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The Internet of Things (IoT) is a paradigm of connectivity and networks for devices with electronics, sensors, and software connected to cyberphysical systems anytime and anywhere. As an innovation, the IoT aids consumers in multiple ways in the retail sector, satisfying utilitarian and hedonic shopping motivations, and as a result, exploring their shopping motivation is necessary. This study deploys the unified theory of acceptance and use of technology (UTAUT) embedded in perceived enjoyment, trust, and risk to estimate Malaysian consumers' attitude toward the IoT for online fashion shopping, eventually leading to utilitarian and hedonic shopping motivations. Quantitative survey data collected from Malaysian IoT shoppers were analyzed using partial least squares structural equation modeling. The outcomes revealed that IoT performance expectancy, effort expectancy, facilitating conditions, and perceived enjoyment significantly and positively affected attitude toward the IoT. However, the impact of social influence, perceived risk, and perceived trust on attitudes toward the IoT appeared to be insignificant. Attitude toward the IoT significantly influenced utilitarian shopping motivation, followed by value- and adventure-related shopping motivations. This assimilated study model highlights how attitude toward the IoT mediates the relationships among IoT performance expectancy, effort expectancy, facilitating conditions, and perception of enjoyment regarding shopping motivations. The current study adds to the literature by enhancing UTAUT3 and exposing allied shopping motivations toward IoT-facilitated fashion shopping. This study offers practical implications for developing trust and reducing risk to harness positive attitudes toward the IoT to enhance fashion shopping. Related shortcomings and practical implications are outlined at the end of this paper. [ABSTRACT FROM AUTHOR]

Drones, formally known as Unmanned Aerial Vehicles (UAVs), are versatile technology increasingly associated with many modern-day applications. The advancements in technology have brought about a revolution in drones, extending their applications in various fields, including forensic science. Despite their potential, the full extent of drone capabilities in forensic science remains unclear and limited by a lack of defined evidence. Therefore, this article aims to provide a comprehensive review of the current literature on the use of drones in forensic science, while also highlighting the challenges and limitations of their deployment. This review seeks to identify areas for further research and development in the use of drones in forensic science by exploring the key issues. The use of Arksey and O’Malley’s framework updated by Joanna Briggs Institute for Scoping Reviews methodology shows that drones have proven to be a valuable technology in various forensic-related events, including clandestine graves detection, crime scene investigations, traffic accident investigations, disaster assistance, and pollution detection. However, there is still inadequate information on the use of drones in forensic science, particularly in enhancing the Disaster Victim Identification (DVI) procedure during the initial phase of a disaster. Therefore, this paper aims to provide insights into the potential applications of drones in forensic science and promote their integration into related fields.Article Highlights: The article represents the first comprehensive review that categorises drone applications in forensic science. Deployment of drones in forensic science remains relatively limited, despite the advancement in drone technology. Integration of external sensors and software allows drones to perform a wider range of contemporary applications. [ABSTRACT FROM AUTHOR]

With the help of pervasive computing, human living has changed into a smarter way using the developments in IoMT, telecommunication technologies, and wearable sensors for ensuring improved healthcare services. IoMT is comprised of certain potentiality for the revolution in the healthcare industry. IoMT is associated with caregivers, healthcare providers, patients, and wearable sensors with software and ICT. The healthcare industry is also a well-known expanding market that has huge demands. It ensures the potential services towards the patients and also provides its contributions to the profits of the health sector. According to the technical advancements, a healthcare system must be developed based on decision-making capacity. Numerous researchers have also focused on involving cognitive behavior in IoT technology. Thus, in this paper, a new smart healthcare system with the help of IoT devices is suggested. Initially, the data is collected from IoMT devices, which are fed to further processing. Secondly, the data pre-processing is carried out to remove the corrupted data and for removing the noise from the data. Thirdly, the features are collected from the pre-processed data through wavelet entropy computation, and deep features are gathered using CNN. Fourthly, both extracted wavelet entropy features and deep features have undergone an adaptive fusion process using an improved meta-heuristic algorithm, thus termed adaptive wavelet entropy deep feature fusion. Finally, the classification is performed through I-RNN to get the disease-related outcomes, where the weight of RNN is optimized using a new MVS-AVOA. Through the evaluation, the performance analysis of the proposed MVS-AVOA-RNN has 41.5% better than Naive Bayes, 26.8% better than SRU, 18.3% superior to LSTM, and 5.4% enriched than RNN. Thus, the obtained result reveals that the proposed optimized RNN with an advanced feature set supersedes the aforementioned techniques. [ABSTRACT FROM AUTHOR]

