beneficial, such measurements of student perception on the effectiveness of learning on metaverse platforms can offer meaningful recommendations for tech-savvy educators. [ABSTRACT FROM AUTHOR]

Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC's data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an effective MC trajectory. Much attention has been received to MC's path planning through SPs in a static environment where the path is determined during the initial phase, but they do not emphasize the nodes' coverage rate and cannot be adapted to network topology changes. In this context, we propose an Efficient Trajectory Planning method for Coverage Enhanced Data collection in WSN (ETP-CED). We introduce an enhanced method based on integrated Particle Swarm Optimization and Ant Colony Optimization for selecting the best set of SPs and planning efficient MC trajectory. ETP-CED is adaptive to node failures, allowing the nodes to reposition themselves to patch up coverage holes in the network. MC readjusts its planned path when there are less nodes in the network due to node failures, thereby shortening the trajectory length and speeding up data delivery. The results show that ETP-CED outperforms existing methods in the aspects of nodes' coverage and data collection efficiency. [ABSTRACT FROM AUTHOR]

Wireless Technology is an integral part of today's applications. It encompasses commercial and human-oriented environmental monitoring, military applications, robotics, and healthcare monitoring systems, home or factory automation, and logistics. A wireless sensor network is a network system that uses wireless sensor nodes to monitor physical or environmental conditions such as voice, temperature, and spatial dispersive movements. Each node can locally sense its environment, process information and data, and send the data to one or more collection points within the WSN. The existing solution is categorized into member nodes and group/cluster heads (CH). The CH election process increases the overhead of the network and reduces the network lifetime. The processing and energy limitations of the nodes are considered for the CH election process. In this cluster formation methods aiming at the Cluster head selection process and providing trust in hierarchical WSN are proposed. The Energy Efficient Aggregation Data Convening Routing (E2ADCR) is used to estimate the routing path, and aggregate data collection to improve the network lifetime. The major advantage of this technique is to avoid the malicious or selfish node from becoming a dominant cluster in a group of clusters. Initially sink node selection forwards the Configuration Message (CM) to every node on the network to construct the performing node. In this, cluster selection is based on connection density, degree of the node angle, and residual energy (Quality Factor) that is evaluated from the link robustness, energy, and degree of the node. Multi-hop link transmission support path optimization technique is estimated in the path when the obstacle is present in the WSN. To introduce an Aggregated Support-based Data Collection to evaluate each packet flow monitor on the network to if any unrelated packet will eliminate to forward to the sink node. The proposed routing path of computational simplicity is achieved by a simple method. The new routing protocols, which were developed during this research, have better energy efficiency. [ABSTRACT FROM AUTHOR]

Non-contact sensing can be a rapid and convenient alternative for determining structure response compared to conventional instrumentation. Computer vision has been broadly implemented to enable accurate non-contact dynamic response measurements for structures. This study has analyzed the effect of non-contact sensors, including type, frame rate, and data collection platform, on the performance of a novel motion detection technique. Video recordings of a cantilever column were collected using a high-speed camera mounted on a tripod and an unmanned aerial system (UAS) equipped with visual and thermal sensors. The test specimen was subjected to an initial deformation and released. Specimen acceleration data were collected using an accelerometer installed on the cantilever end. The displacement from each non-contact sensor and the acceleration from the contact sensor were analyzed to measure the specimen's natural frequency and damping ratio. The specimen's first fundamental frequency and damping ratio results were validated by analyzing acceleration data from the top of the specimen and a finite element model. [ABSTRACT FROM AUTHOR]

People spontaneously divide everyday experience into smaller units (event segmentation). To measure event segmentation, studies typically ask participants to explicitly mark the boundaries between events as they watch a movie (segmentation task). Their data may then be used to infer how others are likely to segment the same movie. However, significant variability in performance across individuals could undermine the ability to generalize across groups, especially as more research moves online. To address this concern, we used several widely employed and novel measures to quantify segmentation agreement across different sized groups (n = 2–32) using data collected on different platforms and movie types (in-lab & commercial film vs. online & everyday activities). All measures captured nonrandom and video-specific boundaries, but with notable between-sample variability. Samples of 6–18 participants were required to reliably detect video-driven segmentation behavior within a single sample. As sample size increased, agreement values improved and eventually stabilized at comparable sample sizes for in-lab & commercial film data and online & everyday activities data. Stabilization occurred at smaller sample sizes when measures reflected (1) agreement between two groups versus agreement between an individual and group, and (2) boundary identification between small (fine-grained) rather than large (coarse-grained) events. These analyses inform the tailoring of sample sizes based on the comparison of interest, materials, and data collection platform. In addition to demonstrating the reliability of online and in-lab segmentation performance at moderate sample sizes, this study supports the use of segmentation data to infer when events are likely to be segmented. [ABSTRACT FROM AUTHOR]

