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Abstract: As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

Despite total shoulder arthroplasty (TSA) and reverse TSA (rTSA) being fundamentally different procedures, and indicated in different pathologies (rTSA for rotator cuff deficiency [RCD] and proximal humeral fractures [PHFx] and anatomic TSA [aTSA] for glenohumeral osteoarthritis [GHOA]), they have the same Current Procedural Terminology (CPT) code (23472). This paper's aim is to investigate differences in operative time and work-related value units (wRVUs) per hour among these pathologies, and ultimately determine if there is a need to assign separate CPTs for aTSA and rTSA. A retrospective cohort of data from the American College of Surgeons–National Surgical Quality Improvement Program was collected, all patients who underwent aTSA or rTSA (CPT: 23472) between the years of 2006 and 2019 for diagnoses of GHOA, RCD, and PHFx were included. Data collected included patient age, body mass index, operative time, and wRVUs per hour. Compared to GHOA (reference group), the average operative time for the RCD cohort was 12.242 minutes shorter (P <.001), while the wRVUs were higher by 1.627 (P <.001). The average operative time for rTSAs in the PHFx cohort were 17.615 minutes longer (P <.001), while the wRVUs were lower by 2.205 (P <.001). The average operative time for rTSAs for both RCDs and PHFx were longer than that for aTSAs for GHOA. Additionally, wRVUs were lower for rTSAs for RCD and PHFx compared to aTSAs for GHOA. This elucidates inconsistency in reimbursement structure for the procedures, which should be revisited. [ABSTRACT FROM AUTHOR]

Studies published in the last year in the field of cystic fibrosis have provided more data on the safety and efficacy of a number of therapies, including mutation-specific drugs. There have also been a number of publications on monitoring of early lung disease including the use of lung clearance index and magnetic resonance scanning. Evidence suggests early lung changes may remain relatively static over the first year of life. There are important outcome differences across national patient registries and there is also the increasing recognition of psychological illnesses and possible drug interactions as treatment becomes more complicated and survival improves. [ABSTRACT FROM AUTHOR]

Wrist fractures are common injuries associated with high disability in the early recovery period. The impact of wrist fractures on safe return to drive is not understood. (1) To compare the proportion of adults who were drivers in car crashes before and after wrist fracture; (2) To examine potential factors (demographic and/or clinical) associated with increased odds of being a driver in a car crash following wrist fracture. Retrospective cohort study. Three state-wide government datasets (MainRoads Western Australia [WA], Hospital Morbidity Data Collection and the Emergency Department Data Collection) were used to obtain and link demographic, clinical and car crash information relating to adults with a wrist fracture sustained between 2008 and 2017. McNemar's tests were used to compare the proportion of drivers in a car crash within the 2 years prior to and following the fracture date. Multivariable logistic regressions were used to identify if any variables were associated with increased odds of crashing in the post-fracture period. Data relating to 37,107 adults revealed a 3.3% (95% CI 3.0%–3.6%, p < 0.05) decrease in the proportion of drivers in a car crash following wrist fracture, persisting for the entire 2 years post-fracture, when compared to the proportion who crashed before their fracture. Those with more severe wrist fracture injury patterns had 79%(95% CI 1.07–3.0, p = 0.03) higher odds of having a crash in the first 3 months following their injury, compared to those with isolated wrist fracture injuries. These results inform and update return to drive recommendations. The reduced proportion of drivers involved in crashes following wrist fracture persisted for 2 years; longer than the expected physical recovery timeframe. It is important that hand therapists actively educate the sub-group of adults with more severe wrist fracture injury patterns of the increased likelihood of car crash for the 3 months following their fracture. • This the first paper to report on car crashes following wrist fracture. • We provide much needed information to assist with return to drive decision-making. • Those with severe injury had 79% higher odds of crashing in the initial 3-months. • Education is recommended for the sub-group with 3-months of increased crash risk. [ABSTRACT FROM AUTHOR]

Acromion and scapular spine fractures (ASFs) after reverse total shoulder arthroplasty have been reported at a rate of 3.9 percent. Radiographs have been shown to be unreliable at identifying these fractures, with an estimated 20% false negative rate. A computed tomography (CT) scan is typically performed when initial radiographs are inconclusive and clinical suspicion for ASF remains high. However, the accuracy of identifying ASF fractures after reverse shoulder arthroplasty (RSA) with CT has not been studied. The purpose of this paper is to evaluate the ability of CT scans to detect clinically suspected ASF fractures after RSA when initial radiographs are inconclusive. A retrospective review of our institution's shoulder and elbow repository identified 111 patients treated with RSA from 2006 to 2022 that subsequently sustained an ASF. Upon clinical suspicion of an ASF, a 4-view radiographic series was obtained. If no fracture was clearly identified, a thin-cut CT was obtained. Additional four-view radiographic series were taken at each subsequent follow-up. Rate of detection by CT scan was defined using the number of cases with positive scans out of the number in which both a CT scan was performed, and a fracture was identified on either CT scan or other additional follow-up imaging. After inconclusive radiographs, a CT was ordered for 61 patients (55%) at a median of 1.3 weeks after the onset of signs/symptoms suspicious for ASF. Fractures were identified in 52 cases (85.2%) at a median of 1.6 weeks after the onset of sign/symptoms. The fracture was not seen on CT in 9 cases at a median of 0.9 week after the onset of sign/symptoms, representing a false negative rate of 14.8%. These fractures were later identified on radiographs at a median of 5.9 weeks following the CT scan. No statistical differences were observed in sensitivity of CT scan between fracture types. Fractures initially missed on CT scan eventually displaced and became nonunions. CT scans can be helpful in identifying and characterizing ASF following RSA, with an overall rate of detection (sensitivity) of 85%. Consideration of alternative advanced imaging may be needed for patients with negative CT scans and a high index of clinical suspicion for ASF. [ABSTRACT FROM AUTHOR]