Sign language recognition is considered the most important and challenging application in gesture recognition, involving the fields of pattern recognition, machine learning and computer vision. This is mainly due to the complex visual–gestural nature of sign languages and the availability of few databases and studies related to automatic recognition. This work presents the development and validation of a Brazilian sign language (Libras) public database. The recording protocol describes (1) the chosen signs, (2) the signaller characteristics, (3) the sensors and software used for video acquisition, (4) the recording scenario and (5) the data structure. Provided that these steps are well defined, a database with more than 1000 videos of 20 Libras signs recorded by twelve different people is created using an RGB-D sensor and an RGB camera. Each sign was recorded five times by each signaller. This corresponds to a database with 1200 samples of the following data: (1) RGB video frames, (2) depth, (3) body points and (4) face information. Some approaches using deep learning-based models were applied to classify these signs based on 3D and 2D convolutional neural networks. The best result shows an average accuracy of 93.3%. This paper presents an important contribution for the research community by providing a publicly available sign language dataset and baseline results for comparison. [ABSTRACT FROM AUTHOR]

Introduction: As the demand for rehabilitation in orthopaedics increases, so too has the development in advanced rehabilitation technology. However, to date, there are no review papers outlining the broad scope of advanced rehabilitation technology used within the orthopaedic population. The aim of this study is to identify, describe and summarise the evidence for efficacy for all advanced rehabilitation technologies applicable to orthopaedic practice. Methods: The relevant literature describing the use of advanced rehabilitation technology in orthopaedics was identified from appropriate electronic databases (PubMed and EMBASE) and a narrative review undertaken. Results: Advanced rehabilitation technologies were classified into two groups: hospital-based and home-based rehabilitation. In the hospital-based technology group, we describe the use of continuous passive motion and robotic devices (after spinal cord injury) and their effect on improving clinical outcomes. We also report on the use of electromagnetic sensor technology for measuring kinematics of upper and lower limbs during rehabilitation. In the home-based technology group, we describe the use of inertial sensors, smartphones, software applications and commercial game hardware that are relatively inexpensive, user-friendly and widely available. We outline the evidence for videoconferencing for promoting knowledge and motivation for rehabilitation as well as the emerging role of virtual reality. Conclusions: The use of advanced rehabilitation technology in orthopaedics is promising and evidence for its efficacy is generally supportive. [ABSTRACT FROM AUTHOR]

With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal. [ABSTRACT FROM AUTHOR]

As digital fabrication and physical computing continue to emerge, prototyping of the interactive kinetic artifacts has become common for makers. However, novice makers have difficulties in integrating components: physical mechanisms, actuators, sensors, and software code. We present a prototyping tool, M.Integrator, which supports a synthetic approach to building interactive kinetic artifacts. It helps makers consider prototypes as a whole from the early stage of the fabrication process. The sensor-based movement of the interactive kinetic artifact can be automatically converted to software code using the visual authoring interface. A maker can interactively test the interactive kinetic artifact by combining virtual and physical components. Instant physical testing is carried out by a customized prototyping board. In the user study with twelve makers, the tool allowed makers to complete the task in a short evaluation session. The visual authoring interface enabled them to generate the sensor-based movement after only a brief explanation. We found that the tool supported synthetic and iterative prototyping. Combinations of virtual and physical components supported the interactive simulation throughout the session. We conclude by discussing the implications for future digital fabrication of the interactive kinetic artifact. [ABSTRACT FROM AUTHOR]

