Search Results (206)

Marine biodiversity is of critical importance to global ecosystems. The Indo-Pacific Convergence Zone (IPCZ), a global marine biodiversity hotspot, faces escalating threats from human activities and climate change. This underscores the pressing need to develop effective conservation strategies for marine biodiversity in the IPCZ. This study integrates spatial analysis of ecological sensitivity (coral reefs, mangroves, and seagrass) and anthropogenic pressures (shipping/fishing intensity) to identify biodiversity hotspots and conservation gaps. Using datasets from UNEP-WCMC, OBIS, and Global Fishing Watch, we applied GIS-based multi-criteria evaluation to 5408 grid cells (0.5° resolution) across the IPCZ. Results revealed that 14.7% of the study area constitutes biodiversity hotspots, primarily in coastal Philippines, Indonesia’s Lesser Sunda Islands, and northern Australia. However, only 6% of the IPCZ is currently protected, with merely 13.88% of hotspots overlapping existing marine protected areas (MPAs). Anthropogenic pressure hotspots (e.g., Malacca Strait) showed limited spatial overlap with biodiversity hotspots, suggesting species displacement from high-disturbance zones. Priority conservation areas were delineated by balancing ecological significance and economic activity conflicts. We propose targeted strategies, including buffer zones, seasonal no-take areas, and green shipping technologies, to reconcile conservation with sustainable development. This framework provides actionable insights for enhancing MPA networks in biogeographic transition zones. Full article

YouTube is the most popular social media platform for children and adolescents, yet relatively little research has been conducted on adolescents’ use and their motivations for use. Prior research has predominantly focused on measuring the quantity of time spent on the platform, and less is known about the motivations and self-awareness of using YouTube as children turn into tweens and teens. Stemming from a larger survey study of adolescent social technology use, we interviewed a subset of 35 youths (50% female, 49% male, 1% non-binary) to qualitatively explore the benefits and challenges of YouTube use. Thematic analyses revealed the wide range of emotional responses and regulation that were attributed to YouTube use, including humor, fear, anger, insecurity, and anxiety. Some participants experienced wishful identification with YouTube influencers, and others viewed YouTube as entertainment or an escape from boredom. Sleep regulation was mixed, in that for some, YouTube was a distraction from getting enough sleep, and for others, it helped them fall asleep. Parental monitoring was a developmental challenge as the youths described their parents as lacking knowledge about the length of time they spend on the platform and/or the risky YouTube content that they watch, particularly as they got older. These exploratory findings may be pertinent for parents, educators, and clinicians. Full article

As maritime operations become increasingly reliant on interconnected information technology (IT) and operational technology (OT) systems, ensuring cybersecurity on vessels has become more critical than ever. One of these systems is the Integrated Navigation System (INS), which assists the Officer of Watch (OOW) on the bridge in ensuring safe navigation. The INS comprises several components that may be susceptible to cyber attacks, hence it faces cyber risks that need to be mitigated. Cyber risks are understood differently, depending on perspective. In this paper, we determine the perspective that the research community has of cyber risk, focusing on the INS, and that of professionals representing the maritime industry, and analyze similarities and differences. To this end, we conduct a systematic literature review and interviews with maritime professionals. This study provides useful insights for researchers and professionals seeking to understand the cyber risks of the INS. Full article