Introduction Establishing and maintaining a cancer registry in a tertiary high volume centre is fraught with difficulty, inaccuracy and missed data entry. Further, the raw unstructured data must be converted into a structured digital data, so that scientists can identify trends in cancer diagnoses and treatment responses. Objective We test the feasibility of establishing a cancer registry of Head and Neck malignancy patients through a research oriented artificial intelligence (AI) enabled data collection platform, using its smartphone application version. Materials and Methods This prospective observational study was conducted in the Department of Otolaryngology & Head and Neck Surgery, Post Graduate Institute of Medical Education And Research, Chandigarh in collaboration with Departments of Radiotherapy and Community Medicine. After taking due clearance from the Institute ethical committee, HNC patients, who were biopsy proven, were enrolled from October 2019 up to March 2021. The obtained data was entered, followed up and analysed through Jiyyo Research application which is a commercially available dedicated research oriented AI enabled data collection platform. Results The Jiyyo Research site was browsed and after proper registration, the patient data was entered into a proforma/questionnaire. The entered patient details were browsed for review, follow up and addition of new information. The whole process of data capture for each patient, took approximately 5-8 min, while any updates or review for the same patient required less than a minute. Search and data retrieval was very quick, and can be done in 1-2 min. Through this platform, a total of 1214 HNC patients were collected, followed and analysed during the study period. Conclusion It was feasible to establish a Head and Neck Cancer Registry using an AI based smartphone app. This AI based tumor registry could benefit in further studies with longer follow up of 5 and 10 years and in future AI studies. [ABSTRACT FROM AUTHOR]

Tracking mobile objects in road networks is one of the important applications of wireless sensor networks (WSNs). It includes road safety, smart transportation, vehicle safety, vehicle tracking, traffic monitoring, etc. In this work, we assume that the sensors are installed on vehicles which are moving on the road. These mobile sensors need to be tracked/visited periodically within a pre-defined time interval. In our scenario, the mobility or trajectory of the mobile sensors are unpredictable. In many applications nowadays, the mobile sensors have unpredictable mobility. In this paper, our aim is to collect data by visiting the mobile sensors periodically using fewer number of mobile sinks. The mobile sinks subsequently deliver the collected data to a stationary base station. Time-bound periodic data collection from the mobile sensors along with their unpredictable motion is even more challenging than the data collection from static sensors. Here, we propose a deterministic data gathering algorithm using the solution of Chinese Postman Problem. We measure the performance of the proposed solution. Due to non-availability of any existing solution, we have compared our algorithm with a heuristic algorithm and a variation of an existing solution. The experiment results show that our proposed data gathering algorithm performs well. [ABSTRACT FROM AUTHOR]

Background: Spinal muscular atrophy (SMA) is a severe neuromuscular disease that is inherited in an autosomal recessive manner with an estimated incidence of 1 in 10,000 live births. The traditional classification of SMA includes five types (Types 0–4 SMA) based on patient age at disease onset and the highest motor milestone achieved. Spinal muscular atrophy leads to progressive muscle denervation, skeletal muscle atrophy and loss of motor function and ambulation, though phenotypes vary along a disease continuum. Regardless of disease severity, or access to treatment, a multidisciplinary approach to care is required to ease the burden of disease. To date, limited global data exist regarding the cost and resource use associated with SMA management. Objective: We planned to perform a systematic literature review to identify studies on cost and healthcare resource use associated with SMA. Methods: A comprehensive search was conducted in 2019 using several electronic databases in addition to supplementary sources and updated in 2021 in order to capture recently published studies. Electronic searches performed in Embase, MEDLINE, Evidence-Based Medicine Reviews and EconLit via the Ovid platform were supplemented by searches of the grey literature (reference lists, conference proceedings, global Health Technology Assessment body websites and other relevant sources). Study eligibility criteria were based on the population, interventions, comparators and outcomes (PICO) framework. Quality assessment of full-text publications was evaluated with reference to a published checklist. To accommodate heterogeneity across studies including countries, currencies, populations, time units and methods of reporting used, costs were reported in Euros in 2019. Results: A total of 51 publications, comprising 49 unique studies of patients with SMA that met all eligibility criteria were included in the final selection. The publications comprised data from 14 countries and seven additional studies that reported multi-national data. Because of the heterogeneity between the different types of SMA, data were frequently reported separately for individuals with Type 1 or early-onset SMA and for Types 2, 3, and 4 SMA or later-onset SMA. Generally, direct medical costs and resource use were reported to be highest for patients with Type 1 SMA, decreasing incrementally for patients with Type 2 and Type 3 disease. Where cost categories were similar, direct costs were much lower in Europe than in the USA. Indirect costs were primarily associated with informal care, which was a substantial burden on patients and families in terms of both cost and time. Cost drivers were generally found to be dependent on SMA type. Conclusions: Long-term robust studies are required to fully elucidate the economic burden of SMA. Considering that motor function can vary broadly, especially in Type 2 SMA, it would be beneficial to understand how costs and resource use are affected by different degrees of ambulation. Reporting data in terms of achieved motor function could also mitigate the challenges of comparing global data studies of small populations. Global, regional, and/or local data collection platforms and disease registry networks could play an important role in helping to address current data gaps. [ABSTRACT FROM AUTHOR]