Premature frailty is a critical challenge for young breast cancer survivors (YBCSs), impacting their health and perpetuating gender inequality through heightened vulnerability and marginalization. While digital health shows promise in frailty screening, its effectiveness for comprehensively managing frailty remains inconclusive. This randomized controlled trial, registered at the Chinese Clinical Trial Registry (ChiCTR2200058823), tests the "AI-TA" program's efficacy on premature frailty and quality of life in YBCSs. The intervention group received a gender- and generation-sensitive program combining artificial intelligence interactions and humanities skills. The control group received 12 weeks of online information support. Both groups improved in frailty dimensions (P < 0.05); the intervention group showed notable enhancements in psychological (P = 0.013) and social frailty (P < 0.001). Quality of life also improved more in the intervention group from T1 to T2 (β = 15.384, 95% CI:13.028–17.740, P < 0.001). Results show a gender- and generation-sensitive digital humanistic program can optimize frailty management, promoting survivorship and gender equity. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Internet of Things networks and its significant cybersecurity challenges, the proposal of models capable of studying malware propagation within these structures has become highly relevant. This paper aims to formulate and implement an SEIRS-NIMFA model to analyze the dissemination of malware infections with a latency period. To accomplish this, we mathematically articulated an SEIRS epidemiological model using an individual-based approach and implemented it using Python. In addition, this paper examines how varying the network size and density, the initially infected device, and several model parameters influence the propagation dynamics. Moreover, to address the Markov chain approach's high temporal and spatial complexity, we use the n-intertwined mean-field approximation method. Our findings demonstrate that our proposal can effectively aid decision-making in implementing security measures in real-world situations. Finally, our proposal and its implementation are open to further enhancements, broadening their potential applications. [ABSTRACT FROM AUTHOR]

In India, there is an urgent need to meet the food production due to the country's rapidly expanding population. There is a downturn in the farming sector and the farmers are shifting to other firms due to insufficient output in the sector. Furthermore, traditional agricultural practices followed in India are insufficient to meet the increasing food demand. The costs and operational times generally increase as the majority of agricultural activities depend significantly on human labor. The increasing prevalence of machinery on farms is not a substitute for the necessity for human labor completely. This highlights the critical importance of agricultural automation. This study highlights the significant impact of UAVs on precision and smart agriculture through automation. The implementation of drones in diverse agricultural tasks has the potential to reduce labor demands and streamline operational efficiency. This review offers a comprehensive overview of conceptual design, command flow operation, micro-controller boards, remote-control systems, and attachments such as sensors, cameras, and motors in UAVs, aimed at automating agricultural activities. The integration of the Internet of Things (IoT) in unmanned aerial vehicles (UAVs), alongside artificial intelligence and machine learning, enhances accuracy and precision in agricultural operations. This study also addresses future advancements, limitations, and challenges that farmers face in adapting to UAVs. [ABSTRACT FROM AUTHOR]

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified. [ABSTRACT FROM AUTHOR]

Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. Recently, deep learning has emerged as a promising framework for analyzing breast thermograms. However, the performance of deep neural networks heavily relies on the volume of training images, and the limited number of labeled thermograms necessitates the generation of synthetic images for training. This article introduces a novel method that eliminates redundant or destructive information in synthetic thermograms while preserving essential and independent features using deep autoencoder concepts. Consequently, the proposed strategy enhances the representation of synthetic images for training deep networks, leading to improved breast cancer detection in thermograms. Evaluating the performance of the proposed method on the DMR-IR dataset shows a significant improvement in detecting cancerous breasts in thermograms compared to benchmark methods. Integrating this innovation into the basic model achieved an average accuracy of 92.3%, sensitivity of 93%, and specificity of 91.4%, surpassing the basic model's 89.1%, 86%, and 92.5%, respectively. The proposed method demonstrated superior performance in mitigating overfitting, as evidenced by a smaller gap between training and validation curves, resulting in a 7% improvement in accuracy and a 3.2% increase in sensitivity. Although there was a slight decrease of 1.1% in specificity, it was outweighed by other parameter improvements. Additionally, the method led to a significant reduction in the dispersion range of test results, approximately six times less than alternative approaches. [ABSTRACT FROM AUTHOR]

