Monitoring Of Behavior Research Articles

Abnormal behavior monitoring enhanced smart university stadium under the background of “Internet plus”

AbstractWith the rapid development of the Internet of Things and 5G technology, smart university gymnasiums have become more and more important. However, it has become increasingly difficult for university gymnasium management, especially to detect abnormal behavior with dense crowds under limited venue space. To handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) abnormal behavior detection system which consists of the 5G camera, 5G transmission network and cloud platform. The 5G camera captures and transmits the video to the cloud platform by exploiting the 5G wireless sensor network. In the cloud platform, a hybrid variational autoencoder backbone which exploits the pre‐trained VGG16 and Transformer model is deployed to detect abnormal behaviors. Moreover, by introducing adversarial training mechanisms, the robustness of the proposed model is effectively improved. The experimental results on our self‐built gymnasium abnormal behavior dataset show that the proposed model can correctly identify most of the abnormal behaviors in the gymnasium compared to other models.

Soundscape characteristics of RAS tanks holding Atlantic salmon (Salmo salar) during feeding and feed withdrawal

Behavioural monitoring can provide crucial information on welfare and feeding in aquaculture. Passive acoustic monitoring of behaviour can be particularly useful in recirculating aquaculture systems (RAS), as they often have turbid water that impairs visual monitoring. Currently, little is known about the sounds that make up the soundscapes in RAS tanks holding Atlantic salmon (Salmo salar). In this study, hydrophones were used to continuously record the soundscape in eight single tank RAS holding Atlantic salmon parr for 15 days, with the fish in four of the tanks being fasted by feed withdrawal for five days from the sixth to the tenth day. The results show that soundscapes in RAS tanks are affected by feeding. Two main sound sources were identified during feeding in RAS tanks, one related to pellets delivery and the other to fish behaviour. The sound of pellets hitting the water surface had energy concentrated at frequencies between 1.7 and 4.0 kHz, with peak frequency decreasing and amplitude increasing with increasing number of pellets hitting simultaneously. The feeding sounds of Atlantic salmon had energy concentrated at frequencies between 6.5 and 9.4 kHz.More complex soundscapes were recorded during feeding events. These were characterized by variations in amplitude and frequency that have been described by using acoustic indexes in RAS tanks for the very first time. The Acoustic Complexity Index (ACI), the Acoustic Entropy Index (H) and the Normalized Difference Soundscape Index (NDSI) showed distinct changes in the soundscape related to feeding events; ACI increased while H and NDSI decreased compared to the times in between scheduled feeding times. The sound types identified in this study and the outcomes of the acoustic indices indicate a possibility to monitor system performance as well as fish behaviour in the tank soundscapes in RAS. Soundscape monitoring can contribute to match feeding closer to fish appetite, improve water quality, and reduce risks that deviations in the system performance can have on fish welfare during production.

Exploring Behaviour Change of Healthcare Professionals while Implementing a Complex Nursing Intervention: A Survey Study

In this study we explore the change in behaviour in terms of capability, opportunity and motivation of all healthcare professionals involved in the implementation of the complex nursing intervention PREDOCS in twelve cardiac surgery centres in the Netherlands. A multicentre survey in twelve Dutch cardiac surgery centres, implementing a complex nursing intervention: the PREDOCS consultation. Convenience samples of involved healthcare professionals completed the questionnaire, in 2018 (n = 92) and 2019 (n = 73). The COM-B-QP questionnaire based on COM-B items, MIDI items and open fields was developed and adapted to context-specific factors of every participating hospital. Depending on their role, participants rated their level of agreement with 14–26 predefined statements. The median and interquartile range and the difference/delta in medians at two-time points were calculated. At both moments in time, high scores were identified with no significant difference in five COM-B items: psychological capability, physical and social opportunity and reflective and automatic motivation. Professionals were generally well informed about the PREDOCS consultation and the PREDOCS consultation fits within their current work and profession. Staff shortages, staff turnover and insufficient insight into the effect were reported as reasons that hindered further implementation. We measured no significant change in the behaviour of healthcare professionals over time. It is unclear to what extent staff shortages, staff turnover and insufficient insight into effect influenced behaviour. Given the complexity of implementation, repeated monitoring of behaviour seems useful, so that continuous adjustment of the implementation strategy can take place.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