Web-based data collection is increasingly popular in both experimental and survey-based research because it is flexible, efficient, and location-independent. While dedicated software for laboratory-based experimentation and online surveys is commonplace, researchers looking to implement experiments in the browser have, heretofore, often had to manually construct their studies' content and logic using code. We introduce lab.js, a free, open-source experiment builder that makes it easy to build studies for both online and in-laboratory data collection. Through its visual interface, stimuli can be designed and combined into a study without programming, though studies' appearance and behavior can be fully customized using html, css, and JavaScript code if required. Presentation and response times are kept and measured with high accuracy and precision heretofore unmatched in browser-based studies. Experiments constructed with lab.js can be run directly on a local computer and published online with ease, with direct deployment to cloud hosting, export to web servers, and integration with popular data collection platforms. Studies can also be shared in an editable format, archived, re-used and adapted, enabling effortless, transparent replications, and thus facilitating open, cumulative science. The software is provided free of charge under an open-source license; further information, code, and extensive documentation are available from https://lab.js.org/. [ABSTRACT FROM AUTHOR]

Introduction: Functional motor disorders (FMDs) are usually categorized according to the predominant phenomenology; however, it is unclear whether this phenotypic classification mirrors the underlying pathophysiologic mechanisms. Objective: To compare the characteristics of patients with different FMDs phenotypes and without co-morbid neurological disorders, aiming to answer the question of whether they represent different expressions of the same disorder or reflect distinct entities. Methods: Consecutive outpatients with a clinically definite diagnosis of FMDs were included in the Italian registry of functional motor disorders (IRFMD), a multicenter data collection platform gathering several clinical and demographic variables. To the aim of the current work, data of patients with isolated FMDs were extracted. Results: A total of 176 patients were included: 58 with weakness, 40 with tremor, 38 with dystonia, 23 with jerks/facial FMDs, and 17 with gait disorders. Patients with tremor and gait disorders were older than the others. Patients with functional weakness had more commonly an acute onset (87.9%) than patients with tremor and gait disorders, a shorter time lag from symptoms onset and FMDs diagnosis (2.9 ± 3.5 years) than patients with dystonia, and had more frequently associated functional sensory symptoms (51.7%) than patients with tremor, dystonia and gait disorders. Patients with dystonia complained more often of associated pain (47.4%) than patients with tremor. No other differences were noted between groups in terms of other variables including associated functional neurological symptoms, psychiatric comorbidities, and predisposing or precipitating factors. Conclusions: Our data support the evidence of a large overlap between FMD phenotypes. [ABSTRACT FROM AUTHOR]

The high-speed welding of friction stir welding can improve the production efficiency of the equipment. The orthogonal experiment method was used to study the high-speed welding process parameters of aluminum alloy. The mechanical properties of welded joints were tested, and the influence of process parameters on welding quality was analyzed. A data collection platform was designed, and the spindle axial force of the spindle and the three-directional force of the worktable were collected during the welding process. The relationship between the process force and process parameters was studied. From the perspective of macroscopic metallography, the causes of welding defects and the influence of process parameters are studied. The research results show that the mechanical properties of the welded joint are optimal and meet the requirements of welding quality when the process parameters are the tool rotational speed of 5000 rpm, the welding speed of 3000 mm/min, and the plunge depth of 0.25 mm. [ABSTRACT FROM AUTHOR]