This study focuses on the challenges, technological solutions, and sustainability prospects of solid waste management practices in eleven smart cities in Tamil Nadu, India, through a case study. It was through a systematic review and stakeholder interviews that we assessed the current waste management infrastructure, collection, and processing capabilities in these cities. The analysis concluded that measures for waste segregation remain poor, with collection efficiencies ranging between 80 and 100% and segregation rates across the sampled cities from below 50% to 100% for varying cities. Constraints also include poor infrastructure, lack of public awareness, and financial problems. We develop an integrated waste management framework that advocates source segregation, localized processing, and waste-to-energy plans. The study has shown that the efficient use of smart technologies, the development of public–private alliances, and the principles of the circular economy would significantly enhance the effectiveness of waste management practices in the smart cities of Tamil Nadu. Policy makers and urban planners focused on ameliorating municipal solid waste management systems in fast-growing urban areas can benefit from these findings.Article Highlights: Smart cities in Tamil Nadu face significant challenges in solid waste management, including inadequate infrastructure and low public awareness. Technological interventions and public-private partnerships offer opportunities to improve waste collection and processing efficiency. Implementing circular economy principles and waste-to-energy initiatives can lead to more sustainable waste management practices. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0 (I4.0) and Industry 5.0 (I5.0), a supply chain (SC) is digital and dynamic with a multi-structural framework, and should be resilient in the case of an abrupt disruption. To uncover SC characteristics under this environment, this paper proposes a five-layer interdependent supply chain network (SCN) to depict this multi-structural framework of a SC, then analyzes the topological features and dynamic evolution process of the multilayer SCN. To explore the impact of SCN structure on mitigating disruption propagation, this study presents the risk propagation mechanism in the multilayer SCN. Simulations considering four risk scenarios are carried out based on the Tesla SCN and its dynamic evolution to reveal the resilience of the multilayer SCN for mitigating the ripple effect of disruption propagation. Results show that (1) the ripple effect caused by node failure differs from that caused by the failure of the sub-network layer, which is more serious and significantly different with different network structures of SCN; (2) under the same SCN structure, different sub-networks have different impacts on the entire SCN in terms of mitigating the ripple effect; (3) in the five-layer SCN proposed in this study, product and technological structures are crucial for maintaining the stability of the SCN. Organizational, financial, and information structures are most unstable elements in the SCN. Once the nodes in these three structures fail, risk easily spreads through the entire SCN. Suggestions are provided to encourage a fresh approach to improving the resilience of the SCN. [ABSTRACT FROM AUTHOR]

Predicting Soil Organic Carbon (SOC) accurately and generating SOC distribution map holds potential for assisting farmers in assessing soil fertility, optimizing and enhancing the resource use efficiency. This study used Mica Sense Red Edge sensor mounted onboard Idea forge Q4c Unmanned Aerial System (UAS) to assess the distribution of SOC in the experimental site. Random Forest (RF) and Support Vector Machine (SVM) techniques were developed with both UAS as well as Sentinel datasets for SOC prediction. Overall, the UAS dataset exhibited greater accuracy in prediction of SOC compared to Sentinel Datasets. Random forest model provided an accurate prediction of SOC when used with the UAS dataset (RPD = 1.09, R2CV = 0.25, RPIQ = 2.57 and RMSECV = 0.06), whereas the Sentinel 2A dataset provided a better prediction of SOC with SVM model (RPD = 0.96, R2CV = 0.10, RPIQ = 0.96 and RMSECV = 0.07). The prediction map of SOC was generated using the UAS dataset with the RF model because it was found to be more accurate compared to the Sentinel and SVM model. The accuracy assessment indicators indicated that UAS based SOC prediction is having the potential in achieving more accurate predictions of SOC, which will offer an optimized agricultural practice and insights for supporting informed decision-making. [ABSTRACT FROM AUTHOR]

Biopsy-based histology has been the foundation of disease diagnosis and management for over a century. A long-sought goal in dermatology is the development of an imaging modality with sufficient resolution and compositional detail to noninvasively interrogate skin histology in vivo. Here, we describe a system that achieves this goal using cross-sectionally scanned, multimodal microscopy (cross-modal). Cross-modal combines multiphoton and reflectance confocal microscopy into one compact system with coordinated three-axis scanning that preserves optical resolution in cross-section. A custom pinhole-free mechanism employing finite-infinite conjugates further simplifies and stabilizes confocal alignment. Evaluated in participants ages 9–81 and Fitzpatrick skin types (FST) 1–5, cross-modal images revealed histological details analogous to those obtained from traditional biopsied tissue. We observed dermal elastosis in sun-damaged skin, elevated melanin in pigmented skin, basaloid nests in basal cell carcinoma, and elongated rete ridges in seborrheic keratosis, supporting cross-modal's potential to deliver histological insights noninvasively. [ABSTRACT FROM AUTHOR]

Facing sustainability challenges and the demand for green and high-quality food, smart agriculture has become a key solution, and understanding consumer preferences for its products is crucial for sustainable development. By employing a structural equation model and using the sample data of an online survey conducted in China, this study investigated consumers' intention to purchase smart agricultural products, thereby examining the effects of value co-creation, cue utilization, attitude, and future orientation. According to the results, cue utilization positively affects value co-creation and attitude. In addition, value co-creation and attitude can promote consumers' purchase intention. Moreover, future orientation positively moderates the effect of attitude on purchase intention. These findings have substantial practical implications for formulating marketing strategies aimed at promoting the consumption of smart agricultural products. By strengthening the effects of cue utilization, value co-creation, and future orientation, marketers can specifically target consumers and facilitate the transition to more sustainable consumption patterns. [ABSTRACT FROM AUTHOR]