Dynamic behavior monitoring of twin supertall buildings during Super Typhoon Soksuri using social sensing data

This paper presents the dynamic behavior monitoring of the 300-m-tall Xiamen twin towers during Super Typhoon Soksuri by means of social sensing, which involves two individuals equipped with six smart devices to conduct acceleration measurement and video footage filming. The measured acceleration data are first corrected by the resampling technique since the sampling rate of smart-device-based measurement is unstable, and the corrected building acceleration obtained from different smart devices shows good consistency. Based on the corrected building acceleration responses, the amplitude-dependent and time-variant structural dynamic properties of the monitored twin towers are revealed by the random decrement technique and the Bayesian spectral density approach, respectively. The determined structural dynamic properties are further compared with seven existing prediction models to evaluate their accuracy. Moreover, by employing a computer-vision technique (i.e., the Kanade-Lucas-Tomasi optical-flow approach), the structural dynamic displacement was extracted from the video footage filmed during Soksuri, and its accuracy was further validated through comparison with direct acceleration measurements. This paper aims to demonstrate the feasibility and effectiveness of social sensing for dynamic behavior monitoring of supertall buildings.

Revisiting vision-based violence detection in videos: A critical analysis

An ever-increasing installation of surveillance cameras at different places for ensuring public safety, security and asset protection has triggered the need for intelligent video surveillance to monitor the people and their behavior. Violence detection is a prominent application of intelligent surveillance as it plays a vital role in public safety, behavior monitoring, and law enforcement. It deals with identifying whether the violent event or behavior occurred in video sequence or not. Various researchers have developed different techniques and features for the detection of violence in recent years. The purpose of this paper is to provide an expository study of various state-of-the-art approaches for detecting violence in videos. It is evident from the ongoing effort in this field and with the advancement in computer vision technology, previous approaches get surpassed and new methods and features always keep on developing. Therefore, in order to complement ongoing research, it is also imperative to conduct a comprehensive analysis of different works from time to time. In this survey, each work has been critically analyzed, along with its pros and cons. The violence detection techniques have been divided under three categories: handcrafted features based, deep learning and hybrid violence detection approaches that have been extensively reviewed in various sub-categories. The major challenges faced by researchers and the steps involved in the process of violence detection are also discussed. Moreover, unlike typical practices of comparing the performance of models within same datasets, we examined a few models for cross-dataset assessment and revealed their limited generalization. This emphasizes the crucial need for robust model generalization to ensure efficacy across varied real-world scenarios. Finally, the paper carried out a discussion of various research gaps in the current approaches and the possible solutions to be taken to resolve them, laying a solid foundation for future work in this area.

Atomic Force Microscopy Lifetime Analysis: An Intuitive Method for Evaluating Receptor Tyrosine Kinase Dimer-Targeting Inhibitors.

Overexpression of receptor tyrosine kinases (RTKs) or binding to ligands can lead to the formation of specific unliganded and liganded RTK dimers, and these two RTK dimers are potential targets for preventing tumor metastasis. Traditional RTK dimer inhibitor analysis was mostly based on end point assays, which required cumbersome cell handling and behavior monitoring. There are still challenges in developing intuitive process-based analytical methods to study RTK dimer inhibitors, especially those used to visually distinguish between unliganded and liganded RTK dimer inhibitors. Herein, taking the mesenchymal-epithelial transition factor (MET) receptor, an intuitive method for evaluating MET inhibitors has been developed based on atomic force microscopy (AFM) lifetime analysis. The time interval between the start of the force and the bond break point was regarded as the bond lifetime, which could reflect the stability of the MET dimer. The results showed that there was a significant difference in the lifetime (τ) of unliganded MET dimers (τ1 = 207.87 ± 4.69 ms) and liganded MET dimers (τ2 = 330.58 ± 15.60 ms) induced by the hepatocyte growth factor, and aptamer SL1 could decrease τ1 and τ2, suggesting that SL1 could inhibit both unliganded and liganded MET dimers. However, heparin only decreased τ2, suggesting that it could inhibit only the liganded MET dimer. AFM-based lifetime analysis methods could monitor RTK dimer status rather than provide overall average results, allowing for intuitive process-based analysis and evaluation of RTK dimers and related inhibitors at the single-molecule level. This study provides a novel complementary strategy for simple and intuitive RTK inhibitor research.