Quality-relevant process monitoring provides important guarantees for the safety of industrial operation, which is based on the assumption that data collection is complete and low-order autocorrelated. However, real industrial processes always exhibit complex characteristics such as multi-rate sampling and high-order dynamic, which pose great challenges for process monitoring. To this end, a multi-rate high-order dynamic twin-latent-variable probabilistic (MHDTVP) model is presented in this paper to extract data correlations among multi-rate measurements from quality-relevant and irrelevant perspectives. Moreover, to reveal the dynamics in the multi-rate sampling process, an autoregressive twin-latent-variable structure is designed to extract both quality-relevant and quality-irrelevant high-order dynamic features. In the MHDTVP model, parameters are trained through an efficient expectation maximization (EM) iteration framework. Finally, the performance conclusions of MHDTVP are validated with the Tennessee Eastman process (TEP) and Thermal Power Plant (TPP). The experimental results demonstrate that the proposed model exhibits superior monitoring efficiency for multi-rate dynamic processes compared to similar approaches. [Display omitted] • A novel twin-latent-variable probabilistic model is proposed for the purpose of addressing quality-relevant and quality-irrelevant dynamic process monitoring. • To handle measurements with multiple sampling rates, a multi-rate strategy is designed based on the proposed model. • To reveal high-order autocorrelation, the model breaks the bottleneck of traditional first-order dynamic modeling in multi-rate processes. [ABSTRACT FROM AUTHOR]

EU-funded project FRANCIS focuses on involving citizens in the development of so-called Frugal Innovations, i.e. simple, affordable and sustainable solutions. It does so by organising open-innovations challenges that are managed through an IT-platform and supported by various recruitment and ideation formats. Usually, Citizen Science only involves citizens for minor tasks such as data collection. FRANCIS jump ahead in science is that the project invites them to propose and work on new solutions that have a real chance to enter the market. Before starting the challenges, the team of FRANCIS already made some assumptions about how to encourage and support participants in the best possible way based on existing publications and first findings from primary research. Now that the first challenge is running, the team had the opportunity to check whether they have proven to be true so far. This paper presents the interim lessons learned and details the way forward [ABSTRACT FROM AUTHOR]

Collecting and structuring data on which to perform specific data analyses is a long and complicated process. It requires substantial advanced planning of what data and what data characteristics will be needed for the study. It is important to specify in what format they should be collected. On the other side, there are huge amounts of data stored in a variety of applications. This data is clean and consistent. In many cases, it can be used for new analyses, different from the original purposes for which the information was collected. This is particularly true for data in healthcare. Medical information accumulated in multiple systems can be used for further research and analysis. This paper presents a methodology, appropriate data models, and relevant software decisions that support the reuse and explorations of medical data for specific treatment assessments based on data routinely collected from clinical systems. Some initial results are presented and discussed. [ABSTRACT FROM AUTHOR]

Automatic detection and prediction of falls specifically in elderly population who live alone can prevent untoward incidents by taking action at appropriate time. Validating these automated systems requires a labeled dataset. The available datasets are mostly based on inertial features. A health-related concern may also be a reason behind a fall. Considering physiological features along with inertial features can increase the accuracy of the system. In this paper, a labeled multiclass dataset is presented based on physiological and Inertial features of the body (PIF v2). Two trials are performed by each participant. The participants are young as well as elderly performing activities and falls in real-time using the prototype containing various sensors designed for collecting inertial as well as physiological features. Two trials are performed by each participant. 27 subjects (15 males and 12 females) between the age 10 years to 75 years participated in data collection. A detailed comparison between PIF v1 where the data were collected individually using each sensor and PIF v2 is given along with statistical analysis of PIF v2. The statistical analysis of various features with a t-test shows that the different activities and falls can be differentiated using inertial as well as physiological features. [ABSTRACT FROM AUTHOR]

• New developments make practice-based research more feasible than before. • Data collected during measurement-based care can contribute to research. • A primer for conducting research in private practice is provided. Mental health professionals who work in private practice and other clinical settings have huge opportunities to contribute to the science of our field. But they rarely do so. This article describes ways that practitioners who have research training can capitalize on recent developments in practice, science, and technology to conduct research in their private practice. I describe a model for conducting research as a practitioner that entails tightly integrating the research into clinical practice, and I point out why conducting research in your private practice is worth doing. The remainder of the paper provides a primer, describing strategies for implementing in a clinical setting all the elements of the research enterprise: addressing ethical and legal issues, keeping up to date with the scientific literature, selecting a good research question, conducting a single-case experimental design, finding collaborators and assistants, collecting the data, analyzing the data, writing the paper and getting it published, and handling time and money. Although this paper focuses on research in a solo or group private practice setting, many of the strategies described here can also prove useful in the conduct of research in hospital or community settings. [ABSTRACT FROM AUTHOR]

Collecting and utilizing data in industrial production are becoming increasingly important. One promising approach to utilize data is the concept of digital twin (DT). DTs are virtual representations of physical assets, updated by real data and enhanced by models. This paper provides an overview of DTs for cutting processes. After giving a definition, we discuss requirements derived from representative use cases. As process models are central for DT creation, we present an overview of the latest research as well as conditions for how it can be implemented in industrial environments. The paper concludes with main challenges for future research. [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 implementation of intelligent techniques produces good results in automating fault finding and predicting future outcomes. These approaches have been on the increase in the past years, especially so to detect faults within Compressed Air Systems (CASs). With the use of intelligent techniques, one could minimise the manual and time-consuming aspect of CAS maintenance, improve the environmental impact of the system, while minimising downtime. This paper proposes a general framework for the implementation of intelligent analysis techniques within a real-world system. Such an approach has been implemented on the demand-side of a CAS. In literature, no open datasets are available for use by artificial intelligence models. Hence, as part of this research, a fault generating and monitoring system has been connected to an existing production machine in a manufacturing site to collect the required data. Two classification machine learning methods were implemented and compared across a number of performance metrics. Both the general framework, and its implementation, provide a stepping stone in integrating smart systems with real-time intelligent data analytics for the demand-side of a CAS. These systems would provide a sustainable CAS operation through the effective detection of anomalies and their timely repair. [ABSTRACT FROM AUTHOR]