Extended reality (XR) applications for education are usually associated with motivation, engagement, knowledge and experiences that are difficult to achieve in the real world. One example of such knowledge in military education is the threat-based approach to protection of civilians in armed conflicts. The Norwegian Defence University College (NDUC) educates officers on the threat-based approach. This approach challenges conventional military practice, introducing new ways of thinking about what military forces can and cannot do to protect civilians from violence. Today, militaries are expected to protect civilians from perpetrators that target them as part of their warfare, expanding their responsibilities under International Humanitarian Law. This expansion of tasks represents "troublesome knowledge" for many military officers and demands a deeper understanding of the rationales and strategies driving perpetrators' targeting of civilians. To overcome this pedagogical challenge, we developed an XR-supported educational program combining immersive experiences and pedagogical approaches, including combined 360°-videos and embodiment, dialogue with virtual humans and collaborative VR-landscapes to encourage immersive peer-to-peer learning. The results are encouraging, suggesting high levels of acceptance by learners of the threat-based concept, an effect on stimulating critical discussion and a positive reaction to XR-supported learning. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology that has multiple applications. Multiple Internet of Things (IoT) sensors gather environmental data. The hubs collect environmental data from sensor nodes. Global sensors send notifications to a central command center regarding any alterations. Nevertheless, the system may be hindered by constraints such as computing limitations, power restrictions, transmission limitations, and restricted sensor store capacity. Our goal is to decrease energy consumption in IoT systems by utilizing swarm intelligence. The effectiveness of swarm intelligence, which is based on stochastic global hunt phenomena, relies on the randomization of nodes in IoT networks. Our solution analyzes crucial metaheuristics for IoT network performance. We are researching techniques to minimize energy consumption in sensors in order to prolong the lifespan of IoT networks. This analysis selects the most efficient node for IoT energy use. Efforts should be made to enhance the exploration of wolves by promoting the exchange of knowledge, similar to how bees do, but yet preserving their hunting techniques. Modify the settings of GWO (Grey Wolf Optimizer) to prevent premature convergence and enhance the speed of searching the probable search zone. We assess the performance of ABC, GWO, GWO-GA, and PSGWO algorithms in comparison to HABCGWO. Findings: The suggested framework achieves a decrease in energy usage of 19.8% for ABC, 21.6% for GWO, 33.9% for GWO-GA, and 27.8% for PSGWO. Conclusion: This study enhances the performance of IoT nodes by prioritizing energy efficiency, network longevity, the number of active nodes, temperature, and throughput. Through the utilization of simulations and metaheuristics, we have observed that HABCGWO leads to a rise in energy usage and a decrease in energy consumption. [ABSTRACT FROM AUTHOR]

With the increasing adoption of Internet of Things (IoT) networks, ensuring their security has become a critical concern due to resource limitations and the growing complexity of malicious attacks. Intrusion Detection and Prevention Systems play a pivotal role in safeguarding network performance, but traditional methods often struggle with attack severity and classifying unknown packets. In this research, we introduce the Attention-IDS model, a comprehensive solution comprising five stages: two-fold authentication, local density-based clustering, flow-based feature extraction, intrusion detection system (IDS), and intrusion severity detection. Leveraging IoT devices and user-based authentication, our model effectively detects and prevents unauthorized access attempts, while ensuring enhanced security through the utilization of the Combine Counter Mode algorithm on the blockchain. The IDS stage, powered by the Isolation Forest algorithm, accurately classifies features as normal, malicious, or unknown. Leveraging the proposed Attention-based ResNet model, our approach intelligently classifies unknown packets into normal and malicious categories, employing feature extraction, selection, and classification. Additionally, the Extended Kalman Filter determines intrusion severity, enabling network-wide notification alarms for frequent intrusions and targeted responses for rare intrusions. Extensive simulations using the NS3.26 network simulator demonstrate the superior performance of Attention-IDS compared to existing methods. [ABSTRACT FROM AUTHOR]

Physical activity or structured exercise is beneficial in a wide range of circumstances. Nevertheless, individual-level data on differential responses to various types of activity are not yet sufficient in scale, duration or level of annotation to understand the mechanisms of discrete outcomes nor to support personalized recommendations. The Apple Heart & Movement Study was designed to passively collect the dense physiologic data accessible on Apple Watch and iPhone from a large real-world cohort distributed across the US in order to address these knowledge gaps. [ABSTRACT FROM AUTHOR]

