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Abstract Understanding the complex interactions between external and internal factors that influence pollinator foraging behaviour is essential to understand ecosystem functioning, design agricultural practices or develop effective conservation strategies. However, it remains challenging to collect large and reliable data sets with reasonable personnel and workload. In this study, we present a wireless and cost‐effective robotic flower equipped with internet of things (IoT) technology that automatically offers nectar to visiting insects while monitoring visitation time and duration. The robotic flower is easy to manipulate and settings such as nectar refill rates can be remotely altered, making it ideal for field settings. The system transmits data completely wirelessly and autonomously, is mobile and easy to clean. The prototype settings allow for approximately 2 weeks of uninterrupted data collection for each battery charge. As a proof‐of‐concept application, a foraging preference dual choice experiment with bumblebees was performed. On average, more than 7000 flower visits per colony were registered daily with a set‐up consisting of 16 robotic flowers. The data show a gradual preference shift away from the pre‐trained low concentration, confirming the hypothesis of favouring sugar water with higher concentration. The robotic flower provides accurate and reliable data on insect behaviour, significantly reducing the price and/or labour costs. Although primarily designed for (bumble)bees, the system could be easily adapted for other flower‐visiting insects. The robotic flower is user‐friendly and can be easily adapted to address a wide range of research questions in pollination ecology, conservation biology, biocontrol and ecotoxicology, and allows for detailed studies on how nectar traits, flower colour and shape or pollutants affect foraging behaviour.

Understanding the complex interactions between external and internal factors that influence pollinator foraging behaviour is essential to understand ecosystem functioning, design agricultural practices or develop effective conservation strategies. However, it remains challenging to collect large and reliable data sets with reasonable personnel and workload.In this study, we present a wireless and cost‐effective robotic flower equipped with internet of things (IoT) technology that automatically offers nectar to visiting insects while monitoring visitation time and duration. The robotic flower is easy to manipulate and settings such as nectar refill rates can be remotely altered, making it ideal for field settings. The system transmits data completely wirelessly and autonomously, is mobile and easy to clean.The prototype settings allow for approximately 2 weeks of uninterrupted data collection for each battery charge. As a proof‐of‐concept application, a foraging preference dual choice experiment with bumblebees was performed. On average, more than 7000 flower visits per colony were registered daily with a set‐up consisting of 16 robotic flowers. The data show a gradual preference shift away from the pre‐trained low concentration, confirming the hypothesis of favouring sugar water with higher concentration.The robotic flower provides accurate and reliable data on insect behaviour, significantly reducing the price and/or labour costs. Although primarily designed for (bumble)bees, the system could be easily adapted for other flower‐visiting insects. The robotic flower is user‐friendly and can be easily adapted to address a wide range of research questions in pollination ecology, conservation biology, biocontrol and ecotoxicology, and allows for detailed studies on how nectar traits, flower colour and shape or pollutants affect foraging behaviour. [ABSTRACT FROM AUTHOR]

Collagen type I, the main component of the extracellular matrix in vertebrates, is widely used in tissue engineering applications. This is on account that collagen molecules can self‐assemble under certain conditions into 3D fibrillar hydrogels. Although there is an extensive body of literature studying collagen self‐assembly, there is a lack of systematic understanding on how different experimental factors, such as pH and temperature, and their cumulative effects guide the self‐assembly process. In this work, a comprehensive workflow to study the interactive effects of several assembly parameters on the collagen self‐assembly process is implemented. This workflow consists of: 1) efficient statistical sampling based on Design of Experiments, 2) high‐throughput and automated data collection and 3) automated data analysis. This approach enables to screen several parameters simultaneously and derive a set of mathematical equations that link parameters with the kinetics and morphological aspects of collagen self‐assembly, and can be used to design collagen constructs with predefined characteristics. [ABSTRACT FROM AUTHOR]

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field. [ABSTRACT FROM AUTHOR]