Development of flexible multi-phase barium titanate piezoelectric sensor for physiological health and action behavior monitoring

Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 10 4 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 10 4 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.

Ambient nano RF-Energy driven self-powered wearable multimodal real-time health monitoring

As the trend of population aging intensifies, the demand for continuous and efficient health monitoring for solitary elderly individuals and those with limited self-care capabilities is growing. Traditional wearable health monitoring devices primarily rely on battery power, which not only incurs high maintenance costs but also risks interruption of monitoring due to battery depletion. To address these issues, this paper introduces a self-powered flexible wearable monitoring device utilizing far-field Radio Frequency Energy Harvesting (RFEH) technology. This device powers integrated sensors and Bluetooth by harvesting RF energy from ambient Wi-Fi and other wireless signals, enabling real-time monitoring of the wearer's physical behaviour and health status with immediate feedback via mobile terminals. Under conditions of 100 cm distance and a power intensity of 0.8 dBm, the system can charge a 220 μF capacitor to 4.12 V within just 23.24 seconds, ensuring stable operation of the device. Moreover, the monitoring device is equipped with a low-power wireless sensor system capable of sampling up to 100 Hz, which accurately and promptly tracks and analyzes key health indicators such as walking, physiological activities, and respiratory status. This technology provides a reliable health monitoring solution for elderly individuals living alone and those with difficulties in self-care, significantly enhancing the effectiveness of remote medical services, improving their quality of life, and reducing the occurrence of emergency medical events. This research not only advances wearable device technology but also paves new paths for health management in an aging society.

Digital Twin—A Review of the Evolution from Concept to Technology and Its Analytical Perspectives on Applications in Various Fields

Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates the current state of DT technology but also anticipates its future trajectory and impact across multiple domains.

Acute nicotine vapor normalizes sensorimotor gating and reduces locomotor activity deficits in HIV-1 transgenic rats.

Despite improved life expectancy of people with HIV (PWH), HIV-associated neurocognitive impairment (NCI) persists, alongside deficits in sensorimotor gating and neuroinflammation. PWH exhibit high smoking rates, possibly due to neuroprotective, anti-inflammatory, and cognitive-enhancing effects of nicotine, suggesting potential self-medication. Here, we tested the effects of acute nicotine vapor exposure on translatable measures of sensorimotor gating and exploratory behavior in the HIV-1 transgenic (HIV-1Tg) rat model of HIV. Male and female HIV-1Tg and F344 control rats (n=57) were exposed to acute nicotine or vehicle vapor. Sensorimotor gating was assessed using prepulse inhibition (PPI) of the acoustic startle response, and exploratory behavior was evaluated using the behavioral pattern monitor (BPM). Vehicle-treated HIV-1Tg rats exhibited PPI deficits at low prepulse intensities compared to F344 controls, as seen previously. No PPI deficits were observed in nicotine-treated HIV1-Tg rats, however. HIV-1Tg rats were hypoactive in the BPM relative to controls, whilst nicotine vapor increased activity and exploratory behavior across genotypes. Cotinine analyses confirmed comparable levels of the primary metabolite of nicotine across genotypes. Previous findings of PPI deficits in HIV-1Tg rats were replicated and, importantly, attenuated by acute nicotine vapor. Evidence for similar cotinine levels suggest a nicotine-specific effect in HIV-1Tg rats. HIV-1Tg rats had reduced exploratory behavior compared to controls, attenuated by acute nicotine vapor. Therefore, acute nicotine may be beneficial for remediating sensorimotor and locomotor activity deficits in PWH. Future studies should determine the long-term effects of nicotine vapor on similar HIV/NCI-relevant behaviors.

Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis.

The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are "Computers and Electronics in Agriculture" and "Journal of Dairy Science". It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.

Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.

The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%.

Financial Incentives and Treatment Outcomes in Adolescents With Severe Obesity

Adolescent severe obesity is usually not effectively treated with traditional lifestyle modification therapy. Meal replacement therapy (MRT) shows short-term efficacy for body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) reduction in adolescents, and financial incentives (FIs) may be an appropriate adjunct intervention to enhance long-term efficacy. To evaluate the effect of MRT plus FIs vs MRT alone on BMI, body fat, and cardiometabolic risk factors in adolescents with severe obesity. This was a randomized clinical trial of MRT plus FIs vs MRT alone at a large academic health center in the Midwest conducted from 2018 to 2022. Participants were adolescents (ages 13-17 y) with severe obesity (≥120% of the 95th BMI percentile based on sex and age or ≥35 BMI, whichever was lower) who were unaware of the FI component of the trial until they were randomized to MRT plus FIs or until the end of the trial. Study staff members collecting clinical measures were blinded to treatment condition. Data were analyzed from March 2022 to February 2024. MRT included provision of preportioned, calorie-controlled meals (~1200 kcals/d). In the MRT plus FI group, incentives were provided based on reduction in body weight from baseline. The primary end point was mean BMI percentage change from randomization to 52 weeks. Secondary end points included total body fat and cardiometabolic risk factors: blood pressure, triglyceride to high-density lipoprotein ratio, heart rate variability, and arterial stiffness. Cost-effectiveness was additionally evaluated. Safety was assessed through monthly adverse event monitoring and frequent assessment of unhealthy weight-control behaviors. Among 126 adolescents with severe obesity (73 female [57.9%]; mean [SD] age, 15.3 [1.2] years), 63 participants received MRT plus FIs and 63 participants received only MRT. At 52 weeks, the mean BMI reduction was greater by -5.9 percentage points (95% CI, -9.9 to -1.9 percentage points; P = .004) in the MRT plus FI compared with the MRT group. The MRT plus FI group had a greater reduction in mean total body fat mass by -4.8 kg (95% CI, -9.1 to -0.6 kg; P = .03) and was cost-effective (incremental cost-effectiveness ratio, $39 178 per quality-adjusted life year) compared with MRT alone. There were no significant differences in cardiometabolic risk factors or unhealthy weight-control behaviors between groups. In this study, adding FIs to MRT resulted in greater reductions in BMI and total body fat in adolescents with severe obesity without increased unhealthy weight-control behaviors. FIs were cost-effective and possibly promoted adherence to health behaviors. ClinicalTrials.gov Identifier: NCT03137433.

Development of a Fleet IoT-Based System Management in Institutions

IoT-based systems are increasingly important for managing assets and activities, and implementing such a system can greatly benefit institutions like WASAC. The purpose of this system is to effectively manage WASAC's fleet, improving vehicle tracking, dispatching, fuel management, maintenance planning, driver behavior and safety, accident reduction, vehicle lifespan, customer service, unauthorized vehicle usage control, and data analysis and reporting. Currently, WASAC relies on paper-based methods for data recording and fuel management, resulting in financial losses and inadequate driver monitoring. By developing an IoT-based system, several sensors and devices such as a CAN adapter FMB140, automatic alcohol test sensor, fuel sensor, relay, I-button, and buzzer will be employed. These devices and sensors will be connected to the FMB140 IoT device, which will capture data and store them in a server database. This data will then be used to create an interactive web-based application using socket programming for fleet managers and the monitoring team. The application will be developed using PHP and React technologies. By implementing this IoT in fleet management will enhance safety and security measures for institutions specifically within the WASAC fleet management system, Real-time tracking, geofencing, prevention of fuel theft, driver behavior monitoring, and remote engine cut-off will significantly improve driver and vehicle safety while boosting fleet productivity. Overall, the IoTbased system will streamline operations, reduce costs, and enhance efficiency for WASAC's fleet management.