Big data collection and storage have become one of the most obvious challenge in this era. Indeed, much of that data is collected thanky to a huge number of connected devices in sensing-based IoT applications. Thus, in order to deal with data growth in such applications, researchers have focused on data reduction approach as an efficient solution for minimizing the amount of data collection and saving the limited sensor energy in such networks. Mainly, data reduction approach relies on various kinds of data processing techniques such that aggregation, compression, prediction, clustering, sensing frequency adaptation and spatial-temporal correlation. However, each of those techniques has its own advantages and disadvantages regarding sensor energy saving, data reduction ratio, data accuracy, complexity, etc. In this paper, we propose a hybrid data collection and energy saving mechanism, called All-in-One, for sensing-based IoT applications. The proposed mechanism takes advantages from existing data reduction techniques while optimizing various performance metrics. All-in-One relies on the cluster network architecture and works on three main phases: on-period, in-period and in-node. The first phase, e.g. on-period, allows each sensor node to search the similarity among its periodic collected data then to reduce its data transmission to the Cluster-Head (CH) by applying either data aggregation, compression or prediction technique. The second phase, e.g. in-period, allows each sensor to study the variation of the monitored condition then to reduce its data collection according to two techniques, on-off transmission or adapting sensing frequency. The last phase, e.g. in-node, is applied at the CH level and aims to remove the redundancy among data collected by neighboring nodes, based on in-network correlation or data clustering techniques, before sending the data to the sink. We conducted simulations on real sensor data in order to evaluate the efficiency of our mechanism, in terms of several performance metrics, compared to other exiting techniques. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. Wireless sensor networks are relatively simple, scalable networks with many applications in research. They can provide many benefits, including ad hoc distribution, lower costs, and higher flexibility. In a scenario where time is of the essence and dedicated base stations cannot be established, mobile sinks must be used to gather data. IoT systems are based on a collective organization in which devices collaborate to provide better and more accurate decisions. It is important to ensure that the information being shared is legitimate to avoid any significant degradation in system performance because of false or inaccurate information. Building trust—the "assurance" between two devices that the information being shared can be used with confidence that it is accurate—will create a trustworthy, secure system in which all devices are identified and no information is accepted from any unauthorized device. The key contribution of this work is a new, dynamic, trust-based clustering mechanism by which nodes can securely connect to one another and begin transmitting data to a sink while it is available. To demonstrate the utility of this mechanism, we examine two possible attacks on a trust-based network and present a heuristic solution for minimizing the negative effects of such attacks in an energy-efficient way. Our results show improved network performance through reduction of the number of cycles required to isolate or mitigate the effect of malicious nodes in the network, thus reducing the energy consumption in the network with a concomitant increase in its lifespan. Our cluster methodology also has the effect of spreading energy consumption among nodes, thereby reducing early fall-off of nodes and network holes. [ABSTRACT FROM AUTHOR]

This study examines the research methods, topics, and trends of empirical MOOC research to gain a comprehensive understanding of the MOOC phenomenon through reviewing 541 empirical MOOCs research published from 2009 to 2019. The results indicate that: (1) the majority of studies adopted quantitative research methods followed by mixed research methods and qualitative research methods, (2) the most frequently adopted data collection method was survey, followed by platform database and interviews, (3) almost half of the studies used at least two data collection methods such as survey and interview, (4) the majority of researchers adopted descriptive statistics for data analysis, followed by inferential statistics and content analysis, (5) the research topics primarily focused on students, followed by design-focused, context and impact-focused, and instructor-focused. Among these student-focused topics, learner retention, learning experience, social learning, and engagement were the most mentioned, and (6) the affiliations of the first authors of the MOOC studies were mainly from the U.S. followed by China and Spain. Implications and future research were discussed. [ABSTRACT FROM AUTHOR]

In this article, we explore the long-term preservation implications of application programming interfaces (APIs) which govern access to data extracted from social media platforms. We begin by introducing the preservation problems that arise when APIs are the primary way to extract data from platforms, and how tensions fit with existing models of archives and digital repository development. We then define a range of possible types of API users motivated to access social media data from platforms and consider how these users relate to principles of digital preservation. We discuss how platforms' policies and terms of service govern the set of possibilities for access using these APIs and how the current access regime permits persistent problems for archivists who seek to provide access to collections of social media data. We conclude by surveying emerging models for access to social media data archives found in the USA, including community driven not-for-profit community archives, university research repositories, and early industry–academic partnerships with platforms. Given the important role these platforms occupy in capturing and reflecting our digital culture, we argue that archivists and memory workers should apply a platform perspective when confronting the rich problem space that social platforms and their APIs present for the possibilities of social media data archives, asserting their role as "developer stewards" in preserving culturally significant data from social media platforms. [ABSTRACT FROM AUTHOR]

We study a type of double truncation where units are observed if and only if their death event occurs within a specific timespan. The resulting missing data mechanism is nonignorable and thus has to be reconsidered. Based on the density function of observed lifetimes and the random sample size, we derive a likelihood model that enables simultaneous estimation of the lifetime distribution and the parameters governing the birth process. In particular, knowledge of the population size is not required. We show that the model is identifiable under certain conditions by using results on exponential families. Bayesian estimators and corresponding standard errors for all involved parameters become available by using MCMC simulation. We describe how the simulation can be performed efficiently while maintaining sufficiently good mixing behaviour of the resulting chains. Both finite-sample and asymptotic properties of the investigated estimators are examined through a simulation study. The proposed method is applied to estimate the lifetime distribution of German companies. [ABSTRACT FROM AUTHOR]