LoRa WAN (Long range wide area network) performance in IoT (Internet of Things) tends to be an effective communication at low power and less cost. It assists all the nodes to interrelate within a range of 5 km if received signal strength is up to − 154 dBm. The calculated link budget of LoRa WAN predict LOS/ NLOS signal in the range of less than − 154 dBm at path loss of maximum 160 dB. Path loss of a signal is calculated with standard model and area specific path loss model is proposed. The real time analysis based on received signal strength resulted a standard deviation of signal in the range of 11.8 dB for a fading environment. Path loss with standard models in deep fading areas varies in between 200 and 360 dB. Area specific path loss model for free space is proposed within the range of LoRa WAN and it varies between 100 and 154 dB. The outage in deep fading area tends to deep almost 80% at 1 km range and data transfer rate is about 17 % at 3 dB noise figure. The received parameters are also compared with LTE and GSM networks which operate at L band frequency. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive implementation of Simultaneous Localization and Mapping (SLAM) techniques on the TurtleBot robot within the Robot Operating System (ROS) framework. The study aims to advance the capabilities of the TurtleBot, a popular and cost-effective robot, by integrating hardware and software components, including laser and odometry sensors. The SLAM algorithm, specifically Gmapping, is employed for mapping while utilizing ROS visualization tools like Rviz. The robot's simulation in Gazebo enhances testing in controlled environments. Leveraging teleoperation for data collection, the research delves into the challenges and considerations specific to SLAM on the TurtleBot platform, addressing a notable research gap. The study extends the exploration by investigating potential future enhancements and benefits, showcasing the adaptability and versatility of SLAM-integrated robotic systems. Simulation results detail the successful execution of SLAM through teleoperation, providing insights into mapping accuracy, computational performance, and the overall quality of the generated maps. The work concludes with a discussion on the distance travelled, future prospects, and the profound impact of SLAM on robotic navigation. [ABSTRACT FROM AUTHOR]

Autonomous on-demand services as part of public transport are discussed to improve public transport substantially. A household survey in Karlsruhe, Germany, was conducted among inhabitants of a residential area where a combined autonomous and on-demand minibus service with automation level 4 was offered. The study investigates the residents' appraisal of this service and reasons for using and not using it. Results indicate that people generally have a positive attitude towards it and are willing to use it in the future. Difficulties are found in travel speed, availability, and complexity of using such a new service. Favorable factors in the intention to use the service are having a mobility impairment, being open to other forms of new mobility, and not having a car in the household. In the future, to be successful, such services should improve travel times and reliability and address issues of their primary target group, such as the high complexity of accessing these services. [ABSTRACT FROM AUTHOR]

The occurrence of rockburst disasters is closely related to the zonal deformation and failure of rock masses. To effectively monitor and understand the critical zones of surrounding rock damage, it is imperative to establish an extensive network of strain measurement stations. In this study, a comprehensive approach was adopted, integrating acoustic emission (AE), digital image correlation, and fiber Bragg grating demodulation techniques. According to the different deformation stages of the sample, the changes in AE counts and internal strains were analyzed, and the position changes of the AE event location, internal high-strain field, and surface-high-strain field were analyzed, and it was found that there was good spatial correlations among them. Based on the AE events' counts and localization and internal strain, two new damage variables were proposed, and their numerical values and positions were used to analyze the evolution path of coal sample damage, which was found to match the actual fracture area of the specimen. Therefore, a novel comprehensive monitoring method combining the localization of AE events and internal strain fields is proposed to determine the position of high-damage areas. These research findings are of significant importance for accurately predicting the key areas leading to overall instability in coal–rock masses. [ABSTRACT FROM AUTHOR]

The term "artificial intelligence" (AI) refers to "smart" high-tech that is mindful of and able to learn from its surroundings. It is the most revolutionary technology that humans have ever created. Common AI approaches involving machine learning and deep learning techniques can be effectively applied to resolve today's various cybersecurity issues. Furthermore, the metaverse is all about how people communicate and engage with one another through technology. This survey explores the role of AI with its emerging applications and their various technologies, such as the metaverse, healthcare, IoT, gaming, and many more. To determine the strengths, flaws, opportunities, and risks that are inherent in artificial intelligence technologies, using an extensive literature survey, the SWOT (Strengths, Weaknesses, Opportunities, and Threats) assessments have been undertaken in this survey paper. Finally, the survey paper summarises the current state of knowledge of AI applications and discusses the findings present in recent research to ensure a favourable change in artificial intelligence advances and applications. Some technical AI challenges, like high-speed, high-performance hardware and reducing the amount of training data, etc., are also discussed with future scope. [ABSTRACT FROM AUTHOR]