The crossroads of tradition and modern technology: integrative approaches to studying carnivores in low density ecosystems

The study of large carnivores in semi-arid ecosystems presents inherent challenges due to their low densities, extensive home ranges, and elusive nature. We explore the potential for the synthesis of traditional knowledge (i.e. art of tracking) and modern technology to address challenges in conservation and wildlife research in these challenging environments. Our research focuses on the African lion (Panthera leo) in the Central Kalahari region of Botswana as a model system to demonstrate the potential of this integrative approach. Combining GPS tracking and traditional San trackers’ expertise, we present two case studies: (1) the individual identification of lions via a combination of tracking and footprint analysis and (2) the monitoring of territorial behavior through a combination of GPS technology (i.e. GPS collars and handheld GPS devices) and non-invasive tracking. These approaches enhance our understanding of carnivore ecology as well as support conservation efforts by offering a non-invasive, cost-effective, and highly accurate means of monitoring populations. Our findings underscore the value of merging traditional tracking skills with contemporary analytical and technological developments to offer new insights into the ecology of carnivores in challenging environments. This approach not only improves data collection accuracy and efficiency but also fosters a deeper understanding of wildlife, ensuring the conservation and sustainable management of these species. Our work advocates for the inclusion of indigenous knowledge in conservation science, highlighting its relevance and applicability across various disciplines, thereby broadening the methodologies used to study wildlife, monitor populations, and inform conservation strategies.

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

AbstractThis paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.

Exploring the influence of user characteristics on verbal aggression towards social chatbots

ABSTRACT Chatbots possess great potential benefits, yet concerns persist regarding users adopting inappropriate, offensive language. This research delved into the influence of user characteristics on verbally aggressive behaviours towards social chatbots. Employing a mixed-method study, we examined individual characteristics such as personal dispositions, offensive language patterns, academic majors, and prior experiences with conversational agents. Findings from a ten-day field experiment involving 33 participants using a real-world Telegram-based chatbot app unveiled that users' anthropomorphism, computer-related major, and gender significantly impact their moral emotions and evaluations of the chatbot's capabilities. Moreover, employing offensive language towards the chatbot detrimentally impacted users' perceptions of its abilities, helpfulness, and likability. The research findings advocate for ongoing monitoring and effective resolution of users' behaviours regarding the use of offensive language in their interactions with a chatbot. Additionally, the results underscore the importance of incorporating diverse perspectives into chatbot design to address biases and offensive utterances.

The Impact of Monitoring on Reseller Slackness: The Mediating Role of Channel Conflict

ABSTRACT Purpose The purpose of this study is to examine how monitoring influences reseller slackness. We try to answer the following: (1) How do monitoring (behavior monitoring and output monitoring affect reseller slackness? (2) What is the mechanism by which monitoring affects reseller slackness? Methodology/approach The study proposed a theoretical framework using behavior monitoring, output monitoring, and channel conflict as the antecedents, as well as reseller slackness as a consequence. To validate the model, we collected data from resellers across multiple industries in China and analyzed data from 222 valid responses using multiple regression methods. Findings Supplier behavior monitoring decreases reseller slackness, whereas supplier output monitoring increases it. In addition, channel conflict mediates the impact of monitoring on reseller slackness. Research implications Based on the expectation that the partnership will grow in the long term, both partners should minimize channel conflict, and resellers need to curb their slack behavior. While output monitoring can give resellers more freedom to make decisions, this study shows that output monitoring can exacerbate inter-firm conflict and promote reseller slackness. Therefore, managers should be cautious about using output monitoring strategies for reseller slackness management. Practical implications Our findings suggest that behavior monitoring can mitigate channel conflict and reduce reseller slackness. However, output monitoring can facilitate channel conflict, promoting reseller slackness. As a result of these findings, suppliers can better manage reseller slackness by understanding the effects of different types of monitoring. Originality/value This study makes two contributions to existing literature. Firstly, it empirically examines the role of behavior and output monitoring on reseller slackness, thus enriching the existing literature on reseller slackness. Secondly, it further clarifies the specific pathways through which monitoring affects reseller slackness and lays the foundation for future research on managing it.

Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform

The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented.