Online Status Research Articles

Expansion of perceived passable width in older adults.

Humans can perceive whether an aperture is passable; this ability is known as passable width perception. Previous studies have shown that passable width in older adults is larger when walking and suggested the effect of larger body sway in older adults while walking on passable width expansion. However, no studies have directly investigated this. Thus, the present study conducted two experiments to investigate the effect of body instability on passable width perception in young and older adults. In Experiment 1, young and older adult participants kept standing upright with their feet together (i.e., Romberg stance) or with their feet in line (i.e., Tandem stance) while observing various aperture widths presented on a large display at a distance of 3m. The participants were required to judge whether the aperture was passable without turning their shoulders. During the task, body sway was measured using a motion capture device. The results showed larger sway in the Tandem stance than in the Romberg stance, irrespective of age group; however, a change in perceived passable width was observed only in older adults: it expanded in the unstable condition compared to the stable condition. In Experiment 2, the young adults performed the same task with a more unstable stance (i.e., one-leg standing) than that adopted in Experiment 1. Nevertheless, it did not affect their passable width perception. These results suggest that only older adults exploit the online status of their body sway to estimate their safety margins even during a non-walking situation. Future studies on passable width perception are expected to measure body sway.

The New Method for Ground Insulation Monitoring of the Secondary Circuit in Substations

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%.

From Activism to Influence: Unpacking the Romanticisation of Mental Illness Online

Social media has become an integral part of daily life, particularly among youths, shaping interactions and influencing mental well-being. Unfortunately, this has also led to a rise in cyberbullying, particularly concerning the sharing of personal struggles, such as depression. Despite these challenges, some youths have adeptly navigated social media to promote mental health awareness and activism. However, a subset of these activists has become popular influencers, sometimes prioritising maintaining their online status over their advocacy roles. Consequently, there is a concern that some may inadvertently glorify or romanticise depression to sustain their popularity. In this article, I explore the influence of social media on mental health activism, focusing on its effects on the mental well-being of youths. I examine whether the portrayal of depression by influencers or mental health activists contributes positively to destigmatisation efforts or if it inadvertently perpetuates misconceptions. The data were collected from the different social media platforms of nine participants. Semi-structured interviews were conducted for an in-depth understanding of the mined data. The data were analysed using thematic analysis. The themes that emanated from the findings were: disguised “activism” and killing two birds with one stone, increased popularity versus activism, and irrelevant content. The study highlights the importance of investigating the impact of social media on mental health activism by recognising the potential implications on the mental well-being of youths. Social media influencers aim to change perceptions of depression, but there is still a growing concern whether the proposed new image of depression is helpful or misleading.

Research of IoT Technology Based Online Status Monitoring on Hydropower Station Equipment

The rapid proliferation of the Internet of Things (IoT) has revolutionized the field of status monitoring for electrical equipment such as hydropower station equipment, offering enhanced efficiency and reliability in maintenance and operations. In this paper, we investigate the utilization of IoT transmission technology enhanced by multiple relays, denoted as $M$ relays, to augment the monitoring of hydropower station equipment. To further optimize the performance of the system, we employ partial relay selection, commonly referred to as selection combining. This study delves into the analysis of the system performance by deriving analytical data rates, with a focus on quantifying the benefits of employing partial relay selection in IoT transmission for electrical equipment status monitoring. Our analytical approach enables a comprehensive evaluation of system efficiency, considering factors such as data rate, reliability, and power consumption. Through our analysis, we aim to provide valuable insights into the trade-offs and advantages of incorporating partial relay selection into IoT systems, ultimately assisting in the development of more effective and efficient solutions for status monitoring on electrical equipment. By examining the impact of $M$ relays and partial relay selection on IoT transmission technology, this research contributes to the ongoing efforts to enhance the reliability and robustness of status monitoring for electrical equipment, ultimately advancing the capabilities of IoT-based solutions in the context of electrical systems and equipment maintenance.

Dispositional and Social Correlates of Digital Status Seeking Among Adolescents.

Social media have transformed peer relationships among adolescents, providing new avenues to attain online status indicators such as likes and followers. This study aimed to explore the associations between various dispositional and social factors and digital status-seeking behaviors among a sample of adolescents (N = 731; Mage = 14.69, 48.7 percent female), as well as explore potential gender differences in the examined associations. Sociometric nominations for digital status-seeking, likeability, and popularity were collected, and participants self-reported their social media use frequency, awareness of social media positivity bias, reward sensitivity, and gender. The findings revealed a positive relationship between sociometric popularity and digital status-seeking, whereas likeability displayed a negative association with digital status-seeking. These results emphasize the importance of distinguishing between different social status indicators in understanding online behaviors. Reward sensitivity did not show a significant link to digital status-seeking, and awareness of social media positivity bias heightened the likelihood of being nominated as a digital status-seeker. These findings underscore the need for further research, especially focusing on girls who appear to be more vulnerable to engaging in digital status-seeking behaviors.

High and low levels of adolescent peer status are associated longitudinally with socioevaluative concern.

This study examined linear and curvilinear longitudinal associations between peer status (i.e., likeability and popularity) and socioevaluative concern, a socio-cognitive feature characterized by attunement to judgment from peers. A sample of 716 adolescents (Mage = 16.01, SD = 1.25; 54% female; 46.5% White; 69.5% reduced-price lunch) was assessed twice annually. Likeability and popularity were assessed with peer nominations at Time 1. Measures of general (rejection sensitivity, peer importance) and online (digital status seeking, online status importance) socioevaluative concern were obtained at Times 1 and 2. High and low levels of likeability were longitudinally associated with increased peer importance, while high and low levels of popularity were associated with increased digital status seeking, and decreased online status importance for girls.

Image processing-based detection method of learning behavior status of online calssroom students

Due to the outbreak of the new crown epidemic, online teaching is booming, but compared with traditional offline teaching, there are many problems, such as the difficulty of detecting the voice status of students. Therefore, the research on students’ online status detection system is of great significance. In this paper, based on image processing, the detection method of online classroom students’ learning behavior status is studied, and the learning status of students is detected from the perspective of face detection and face recognition fatigue detection. In this study, the students’ learning status is detected by the facial expressions in the video during the students’ learning process. When the students have negative emotions and become tired, the system can detect and record them in time and issue a warning. Therefore, this research can well solve the problems existing in online teaching, and to a certain extent, the teaching quality has been greatly improved.

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.

‘Brand, community, lifestyle’: Fashioning an authentic, body positive influencer brand on Instagram

Fashion influencers on Instagram create intimate and ‘authentic’ content to develop a branded persona, which they can use to build follower relationships and monetize their account through brand partnerships. However, some influencers are leveraging their online status and relationships to form their own fashion brands. This research examines three case studies of influencer-founded fashion brands: Day/Won, a size-inclusive athletic clothing company founded by model Candice Huffine; Premme, a plus-size clothing brand founded by fashion influencers Gabi Gregg and Nicolette Mason; and Megababe, a body positive beauty brand founded by fashion influencer Katie Sturino, to examine how they communicate with followers and reference body positive discourses to represent themselves as ‘authentic’ and inclusive brands. Over 500 Instagram posts from these influencers’ personal and brand accounts were captured and manually coded and analysed using constructivist grounded theory for practices that the accounts used to establish authentic communication with followers. The findings suggest that influencer and brand accounts align their use of engagement, embodiment and expression strategies in order to develop and convey an authentically body positive identity.

A photoacoustics-enhanced drilling probe for radiation-free pedicle screw implantation in spinal surgery.

This article proposes a novel intra-operative navigation and sensing system that optimizes the functional accuracy of spinal pedicle screw implantation. It does so by incorporating radiation-free and multi-scale macroscopic 3D ultrasound (US) imaging and local tissue-awareness from in situ photoacoustic (PA) sensing at a clinically relevant mesoscopic scale. More specifically, 3D US imaging is employed for online status updates of spinal segment posture to determine the appropriate entry point and coarse drilling path once non-negligible or relative patient motion occurs between inter-vertebral segments in the intra-operative phase. Furthermore, a sophisticated sensor-enhanced drilling probe has been developed to facilitate fine-grained local navigation that integrates a PA endoscopic imaging component for in situ tissue sensing. The PA signals from a sideways direction to differentiate cancellous bone from harder cortical bone, or to indicate weakened osteoporotic bone within the vertebrae. In so doing it prevents cortical breaches, strengthens implant stability, and mitigates iatrogenic injuries of the neighboring artery and nerves. To optimize this PA-enhanced endoscopic probe design, the light absorption spectrum of cortical bone and cancellous bone are measured in vitro, and the associated PA signals are characterized. Ultimately, a pilot study is performed on an ex vivo bovine spine to validate our developed multi-scale navigation and sensing system. The experimental results demonstrate the clinical feasibility, and hence the great potential, for functionally accurate screw implantation in complex spinal stabilization interventions.

Nomophobia (No MObile PHone PhoBIA) Among Medical Students: A Literature Review

Smartphones have become more popular due to advances in high-speed Internet connectivity. Smartphones are popular among young people, including medical students, as virtual devices. As a result, excessive use of smartphones has expressed concerns about its addictive potential, a condition known as nomophobia. Nomophobia (No MObile PHone PhoBIA) is the worry or fear people feel when they do not have their mobile phone or are unable to use it. The term was introduced in 2008 and is considered a modern phobia. Nomophobia is also a general pathology among students, particularly those in medicine. This literature review examines the prevalence of nomophobia, symptoms, causes of nomophobia among medical students, and the impact of nomophobia on student achievement. Existing literature on nomophobia in the PUBMED and Google Scholar databases was examined using the search terms ‘nomophobia’ OR ‘mobile phone use’ OR ‘mobile phone addiction’ AND 'medical students.' Several studies have found that medical students have nomophobia. More than 50% of medical students in this review have moderate nomophobia, while 5-20% have severe nomophobia. The most common symptoms of nomophobia were anxiety, agitation, tachycardia, inability to turn off the mobile phone, constantly checking the phone, charging the battery even when the phone is nearly fully charged, fear of not being able to connect to a data network, concerns about being removed from online status or identity, and avoid activities to spend time on a mobile device. Students with severe nomophobia reported poor academic performance; students with a higher level of nomophobia performed poorly academically and vice versa. Medical students had a greater incidence of addiction to mobile phones, which affected their health and academic achievement. More emphasis should be placed on early detection and intervention to overcome nomophobia.

Going online: Forecasting the impact of websites on productivity and market structure

We develop a unifying framework to investigate the effects of firms' internet presence on productivity and market structure. Using information on website adoption as an indicator of online trading, we treat the decision of entering an e-commerce market equivalent to the decision of entering a foreign market. Our theoretical framework draws from a dynamic model of international trade, which accounts for firms' heterogeneity in productivity levels and in the returns to productivity enhancing investments. We test the predictions of our model using UK and Spanish company account data, over the 1995–2010 period merged with information of companies' online status. The period analysed is associated with the early stage of internet diffusion and our sample countries represent fast (the UK) and slow (Spain) diffusion. Our results show that website adoption is associated with higher productivity growth and with a reduction in market concentration in both countries. The increase in competition operates via a negative selection mechanism, whereby productivity growth is inversely related to the pre-entry productivity levels. We also find that productivity gains decline over time.

On-line status monitoring and surrounding environment perception of an underwater cable based on the phase-locked Φ-OTDR sensing system.

A newly designed phase-locked (PL) Φ-OTDR system was proposed and instrumented. Field tests of water impact, anchor damage towing and tide diagnosing were carried out in a natural freshwater lake as well as the East China Sea. Personnel movement trajectory monitoring and ship flow monitoring were carried out by a buried cable along the floodplain of the Yangtze River. It proved that the proposed system can monitor the real-time status and sense the surrounding environment of existing underwater communication cables, which could be helpful for the maintenance of the cable itself as well as underwater information collection.

Organizational Resource Allocation by Mobile Edge Computing in the Context of the Internet of Things

In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization’s resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user’s information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user’s information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV’s edge computing system are comprehensively discussed to improve the performance and efficiency of the network.