UK Biobank is a population-based cohort of half a million participants aged 40–69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions. Between 2014 and 2023, 100,000 UK Biobank participants are undergoing brain, heart and abdominal MRI, as well as DXA and carotid ultrasound scans. In this review, authors provide a detailed overview of the rationale for the collection of these imaging data, the procedures of data collection and management, and the future directions of the UK biobank imaging enhancement. [ABSTRACT FROM AUTHOR]

The increasing use of social networking platforms has raised the need to develop automated multi-classifications on document network. In this paper, we propose a supervised relation topic model that leverages the links between documents to learn the latent content of documents and enhance performance of prediction. Our model takes advantage of Bayesian generative model to exploit the relation between word feature and link feature in a document network. We evaluate our model on large-scale data collections that include scientific citation community and medical article network. We demonstrate its effectiveness and efficiency on document classification with SLDA model and collective classification approaches. [ABSTRACT FROM AUTHOR]

With the growth of data, data-intensive approaches for sustainability are becoming widespread and have been endorsed by various stakeholders. To understand their implications, in this paper, data-intensive approaches for sustainability will be explored by conducting an extensive review. The current data-intensive approaches are defined as an amalgamation of traditional data-collection methods, such as surveys and data from monitoring networks, with new data-collection methods that involve new information communication technology. Based on a comprehensive review of the current data-intensive approaches for sustainability, key challenges are identified: the lack of data availability, diverse indicators developed from a narrowly viewed base, diverse definitions and values, skewed global representation, and the lack of social and economic information collected, especially among the business indicators. To clarify the implications of these trends, four major research assumptions regarding data-intensive approaches are elaborated: the methodology, epistemology, normativity, and ontology. Caution is required when data-intensive approaches are masked as "objective". Overcoming this issue requires interdisciplinary and community-based approaches that can offer ways to address the subjectivities of data-intensive approaches. The current challenges to interdisciplinarity and community-based approaches are also identified, and possible solutions are explored, so that researchers can employ them to make the best use of data-intensive approaches. [ABSTRACT FROM AUTHOR]

Lymphomas are a large, heterogeneous group of neoplasms with well-defined characteristics, and this heterogeneity highlights the importance of epidemiological data. Knowledge of local epidemiology is essential to optimise resources, design clinical trials, and identify minority entities. Given there are few published epidemiological data on lymphoma in Spain, the Spanish Lymphoma and Autologous Bone Marrow Transplant Group created the RELINF project. The aim of this project is to determine the frequencies and distribution of lymphoid neoplasms in Spain and to analyse survival. We developed an online platform for the prospective collection of data on newly diagnosed cases of lymphoma in Spain between January 2014 and July 2018; 11,400 patients were registered. Diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL) were the most frequent lymphomas in our series. Marginal B cell lymphoma frequency was higher than that reported in other studies, representing more than 11% of mature B cell lymphomas. Peripheral T cell lymphoma not otherwise specified (PTCL-NOS) was the most common subtype of T cell lymphoma, and NK/T cell lymphomas were more frequent than expected (5.4% of total). Hodgkin's lymphoma accounted for 12% of lymphoproliferative syndromes. Overall survival was greater than 90% at 2 years for indolent B cell lymphomas, and approximately 60% for DLBCL, somewhat lower than that previously reported. Survival was poor for PTCL-NOS and angioimmunoblastic T cell lymphoma, as expected; however, it was somewhat better than that in other studies for anaplastic large cell anaplastic lymphoma kinase lymphomas. This is the first prospective registry to report the frequencies, distribution, and survival of lymphomas in Spain. The frequencies and survival data we report here are globally consistent with that reported in other Western countries. These updated frequencies and survival statistics are necessary for developing appropriate management strategies for neoplasias in the Spanish population. [ABSTRACT FROM AUTHOR]