This paper presents a novel control strategy to minimize residual vibrations and overshoot using inertial dampers in repetitive tasks. In this work, vibration data collected during repeating task is utilized to generate a fully pre-scheduled feedforward compensation signal that assists the inertial damper's original feedback controller to further enhance its vibration mitigation capability. Optimal feedforward signal is determined iteratively over successive operations considering the actuator stroke and force limits. Numerical and experimental results validate the approach demonstrating significant (up to 87%) reduction in peak vibration while using equal or less actuator force as compared to the conventional control. [ABSTRACT FROM AUTHOR]

This paper deals with the state fault detection scheme for distribution flow networks subject to continuously varying conditions at boundaries. A robust PDE detection observer for transport flow systems is designed. Directly built on the nonlinear hyperbolic systems of balance laws model with anti-collocated setup, the PDE observer based on backstepping theory provide the on-line estimation of signals that are not measured. The stability of the error equation is proved. The estimation and the observability time are used for fault detection; an adaptive threshold is defined for the purpose. The performances of the observer and the fault detection method are validated on actual flow data collected from a real water distribution system (WDS) for leakage detection. The leak detection time corresponds to the first alarm activation, confirms the effectiveness of proposed approach. [Display omitted] • Robust persistent state fault detection PDE-based observer for transport flow systems is built. • The nonlinearity derives from a class of hyperbolic systems of balance laws is kept. • Backstepping boundary observer with anti-collocated setup is designed. • The online estimation of unmeasured signals and the observability time are used for fault detection. • An adaptive threshold based on disturbances bound, performance index and input signal is set. • Data are collected from a realwater distribution network. • The performances of the observer and the fault detection scheme are validated on actual flow data. [ABSTRACT FROM AUTHOR]

IoT devices which include wireless sensors, software, actuators, and computer devices operated through the Internet, enable the transfer of data among objects or people automatically without human intervention. Since these devices are resource constraint embedded devices, security policies are not implemented adequately upon these devices. The connectivity with the Internet, diversity of hardware, varied operating platforms, and surge in attack surface increases the target space for malicious cyber actors. The threat probability increases substantially since the attacker takes advantage of less secure, vulnerable devices to perform the massive-scale attack on the critical infrastructure. It has been observed that the majority of embedded IoT devices operate upon Linux-flavoured operating environments. This paper reviews the Linux-based IoT malware analysis techniques and tools employed for malware detection, analysis, and classification. Various threat data collection methods have been discussed at length and a thorough study of tools and techniques used in static and dynamic analysis of the Linux malware has been provided. A review of the machine learning methods developed using discrete features to classify the malicious program is one of the essential components of this paper. The paper concluded with a discussion on various open issues and challenges that need to be addressed by the research community at large. [ABSTRACT FROM AUTHOR]

IoT devices which include wireless sensors, software, actuators, and computer devices operated through the Internet, enable the transfer of data among objects or people automatically without human intervention. Since these devices are resource constraint embedded devices, security policies are not implemented adequately upon these devices. The connectivity with the Internet, diversity of hardware, varied operating platforms, and surge in attack surface increases the target space for malicious cyber actors. The threat probability increases substantially since the attacker takes advantage of less secure, vulnerable devices to perform the massive-scale attack on the critical infrastructure. It has been observed that the majority of embedded IoT devices operate upon Linux-flavoured operating environments. This paper reviews the Linux-based IoT malware analysis techniques and tools employed for malware detection, analysis, and classification. Various threat data collection methods have been discussed at length and a thorough study of tools and techniques used in static and dynamic analysis of the Linux malware has been provided. A review of the machine learning methods developed using discrete features to classify the malicious program is one of the essential components of this paper. The paper concluded with a discussion on various open issues and challenges that need to be addressed by the research community at large. [ABSTRACT FROM AUTHOR]

• This paper scrutinizes smart mobility project expecations' knowledge claims. • Smart knowledge claims present datafication as a producer of neutral knowledge. • Expectations based on datafication carry strong inherent legitimacy. • Expectations on societal benefits disappear from view when projects materialize. • Smart knowledge-claims risk excluding non-smart alternatives. This paper studies the performativity of smart mobility expectations in envisioning urban futures. Smart mobility, or ICT-enabled transport services, are increasingly considered a necessary ingredient for sustainability transitions in cities. Expectations of smart mobility's contribution to such a transition are constituted by a strong belief in the transformative potential of data collection and use. These knowledge claims embedded in smart mobility expectations tend to be unchallenged, yet contribute to a particular future vision of urban mobility. Our empirical analysis, which draws on two empirical smart cycling case studies in Utrecht, the Netherlands, and Bordeaux, France, underlines the politics of such smart knowledge claims in two smart cycling projects and identifies distinct processes as to how such claims may shape and structure mobility futures. We observe intimate entanglements between what is being developed in terms of technologies and services; and the societal needs that the projects' expectations promise to fulfil. At the same time, we witness a disentanglement of these interconnected knowledge claims when projects unfold, leaving the promise of (un)achieved societal benefits out of view. Indeed, smart knowledge claims carried strong inherent legitimacy in the cases studied, thereby risking to exclude non-smart alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we developed a novel data collection and analysis approach that uses manual and automated processing of large datasets and combines them to offset the limitations of past research and practice when it comes to identifying actors in complex environmental governance systems. Our empirical context is wildfire governance in Colorado. Wildfire governance represents a pressing challenge for interested actors in the western United States. We characterize Colorado's wildfire governance system by using big data triangulation techniques to identify key actors and their topical domains to reveal meaningful subgroups. Our results highlight the importance of understanding the pitfalls of each source of data and the potential benefits of combining them to gain a more complete understanding of the system. The true potential of multi-source analysis becomes apparent when one pays special attention to the degree of overlap that exists between sources. Each source reveals a unique makeup of actors (both in terms of individuals and organizational types) and different forms of engagement in the system. In addition to providing different sets of actors, we found evidence of different emergent topics in each data source. • We develop a novel method for processing large quantities of publicly available data for actor identification. • Multi-source data triangulation provides specific insights on the structure of the governance system. • Each source reveals a unique makeup of actors, their roles and topical domains in the system. • Minimal overlap between sources supports the need to critically consider biases and pitfalls associated to data sources. [ABSTRACT FROM AUTHOR]