The primary purpose of video surveillance is to monitor public indoor areas or the boundaries of secure facilities to safeguard them against theft, unauthorized access, fire, and various other potential threats. Security cameras, equipped with integrated video surveillance systems, are strategically placed throughout critical locations on the premises, allowing security personnel to observe all areas for specific behaviors that may signal an emergency or a situation requiring intervention. A significant challenge arises from the fact that individuals cannot maintain focus on multiple screens simultaneously, which can result in the oversight of crucial incidents. In this regard, artificial intelligence (AI) video analytics has become increasingly prominent, driven by numerous practical applications that include object identification, detection of unusual behavior patterns, facial recognition, and traffic management. Recent advancements in this technology have led to enhanced functionality, remarkable accuracy, and reduced costs for consumers. There is a noticeable trend towards upgrading security frameworks by incorporating AI into pre-existing video surveillance systems, thus leading to modern video surveillance that leverages video analytics, enabling the detection and reporting of anomalies within mere seconds, thereby transforming it into a proactive security solution. In this context, the AiWatch system introduces digital twin (DT) technology in a modern video surveillance architecture to facilitate advanced analytics through the aggregation of data from various sources. By exploiting AI and DT to analyze the different sources, it is possible to derive deeper insights applicable at higher decision levels. This approach allows for the evaluation of the effects and outcomes of actions by examining different scenarios, hence yielding more robust decisions. Full article

Virtual reality (VR) has existed in the realm of education for over half a century; however, it has never achieved widespread adoption. This was traditionally attributed to costs and usability problems associated with these technologies, but a new generation of consumer VR headsets has helped mitigate these issues to a large degree. Arguably, the greater barrier is now the overhead involved in creating educational VR content, the process of which has remained largely unchanged. In this paper, we investigate the use of ${360}^{\circ }$ video as an alternative way of producing educational VR content with a much lower barrier to entry. We report on the differences in user experience between ${360}^{\circ }$ and standard desktop video. We also compare the short- and long-term learning retention of tertiary students who viewed the same video recordings but watched them in either ${360}^{\circ }$ or standard video formats. Our results indicate that students retain an equal amount of information from either video format but perceive ${360}^{\circ }$ video to be more enjoyable and engaging, and would prefer to use it as additional learning resources in their coursework. Full article

The maritime industry is rapidly advancing toward the initial stages of the digitised era of shipping, characterised by considerable advances in maritime autonomous technology in recent times. This study examines the effectiveness of training packages and the impact of rank during the failure of a sophisticated autopilot control system. For this study, the fault recognition and diagnostic skills of 60 navigational seafarers conducting a navigational watch in a full mission bridge watchkeeping simulator were analysed. Participants had either significant experience as qualified navigational officers of the watch or were navigational officers of the watch cadets with 12 months’ watchkeeping experience. These groups were subdivided into those who were given a training package focused on behavioural aspects of managing automation, such as maintaining situational awareness, and those given a technical training package. The findings were analysed using an Event Tree Analysis method to assess the participants’ performance in diagnosing a navigation fault. Additionally, the fault recognition skills were assessed between groups of training and rank. The study found that participants who received the behavioural training were more successful in both recognising and diagnosing the fault during the exercise. Behavioural training groups outperformed technical training groups, even when technical training participants were experienced seafarers. This difference in performance occurred without any apparent differences in workload or secondary task performance. Understanding the data gathered from the study could lead to the development of future training regimes for navigational officers of the watch and help to optimise the evolution of the seafaring role. Full article

This study investigates how Chinese EFL (English as a foreign language) learners of low- and high-proficiency levels allocate attention between captions and audio while watching videos, and how visual complexity (single- vs. multi-speaker content) influences caption reliance. The study employed a novel paused transcription method to assess real-time processing. A total of 64 participants (31 low-proficiency [A1–A2] and 33 high-proficiency [C1–C2] learners) viewed single- and multi-speaker videos with English captions. Misleading captions were inserted to objectively measure reliance on captions versus audio. Results revealed significant proficiency effects: Low-proficiency learners prioritized captions (reading scores > listening, Z = −4.55, p < 0.001, r = 0.82), while high-proficiency learners focused on audio (listening > reading, Z = −5.12, p < 0.001, r = 0.89). Multi-speaker videos amplified caption reliance for low-proficiency learners (r = 0.75) and moderately increased reliance for high-proficiency learners (r = 0.52). These findings demonstrate that low-proficiency learners rely overwhelmingly on captions during video viewing, while high-proficiency learners integrate multimodal inputs. Notably, increased visual complexity amplifies caption reliance across proficiency levels. Implications are twofold: Pedagogically, educators could design tiered caption removal protocols as skills improve while incorporating adjustable caption opacity tools. Technologically, future research could focus on developing dynamic captioning systems leveraging eye-tracking and AI to adapt to real-time proficiency, optimizing learning experiences. Additionally, video complexity should be calibrated to learners’ proficiency levels. Full article