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as an inevitable part of human life, that includes online learning, smart homes, smart cars, smart grids, smart cities, agriculture, and e-healthcare. It allows us to operate them 24/7 from anywhere. These smart IoT devices streamline our daily lives by automating everything around us. Several security issues have arisen with the continuous growth of non-secure IoT devices. Distributed Denial of Service (DDoS) attack is one of the most prominent security threats to Internet-based services and IoT platforms. It has the potential to break down the victim's server or network by transferring an immense amount of irrelevant traffic from the pool of compromised IoT devices. In this article, we present: (i) A comprehensive cyberattacks taxonomy for IoT platforms, (ii) Systematically demonstrate IoT technology: evolution, applications, and challenges, (iv) Systematic review of existing machine learning (ML) and deep learning (DL)-based detection approaches for large-scale IoT traffic-based DDoS attacks, (v) Characterize publicly available IoT-traffic-specific datasets, and (vi) Discuss various open research issues with possible solutions for detecting IoT traffic-based DDoS attacks, including future directions. [ABSTRACT FROM AUTHOR]

The Metaverse is regarded as a brand-new virtual society constructed by deep media, and the new media art produced by new media technology will gradually replace the traditional art form and play an important role in the infinite Metaverse in the future. The maturity of the new media art creation mechanism must also depend on the help of artificial intelligence (AI) and Internet of Things (IoT) technology. The purpose of this study is to explore the image style transfer of digital painting art in new media art, that is, to reshape the image style by neural network technology in AI based on retaining the semantic information of the original image. Based on neural style transfer, an image style conversion method based on feature synthesis is proposed. Using the feature mapping of content image and style image and combining the advantages of traditional texture synthesis, a richer multi-style target feature mapping is synthesized. Then, the inverse transformation of target feature mapping is restored to an image to realize style transformation. In addition, the research results are analyzed. Under the background of integrating AI and IoT, the creation mechanism of new media art is optimized. Regarding digital art style transformation, the Tensorflow program framework is used for simulation verification and performance evaluation. The experimental results show that the image style transfer method based on feature synthesis proposed in this study can make the image texture more reasonably distributed, and can change the style texture by retaining more semantic structure content of the original image, thus generating richer artistic effects, and having better interactivity and local controllability. It can provide theoretical help and reference for developing new media art creation mechanisms. [ABSTRACT FROM AUTHOR]

High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time–frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment. [ABSTRACT FROM AUTHOR]

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. [ABSTRACT FROM AUTHOR]

This work investigates the impact of environmental changes on the efficiency of the photovoltaic panel system. To analyze the impact, we first study the effect of the current climatic parameters on the evolution of the output energy of two solar photovoltaic modules, such as irradiance, temperature and wind speed. Meteorological data was acquired using specific sensors for temperature, irradiance, and wind speed; these data have been initially conditioned by using of 34972A LXI data acquisition and thereafter, interfaced to a PC using IEEE 488.2 general purpose interface bus (GPIB). We use the LabVIEW real-time interface to display and store the collected data in the PC. Finally, from the data stored, we have carried out the discussions on the experimental results-based data driven analysis using clustered heatmap which supports the approval of this impact using climate forecasts and strategy to adopt for an efficient and economical used and consequently a solar photovoltaic energy under climatic constraints is proposed. [ABSTRACT FROM AUTHOR]

With the rapid development and wide proliferation of sensor devices and the Internet of Things (IoT), machine learning algorithms processing and analysing one or more modalities of sensory signals have become an active research field given its numerous applications, particularly in the domestic intelligent environment (DIE). In the past decades, the research on sensing and interactive devices of DIE and deep learning (DL) based methods have become strikingly popular. Several missions, such as the pro- cessing and analysis of sensing signals related to domestic instruments and the control of certain devices to act upon the results, comprise the main working targets in DIE. The goal of this review is to provide a brief overview of the aforementioned sensors, their related DL algorithms and their applications. To comprehend the ideas behind the use of various devices found in domestic intelligent instruments, we first summarize the available information. Then, to quantify and adapt the residents' knowledge of the household environment, we review data-driven learning techniques based on the aforementioned sensor-based devices and introduce robotic applications that provide helpers and action outputs in the environment. Finally, we investigate the commonly utilized datasets relevant to DIE and human activ- ity recognition (HAR) and explore the challenges and prospects of their applications in the DIE field. [ABSTRACT FROM AUTHOR]