Emotion recognition based on electroencephalography (EEG) signal features is now one of the booming big data research areas. As the number of commercial EEG devices in the current market increases, there is a need to understand current trends and provide researchers and young practitioners with insights into future investigations of emotion recognition systems. This paper aims to evaluate popular consumer-grade EEG devices' status and review relevant studies that examined the reliability of these low-cost devices for emotion recognition over the last five years. Additionally, a comparison with research-grade devices is conducted. This paper also highlights EEG-based emotion recognition research's key areas, including different feature extraction capabilities, characteristics, and machine learning algorithms. Finally, the main challenges for building an EEG-based emotion recognition system, focusing on the data collection process with commercial EEG devices and machine learning algorithms' performance, are presented. [ABSTRACT FROM AUTHOR]

• The paper presents the data collection strategy of the Swiss StudentLife Study. • The study investigated the emergence of undergraduate student communities. • It combined multiple methods of data collection, including surveys and social sensors. • Multidimensional and dynamic networks were measured on different time scales. • Practical challenges and solutions are discussed to help future empirical studies. The Swiss StudentLife Study (SSL Study) is a longitudinal social network data collection conducted in three undergraduate student cohorts (N 1 = 226, N 2 = 261, N 3 = 660) in 2016−2019. The main goal of the study was to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To this end, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, social media data, and field experiments. The dynamics of these social networks were measured on various time scales. In this paper, we present the design and data collection strategy of the SSL Study. We discuss practical challenges and solutions related to the data collection in four areas that were key to the success of our project: study design, research ethics, communication, and population definition. [ABSTRACT FROM AUTHOR]

• Social network research offers a means of understanding the complex health care system. • The use of social network research in health care is increasing. • This paper outlines recommendations for network data collection in health services research. • Key ethical challenges of social network research in health care are around confidentiality of respondents. Social network research can help us understand intractable problems central to the challenges of today's health care system: silo-working, bottlenecks, gaps, poor communication, professional isolation, and other social processes with the propensity to compromise patient safety and quality care. In this paper, we discuss practical and ethical challenges to designing and collecting quality network data faced by health services researchers. The paper develops high-utility recommendations to address these challenges. Design decisions are discussed in light of the dynamism and complexity of health care and the need for more longitudinal, mixed-methods network studies. [ABSTRACT FROM AUTHOR]

• This paper discusses a network study in the IT-Security niche, covering Design, Implementation, Analysis and key foundational issues. • In particular, it focuses on trust-building with participants in a particular social niche, and intersecting requirements that the author had to consider and address. • The paper argues that contextual awareness and understanding are extremely important for niche studies, and underlines the importance of co-design and flexibility. • The author's experience suggests that authors should spend some time learning about the niche they are interested in, potentially using qualitative methods, before designing and administering a network study. As part of a larger project on trust and co-operation in IT-Security, the author collected network data from an international security team specialized in cyber-incident response, leading to the only dataset of its kind. Collecting these data involved a number of challenges. First, the team in question had recently been established and was based in a large international firm. Thus, the author had to navigate legal and procedural issues, and stay on top of a changing environment. Second, actors in IT-Security are acutely aware of how data can be misused, requiring the author to build trust and demonstrate appropriate data handling. Attaining support from security managers was a key step to build trust with participants and the host organization. Third, precautions were necessary due to the project's security implications, as the study data could be abused by adversaries. Consequentially, it was necessary to engage in a co-design process, negotiating technical and procedural precautions. Not all measures initially agreed upon were feasible in practice. [ABSTRACT FROM AUTHOR]

• We discuss two relational data sources in depth, phone wiretaps and police-generated 'events', and the issues of validity and reliability related to each source. • For wiretaps, we discuss self-censorship, group coverage, the link between conversations and behaviour, and sampling. • For police-generated events, we discuss selective law enforcement, changing recording practices, double-counting, the counting of individuals with different degree of culpability, and sampling/extraction. • Wiretaps are normally best suited to study the internal structure of groups and police-recorded events interactions among OC groups in markets. • We discuss the challenge posed by the 'double boundary specification' problem in the context of OC. • We discuss issues related to selecting the time span of data collection. Network studies of organized crime (OC) normally explore two key relational issues: the internal structure of groups and the interactions among groups. The paper first discusses in depth two data sources that have been used to address these questions -- phone wiretaps and police-generated "events"– and reviews issues of validity, reliability and sampling. Next, it discusses challenges related to OC network data in general, focusing on the 'double boundary specification' problem and the time span of data collection. We conclude by arguing that structural analysis cannot be divorced from a deep contextual (qualitative) knowledge of the cases. The paper refers to concrete research dilemmas and solutions faced by scholars, including ourselves. [ABSTRACT FROM AUTHOR]

• Systematic data collection on covert networks is advocated. • Six key aspects of covert network data collection are identified. • Problems with secondary and missing data are discussed. • Biographies, graph databases, and checklists are proposed as starting solutions. Data quality is considered to be among the greatest challenges in research on covert networks. This study identifies six aspects of network data collection, namely nodes, ties, attributes, levels, dynamics, and context. Addressing these aspects presents challenges, but also opens theoretical and methodological opportunities. Furthermore, specific issues arise in this research context, stemming from the use of secondary data and the problem of missing data. While each of the issues and challenges has some specific solution in the literature on organized crime and social networks, the main argument of this paper is to try and follow a more systematic and general solution to deal with these issues. To this end, three potentially synergistic and combinable techniques for data collection are proposed for each stage of data collection – biographies for data extraction, graph databases for data storage, and checklists for data reporting. The paper concludes with discussing the use of statistical models to analyse covert networks and the cultivation of relations within the research community and between researchers and practitioners. [ABSTRACT FROM AUTHOR]