We have carried out an intensive study of photometric (Kepler Mission) and spectroscopic data on the system Kepler-2 (HAT-P-7A) using the dedicated software WinFitter 6.4. The mean individual data-point error of the normalized flux values for this system is 0.00015, leading to the model's specification for the mean reference flux to an accuracy of ∼0.5 ppm. This testifies to the remarkably high accuracy of the binned data-set, derived from over 1.8 million individual observations. Spectroscopic data are reported with the similarly high-accuracy radial velocity amplitude measure of ∼2 m s−1. The analysis includes discussion of the fitting quality and model adequacy. Our derived absolute parameters for Kepler-2 are as follows: M p (Jupiter) 1.80 ± 0.13; R ⋆ 1.46 ± 0.08 × 10 6 km; R p 1.15 ± 0.07 × 10 5 km. These values imply somewhat larger and less condensed bodies than previously catalogued, but within reasonable error estimates of such literature parameters. We find also tidal, reflection and Doppler effect parameters, showing that the optimal model specification differs slightly from a 'cleaned' model that reduces the standard deviation of the ∼3600 binned light curve points to less than 0.9 ppm. We consider these slight differences, making comparisons with the hot-Jupiter systems Kepler-1 (TrES-2) and 13. We confirm that the star's rotation axis must be shifted towards the line of sight, though how closely depends on what rotation velocity is adopted for the star. From joint analysis of the spectroscopic and photometric data we find an equatorial rotation speed of 11 ± 3 km s−1. A slightly brighter region of the photosphere that distorts the transit shape can be interpreted as an indication of the gravity effect at the rotation pole; however we note that the geometry for this does not match the spectroscopic result. We discuss this difference, rejecting the possibility that a real shift in the position of the rotation axis in the few years between the spectroscopic and photometric data-collection times. Alternative explanations are considered, but we conclude that renewed detailed observations are required to help settle these questions. [ABSTRACT FROM AUTHOR]

This paper designs and implements a cloud rendering system. The system supports application of server cluster load balancing, static extension of rendering machine, and design architecture of parallel task scheduling. At the same time, we put forward a new design idea, and we implement a set of communication rules of multi-task renderer and cloud rendering system. On the system, users could browse 3D scene online by mobile terminal equipments. The task of PC terminal users and mobile terminal users to access remote scenes could be high speed and real-time rendering by this system. In the end of this paper, we analyze the efficiency of our algorithm. Our system can achieve a better effect on the number of concurrent users and the average response delay. The average frame rate of system can reach 30–40 frames per second. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods on the public test datasets. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis. [ABSTRACT FROM AUTHOR]

In transaction platform, because of competing for market share, this will inevitably bring a series of problems and risks for companies. Decision of the managers and consumers in third-party payment platform, are issue of great concern to platform development. Using rough set theory, this paper constructs the third-party payment platform transaction's rough complex network, establishes a rough complex networks trust model for platform managers to enhance efficiency, a customer select commodities rough decision model for customer's benefit maximization. In this paper, rough set theory is used to study the uncertainty of complex network trust model, but also given attribute reduction algorithm based on time series analysis to solve the attribute rules extraction in dynamic knowledge system. Simulation examples show that the models and algorithms provide the science decision-making approach for a direct participant of the platform transaction. [ABSTRACT FROM AUTHOR]

This paper connects the technological practice of activity monitor gaming to the Next Generation Science Standards (NGSS) science and engineering practice of "analyzing and interpreting data," and to the foundational constructionist idea of personal meaning. In our larger study, eighth-grade students, ages 12–14, wore physical activity monitor devices for approximately four months, encountered their own and peers' data on device displays and dashboards, and played a game designed by the project team that converted their device data into game bonuses and player actions. The analysis for this article examined focus groups and individual interviews with students from the project to determine alignment between students' thinking and the NGSS "analyzing and interpreting data" practices for grades 6–8: considering limitations related to measurement accuracy and error, measurement tools, and appropriateness of the data collection model. The analysis also identified what types of student experiences and reflections could be characterized as constructionist components of knowledge generation and knowledge reformulation, both of which are key to NGSS recommendations around finding "relevance" in data. Findings revealed that while students engaged in all three types of consideration, as well as creating meaningful interpretations of pattern identification and reasoning from evidence, their reflections and insights did not, for the most part, lead to actionable understandings. Our results have implications for implementing activity monitor technologies and practices in science education, particularly in regard to student meaning and motivation. [ABSTRACT FROM AUTHOR]

Deposition models, such as the Shoreline Deposition Model (SDM) used to quantify nearshore avian injuries resulting from the 2010 Deepwater Horizon (DWH) oil spill, were developed to improve the estimates of nearshore avian mortality resulting from the release of oil into coastal and marine environments. Unlike earlier approaches to injury quantification, such as simple counts of carcasses on the shoreline, a modeling approach allows trustees to evaluate the precision of their estimate (i.e., to develop a confidence interval) and can inform decision-making and the likely utility of additional primary data collection activities through sensitivity analyses. In this paper, we rely on published literature, actual DWH data, and a deposition model simulation to evaluate how different model inputs and assumptions can affect the accuracy and precision of model results. We find that the precision of deposition models is strongly affected by the length of time between subsequent shoreline searches, the underlying magnitude of carcass deposition, carcass persistence probabilities, and carcass detection probabilities. In addition, the accuracy of deposition model results may be affected by natural fluctuations in deposition rates. Given these findings, we recommend that natural resource trustees include an evaluation of future model uncertainty as part of their initial data collection efforts. This will allow them to deploy resources in a way that maximizes the utility of future deposition model results. We also identify several factors that do not need to be assessed immediately following a spill event, thereby potentially freeing resources for more time critical data collection efforts. [ABSTRACT FROM AUTHOR]