The future of work is changing rapidly as result of fast technological developments, decarbonization and social upheavals. Thus, employees need a new skillset to be successful in the future workforce. However, current approaches for the identification of future skills are either based on s small sample of expert opinions or the result of researchers interpreting the results of data-driven approaches and thus not meaningful for the stakeholders. Against this background, we propose a novel process for the identification of future skills incorporating a data-driven approach with expert interviews. This enables identifying future skills that are comprehensive and representative for a whole industry and region as well as meaningful for the stakeholders. We demonstrate the applicability and utility of our process by means of a case study, where we identify 33 future skills for the manufacturing industry in Baden-Wuerttemberg, Germany. Our work contributes to the identification of comprehensive and representative future skills (for whole industries). [ABSTRACT FROM AUTHOR]

Evaluating the knowledge transfer performance of universities facilitates the tracking of and improvement in the factor utilization efficiency of R&D activities. In this study, a heterogeneous stochastic frontier analysis model is used to measure the total factor productivity (TFP) growth rate, output elasticity, and factor bias index of knowledge transfer in Chinese universities from 2007 to 2016. The results show that the overall TFP of knowledge transfer at the university level in China has been improving. The increase in technical efficiency is the primary reason for this phenomenon. The output elasticity of R&D capital input is much larger than that of R&D labor input, indicating that currently, knowledge transfer is still driven by capital. This finding is particularly clear in 211-Project universities. R&D labor in the knowledge transfer process receives more preference in universities (especially non-211-Project universities) in the later period (i.e., 2012–2016). Therefore, the government should encourage the enthusiasm of researchers and improve the substantial contribution of R&D labor, thus facilitating an improvement in the subsequent knowledge transfer performance. [ABSTRACT FROM AUTHOR]

Forest industry plays a critical role for many remote rural regions in Nordic countries. Despite the increasing number of various digital transformation initiatives, many forestry organizations still use very traditional non-digitalized work practices in harvesting, logging, and support operations. Unfortunately, digital transformation is often performed as an ad hoc activity without a holistic approach to ensure ICT quality control. In this paper, we aim to answer the research problem of how digital transformation affects the quality of a forest machine operator's operational processes and service systems. A case study research method with a single case structure was used in this study. The main contribution of the paper is to show that digital transformation shall benefit not only the forest machine operator organization but also their surrounding ecosystem by increasing transparency of operations, reducing travel costs, increasing productivity, and creating a more proactive operating model. Both sociological and technical aspects of digital transformation are discussed in the context of forest service management. Additionally, we show that in order to succeed in digital transformation, forestry organizations must pay attention to various quality aspects holistically: hardware quality, software quality, service quality, data quality, and process/project quality. [ABSTRACT FROM AUTHOR]

Compared to conventional methods of industrial production, the cutting-edge additive manufacturing (AM or 3D printing) process creates new products through rapid prototyping technology based on a computer-aided design model. Regarding electrochemistry, numerous scientists have made use of AM to print electrodes and other conductive materials, to create electrochemical cells, laboratory experimental accessories, and to aid in teaching subject. To produce complicated structures for preparing individualized components of varying sizes, forms, compositions, and architectures. This mini-review article highlights the AM techniques, materials, and applications in electrochemistry fields such as electrodes, sensors, electrochemical cells, energy storage, experimental accessories, and STEM teaching aids. [ABSTRACT FROM AUTHOR]

Wire Arc Additive Manufacturing is a Direct Energy Deposition additive technology that uses the principle of wire welding to deposit layers of material to create a finished component. This technology is finding an increasing interest in the manufacturing industry, especially thanks the low cost and the possibility to build large-scale components. Nowadays, the boosting to transition into smart manufacturing systems and the increasingly computational resources allowed the development of intelligent applications for smart production systems for both in situ inspection and process parameter control. This paper aims to provide an review of applications developed using artificial intelligence techniques for Wire Arc Additive Manufacturing, with particular focus on defect detection software modules, feedback generation for control system and innovative control strategies as reinforcement learning to overcome problems related to model non-linearity and uncertainties. [ABSTRACT FROM AUTHOR]