Smart integration of water, energy, agriculture, and environmental systems can create synergies, increase socio-economic benefits, and minimize environmental impact. However, effective planning of integrated water-energy-food-environment systems (iWEFEs) requires high resolution temporal and spatial data on various environmental and socioeconomic variables. Insufficient data availability and accessibility hampers the implementation of iWEFEs, particularly in remote areas of low- and middle-income countries. Addressing this gap, first, essential variables for the planning of iWEFEs are identified. Next, remote datasets are evaluated and selected regarding their suitability to serve for the planning of iWEFEs using a multi-criteria-analysis considering data accessibility, spatial coverage, spatial resolution, temporal resolution, and temporal coverage. Remote and in-situ data collection for the identified WEFE variables are implemented using a pilot case study of a smallholder farm in the data-scarce tropics of Costa Rica. The remote data collection is automated via APIs to open servers, data analysis and data visualization scripts, and complemented by an online survey. In-situ measurements are recommended to address data gaps in remote sensing, which are especially prevalent in the water domain. The research shall lay the foundation for free, open and automated data collection enabling the planning of iWEFEs worldwide.

Environmental protection is becoming increasingly important in the maritime sector, particularly in the port area. Both sectors have a significant impact on the environment due to activities such as cargo handling, road and rail traffic and marine vessel operations. One of the significant aspects of port operations is emissions to the atmosphere. However, building atmospheric emission inventories in ports is a challenging task that includes intensive data collection campaigns as well as significant financial investments in data processing and analysis. This assists the decision-makers to undertake timely corrective actions and curb adverse impacts. However, current methodologies for building emission inventories have a considerable time lag since emissions are evaluated weeks or months after they have occurred. This paper aims at solving this issue by providing a methodology for building air emission inventories in real-time using IoT data sources with an emphasis on building comprehensive emission inventories in an automated fashion. To validate the approach, an atmospheric emission inventory was built based on Internet of Things (IoT) data for the port of Piraeus. The results indicate that nitrogen oxides (NOX) emissions are prevalent during both operating phases of vessels, followed by sulphur oxides (SOX) emissions. Non-methane volatile organic compounds (NMVOC) and particulate matter (PM) emissions are considerably lower. Emissions during hotelling time are on average 7.1 time higher than the emission generated during the vessel manoeuvring time. In the discussion section, the advantages and constraints of the approach are given with guidelines for further refinement of the proposed methodology. [ABSTRACT FROM AUTHOR]

The utility of X-ray crystal structures determined under ambient-temperature conditions is becoming increasingly recognized. Such experiments can allow protein dynamics to be characterized and are particularly well suited to challenging protein targets that may form fragile crystals that are difficult to cryo-cool. Room-temperature data collection also enables time-resolved experiments. In contrast to the high-throughput highly automated pipelines for determination of structures at cryogenic temperatures widely available at synchrotron beamlines, room-temperature methodology is less mature. Here, the current status of the fully automated ambient-temperature beamline VMXi at Diamond Light Source is described, and a highly efficient pipeline from protein sample to final multi-crystal data analysis and structure determination is shown. The capability of the pipeline is illustrated using a range of user case studies representing different challenges, and from high and lower symmetry space groups and varied crystal sizes. It is also demonstrated that very rapid structure determination from crystals in situ within crystallization plates is now routine with minimal user intervention.

Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction.

The movement of vehicles in and out of the predefined enclosure is an important security protocol that we encounter daily. Identification of vehicles is a very important factor for security surveillance. In a smart campus concept, thousands of vehicles access the campus every day, resulting in massive carbon emissions. Automated monitoring of both aspects (pollution and security) are an essential element for an academic institution. Among the reported methods, the automated identification of number plates is the best way to streamline vehicles. The performances of most of the previously designed similar solutions suffer in the context of light exposure, stationary backgrounds, indoor area, specific driveways, etc. We propose a new hybrid single-shot object detector architecture based on the Haar cascade and MobileNet-SSD. In addition, we adopt a new optical character reader mechanism for character identification on number plates. We prove that the proposed hybrid approach is robust and works well on live object detection. The existing research focused on the prediction accuracy, which in most state-of-the-art methods (SOTA) is very similar. Thus, the precision among several use cases is also a good evaluation measure that was ignored in the existing research. It is evident that the performance of prediction systems suffers due to adverse weather conditions stated earlier. In such cases, the precision between events of detection may result in high variance that impacts the prediction of vehicles in unfavorable circumstances. The performance assessment of the proposed solution yields a precision of 98% on real-time data for Malaysian number plates, which can be generalized in the future to all sorts of vehicles around the globe. [ABSTRACT FROM AUTHOR]

Advances in biologging technologies have significantly improved our ability to track individual animals' behaviour in their natural environment. Beyond observations, automation of data collection has revolutionized cognitive experiments in the wild. For example, radio‐frequency identification (RFID) antennae embedded in 'puzzle box' devices have allowed for large‐scale cognitive experiments where individuals tagged with passive integrated transponder (PIT) tags interact with puzzle boxes to gain a food reward, with devices logging both the identity and solving action of visitors.Here, we extended the scope of wild cognitive experiments by developing a fully automated selective two‐option foraging device to specifically control which actions lead to a food reward and which remain unrewarded. Selective devices were based on a sliding‐door foraging puzzle, and built using commercially available low‐cost electronics.We tested it on two free‐ranging PIT‐tagged subpopulations of great tits Parus major as a proof of concept. We conducted a diffusion experiment where birds learned from trained demonstrators to get a food reward by sliding the door either to the left or right. We then restricted access of knowledgeable birds to their less preferred side and calculated the latency until birds produced solutions as a measure of behavioural flexibility.A total of 22 of 23 knowledgeable birds produced at least one solution on their less preferred side after being restricted, with higher‐frequency solvers being faster at doing so. In addition, 18 of the 23 birds reached their solving rate from prior to the restriction on their less preferred side, with birds with stronger prior side preference taking longer to do so.We therefore introduce and successfully test a new selective two‐option puzzle box, providing detailed instructions and freely available software that allows reproducibility. It extends the functionality of existing systems by allowing fine‐scale manipulations of individuals' actions and opens a large range of possibilities to study cognitive processes in wild animal populations. [ABSTRACT FROM AUTHOR]

Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction. [ABSTRACT FROM AUTHOR]

Animal welfare standards are used within the food industry to demonstrate efforts in reaching higher welfare on farms. To verify compliance with those standards, inspectors conduct regular on-farm animal welfare assessments. Conducting these welfare assessments can, however, be time-consuming and prone to human bias. The emergence of Digital Livestock Technologies (DLTs) offers new ways of monitoring farm animal welfare and can alleviate some of the challenges related to animal welfare assessments by collecting data automatically and more frequently. Whilst automating welfare assessments with DLTs may be promising, little attention has been paid to farmers’ perceptions of the challenges that could prevent successful implementation. This study aims to address this gap by focusing on the trial of a DLT (a 3D machinelearning camera) to automate mobility and body condition scoring on 11 dairy cattle farms. Semi-structured, in-depth interviews were conducted with farmers, technology developers and a stakeholder involved in a farm assurance scheme (N14). Findings suggest that stakeholders perceived important benefits to the use of the camera in this context, from building consumer trust by increasing transparency to improved management efficiency. There was also a potential for greater consistency in data collection and thus for enhanced fairness across the UK dairy sector, particularly on the issue of lameness prevalence. However, stakeholders also raised important concerns, such as a lack of clarity around data ownership, reliability, and use, and the possibility of some farmers being penalised (e.g., if the technology failed to work). More clarity should thus be given to farmers in relation to data governance and evidence provided in terms of technical performance and accuracy. The findings of this study highlighted the need for more inclusive approaches to ensure farmers’ concerns are adequately identified and addressed. These approaches can help minimise negative consequences to farmers and animal welfare, whilst maximising the potential benefits of automating welfare-related data collection.

Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission.

Background: Obtaining patient-reported outcomes (PROs) is becoming a standard component of patient care. For nonacademic practices, this can be challenging. From this perspective, we designed a nearly autonomous patient outcomes reporting system. We then conducted a prospective, cohort pilot study to assess the efficacy of the system. Methods: We created an automated system to gather PROs. All operative patients for 4 surgeons in an upper-extremity private practice were asked to participate. These patients completed the Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) questionnaires preoperatively and received follow-up e-mails requesting patients to complete additional QuickDASH questionnaires at 3, 6, and 12 weeks postoperatively and to complete a 13-week postoperative satisfaction survey. Response rates and satisfaction levels are reported with descriptive statistics. Results: Sixty-two percent of participants completed the 3-week assessment, 55% completed the 6-week assessment, and 43% completed the 12-week assessment. Overall, 35% of patients completed all questionnaires, and 73% completed at least 1 postoperative assessment. The collection of follow-up questionnaires required no additional time from the clinical staff, surgeon, or a research associate. Conclusions: Automated e-mail assessments can collect reliable clinical data, with minimal surgeon or staff intervention required to administer and collect data, minimizing the financial cost. For nonacademic practices, without access to additional research resources, such a system is feasible. Further improvements in communication with patients could increase response rates. [ABSTRACT FROM AUTHOR]

Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission. [ABSTRACT FROM AUTHOR]

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara's Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasmaprotein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations. [ABSTRACT FROM AUTHOR]

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara’s Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.

Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states.

Serial protein crystallography has emerged as a powerful method of data collection on small crystals from challenging targets, such as membrane proteins. Multiple microcrystals need to be located on large and often flat mounts while exposing them to an X-ray dose that is as low as possible. A crystal-prelocation method is demonstrated here using low-dose 2D full-field propagation-based X-ray phase-contrast imaging at the X-ray imaging beamline TOMCAT at the Swiss Light Source (SLS). This imaging step provides microcrystal coordinates for automated serial data collection at a microfocus macromolecular crystallography beamline on samples with an essentially flat geometry. This prelocation method was applied to microcrystals of a soluble protein and a membrane protein, grown in a commonly used double-sandwich in situ crystallization plate. The inner sandwiches of thin plastic film enclosing the microcrystals in lipid cubic phase were flash cooled and imaged at TOMCAT. Based on the obtained crystal coordinates, both still and rotation wedge serial data were collected automatically at the SLS PXI beamline, yielding in both cases a high indexing rate. This workflow can be easily implemented at many synchrotron facilities using existing equipment, or potentially integrated as an online technique in the next-generation macromolecular crystallography beamline, and thus benefit a number of dose-sensitive challenging protein targets.

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Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states. [ABSTRACT FROM AUTHOR]

In forensic science, a robust and sound interpretation and evaluation of transferred fibre evidence requires an understanding of the principles and mechanisms that underpin fibre transfer, yet existing research lacks consistency and repeatability. This study investigates the impact of washing activities on both the release of fibres into wastewater and the transfer of constituent fibres from donor garments to receiver swatches. Using a low-cost friction tester and automated data collection through photography and ImageJ image processing software, controlled conditions were maintained for repeated experiments. Results indicated significant fibre release during wash cycles, with load size and donor garment history playing crucial roles. The donor garments subjected to repetitive washes exhibit a progressive decrease in the number of fibres transferred, independently of the load size. This study underscores the importance of considering a garment's washing history in forensic science contexts, but also for consistency in the way that data are collected. [ABSTRACT FROM AUTHOR]

Serial rotation electron diffraction (SerialRED) has been developed as a fully automated technique for three-dimensional electron diffraction data collection that can run autonomously without human intervention. It builds on the previously established serial electron diffraction technique, in which submicrometre-sized crystals are detected using image processing algorithms. Continuous rotation electron diffraction (cRED) data are collected on each crystal while dynamically tracking the movement of the crystal during rotation using defocused diffraction patterns and applying a set of deflector changes. A typical data collection screens up to 500 crystals per hour, and cRED data are collected from suitable crystals. A data processing pipeline is developed to process the SerialRED data sets. Hierarchical cluster analysis is implemented to group and identify the different phases present in the sample and to find the best matching data sets to be merged for subsequent structure analysis. This method has been successfully applied to a series of zeolites and a beam-sensitive metal–organic framework sample to study its capability for structure determination and refinement. Two multi-phase samples were tested to show that the individual crystal phases can be identified and their structures determined. The results show that refined structures obtained using automatically collected SerialRED data are indistinguishable from those collected manually using the cRED technique. At the same time, SerialRED has lower requirements of expertise in transmission electron microscopy and is less labor intensive, making it a promising high-throughput crystal screening and structure analysis tool.

Cryocrystallography is a technique that is used more often than room‐temperature data collection in macromolecular crystallography. One of its advantages is the significant reduction in radiation damage, which is especially useful in synchrotron experiments. Another advantage is that cryopreservation provides simple storage of crystals and easy transportation to a synchrotron. However, this technique sometimes results in the undesirable adhesion of frost to mounted crystals. The frost produces noisy diffraction images and reduces the optical visibility of crystals, which is crucial for aligning the crystal position with the incident X‐ray position. To resolve these issues, a computer‐controlled device has been developed that drizzles liquid nitrogen over a crystal to remove frost. It was confirmed that the device works properly, reduces noise from ice rings in diffraction images and enables the centering of crystals with low visibility owing to frost adhesion. [ABSTRACT FROM AUTHOR]

A DigitalMicrograph script, InsteaDMatic, has been developed to facilitate rapid automated 3D electron diffraction/microcrystal electron diffraction data acquisition by continuous rotation of a crystal with a constant speed, denoted as continuous rotation electron diffraction. The script coordinates microscope functions, such as stage rotation, and camera functions relevant for data collection, and stores the experiment metadata. The script is compatible with any microscope that can be controlled by DigitalMicrograph and has been tested on both JEOL and Thermo Fisher Scientific microscopes. A proof of concept has been performed through employing InsteaDMatic for data collection and structure determination of a ZSM‐5 zeolite. The influence of illumination settings and electron dose rate on the quality of diffraction data, unit‐cell determination and structure solution has been investigated in order to optimize the data acquisition procedure. [ABSTRACT FROM AUTHOR]

New technologies create and archive digital traces —records of people's behavior—that can supplement and enrich psychological research. Digital traces offer psychological-science researchers novel, large-scale data (which reflect people's actual behaviors), rapidly collected and analyzed by new tools. We promote the integration of digital-traces data into psychological science, suggesting that it can enrich and overcome limitations of current research. In this article, we review helpful data sources, tools, and resources and discuss challenges associated with using digital traces in psychological research. Our review positions digital-traces research as complementary to traditional psychological-research methods and as offering the potential to enrich insights on human psychology. [ABSTRACT FROM AUTHOR]

Owing to the development of brilliant microfocus beamlines, rapid‐readout detectors and sample changers, protein microcrystallography is rapidly becoming a popular technique for accessing structural information from complex biological samples. However, the method is time‐consuming and labor‐intensive and requires technical expertise to obtain high‐resolution protein crystal structures. At SPring‐8, an automated data‐collection system named ZOO has been developed. This system enables faster data collection, facilitates advanced data‐collection and data‐processing techniques, and permits the collection of higher quality data. In this paper, the key features of the functionality put in place on the SPring‐8 microbeam beamline BL32XU are described and the major advantages of this system are outlined. The ZOO system will be a major driving force in the evolution of the macromolecular crystallography beamlines at SPring‐8. An automated data‐collection system named ZOO has been developed. This system enabled faster data collection, facilitated advanced data‐collection and data‐processing techniques, and permitted the collection of higher quality data. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) is quickly gaining ground in ecological research, following global trends toward automated data collection and big data. Using unattended sound recording, PAM provides tools for long-term and cost-effective biodiversity monitoring. Still, the extent of the potential of this emerging method in terrestrial ecology is unknown. To quantify its application and guide future studies, we conducted a systematic review of terrestrial PAM, covering 460 articles published in 122 journals (1992–2018). During this period, PAM-related studies showed above a fifteenfold rise in publication and covered three developing phases: establishment, expansion, and consolidation. Overall, the research was mostly focused on bats (50%), occurred in northern temperate regions (65%), addressed activity patterns (25%), recorded at night (37%), used nonprogrammable recorders (61%), and performed manual acoustic analysis (58%), although their applications continue to diversify. The future agenda should include addressing the development of standardized procedures, automated analysis, and global initiatives to expand PAM to multiple taxa and regions. [ABSTRACT FROM AUTHOR]

Field studies seeking to identify interactions between the environment and behaviors of wild songbirds are often restricted by time, labor, and accessibility of the site; hampering the collection of long‐term, high‐resolution data. Here, we describe the development, utilization, and initial results of a long‐term field study of wild songbird feeding patterns using data collected through an inexpensive microcomputer‐controlled automated feeder. Our studies indicate the "smart feeder" is capable of reliable and accurate data collection on feeding and behavioral metrics over long durations with relation to a wide range of environmental conditions. This enables detailed analysis of songbird's environment–behavior interactions. Our results have identified trends in environment–behavior interactions, microhabitat variations, species‐specific feeding profiles, and differences in the frequency and involvement of displacement events. Computerized feeders enabled us to address environment–behavior interactions, resulting in more detailed data than traditional observational methods. This reinforces conclusions from previous work regarding the potential for automated data collection to be adapted for a wide variety of research studies across the field of ethology. Computer‐automated feeders provide a new way to collect hard‐to‐access data on behavior–environment interactions in songbirds. The PASSER system does just that, enabling a novel way to approach to ecobehavioral questions with a "big data" twist on spatiotemporal environmental variations. [ABSTRACT FROM AUTHOR]

We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the Kilobot and compatible with existing hardware, the Kilogrid is a modular system composed of a grid of computing nodes, or modules that provides a bidirectional communication channel between the Kilobots and a remote workstation. In this paper, we describe the hardware and software architecture of the Kilogrid system as well as its functioning to accompany its release as a new open hardware tool for the swarm robotics community. We demonstrate the capabilities of the Kilogrid using a 200-module Kilogrid, swarms of up to 100 Kilobots, and four different case studies: exploration and obstacle avoidance, site selection based on multiple gradients, plant watering, and pheromone-based foraging. Through this set of case studies, we show how the Kilogrid allows the experimenter to virtualize sensors and actuators not available to the Kilobot and to automatize the collection of data essential for the analysis of the experiments. [ABSTRACT FROM AUTHOR]

Pavlovian eye blink conditioning (EBC) has been extensively studied in humans and laboratory animals, providing one of the best-understood models of learning in neuroscience. EBC has been especially useful in translational studies of cerebellar and hippocampal function. We recently reported a novel extension of EBC procedures for use in sheep, and now describe new advances in a digital video-based system. The system delivers paired presentations of conditioned stimuli (CSs; a tone) and unconditioned stimuli (USs; an air puff to the eye), or CS-alone Bunpaired^ trials. This system tracks the linear distance between the eyelids to identify blinks occurring as either unconditioned (URs) or conditioned (CRs) responses, to a resolution of 5 ms. A separate software application (Eye Blink Reviewer) is used to review and autoscore the trial CRs and URs, on the basis of a set of predetermined rules, permitting an operator to confirm (or rescore, if needed) the autoscore results, thereby providing quality control for accuracy of scoring. Learning curves may then be quantified in terms of the frequencies of CRs over sessions, both on trials with paired CS–US presentations and on CS-alone trials. The latency to CR onset, latency to CR peak, and occurrence of URs are also obtained. As we demonstrated in two example cases, this video-based system provides efficient automated means to conduct EBC in sheep and can facilitate fully powered studies with multigroup designs that involve paired and unpaired training. This can help extend new studies in sheep, a species well suited for translational studies of neurodevelopmental disorders resulting from gestational exposure to drugs, toxins, or intrauterine distress. [ABSTRACT FROM AUTHOR]

The distribution of child sexual exploitation (CE) material has been aided by the growth of the Internet. The graphic nature and prevalence of the material has made researching and combating difficult. Although used to study online CE distribution, automated data collection tools (e.g., webcrawlers) have yet to be shown effective at targeting only relevant data. Using CE-related image and keyword criteria, we compare networks starting from CE websites to those from similar non-CE sexuality websites and dissimilar sports websites. Our results provide evidence that (a) webcrawlers have the potential to provide valid CE data, if the appropriate criterion is selected; (b) CE distribution is still heavily image-based suggesting images as an effective criterion; (c) CE-seeded networks are more hub-based and differ from non-CE-seeded networks on several website characteristics. Recommendations for improvements to reliable criteria selection are discussed. [ABSTRACT FROM AUTHOR]

We present a new software package called Focus that interfaces cryo-transmission electron microscopy (cryo-EM) data collection with computer image processing. Focus creates a user-friendly environment to import and manage data recorded by direct electron detectors and perform elemental image processing tasks in a high-throughput manner while new data is being acquired at the microscope. It provides the functionality required to remotely monitor the progress of data collection and data processing, which is essential now that automation in cryo-EM allows a steady flow of images of single particles, two-dimensional crystals, or electron tomography data to be recorded in overnight sessions. The rapid detection of any errors that may occur greatly increases the productivity of recording sessions at the electron microscope. [ABSTRACT FROM AUTHOR]