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay. [ABSTRACT FROM AUTHOR]

This paper provides an overview of crowdsensing and some of its applications. Crowdsensing is a part of the collecting data situations also; it's built on a data system on multiple customer interactions. Moreover, writing the general information of the smart cities can be used to boost to received number frequency to send messages. This work mentioned the Crowdsensing layers that describe Mobile crowdsensing. The article focuses on crowdsensing layers, developed an application in Coalition Game using crowdsensing in terms of GPS. In addition, this paper discussed the Mobile crowdsensing system and how important the cloud is in serving the wireless network, the Internet of things (IoT), and data collection. Furthermore, this research also has developed a smart crowdsensing parking system that will help by reducing the time-wasting users. [ABSTRACT FROM AUTHOR]

Production planning poses major challenges due to high product diversity, high fluctuations in demand and strong dynamic fluctuations in delivery quantities and times. This makes automation of production planning increasingly necessary to enable optimized sequence planning based on current production data. Production control systems (PRS) offer the possibility of automatic corrective actions in the event of deviations from the specified target dates. Production, PRS and data acquisition are considered as a single control loop and are the subject of various development and research topics. However, automatic interventions in planning and control can lead to serious disruptions if there are errors in the software of the PRS. Therefore, testing and validation of PRS is a crucial factor in development, but is hardly possible in real production environments due to the availability of physical manufacturing facilities and the associated risks. Therefore, in the scope of the paper, a concept for a software framework is proposed to provide a test environment for a PRS. A virtual production environment, which can be combined with the PRS and parameterized in form of a discrete-event simulation, offers the possibility to test the PRS. It is also possible to couple the simulation with a reference calculation to show the behavior of the production environment. The entire framework is designed to enable the identification of critical machines and times as well as, in further steps, the detection of software errors in the PRS. [ABSTRACT FROM AUTHOR]

• Modern consumer electronics increasingly contain digital traces that can be used to infer information about activities in the physical world, which might not be obtainable from any other types of forensic evidence. • Principles for forensically responsible use of such data2activity traces are described, based on practical experience. These include experimental research into accuracy, reliability and rarity of these traces. • Limitations and potential pitfalls in interpretation of data2activity traces are illustrated by two practical examples. • Future research into data2activity traces are indicated and should include development of future-proof data acquisition and storage methodology as well as development of labelling methodology for forensically relevant free-living experiments. The strong integration of consumer electronics in everyday life offers many new investigative opportunities. In particular, digital traces from smartphones, smartwatches and activity trackers can now increasingly be used to infer information about actions performed by their users in the physical world that might not be obtainable from any other types of forensic evidence. While potentially very valuable from an investigative perspective, making forensically justifiable statements about such traces can sometimes be more difficult than expected. Requirements for this have not yet received much attention in the digital forensic literature. To help filling this gap, we describe the principles we use in determining the evidential value of such traces, which emphasize the need for experimental verification. For such research, aimed at determining the evidential value of these traces, we coin the term data2activity. In this paper, we devote attention to the potential and limitations of data2activity traces, focusing on challenges and giving two examples to illustrate potential pitfalls in interpreting data. Finally, future research directions into data2activity traces are indicated that, in our opinion, should be given attention. These include development of future-proof data acquisition and storage methodology, enabling division-of-effort and sharing of information, as well as development of labeling methodology for free-living experiments. [ABSTRACT FROM AUTHOR]

• Cyclists experience 'precarious entitlement' to public space in Dublin. • Cyclists 'provoke responsibility' in other mobile subjects to be respected. • Mobile struggles for recognition do not require collective mobilisation. • Provoking responsibility involves multisensory provocation of mobile subjects. • Provides account of citizenship 'on the move' through cycling practices. In this paper, I present how cyclists in Dublin engage in a process of provoking responsibility in their interactions with other mobile subjects as means of dealing with conditions of precarious entitlement to public space. I begin the paper with an exploration of the extant literature relating to cycling, citizenship and public space, arguing that provoking responsibility can add to such literature on individual and collective cycling mobilities in that it conceptualises how cyclists engage in an individual, everyday struggle for recognition as a legitimate public space user amidst the sharing of public space with differently mobile subjects. Next, I detail the grounded theory methodology, study context (Dublin, Ireland), means of data collection (qualitative interviews), interviewee sample, and 'precarious entitlement theory'. Following this, I posit the category of 'precarious entitlement' to public space – the main concern of utility cyclists in Dublin – and conceptually outline provoking responsibility as a process of dealing with conditions of precarious entitlement, using illustrations from qualitative interviews with cyclists. I include in this outline the key modes of provoking responsibility: accentuating presence, asserting entitlement, indicating transgression, punishing transgression, and displaying responsibility. I conclude that provoking responsibility shows how cyclists can engage in everyday, mobile practices of citizenship in the name of recognition of spatial entitlements and equal status within public space without collective mobilisation; moreover, the category depicts how cyclists engage in not only multisensory practices but also multisensory provocation in their interactions with co-present mobile subjects as means of being recognised and respected. [ABSTRACT FROM AUTHOR]

Escalation in the real world applications using Internet of Things (IoT) now demands low cost and minimum power consuming devices having sensing, communicating, and computing capabilities. Low-rate Wireless Personal Area Network (LR-WPAN) is an emerging solution which can provide these capabilities with low data rate and less complexity. The longevity and connectivity of the network is a critical issue during the data collection in LR-WPAN. An Energy aware Load Balanced Clustering (ELBC) approach is proposed in this paper that enhances the network life time by minimizing the energy consumption. Clustering is done using the Golden Eagle Optimization (GEO) and by taking connectivity, Average energy of cluster, balancing factor and cohesion as parameters for optimal cluster head selection. Mobile Sink based data collection strategy using Gravitational Search Algorithm (GSA) is used to collect data from clusters. The simulation is done in network simulator-3 and the result shows that, the performance of ELBC-GEO is better in terms of network lifetime, average energy of nodes, and data collection delay. [ABSTRACT FROM AUTHOR]

Energy minimization is one of the significant tasks in the WSN which can be achieved through clustering process. Clustering in the WSN is an efficient method to increase the lifespan of the network. In the proposed work the main concentration is given to the network lifetime which is very necessary for the less node failures and high packet deliveries. In this paper the hierarchical structuring is done using clustering approach based on efficient data aggregation process. The aggregation process is defined into two phases. The first phase defines the arrangement of the nodes in the cluster using uniform distribution and second phase defines the selection of the cluster head and aggregation of the data using linear programming. The main objective is to minimize the energy consumption and increase of the robustness of the sensor network. Eventually the performance of the network will be evaluated in terms of low energy consumption, high packet deliveries and low congestions in the WSN. [ABSTRACT FROM AUTHOR]

The blockchain-based access control mechanism (BACM) is gradually becoming an essential paradigm for solving dynamic and trusted access control problems in the open network environment. However, since the current open network environment has such features as dynamic variability and the uncertainty of user identity, most of the existing BACM cannot solve the access control problems in the current open network environment in a dynamic, flexible, proactive, efficient, and fine-grained approach. In this paper, we propose a novel BACM scheme to address such problems. Specifically, we first design a new, proactive, and fine-grained access control model, by utilizing the dynamicity and fine-grain of the attribute-based access control model, flexibility shown by the trust evaluation mechanism in evaluating the trust level of users, and proactivity shown by the game evaluation mechanism in curbing malicious users who suddenly launch malicious access requests. Second, based on the above access control model, we propose a dynamic, flexible, and proactive BACM for the current open network environment, exploiting the trustworthiness and transparency that the smart contract and the transaction mechanism in blockchain technology show during program execution. Further, a double sliding storage window is built, guaranteeing accurate data acquisition by BACM while efficiently allowing it to acquire time-sensitive data during the permission management process. Meanwhile, a pre-authorization concept is introduced to improve the efficiency and flexibility of BACM in processing access control problems. Security analysis demonstrates that our proposed BACM scheme satisfies the simple security issue and the simple availability issue. Experiments on a real user trust record dataset demonstrate the high effectiveness of the proposed BACM scheme in evaluating and deciding on access requests and the superiorities over most existing schemes in dynamicity, fine granularity, flexibility, and proactivity. [ABSTRACT FROM AUTHOR]

This paper describes the concept of a novel research planned to be carried out in Polish accounting firms providing accounting services to micro and small enterprises. The accounting firms consist of around 36,000 active entities providing services to as many as about 2 million entrepreneurs. The principal objective of this research is to determine the extent of robotic process automation in Polish accounting firms, as well as to identify the benefits of and obstacles to its implementation. The research is to be structured into 6 tasks, while the methods to be applied include a literature review, interviews with the owners and accountants working in accounting firms, survey methods/pen-and-paper personal interview and computer-assisted web interview, as well as raw data collection and statistical analysis. The results of a preliminary pilot study in two accounting firms are also presented in the paper. [ABSTRACT FROM AUTHOR]

• The paper presents the data collection strategy of the Swiss StudentLife Study. • The study investigated the emergence of undergraduate student communities. • It combined multiple methods of data collection, including surveys and social sensors. • Multidimensional and dynamic networks were measured on different time scales. • Practical challenges and solutions are discussed to help future empirical studies. The Swiss StudentLife Study (SSL Study) is a longitudinal social network data collection conducted in three undergraduate student cohorts (N 1 = 226, N 2 = 261, N 3 = 660) in 2016−2019. The main goal of the study was to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To this end, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, social media data, and field experiments. The dynamics of these social networks were measured on various time scales. In this paper, we present the design and data collection strategy of the SSL Study. We discuss practical challenges and solutions related to the data collection in four areas that were key to the success of our project: study design, research ethics, communication, and population definition. [ABSTRACT FROM AUTHOR]

The EU has set itself demanding objectives to achieve complete decarbonisation by 2050. The building sector plays an important role for this purpose, hence monitoring its progress towards decarbonisation is essential to identify measures that work and to readjust policies when necessary. However, collecting information to monitor the building sector is a complex task due to the scarcity and low quality of available data. The European digital building logbook (DBL) has great potential in this regard because it allows the collection of data at the building scale, which could be combined at a national level. Thus, this paper proposes a new functionality for the DBL -as an enabler for the collection of data for the assessment of the progress of the main EU policy objectives related to the building sector decarbonisation-, and it evaluates the potential of the DBL for it. To do so, the main policy objectives were associated to a set of progress indicators –mandatory and optional- and matched to DBL indicators. Mandatory indicators are for all Member States and they have been included in the 2023 amended version of the proposal for the Energy Performance of Buildings Directive recast. Optional indicators are considered country-specific in this paper and are sourced from various literature references. Here we have studied the optional progress indicators for the case of Spain. Results show that the DBL has great potential to contribute to the collection of more than half of the mandatory indicators and a very high share of the optional ones for Spain. • Monitoring the decarbonisation progress of national building sectors is crucial. • Mandatory and optional indicators are needed to monitor buildings decarbonisation. • The DBL has great potential for monitoring national building stocks decarbonisation. • The DBL will provide the majority of the decarbonisation progress indicators. [ABSTRACT FROM AUTHOR]

This paper proposes an incipient chatter detection method to meet high dynamic applications' time and reliability constraints, such as high-speed milling involving heavy noise. The herein introduced method relies on a multiple sampling per revolution (MSPR) technique, coupled with two data preprocessing techniques, a modified adaptive cumulative chatter indicator, and a two-risk levels-based threshold. The MSPR technique enables collecting information-rich enough data to characterize the chatter dynamics thanks to a significant amount of data collected in each revolution. Therefore, the MSPR technique allows for acquiring the data using a short-time window, thus reducing the detection delay. Two data preprocessing techniques, i.e., Z-score normalization and mean-centered, are implemented for data integration and chatter information consolidation. The modified adaptive cumulative chatter indicator has three advantages: (a) it accumulates the information on the chatter feature and highlights the appearance of an incipient chatter; (b) it adapts to the variation of the environmental disturbance noises, resulting in enhanced detection reliability; (c) it is faster than the adaptive cumulative log-likelihood ratio (ACLLR) for decision-making statistically. The two-risk levels-based threshold overcomes the limitations of a unique threshold, and allows simultaneously assessing the two risk levels, thus improving detection reliability. We successfully applied the proposed method to detect incipient chatter in a digital high-speed milling process and assessed its effectiveness by comparing it with several existing chatter detection methods. • A method for incipient chatter detection in high-speed milling process based on the cumulative strategy. • Multiple sampling per revolution technique that consolidates incipient chatter information, reduce the detection delay. • Modified adaptive cumulative chatter indicator enables improving the detection reliability. • Threshold based on two-risk levels to enhancing the detection reliability. [ABSTRACT FROM AUTHOR]

Ultrasound-guided biopsy needle identification is a crucial step in clinical treatment planning, but remains challenging due to the difficulty in data acquisition that includes the ultrasound speckle interference pattern and the presence of strong linear anatomical structure. This paper introduces a real-time needle tracking method for visualizing 2D needle shapes and trajectory during interventions. Based on observations of the needle dynamics within a small fraction of dynamic ultrasound images, the current needle placement was estimated: (1) A subspace based background suppression technique was used to identify points representing possible needle locations using the consecutive dynamic frames in a sliding-window fashion. and (2) a Hough transform was then used to filter out false positives and fit the remaining points on the Hough space. Evaluation on datasets from 16 subjects demonstrated that the proposed method produced high-quality needle-only images, significantly reducing the mean trajectory error to 1.89°and the tip position error to 5.1 mm, outperforming temporal subtraction (2.65°and 14.3 mm) and Gabor filtering (2.94°and 13.8 mm). The attention-based U-Net achieved a comparable mean trajectory angle error of 1.82°but yielded a higher mean tip position error of 8.2 mm. Qualitative and quantitative analyses consistently indicated that the proposed method offers enhanced accuracy and robustness across subjects compared to competing methods. [ABSTRACT FROM AUTHOR]

The complexity of high-dimensional and noisy process signals reduces the effectiveness of conventional fault detection methods in industrial processes. Based on the hypothesis that data collected from normal and faulty processes has different characteristics, unsupervised deep neural networks, e.g., autoencoders, have been widely applied in process fault detection and achieved good performance. Many variants have been proposed to improve feature learning by combining different network structures. In this paper, a new transformer model, residual autoencoder-based transformer, is proposed for process fault detection. Firstly, autoencoder and transformer are integrated for better unsupervised feature learning of process signals. Secondly, linear embedding and attention mechanisms with bias are proposed to generate effective features from process signals. Finally, residual connections are constructed between the encoder and decoder of RATransformer to address overfitting in training. Four industrial cases are used to test the performance of RATransformer for process fault detection. The results show that the fault detection rate of RATransformer is at least 1 % higher than other comparison methods. Moreover, the testing results show that the model structure improves the fault detection performance of RATransformer. The complex models like RATransformer can be used in the industrial process when sufficient normal process data is available. An end-to-end training method can be further developed to improve the applicability of RATransformer in process fault detection in the future. • A new transformer model, residual autoencoder-based transformer (RATransformer) is proposed for process fault detection. • The feature learning improves fault detection performance in industrial process. • Residual autoencoder-based transformer provides an effective way for process control. • The effectiveness of RATransformer for fault detection is verified in industrial processes. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are usually deployed in unattended, opaque and even hostile environments; thus, they may face serious threats. In the last few years, physical layer security (PLS) has emerged as a new technique that can improve the security performance of networks. In this paper, we research the physical layer security scheme of UASNs by actively jamming against eavesdropping attacks. Utilizing the large propagation delay of the underwater acoustic (UWA) signal, the jamming signals can interfere with legitimate signals to prevent eavesdropping. Therefore, we propose an active jamming-based helper deployment scheme (AJHDS) for UASNs, which deploys the helpers to the target water area and realizes the secure transmission of the network. Both the simulation and field test results show that the scheme can significantly reduce the interception capability of eavesdroppers. Furthermore, the field sea experiment evaluates that the location of helpers affects the eavesdropper's acquisition of legitimate data packets. [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]

Urban bicycling data collection with mobile digital devices has become ubiquitous in cities across the globe in the last decade. However, planners, developers, data and city scientists often struggle to process, avoid biases, clean datasets, and select relevant data points, when processing, analyzing and modelling vast troves of data, when engineering applications for urban mobility. This paper aims to shed light on these data sources and bases using a descriptive and comparative analysis of the social layers embodied into this data. These ultimately and inadvertently might result in algorithmic analysis and predictive simulations with inadequate sensitivity and specificity outputs for desired requirements. Actors and social groups have vested and specific interests in these bicycling databases, which in turn shape data bases biases or disclosure policies. Therefore, their data will reflect mostly the way they interact with bicycles, cyclists and cities. How pervasive, available for academic research, and which uses are currently made with bicycling bulk data collections in the case of Berlin? Sixteen social groups that operate or hold digital bicycling data systems in that city are investigated. Results show that a clear distinction between the data produced by public and private social groups, as different uses and disclosure polices for it are identified. These contrasts significantly reduce the range of models and applications that can derive from this data. In the development of diagnostic and prognostic systems, public owned are more suitable, whereas private is for commercial application. Digitization of urban bicycling data appeared to produce different data quality and quantity. The brief description and grouping of a specific sociotechnical ensemble dynamic, such as the one conducted here, is particularly useful for the early stages of research, data collection planning and modelling, and in the design of requirements for technology development. [ABSTRACT FROM AUTHOR]