Advances in wearable technology have revolutionized healthcare by enabling the continuous monitoring of patients and personalized healthcare delivery. In the field of gastroenterology, the integration of wearable devices and smartphone applications represents a promising frontier. As technology continues to expand, understanding the current landscape and future directions of wearable technology in gastroenterology is essential for improving patient outcomes and clinical practice. Background/Objectives: Most review articles, thus far, regarding wearable technology in healthcare have been directed towards cardiovascular health. The purpose of this review is to explore the evolving role of wearable technology in the management of gastrointestinal disorders, focusing on remote patient monitoring and the use of smartphone applications. Methods: We conducted a search for studies on wearable technology and included the following search terms: wearable technology, gastroenterology, wearable device, smartphone, application, heart rate variability, biosensor, watch, patch. We included randomized controlled trials, prospective studies, and feasibility studies published from 2018 onwards. We excluded studies in pediatrics or those unrelated to GI disorders. Results: We found that using wearable devices and digital health management may be an effective way to monitor symptoms, reduce hospitalizations, and improve healthcare delivery in several gastrointestinal diseases such as inflammatory bowel diseases, motility disorders, liver diseases, etc. Conclusions: This review proposes that remote patient monitoring through wearable devices and digital health management via smartphone applications could reduce hospitalizations and empower patients, though challenges related to data security, accuracy, and integration with the electronic medical record must be addressed. Full article

Accurate near-surface wind speed and direction measurements are crucial for validating atmospheric models, especially for the purpose of adequately assessing the interactions between the surface and wind, which in turn results in characteristic vertical profiles. Coastal regions pose unique challenges due to the discontinuity between land and sea and the complex interplay of atmospheric stability, topography, and boundary/layer dynamics. This study focuses on a unique database of wind profiles collected over several years at a World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) coastal site in the southern Italian region of Calabria (Lamezia Terme, code: LMT). By leveraging remote sensing technologies, including wind lidar combined with in situ measurements, this work comprehensively analyzes wind circulation at low altitudes in the narrowest point of the entire Italian peninsula. Seasonal, daily, and hourly wind profiles at multiple heights are analyzed, highlighting the patterns and variations induced by land–sea interactions. A case study integrating Synthetic Aperture Radar (SAR) satellite images and in situ observations demonstrates the importance of multi-sensor approaches in capturing wind dynamics and validating model simulations. Data analyses demonstrate the occurrence of extreme events during the winter and spring seasons, linked to synoptic flows; fall seasons have variable patterns, while during the summer, low-speed winds and breeze regimes tend to prevail. The prevailing circulation is of a westerly nature, in accordance with other studies on large-scale flows. Full article

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emissions and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large-scale deployment of methane analyzers across millions of emission sites is prohibitively expensive, and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH₄ERS) has been developed, comprising four commercially available sensing technologies: metal oxide (MOx,), Non-dispersion Infrared (NDIR), integrated infrared (INIR), and tunable diode laser absorption spectrometer (TDLAS). WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC), and this study describes the integration of these sensors into a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH₄ERS unit, report initial sensor responses, and describe future research goals. Meanwhile, future work will use data gathered by multiple WATCH₄ERS units to 1. better understand the cost–benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large-scale methane monitoring strategies. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensors cannot respond fast enough to report concentrations that change in a sub-minute time frame. The NDIR sensor reported a linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration, especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor reported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions. The WATCH₄ERS units will be deployed in real-world environments to investigate the utility of each in the future. Full article