• We explored farmers' attitudes on automated and mobility scoring for farm assurance. • The camera could help improve transparency, efficiency, and consistency in data collection. • This can promote improved welfare management, consumer trust, and fairness. • Concerns over data ownership, reliability, complacency and mandatory use were raised. • Farmers should be included in early discussions to anticipate negative consequences. Animal welfare standards are used within the food industry to demonstrate efforts in reaching higher welfare on farms. To verify compliance with those standards, inspectors conduct regular on-farm animal welfare assessments. Conducting these welfare assessments can, however, be time-consuming and prone to human bias. The emergence of Digital Livestock Technologies (DLT s) offers new ways of monitoring farm animal welfare and can alleviate some of the challenges related to animal welfare assessments by collecting data automatically and more frequently. Whilst automating welfare assessments with DLTs may be promising, little attention has been paid to farmers' perceptions of the challenges that could prevent successful implementation. This study aims to address this gap by focusing on the trial of a DLT (a 3D machinelearning camera) to automate mobility and body condition scoring on 11 dairy cattle farms. Semi-structured, in-depth interviews were conducted with farmers, technology developers and a stakeholder involved in a farm assurance scheme (N14). Findings suggest that stakeholders perceived important benefits to the use of the camera in this context, from building consumer trust by increasing transparency to improved management efficiency. There was also a potential for greater consistency in data collection and thus for enhanced fairness across the UK dairy sector, particularly on the issue of lameness prevalence. However, stakeholders also raised important concerns, such as a lack of clarity around data ownership, reliability, and use, and the possibility of some farmers being penalised (e.g., if the technology failed to work). More clarity should thus be given to farmers in relation to data governance and evidence provided in terms of technical performance and accuracy. The findings of this study highlighted the need for more inclusive approaches to ensure farmers' concerns are adequately identified and addressed. These approaches can help minimise negative consequences to farmers and animal welfare, whilst maximising the potential benefits of automating welfare-related data collection. [ABSTRACT FROM AUTHOR]

Social evolution has led to a stunning diversity of complex social behavior, in particular in vertebrate taxa. Thorough documentation of social interactions is crucial to study the causes and consequences of sociality in gregarious animals. Wireless digital transceivers represent a promising tool to revolutionize data collection for the study of social interactions in terms of the degree of automation, data quantity, and quality. Unfortunately, devices for automated proximity sensing via direct communication among animal-borne sensors are usually heavy and do not allow for the investigation of small animal species, which represent the majority of avian and mammalian taxa. We present a lightweight animal-borne sensor node that is built from commercially available components and uses a sophisticated scheme for energy-efficient communication, with high sampling rates at relatively low power consumption. We demonstrate the basic functionality of the sensor node under laboratory conditions and its applicability for the study of social interactions among free-ranging animals. The first field tests were performed on two species of bats in temperate and tropical ecosystems. At <2 g, this sensor node is light enough to observe a broad spectrum of taxa including small vertebrates. Given our specifications, the system was especially sensitive to changes in distance within the short range (up to a distance of 4 m between tags). High spatial resolution at short distances enables the evaluation of interactions among individuals at a fine scale and the investigation of close contacts. This technology opens new avenues of research, allowing detailed investigation of events associated with social contact, such as mating behavior, pathogen transmission, social learning, and resource sharing. Social behavior that is not easily observed becomes observable, for example, in animals living in burrows or in nocturnal animals. A switch from traditional methods to the application of digital transceiver chips in proximity sensing offers numerous advantages in addition to an enormous increase in data quality and quantity. For future applications, the platform allows for the integration of additional sensors that may collect physiological or environmental data. Such information complements social network studies and may allow for a deeper understanding of animal ecology and social behavior. [ABSTRACT FROM AUTHOR]

The aim of this project was construction of a universal platform for rapid development and implementation of interactive computer-based collection of clinical data. A TabletPC was used to pilot-test the platform and to implement two self-administered questionnaires: SF-12 Health Survey (SF-12) and Health Utilities Index (HUI). Qualitative analysis of the system acceptance in 12 patients showed that computer-assisted data collection in elderly patients with no previous computer experience can be successfully implemented using a TabletPC.