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Taking the example of conspiracy-related communication online as one form of contentious politics, this study examines the data collection challenges for multidimensional comparative research across platforms, time, and cultural embeddings. It compares the architectures and features relevant to data collection, access regimes, and use cultures for a set of digital platforms and communication venues. Differentiating between actor- and content-based strategies, this study discusses the potentials and limitations of these approaches, considering differences in platforms, temporal dynamics, and cultural embeddings as well as several layers of equivalence. The discussion highlights crucial insights into designing data collection strategies in multidimensional comparative studies. [ABSTRACT FROM AUTHOR]

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection. [ABSTRACT FROM AUTHOR]

Access to water has improved globally; however, nearly 800 million people continue to lack access, particularly in resource constrained contexts. Current efforts are not on track to meet the Sustainable Development Goals aim of safe and affordable drinking water for all. Innovative and contextualized solutions are required. This article describes a university–government collaboration in Ethiopia that addressed key data limitations, without which equity-based decision-making was not possible. These efficient and cost-effective ways for obtaining and managing data support improved decision-making, and also identified avenues for improving water system governance through policy shifts and citizen participation. [ABSTRACT FROM AUTHOR]

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 new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance. [ABSTRACT FROM AUTHOR]

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at 10.5439/1969004 (Lappin, 2023) and 10.5439/1985470 (de Boer, 2023), respectively. [ABSTRACT FROM AUTHOR]

This paper explores the development of a Quadcopter-Rover System specifically designed for environmental survey applications. The system combines the capabilities of a quadcopter and a rover to provide a comprehensive and versatile solution for data collection and analysis. The paper presents a detailed overview of the system's modelling, design, and manufacturing of the two main components: the quadcopter and the differential wheel robot (the rover). The quadcopter’s main task is to carry the rover to/from the ground destination and collect aerial data while the rover`s main task is ground exploration and data collection. The paper discusses the development of a robust and efficient control algorithm that enables autonomous and coordinated operation between the quadcopter and the rover. Experimental results demonstrate the system's effectiveness in conducting environmental surveys, showcasing its ability to accurately navigate challenging terrains, and collect valuable data for environmental analysis. The Quadcopter-Rover System offers significant potential in applications such as ecological monitoring, disaster management, and precision agriculture, where comprehensive and efficient data collection is crucial for informed decision-making. [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]

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks. [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]

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]

In today's dynamic business environment, achieving and sustaining operational efficiency is paramount for organizations. To remain competitive and meet evolving customer demands, companies strive to optimize their operations and enhance efficiency. Over time, various indicators have been developed to assess efficiency across multiple operational dimensions, aligning with technological advancements and industrial practices. One such indicator is Overall Equipment Effectiveness (OEE), which evaluates equipment efficiency based on availability, performance, and quality. However, with the advent of the digital industry, the management of data for efficiency calculations has become increasingly complex. In this context, this paper proposes a digitized, real-time OEE calculation system that leverages advanced technologies to continuously monitor OEE, enable real-time decision-making, and drive performance improvements. Harnessing the potential of digitalization, the system facilitates seamless data collection, analysis, and reporting while automating manual processes. By focusing on OEE as a comprehensive efficiency indicator, the proposed system aims to enhance organizational performance and establish a foundation for calculating other efficiency measures. [ABSTRACT FROM AUTHOR]

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UAS). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle - Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 days was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. [ABSTRACT FROM AUTHOR]

PURPOSE: To describe clinical research professionals (CRPs)' experiences with electronic patient-reported outcome (ePRO) data collection systems in oncology clinical trials and identify correlates of CRPs' attitude toward technology. METHODS: An online survey was conducted among 210 CRPs from 125 National Cancer Institute–funded research sites. Measures included CRPs' demographic characteristics, working years, employment locations, and previous experiences with various types of ePROs. Their attitude toward technology was measured by the Technology Attitude Scale-Adapted. The Wilcoxon signed-rank test was used to compare two subdomains of attitude (perceived usefulness [PU] and perceived ease of use [PEU]). Multiple linear regression was used to explore correlates of (1) overall attitude, (2) PU, and (3) PEU. The significance level was 5%. RESULTS: Participants' median age was 41 years (range, 21-67). Most were female (90%) and White (82%). More than half of the participants had previous experiences with web-based ePROs using patients' own devices (72%) or site-/sponsor-provided on-site devices (eg, kiosks or tablets; 64%). CRPs who were 60 years or older (β = −0.32, P <.05) or worked for 10-20 years (β = −0.11, P <.05) had relatively negative attitudes, controlling for other factors. Previous experiences with more ePRO types were associated with more positive attitudes (β = 0.08, P =.02). Similar correlates were found with PU but not with PEU. CONCLUSION: This study revealed that CRPs had various experiences with ePRO systems and attitudes toward technology. Age, working years, and previous experiences with ePROs were correlates of overall attitude toward technology and PU. These findings suggest necessary targeted training to facilitate ePRO use in oncology clinical trials by improving CRPs' awareness and attitude toward technology. [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]

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]

The 110 m QiTai radio Telescope (QTT) will be equipped with multiple Ultra-WideBand (UWB) receivers in the primary and Gregory focus to achieve continuous frequency coverage from 270 MHz to 115 GHz, which poses great challenges to signal acquisition, transmission, and real-time processing. Aiming at 10 GHz and above full-bandwidth acquisition and multi-scientific processing for the QTT UWB signals, an experimental system with high-speed signal acquisition, 100 Gb network multi-path distribution, and fast recording is designed by using advanced direct RF-sampling technology and heterogeneous architecture. The system employs a ZCU111 board to digitize dual-polarization signals with a sampling rate of 4.096 GigaSamples-Per-Second and 12-bit quantization. The collected wideband signals are channelized into 2048 chunks, which are then assembled into 16 sets of digital narrow basebands with 128 MHz bandwidth and transmitted to the processing servers through two 100 Gb ports. A HASPIPE pipeline, UWB_HASHPIPE is designed to receive and store multiple subbands in parallel. Data distribution links can be flexibly configured based on IP addresses and port numbers. The system is verified by pulsar observation experiments on the Nanshan 26 m telescope. 512 MHz bandwidth is selected from the collected L -band receiver signals and recorded in VDIF file format with 8 parallel instances. The test results show that the data integrity is excellent, and the signal-to-noise ratio of the band-merged pulsar profile is stronger than single subband data. This paper provides a high-performance and flexible solution for the design of versatile UWB backends. Meanwhile, the developed platform can be integrated into QTT backends for baseband data collection and Very Long Baseline Interferometry observation. [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]

The construction industry requires comprehensive and accurate as-built information for a variety of applications, including building renovations, historic building preservation and structural health monitoring. Reality capture technology facilitates the recording of as-built information in the form of point clouds. However, the emerging development trends of scan planning and multi-technology fusion in point cloud acquisition methods have not been adequately addressed in research regarding their effects on point cloud registration quality and data quality in the built environment. This study aims to extensively investigate the impact of scan planning and multi-technology fusion on point cloud registration and data quality. Registration quality is evaluated using registration error (RE) and scan overlap rate (SOR), representing registration accuracy and registration coincidence rate, respectively. Conversely, data quality is assessed using point error (PE) and coverage rate (CR), which denote data accuracy and data completeness. Additionally, this study proposes a voxel centroid approach and the PCP rate to calculate and optimize the CR, tackling the industry's challenge of quantifying point cloud completeness. [ABSTRACT FROM AUTHOR]

Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targeting the general population. Based on data from the Panel Study Labour Market and Social Security (PASS), an annual probability-based mixed-mode survey on the labor market and poverty in Germany, we find that smartphone ownership and ownership of smartphones with specific OSs are correlated with a number of sociodemographic and substantive variables. The use of weighting techniques based on sociodemographic information available for both owners and nonowners reduces these differences but does not eliminate them. [ABSTRACT FROM AUTHOR]

The Work Sampling (WS) technique, used worldwide to understand how workers spend their time, represents a time-consuming and costly activity. Therefore, several researchers work on different approaches to automate the data collection using sensor-based and vision-based technologies. The challenge of all the sensor-based approaches is that they do not provide the share of time in different work categories. The lack of knowledge on a possible correlation between Direct Work and, e.g., presence, location, or worker movement represents a gap in the current body of knowledge. Thus, this research aims to understand the correlation between Direct Work as the independent predictor variable; and Movement as the dependent response variable. The authors used the data gathered through the application of WS in five case studies on building renovation projects in Denmark. To explain this correlation. The authors selected a combination of four quantitative techniques: (1) curve estimation; (2) linear regression; (3) ANOVA analysis; and (4) t-test. The correlation of the result is discussed considering three assumptions: (1) the structure of the day; (2) global vs. local; and (3) Movement vs. Transporting and Walking. The result shows a significant correlation between Direct Work and Movement with an average R2 of 0.328. This is considered acceptable predictability taking the socio-technical system aspect of a construction site into account. [ABSTRACT FROM AUTHOR]

The collection and use of data on climate change and its impacts are crucial for effective climate adaptation and climate risk management. The revolution in internet access, technology and costs has led to a shift from using traditional paper‐based data collection to the use of Mobile Data Collection using Personal Digital Assistants (PDA) such as smartphones and tablets. In this paper, we report our experiences using both approaches for a household and business survey during a climate adaptation study in two Nigerian cities—Makurdi and Calabar. The focus of this paper is to evaluate and compare the effectiveness of using traditional paper‐based data collection and PDAs as data collection tools for climate change study in African societies. In Calabar, data were collected using paper questionnaires, while in Makurdi the questionnaires were developed on Open Data Kit (ODK) and administered using PDAs. Results show that data collection using PDA was faster, cheaper, more accurate and resulted in fewer omissions than paper‐based data collection. There was a time saving of four (4) minutes per questionnaire and a 24% cost saving when using PDA. PDA provides additional benefits where platforms can collect images, videos and coordinates. This significantly improved the credibility of the data collection process and provided further data that allowed for the mapping of environmental phenomena by linking survey research with geo‐referenced data in a geographic information systems platform to provide spatial representations of social and environmental system convergence. PDA offers a tool for collecting data that will make necessary socio‐environmental data available in a faster, reliable and cheaper manner; future research can build on this study by discovering other possible but less highlighted benefits of PDA. Although, with great benefits, there are lessons to be learnt and issues to consider when deploying PDA in large‐scale household surveys. [ABSTRACT FROM AUTHOR]

In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture. [ABSTRACT FROM AUTHOR]

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. [ABSTRACT FROM AUTHOR]

Research that involves a large and broad sample of museums can produce a representative picture of the entire museum sector and lead to global insights that may not be attainable through a more local lens. However, many museum research projects use a small sample of museums, meant to represent the entire field. We propose a research method that distributes data collection across a broad swath of museums to provide local detail that can be used to assemble a collective picture on a topic of interest to the field. This method, called crowdsourced data collection, was used in a yearlong study of zoos and aquariums in North America, in which 95 institutions were asked to collect data for one to two survey modules per month. We hoped this approach would produce data comparable to data gathered with conventional methods and reduce burden on participating institutions. We found the method replicated nationally representative studies with two validated scales. While only one third of the institutions completed all modules, institutions typically did 8-9 modules, with only slight decreases in the probability of completing the study over time. These results suggest researchers can use crowdsourced data collection to reliably study the museum sector. We also discuss the challenges of this method for researchers and institutions participating as data collection sites. [ABSTRACT FROM AUTHOR]

Today, the use of information and communication technology is very important in making the internet of things (IoT) elements distributable around the earth. With the development of IoT topics, today unmanned aerial vehicles (UAV) are utilized as a platform for gathering data from various IoT devices located worldwide. Determining the number and optimal locations of drones can minimize energy consumption in this data-collection system in the IoT. Using a promising multi-objective optimization algorithm (MOA) can achieve this goal. In this research, a bio-inspired MOA, termed the multi-objective spotted hyena optimizer (MOSHO), is employed on the data-collection platform for a group of IoT devices in a geographical area. The results of this method have been compared with other evolutionary MOAs. The analysis of the results shows that the MOSHO has a noteworthy consequence on the process of optimal energy consumption in this system, in addition to a high convergence associated with better diversity and robustness. The results of this research can be used to identify the optimization parameters in this system. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users' demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed. [ABSTRACT FROM AUTHOR]

At present, many budget companies have realized high-level computerized accounting and have high-speed and advanced database management systems. In order to adapt to the growth of budget entity informatization, the financial department must improve its own financial budget level. Introduce information technology, and develop corresponding financial budget software to better perform its own financial calculation function. This paper explores and analyzes the full collaborative matching algorithm of enabling big data financial budget and builds a financial big data financial budget platform through offline and online data collection. Combined with the matching algorithm and linear regression prediction algorithm in data analysis, the financial data is analyzed in depth. Finally, the PSO-SA algorithm runs 10 rounds on the datasets with matching scales d = 10 and 30, respectively. It is found that the fluctuation is large when d = 10 and relatively stable when d = 30. When d = 10 , the maximum value is 7.2354, and the minimum value is 6.9969. When d = 10 , the maximum value is 26.6403, and the minimum value is 23.9599. It can be concluded that when d = 0 , PSO-SA can obtain a relatively good matching scheme, but it is easy to fall into a local optimal solution. The superposition effect of "data + computing power + algorithm + scenario" can help enterprises make better decisions. Embed complex analysis into daily management and trading scenarios to build a financial empowerment platform. Make the increasingly complex work more automated and intelligent, and improve financial efficiency. [ABSTRACT FROM AUTHOR]

Natural illumination has an important place in home automation applications. Among other advantages, it contributes to better visual health, energy savings, and lower CO2 emissions. Therefore, it is important to measure illuminance in the most accurate and cost-effective way. This work compares several low-cost commercial sensors (VEML 7700, TSL2591, and OPT3001) with a professional one (ML-020S-O), all of them installed outdoors. In addition, a platform based on the Internet of Things technology was designed and deployed as a centralized point of data collection and processing. Summer months have been chosen for the comparison. This is the most adverse situation for low-cost sensors since they are designed for indoor use, and their operating range is lower than the maximum reached by sunlight. The solar illuminance was recorded every minute. As expected, the obtained bias depends on the solar height. This can reach 60% in the worst circumstances, although most of the time, its value stays below 40%. The positive side lies in the good precision of the recordings. This systematic deviation makes it susceptible to mathematical correction. Therefore, the incorporation of more sensors and data that can help the global improvement of the precision and accuracy of this low-cost system is left as a future line of improvement. [ABSTRACT FROM AUTHOR]

The energy of Wireless Sensor Network (WSN) in edge network is limited. In order to save energy, data bundling is proposed to reduce the energy consumed in setting up connection links by joining several packets into a single unit. However, although current data bundling schemes can reduce energy consumption, they will increase the delay of data routing, so it makes the multi-hop end to end delay a lot in WSN. Also, there is no appropriate solution to tackle the delayed growing problem caused by data bundling. And the energy consumption of the Acknowledgment (ACK) transmission is not comprehensively considered either. Regarding to those issues of previous researches, in this paper, a Data and ACK Bundling Scheme (DAB) is proposed to aggregate several data or ACK packets into a single unit to reduce the energy consumption as well as the end to end delay of the WSN. In the proposed DAB scheme, not only multiple packets but also ACK bundling into a single unit can reduce energy consumption. More importantly, the parameters of bundling technology are optimized from a global perspective in DAB scheme, so that WSN can not only improve the network lifetime, but also reduce the end to end delay which is impossible in previous studies. The theoretical analysis and experimental results illustrate that, while ensuring the reliability of data collection, our DAB scheme improves the energy efficiency by 29.03 % and reduces the delay by 22.29 % when compared with our traditional data collection scheme. [ABSTRACT FROM AUTHOR]

With the rise of smart city applications, the accessibility of users' location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation. [ABSTRACT FROM AUTHOR]

As a data-centric network, the Mobile Crowd Sensing (MCS) collects and uploads sensing data through intelligent terminal devices carried by workers. However, due to resource limitations, the confidentiality, integrity and communication cost issues of sensing data have not been well coordinated and resolved in the actual MCS data collection process. In this regard, this paper proposes an edge computing-assisted MCS Chaotic Compressed Sensing Secure Data Collection scheme (CCS-SDC), which supports the secure collection of sensing data and saves communication cost. In CCS-SDC, workers first use the encryption algorithm based on chaos theory to encrypt the collected sensing data, and then adopt the hash location algorithm based on chaos theory to calculate the corresponding hash verification code of the sensing data. After receiving the encrypted sensing data transmitted by the worker, the edge server recomputes the hash verification code of the encrypted sensing data and verifies the integrity of the data, which can locate the changed sensing task data to a certain extent. Then the sensing data is compressed and sampled based on the generated chaos measurement matrix to reduce the amount of data transmission and further enhance the confidentiality of the sensing data. In addition, the same hash positioning algorithm is used between the edge server and the sensing platform to protect data integrity. For the changed data located by integrity verification, in addition to choosing to let workers re-sense and submit, the sensing platform can also choose to discard the changed sensing data under appropriate circumstances, and still reconstruct and decrypt the remaining data through the proposed algorithm to obtain effective original sensing data. The experimental evaluation results on real data sets show that CCS-SDC achieves the best effects, not only achieving lower sensing data communication cost than other related schemes, but also better protecting the confidentiality and integrity of sensing data, which is very useful for resource-constrained MCS data collection scenarios. [ABSTRACT FROM AUTHOR]

Voluntary detection of emerging disease outbreaks is considered essential for limiting their potential impacts on livestock industries. However, many of the strategies employed by animal health authorities to capture data on potential emerging disease threats rely on farmers and veterinarians identifying situations of concern and then voluntarily taking appropriate actions to notify animal health authorities. To improve the performance of these systems, it is important to understand the range of socio‐cultural factors influencing the willingness of individuals to engage with disease reporting such as trust in government, perceived economic impacts, social stigma and perceptions of 'good farming'. The objectives of this systematic review were to assess how different social research methodologies have been employed to understand the role these socio‐cultural dimensions play in voluntary disease reporting and to discuss limitations to address in future research. The review uncovered 39 relevant publications that employed a range of quantitative and qualitative methodologies including surveys, interviews, focus groups, scenarios, observations, mixed‐methods, interventions and secondary data analysis. While these studies provided valuable insights, one significant challenge remains eliciting accurate statements of behaviour and intentions rather than those that reflect desirable social norms. There is scope to develop methodological innovations to study the decision to report animal disease to help overcome the gap between what people say they do and their observable behaviour. A notable absence is studies exploring specific interventions designed to encourage disease reporting. Greater clarity in specifying the disease contexts, behavioural mechanisms and outcomes and the relationships between them would provide a more theoretically informed and policy relevant understanding of how disease reporting works, for which farmers, and in which disease contexts. [ABSTRACT FROM AUTHOR]

With the continuous deepening and development of tobacco manufacturing informatization construction, tobacco manufacturing enterprises have the characteristics of strong production capacity, high degree of production automation, and advanced enterprise-level management information system. In order to cooperate with the market and brand strategy of the tobacco industry, the State Tobacco Monopoly Administration has carried out serious strategic thinking on informatization and customized the overall plan and strategic goals. In recent years, China's informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. In the deployment and development of mobile services, it is often local and municipal mobile companies that, in order to meet the different needs of the market, immediately carry out corresponding services, resulting in one service for each municipal subsidiary in the same province or one platform for a class of services. Situation is good for quickly meeting user needs at that time. The exchange and communication of enterprise personnel information are becoming more and more frequent, and the collection of enterprise information through mobile has become a new way of enterprise information collection. The work orders, process parameters, documents, notifications, and instructions generated by the planning layer are sent to the production control layer to respond, guide, and trigger the production site events. The ultimate goal of mobile data collection is to gather the collected data into the enterprise production data center, which plays an important role in mobile devices, mobile performance, mobile planning, mobile balancing, mobile prediction, and so on. Based on the unified mobile application data collection method, this paper analyzed and analyzed the design of application architecture in the tobacco enterprise platform and further described the business functions, security performance, and system characteristics of the platform. [ABSTRACT FROM AUTHOR]

Currently, a large number of mature methods are available for gaze estimation. However, most regular gaze estimation approaches require additional hardware or platforms with professional equipment for data collection or computing that typically involve high costs and are relatively tedious. Besides, the implementation is particularly complex. Traditional gaze estimation approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are primarily based on the characteristics of pupil and iris, which uses pupil shapes or infrared light and iris glint to estimate gaze, requiring high-quality images shot in special environments and other light source or professional equipment. We herein propose a two-stage gaze estimation method that relies on deep learning methods and logistic regression, which can be applied to various mobile platforms without additional hardware devices or systematic prior knowledge. A set of automatic and fast data collection mechanism is designed for collecting gaze images through a mobile platform camera. Additionally, we propose a new annotation method that improves the prediction accuracy and outperforms the traditional gridding annotation method. Our method achieves good results and can be adapted to different applications. [ABSTRACT FROM AUTHOR]

In order to solve the problem of data collection of intelligent construction of large-scale projects, the author proposes the application of BIM technology in the collection of intelligent construction data of large-scale projects; this method proposes to take advantage of the technical advantages of BIM; starting from the needs of the commercial complex operation and management stage, a BIM-based commercial complex operation management model is established, and the application content of this model to improve the efficiency of commercial complex operation and management is studied. The experimental results show that according to the design conditions and the depth of BIM application, the standard elements and variable factors can be divided into 70% and 30%, respectively. Conclusion. BIM can realize the refinement, intelligence, and quality of commercial complex operation. [ABSTRACT FROM AUTHOR]

With the development of the times, traditional fitness running data collection methods have shown a series of problems, such as low data accuracy, slow collection speed, and insufficient search ability of modeling algorithms. These problems have led to a sharp rise in the difficulty of building a more accurate fitness running data model. Therefore, this study proposed a set of methods for fitness running data collection and evolution modeling based on digital information technology. First, it filters out the impulse noise during the transmission of the three-dimensional acceleration signal caused by the accidental state change of the technology, by using the data collection method for fitness running based on the digital information platform, relying on a multisensor and median filter algorithm. Second, it filters out the gravitational component of this platform based on the orientation sensor. Then, according to the results of the first step, an algorithm is constructed for the model of fitness running data evolution. The purpose is to expand the search range so that the fitness running data model with better performance can be obtained more effectively. The experimental results show that the method based on digital information technology designed in this study has an overwhelming advantage over the traditional acquisition method, the constructed model is more accurate, and the efficiency of data acquisition is increased by 88%. Therefore, with the aid of digital information technology, the design of the data model can reduce the probability of mold repair and the time consumption of mold trials and reduce the design cost. [ABSTRACT FROM AUTHOR]

In this paper, a multisensor data acquisition system based on FPGA is designed to provide an overview of the current development of the company based on the theoretical knowledge of enterprise development strategy. Through a comprehensive investigation of the company's internal environment, the advantages and disadvantages of the company at this stage are clarified, and the opportunities and threats to the company's development are analyzed in the context of the external macroenvironment and industry development. The coupling coordination degree from 2011 to 2025 was measured using the coupling coordination degree model based on the original data and the prediction results, and the results showed that from 2011 to 2025, the coupling coordination degree of Hebei Province showed a continuous growth and experienced the transition from low coordination, basic coordination, to medium coordination in the coupling coordination stage, aiming at the most innovative fields, mastering the most advanced technologies, cooperating with industry giants, innovating, and building digital ecology, creating, and forming core competitiveness based on core independent products and platform-based solutions, and further promoting transformation and upgrading and high-quality development. In this paper, the data acquisition system is tested by building an experimental platform. The main purpose is to test the stability and accuracy of each module of the data acquisition system. After each module test of the system meets the design requirements, the parallel mechanism attitude transformation test experiment is completed, including the moving platform moving test experiment and the moving platform rotation test experiment. The experimental results show that the designed data acquisition system has stable program and reliable function. [ABSTRACT FROM AUTHOR]

Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the "multiscale model to model cloud comparison using precision maps" plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole. [ABSTRACT FROM AUTHOR]

Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent studies investigating hypothetical willingness to complete mobile data collection tasks via an app suggest there may be substantial resistance, in particular, due to concerns around data privacy. There is not much evidence about how privacy concerns influence actual decisions to participate in app-based surveys. Theoretical approaches to understanding privacy concerns and survey participation decisions would suggest that the influence of the former over the latter is likely to vary situationally. In this paper, we present results from a methodological experiment conducted in the context of a three-wave probability-based online panel survey of the general population as part of the 2019 Swiss Election Study ("Selects") testing different ways of recruiting participants to an app. Questions included at wave 1 about online data privacy concerns and comfort sharing different types of data with academic researchers allow us to assess their impact on both hypothetical willingness to download a survey app for completing questionnaires, to take and share photos, and to share the smartphone's GPS location and actual completion of these tasks. Our findings confirm that general concerns about online data privacy do influence hypothetical willingness to complete mobile data collection tasks, but may be overridden by how comfortable people feel about sharing specific types of data with researchers. When it comes to actual compliance with task requests, however, neither privacy concerns nor comfort sharing data seem to matter. We conclude with recommendations for exploring these relationships further in future app-based 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]

• Trellis network data collection platform enables name disambiguation. • Trellis network data collection platform enables multiple language interviews. • Trellis report allows for real-time monitoring of data collection. • Trellis network data collection platform can be used offline in remote areas. Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis ' ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location. [ABSTRACT FROM AUTHOR]

Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability. [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]

One of the most significant sources of information from customers is customer complaints. Successful and effective complaint management can end complaint crises and ensure client loyalty, which is a sign of great service performance. In this paper, we proposed a novel customer temperament-centered and e-CCH system-based data collection and data mining method titled "3D" model for customer complaint data analysis. Three phases are (1) Development and launch of e-Customer Complaint Handling system, (2) Data collection and transfer of learning by e-Customer Complaint Handling system, and (3) Data mining by e-Customer Complaint Handling system. An advanced e lectronic C ustomer C omplaint H andling System called the e-CCH system was then developed and launched. This system adapts the seasonal associations model based on Hippocrates's customer temperament theory to the whole stages of customer complaint reporting and handling. With this system, we conducted a dataset collection work from restaurant chains of two brands over four years. As a result, we collect thousands of real-world temperament-centred customer complaint cases by four years to form the one-of-a-kind CCH dataset. This one-of-a-kind CCH dataset was open-sourced with detailed customer complaint attributes and heuristic decision-making for valuable industrial handling manner. After further analysis of this dataset, we found that customers with different temperament types tend to have different types of complaints. In addition, adapting the temperament theory to the e-CCH system can classify customer types better and provide personalized solutions. To our best knowledge, this rich and the one-of-a-kind CCH dataset reported in this paper is the first comprehensive study of customer complaint handling in an industrial service management context. Meanwhile, data mining with cross analysis and correspondence analysis and an ChatGPT experiment for transfer of learning based on this yearly and one-of-a-kind industrial customer complaint dataset was analyzed and discussed. In addition, how this dataset may contribute to more realistic complaint-handling theoretic studies for better service failure recovery and interactive marketing is discussed in-depth. [ABSTRACT FROM AUTHOR]