A dominant marketing paradigm has appeared in the form of live streaming e-commerce, which holds a significant user base and exhibits an upward trend in sales figures. Based on the technology acceptance model (TAM) and perceived value theory, combined with KANO and the analytic hierarchy process (AHP), this study extracts consumer demand characteristics through a questionnaire survey of 402 Chinese Douyin consumers who watch clothing live streaming online and participate in purchases. The study categorizes consumer demands into five dimensions with 17 indicators: perceived usefulness, perceived ease of use, perceived emotional value, perceived economic value, and e-commerce anchor characteristics, and a hierarchical framework for consumer demands in clothing-focused live streaming e-commerce is constructed. Four critical deep influence factors were indicated by the findings: information acquisition, after-sales support, smooth communication, and emotional experience. These indicators are the primary factors for future optimization of live streaming e-commerce platforms. The results of this study provide effective data analysis and suggestions related to consumer purchasing needs in clothing-based live streaming e-commerce platforms so as to improve customer satisfaction and thus turnover, as well as to provide a theoretical method for reference in subsequent consumer research on live streaming e-commerce platforms. Full article

Chemical sensors, relying on electrical conductance changes in a gas-sensitive material due to the surrounding gas, have the (dis-)advantage of reacting with multiple target gases and humidity. In this work, we report CMOS-integrated SnO₂ thin film-based gas sensors, which are functionalized with mono-, bi-, and trimetallic nanoparticles (NPs) to optimize the sensor performance. The spray pyrolysis technology was used to deposit the metal oxide sensing layer on top of a CMOS-fabricated micro-hotplate (µhp), and magnetron sputtering inert-gas condensation was employed to functionalize the sensing layer with metallic NPs, Ag-, Pd-, and Ru-NPs, and all combinations thereof were used as catalysts to improve the sensor response to carbon monoxide and to suppress the cross-sensitivity toward humidity. The focus of this work is the detection of toxic carbon monoxide and a specific hydrocarbon mixture (HC_mix) in a concentration range of 5–50 ppm at different temperatures and humidity levels. The use of CMOS chips ensures low-power, integrated sensors, ready to apply in cell phones, watches, etc., for air quality-monitoring purposes. Full article

The objective of the study was to examine the relationship between rumination time and various parameters related to eating and locomotion, including other chews, eating chews, eating time, drinking gulps, bolus counts, chews per minute, activity, and activity change, utilizing RumiWatch technology. The RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Feeding Technology, Liestal, Switzerland) was utilized to record time and frequency related to rumination, eating, and movement behaviors. The RumiWatch system (RWS) was put into operation from 1 June 2023 to 30 June 2023. The first two weeks, from 1 June to 14 June 2023 at 7 a.m., served as a period for the cows to acclimate to the RWS, acting as an adjustment phase. Monitoring activities with the RWS commenced on 7 a.m. and lasted until the end of the month, 30 June 2023, with data being recorded daily on an hourly basis. Our findings indicate a significant negative correlation between rumination time and other activity time (r = −0.50), which represents the duration cows allocate to behaviors outside of eating, chewing cud, or distinct movement activities. Additionally, a significant negative correlation was observed between rumination time and eating time (r = −0.54). Furthermore, we observed strong positive correlations with rumination chews (r = 0.84) and bolus (r = 0.75). A weaker positive correlation was found with chews per minute (r = 0.29), while no significant correlation was detected with drinking gulps (r = 0.10). Based on our findings, we recommend the implementation of the RumiWatch System for monitoring rumination and feeding behaviors in lactating dairy cattle. This technology provides valuable insights into cow health and welfare, enabling early detection of potential health issues and improving herd management practices. Full article