• the majority of participants use closeness as a basic recall principle. • one third of participants do not follow the degrees of closeness throughout. • recalls follow degree of closeness, roles and foci, and relationship properties. • recall patterns vary according to socio-economic status of participants. • closeness concepts refer to relationship properties, dynamics, and normative frames The paper investigates how study participants handle the so-called "hierarchical mapping technique", an affective name generator developed by Antonucci (1986) , which is accompanied by a diagram enabling respondents to compare alters with regard to different degrees of closeness. By applying the thinking-aloud method, we identified three patterns in the order of recalling alters: closeness as overarching schema (with either role relationships or relationship properties as subordinate schema), roles and foci as overarching schema, and a fraying schema. In addition, we investigated how study participants understand and interpret "closeness". The meanings of closeness can refer to various relationship properties, cultural framing, and relationship dynamics. Results show that specific meanings of closeness are related to different recall patterns. Furthermore, recall patterns vary according to the socio-economic status of the participants. Finally, implications for the construction of name generators and data collection are discussed. [ABSTRACT FROM AUTHOR]

• The Network Canvas suite consists of three applications: Architect ; Interviewer ; Server. • These applications simplify the collection of complex social network data. • Our work prioritizes flexibility, intuitive design, integrated workflow, and collaboration. Self-reported social network analysis studies are often complex and burdensome, both during the interview process itself, and when conducting data management following the interview. Through funding obtained from the National Institute on Drug Abuse (NIDA/NIH), our team developed the Network Canvas suite of software – a set of complementary tools that are designed to simplify the collection and storage of complex social network data, with an emphasis on usability and accessibility across platforms and devices, and guided by the practical needs of researchers. The suite consists of three applications: Architect: an application for researchers to design and export interview protocols; Interviewer: a touch-optimized application for loading and administering interview protocols to study participants; and Server: an application for researchers to manage the interview deployment process and export their data for analysis. Together, they enable researchers with minimal technological expertise to access a complete research workflow, by building their own network interview protocols, deploying these protocols widely within a variety of contexts, and immediately attaining the resulting data from a secure central location. In this paper, we outline the critical decisions taken in developing this suite of tools for the network research community. We also describe the work which guides our decision-making, including prior experiences and key discovery events. We focus on key design choices, taken for theoretical, philosophical, and pragmatic reasons, and outline their strengths and limitations. [ABSTRACT FROM AUTHOR]

Collecting social network data is challenging, not least because conventional approaches rely on human participation. However, there are instances where access to research subjects is restricted or non-existent, especially in the high-stakes commercial world. This paper outlines the collection of network data from a relatively obscure financial document – the offering circular. I consider the implications of dealing with a non-participatory population and data that is not produced for social network research. In exploring the process of translating data into social network data I highlight the importance of retaining context and qualitative descriptions in the data. I also consider how different coding strategies impact the network. Finally, I discuss the triangulation, data anonymity and potential ethical-legal implications of collecting data from documents. [ABSTRACT FROM AUTHOR]

• Collecting network chain data involves collecting a path in an unobserved whole network. • There are multiple types of chain data collection designs, with each design yielding a unique type of data. • Strategies exist to improve response rates and limit the number of censored chains. • Respondents should be allowed to skip questions, name organizations, and name prior respondents. Although most network studies involve the collection of either ego or whole network data, a smaller subset of work has focused on the collection of network chain data. Collecting network chain data involves collecting a path in an unobserved whole network , and can be useful for capturing phenomena like degrees of separation or search processes. In this paper, we draw on past network chain data collection studies and reviews to propose a design framework for network chain data collection. Next, we use this framework to describe the qualitative collection of network chain data from a pilot sample of public school educators, and the quantitative collection of network chain data from a statewide sample of 600 public school principals and superintendents. Drawing on lessons learned from these data collection efforts, we discuss specific data collection strategies for improving the quality of network chain data, reflecting on what worked and what didn't, offering recommendations for future studies involving network chain data collection. [ABSTRACT FROM AUTHOR]

• Collection of good data is often plagued with trouble even with the best intentions. • Good data collection requires careful planning, but success is not always guaranteed. • Our key message to colleagues is "be flexible, be prepared and be agile". This paper follows a research project meant to focus on understanding how universities and public research organisations (PRO) collaborate with firms in order to innovate and develop products or services using the same technology, an invention called Controlled Radical Polymerisation (CRP). We say 'meant to' quite deliberately as this project was plagued with a range of data collection issues. We aim to give the reader a sense of the complications our research team faced as we share and highlight the issues we did not properly consider in the process of collecting our data, and the strategies we implemented to save the project and collect useful data. These issues included the benefits of the choice of a face-to-face methodology, using our own networks to recruit participants, the underestimated influence of secrecy when collaborating with industry on an innovation project, the value of having a "local champion" and having specific procedures in place for operational matters; but most of all the importance of being flexible and agile. We share the lessons we learned during this journey. Importantly, we seek to give some practical advice and a sense of the reality of data collection – a reality which is often smoothed over and written up as unproblematic in academic publications. [ABSTRACT FROM AUTHOR]

• Constitutive features of social relations are lost when data naturally produced by sequential social interaction are aggregated into network ties. • Audio-visual recording is a technology that facilitates collection, storage and retrieval of complex time-sensitive information on social relations. • Video-recorded interaction within surgical teams is used to illustrate how social relations may be studied using data on continuous-time interaction. Some of the constitutive features of social relations fade from view when information naturally produced by sequential social interaction is translated into network ties. Building on core concepts and ideas developed within conversation analysis, in this paper we argue that this happens because the sequential, multimodal and embodied character of social relations can be fully understood only with reference to the sequential constrains that are generated by – and at the same time shape the micro-dynamics of social interaction. We suggest that the translation of social interaction into social networks precludes analysis of the multiple interfaces that sustain social relations (multimodality), and the material resources around which social relations are organized (embodiment). We highlight audio-visual recording as a data collection technology that facilitates storage, retrieval, and analysis of complex information on social relations that is typically absent from social network data. An illustrative video-supported case study based on the observation of social and task-related interaction among members of surgical teams provides the empirical context that supports and motivates our general reflection on network data collection strategies and technologies to study social interaction. The analysis highlights the need for social networks research to return to the study of social relations. [ABSTRACT FROM AUTHOR]