Equipment Modeling Technology in Power Internet of Things

With the advancement of smart IoT system, equipment modeling technology that adapts to the power Internet of Things becomes more and more important. In the power grid system, various types of equipment access processes are not standardized, the collected data format is not uniform, and the data is difficult to share. There is an urgent need to build a set of equipment modeling methods suitable for the power Internet of Things. By comparing traditional equipment modeling methods, we propose the static modeling structure of equipment in the power Internet of Things, and constructs a description method of terminal equipments on the cloud platform; combined with the construction requirements of the smart IoT system, design the Cloud-side collaborative dynamic modeling technology which based on equipment online status management, resource status management, alarm management and application management. At the same time, integrated equipment management methods for static modeling and dynamic modeling are realized in combination with application cases, providing technologies basis for unified device access, unified data transmission, and open sharing of data.

A modified model predictive control method for frequency regulation of microgrids under status feedback attacks and time-delay attacks

As a promising technology to integrate renewable energy and enable decentralized energy management, microgrid (MG) offers an appealing network architecture due to its potential economic, environmental, and technical benefits. However, with the increasing deployment of intelligent devices and the growing network interconnectivity, communication channels and controllers for MGs become more vulnerable to emerging cyber threats. Two types of attacks are considered in this paper, including the data integrity attacks on the system status feedback and time-delay attacks which may disrupt the control of the MGs and lead to adverse consequences. A modified model predictive control (MPC) scheme is proposed for the secondary frequency control of MGs based on the online status switching method and generalized cross correlation (GCC) estimation to detect the real system status and time delay injected to the control system. The Euclidean metric is used in the online status switching method to obtain the real system states. Meanwhile, the GCC based delay estimation is developed to detect and track the time delay posed by attacks in the real-time operation. Case studies under different scenarios of the attacks are conducted, and the simulation results verify the effectiveness of the proposed MPC scheme under the cyberattacks.

Age of Information Minimization for an Energy Harvesting Source With Updating Erasures: Without and With Feedback

Consider an energy harvesting (EH) sensor that continuously monitors a system and sends time-stamped status update to a destination. The sensor harvests energy from nature and uses it to power its updating operations. The destination keeps track of the system status through the successfully received updates. With the recently introduced information freshness metric “Age of Information” (AoI), our objective is to design optimal online status updating policy to minimize the long-term average AoI at the destination, subject to the energy causality constraint at the sensor. Due to the noisy channel between the sensor and the destination, each transmitted update may be erased with a fixed probability, and the AoI at the destination will be reset to zero only when an update is successfully received. We first consider status updating without feedback available to the sensor and show that the Best-effort Uniform updating (BU) policy is optimal in a broadly defined class of online policies. We then investigate status updating with perfect feedback to the sensor and prove similar optimality of the Best-effort Uniform updating with Retransmission (BUR) policy. In order to prove the optimality of the proposed policies, for each case, we first identify a lower bound on the long-term average AoI among a broad class of online policies, and then construct a sequence of virtual policies to approach the lower bound asymptotically. Since those virtual policies are sub-optimal to the original policy, the original policy is thus optimal.

Design of electronic delay ignition device for a certain type of HESH

Aiming at the low precision of the gunpowder delay body of a certain type of HESH (High-explosive squash-head), an electronic delay ignition device was used to replace the traditional delay body. The design uses an independent setter to charge the electronic delay ignition device, set the delay time and detect the online status,etc. The delay time of HESH can be adjusted within the predetermined range, and the precision of the delay ignition time can be improved from 0.6s to 0.03s,which can obviously improve the performance of HESH.

Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid

With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.

Predictive Wireless Based Status Update for Communication-Agnostic Sampling

In a wireless network that conveys status updates from sources (i.e., sensors) to destinations, one of the key issues studied by existing literature is how to design an optimal source sampling strategy on account of the communication constraints which are often modeled as queues. In this paper, an alternative perspective is presented—a novel status-aware communication scheme, namely parallel communications , is proposed which allows sensors to be communication-agnostic. Specifically, the proposed scheme can determine, based on an online prediction functionality, whether a status packet is worth transmitting considering both the network condition and status prediction, such that sensors can generate status packets without communication constraints. We evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform, which is integrated with a collaborative autonomous driving simulator, i.e., Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control models and road conditions. The results show that with online status predictions, the channel occupancy is significantly reduced, while guaranteeing low status recovery error. Then the framework is applied to two scenarios: a multi-density platooning scenario, and a flight formation control scenario. Simulation results show that the scheme achieves better performance on the network level, in terms of keeping the minimum safe distance in both vehicle platooning and flight control.