Low-cost technology devices, such as smartphones (SPs) and smart watches (SWs), are widely used today to monitor various health effects and environmental risk factors associated with them. However, the efficacy of using these devices as monitoring tools is largely unknown. The present study attempts to narrow this knowledge gap by reviewing recent studies in which low-cost technological tools were used to monitor sleep and associated environmental risk factors. The study focuses on peer-refereed articles that appear in three major scientific databases, Web of Science, Scopus, and ScienceDirect, and were published between 2002 and 2022. Of the 15,000+ records retrieved from these databases by the systematic literature review (PRISMA) search, 15 studies were identified as the most relevant and consequently analyzed. The analysis shows that nighttime light pollution and noise are environmental factors that are most commonly monitored by low-cost technology tools (eight studies), followed by temperature monitoring (seven studies), humidity monitoring (seven studies), and CO₂ monitoring (four studies). In eight studies, tandems of SPs and SWs were used to monitor sleep, while in six studies, data obtained from SPs and SWs were compared with records obtained from conventional monitoring devices. In general, SP and SW measurements were found to be fairly accurate for monitoring sleep and light pollution and less accurate for monitoring noise. At the same time, no studies conducted to date and analyzed in this review demonstrated the effectiveness of SPs and SWs in monitoring ambient temperature, humidity, and air pressure. Our general conclusion is that although SPs and SWs often lack the precision of professional instruments, they can nevertheless be used for large-scale field research and citizen science initiatives, while their feasibility and effectiveness for monitoring several environmental attributes have yet to be determined. Full article

Backgrounds: Virtual reality (VR) has become a transformative technology with applications in gaming, education, healthcare, and psychotherapy. The subjective experiences in VR vary based on the virtual environment’s characteristics, and electroencephalography (EEG) is instrumental in assessing these differences. By analyzing EEG signals, researchers can explore the neural mechanisms underlying cognitive and emotional responses to VR stimuli. However, distinguishing EEG signals recorded by two-dimensional (2D) versus three-dimensional (3D) VR environments remains underexplored. Current research primarily utilizes power spectral density (PSD) features to differentiate between 2D and 3D VR conditions, but the potential of other feature parameters for enhanced discrimination is unclear. Additionally, the use of machine learning techniques to classify EEG signals from 2D and 3D VR using alternative features has not been thoroughly investigated, highlighting the need for further research to identify robust EEG features and effective classification methods. Methods: This study recorded EEG signals from participants exposed to 2D and 3D VR video stimuli to investigate the neural differences between these conditions. Key features extracted from the EEG data included PSD and common spatial patterns (CSPs), which capture frequency-domain and spatial-domain information, respectively. To evaluate classification performance, several classical machine learning algorithms were employed: ssupport vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), naive Bayes, decision Tree, AdaBoost, and a voting classifier. The study systematically compared the classification performance of PSD and CSP features across these algorithms, providing a comprehensive analysis of their effectiveness in distinguishing EEG signals in response to 2D and 3D VR stimuli. Results: The study demonstrated that machine learning algorithms can effectively classify EEG signals recorded during watching 2D and 3D VR videos. CSP features outperformed PSD in classification accuracy, indicating their superior ability to capture EEG signals differences between the VR conditions. Among the machine learning algorithms, the Random Forest classifier achieved the highest accuracy at 95.02%, followed by KNN with 93.16% and SVM with 91.39%. The combination of CSP features with RF, KNN, and SVM consistently showed superior performance compared to other feature-algorithm combinations, underscoring the effectiveness of CSP and these algorithms in distinguishing EEG responses to different VR experiences. Conclusions: This study demonstrates that EEG signals recorded during watching 2D and 3D VR videos can be effectively classified using machine learning algorithms with extracted feature parameters. The findings highlight the superiority of CSP features over PSD in distinguishing EEG signals under different VR conditions, emphasizing CSP’s value in VR-induced EEG analysis. These results expand the application of feature-based machine learning methods in EEG studies and provide a foundation for future research into the brain cortical activity of VR experiences, supporting the broader use of machine learning in EEG-based analyses. Full article