Enhancing the network lifetime of wireless sensor networks is an essential task. It involves sensor deployment, cluster formation, routing, and effective utilization of battery units. Clustering and routing are important techniques for adequate enhancement of the network lifetime. Since the existing clustering and routing approaches have high message overhead due to forwarding collected data to sinks or the base station, it creates premature death of sensors and hot-spot issues. The objective of this study is to design a dynamic clustering and optimal routing mechanism for data collection in order to enhance the network lifetime. A new dynamic clustering approach is proposed to prevent premature sensor death and avoid the hot spot problem. In addition, an Ant Colony Optimization (ACO) technique is adopted for effective path selection of mobile sinks. The proposed algorithm is compared with existing routing methodologies, such as LEACH, GA, and PSO. The simulation results show that the proposed cluster head selection algorithm with ACO-based MDC enhances the sensor network lifetime significantly. [ABSTRACT FROM AUTHOR]

Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in terms of optimal resource provisioning that predicts and automatically adjusts the computing resources on the basis of sensory data volume and application's type. In this paper, we propose a hybrid autonomic resource provisioning framework, which is the combination of autonomic computing, fuzzy logic control and linear regression model. The proposed framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. The effectiveness of the proposed approach is evaluated under a real workload trace. The experimental results show that the proposed approach minimizes the cost by at least 27% and SLA violations by at least 78% as compared to other approaches. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a network formed by smart devices whose core contains embedded technology in order to collect sensory information and exchange it with every single device present within the network. One major challenge in IoT is data stabilization of the sensory data collected by smart devices before it is fed into the cloud. Data collected by sensing devices suffer with random glitches, variation in volume; inter packet delay and data packet loss due to inline power fluctuations. The cleaning and filtration of such noisy characteristics must be done before restoring this raw data to some global repository. This paper advises a method for clean monitoring of captured environmental parameters and proposes few algorithms that help in stabilizing the captured sensory data on the basis of data packet volume, inter packet delay, data type and random amplitude levels. Such an approach supports service level implementation required by an enterprise for deploying error free stabilized data collection without any ambiguity in order to compute an accurate and sustainable result required for effective environmental parameter analysis. [ABSTRACT FROM AUTHOR]

Recently, the energy efficiency is improved in the clustered wireless sensor networks (WSNs) using sink mobility in restricted path. However, due to path restriction, a constant speed is assigned with mobile sink and this has limited the time for communication to collect the sensor data in randomly deployed sensor networks. Further, the collection of sensor data increases the consumption of power in such network. Hence to improve this cluster based block wise compressed path constrained sensing is introduced in clustered sensor networks. Here, two techniques are deployed to reduce the consumption of power in sensor network. To limit the communication time in collecting the sensor data, the shortest path tree computation is used. Also, to reduce the inherent data sparsity block wise compression over spatially correlated data is used. The collection of data is done by the cluster heads and forwarded to the base stations (BSs) using shortest path tree computation. This is formulated as a mixed linear integer programming problem, which is solved using adaptive amoeba algorithm. The block wise compression method uses compressed sensing (CS) in clustered WSN and the measurement is done through block diagonal matrix. The forwarding of CS measurements is done through shortest path algorithm and this relays the measurements to the BSs. The validation is carried out in terms of total consumed power due to the effect of sparsity and transferring the CS measurements to BS. The performance is evaluated based on optimal clustering for attaining reduced power consumption. The experimental results show that the proposed method has higher throughput with increased energy efficiency than the other conventional methods. [ABSTRACT FROM AUTHOR]

The phase I main canal of the Middle Route of the South-to-North Water Diversion Project crosses the coal mining area in Yuzhou city, and thus it is very important to predict the surface subsidence of the goaf. The GIS-based prediction system for Yuzhou goaf area subsidence has been developed by data collection, and the subsidence, horizontal movement, tilt and curvature are predicted for five mined out areas. The results show that the surface movement and deformation in the strike and dip directions are not more than the stability indexes specified in the coal mining regulation under buildings, water-bodies and railways, indicating that the residual deformation will not cause damage against the canal. The forecasting and monitoring values of Guo coal mine goaf and original Xinfeng goaf, located in Liang Town, Yuzhou City, are relatively large, thus the canal in this region may be affected and the safety monitoring should focus on this region. [ABSTRACT FROM AUTHOR]

Today, wireless sensor networks (WSNs) have been widely used in monitoring various applications, such as environment, military and health-care, etc. The explosive growth of the data volume generated in these applications has led to one of the most challenging research issues of the big data era. To deal with such amounts of data, exploring data correlation and scheduling strategies have received great attention in WSNs. In this paper, we propose an efficient mechanism based on the Euclidean distance for searching the spatial-temporal correlation between sensor nodes in periodic applications. Based on this correlation, we propose two sleep/active strategies for scheduling sensors in the network. The first one searches the minimum number of active sensors based on the set covering problem while the second one takes advantages from the correlation degree and the residual energy of the sensors for scheduling them in the network. Our mechanism with the proposed strategies were successfully tested on real sensor data. Compared to other existing techniques, the simulation results show that our mechanism significantly extends the lifetime of the network while conserving the quality of the collected data and the coverage of the monitored area. [ABSTRACT FROM AUTHOR]

In rechargeable wireless sensor networks (R-WSNs), due to limited and dynamic energy supplyment, a sensor can not be always have enough energy when a network can gather excessive energy from the environment. At the same time, it is critical for higher data collection rate when sensors are working at a very low duty cycle due to sporadic availability of energy. Therefore, the sensors with surplus energy can be scheduled for strengthening packet delivery efficiency and improving data collection rate. Considering the data has some relation and redundancy, in this paper, an algorithm is proposed to achieve a high data generation rate for data-gathering trees based on data aggregation technology which can maximize data gather rate as an optimization problem for improving data generation rate in rechargeable wireless networks. An initial data-gathering tree is established and the maximum data collection rate routing is achieved by adjusting the heavily loaded and medium heavily loaded nodes. The data collection rate of the data-gathering tree produced by the proposed algorithm has been shown to be significantly higher than that of the initial tree. The simulation and experiments demonstrate that the proposed algorithm is efficient to maximize data collection rate in R-WSNs. [ABSTRACT FROM AUTHOR]

Nowadays, the major challenge in machine learning is the ‘Big Data’ challenge. The big data problems due to large number of data points or large number of features in each data point, or both, the training of models have become very slow. The training time has two major components: Time to access the data and time to process (learn from) the data. So far, the research has focused only on the second part, i.e., learning from the data. In this paper, we have proposed one possible solution to handle the big data problems in machine learning. The idea is to reduce the training time through reducing data access time by proposing systematic sampling and cyclic/sequential sampling to select mini-batches from the dataset. To prove the effectiveness of proposed sampling techniques, we have used empirical risk minimization, which is commonly used machine learning problem, for strongly convex and smooth case. The problem has been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each using two step determination techniques, namely, constant step size and backtracking line search method. Theoretical results prove similar convergence for systematic and cyclic sampling as the widely used random sampling technique, in expectation. Experimental results with bench marked datasets prove the efficacy of the proposed sampling techniques and show up to six times faster training. [ABSTRACT FROM AUTHOR]

Despite the presumed ability of insight problems to elicit the subjective feeling of insight, as well as the use of so-called insight problems to investigate this phenomenon for over 100 years, no research has collected normative data regarding the ability of insight problems to actually elicit the feeling of insight in a given individual. The work described in this article provides an overview of both classic and contemporary problems used to examine the construct of insight and presents normative data on the success rate, mean time to solution, and mean rating of aha experience for each problem and task type. We suggest using these data in future work as a reference for selecting problems on the basis of their ability to elicit an aha experience. [ABSTRACT FROM AUTHOR]

In multihop scenarios, the sensor nodes nearer to the base station (BS) are overloaded because they handle their own data as well as the information obtained from far away nodes. This induces a higher energy depletion rate in nodes near to the BS causing early death of these nodes resulting in hot spot/energy hole problem in wireless sensor network (WSN). This paper proposes a novel strategy using unequal fixed grid-based cluster along with a mobile data mule for data collection from the cluster head (CH). A CH is selected in such a manner that the cumulative transmission distance for member nodes within the cluster is minimum. The paper has attempted to optimize the values for CH change time or round number (f) and also established a relationship between different size clusters by using a factor (r), as they are playing an important role in the overall performance improvement of the WSN. Integrating a mobile data mule in the protocol enhances its efficiency of handling hot spot problem and makes it more energy effective. Two different WSN-scenarios have been considered based on the movement pattern of the data mule. The results obtained through simulation in both scenarios prove the success of our scheme in terms of energy efficiency, load balancing and network lifetime as compared to the existing protocols. The paper also providing a balance trade-off between delay and high overheads by using a single mule with simple predefined path. It also minimizes the hot spot problem as it sustains more than 3000 rounds, which is far better than the existing methods. [ABSTRACT FROM AUTHOR]