Connectivity Problems Research Articles

Cyber Physical Echoes - Harnessing Digital Twin Intelligence for Real Time System Optimization

Digital Twin Technology (DTT) redefines the landscape of cyber-physical systems (CPS) by providing real-time, virtual replicas of physical processes and their counterparts in complex real-world networks for viewing, analyze and optimize digital twin complex systems through real-time data integration for predictive insights, making them more valuable in industries such as construction, healthcare, smart cities, energy management, etc. DTT provides they could streamline manufacturing techniques, enhance operational efficiency, and assist data-pushed selection-making, in order that normal CPS -Increases flexibility and responsiveness For instance, the ability of digital twins in construction mapping the development industry, helping to prioritize problems and streamline operations, at the same time as looking forward to demand and supporting the planning of smart towns Despite these advantages, the use of DTT role in CPS affords demanding situations are , inclusive of statistics privateness worries, connectivity problems, and the want for scalable solutions that can control massive, disparate databases. Keywords: Digital-Twin Technology, Cyber Systems, Real-Time Data Integration.

Problem Posing as a Way of Promoting Individual Mathematical Thinking in STEM Contexts – The Case of Climate Change

AbstractProblems encountered in Science, Technology, Engineering and Mathematics (STEM) contexts cannot be adequately described or solved with the knowledge of a single discipline. Instead, a high level of inter- and transdisciplinary knowledge and methods is required to overcome them. These help to pose problems about the complex challenges and solve them in a creative way. The better the knowledge within one and of different disciplines is interlinked, the more targeted questions can be formulated and answered. Mathematical concepts serve as the foundation for many of the pressing problems of our time. At the same time, these problems offer a wide range of opportunities for individualized exploration. They are equally suitable for students with different interests and levels of ability, as everyone can identify and work on an individual problem within the given context. However, numerous studies have shown that posing adequate mathematical problems must be learnt as well as knowledge from different disciplines is not automatically linked or transferred to other situations. The ability to grasp the formal structure of a problem, recognize problems, and find connecting problems are characteristics of mathematically gifted children and young people that need to be promoted. In our theory-based contribution, we use a concrete context to illustrate which possible mathematically rich problems can be posed by students of different ages and abilities. This approach facilitates the development of their individual abilities according to their interests and potential.

Understanding User Experience with Smart Home Products

ABSTRACT There has been a surge in the popularity of smart home products. Existing literature primarily focuses on the initial adoption of these products, with less attention given to the post-adoption stage. This has created a research gap in understanding user experience (UX) and the key dimensions of user satisfaction and dissatisfaction. This study aims to address this gap by examining both positive and negative user experiences with smart home products. Using robotic vacuum cleaners as a case study, we analyze key UX dimensions derived from their positive and negative reviews on Amazon.com that influence user satisfaction and dissatisfaction, respectively. Dimensions contributing to satisfaction include “functionality,” “smartness,” and “enhanced capacity (mopping feature).” In contrast, “insufficient smartness,” “poor customer service,” and “connectivity problems” are critical dimensions for user dissatisfaction. Notably, “smartness” serves as a double-edged sword, potentially leading to high satisfaction but also significant dissatisfaction if poorly implemented. Our analysis also includes a comparative examination of robotic vacuums with traditional vacuums, highlighting the different UX dimensions associated with each product type. Using fuzzy-set qualitative comparative analysis (fsQCA), we further examine the configurations of these UX dimensions that contribute to satisfaction and dissatisfaction. To generalize our findings, we applied our framework to smart thermostats and proposed a UX model for smart home products. Our findings not only fill the research gap in smart home literature but also offer valuable insights for practitioners in designing smart home products and developing marketing strategies. Our research method can be applied to understand user experience with various types of smart home products and other innovative technologies.

Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)

INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.

Optimization method of cable structure demolition driven by digital twin evolution model

The large-span cable structure has the problems of complex component connection, cumbersome demolition process and difficult process coordination, which lead to high construction safety risk and low efficiency. Moreover, the optimization of the construction process scheme is difficult, and the convergence and visualization of the finite element analysis are insufficient, which hinders the construction efficiency. This study proposes a cable structure demolition optimization method driven by the digital twin (DT) evolution model. From the perspective of virtual-real mapping, the DT evolution process of cable structure demolition is clarified. The virtualization method of physical demolition process is formed by integrating visual modeling, simulation modeling and data modeling. Taking full account of the temporal and spatial evolution of the demolition process, a DT evolution model of the demolition process is established. Starting from the information flow, component flow, control flow and their relationship, the problems existing in the process of demolition execution are analyzed. Driven by the twin evolution process, a data mapping method for the demolition process is proposed. The safety risk is characterized by the mechanical properties (MPs) of the structure, and the expression language of the data association relationship is established. The particle swarm optimization back propagation neural network (PSO-BPNN) is improved, and an intelligent analysis method of MPs in the whole process of demolition is proposed. The best construction scheme is obtained by integrating the changes in MPs between the various demolition steps. The information of the demolition evolution process is divided into functional modules, and a DT platform is developed to efficiently guide the actual construction. Taking the demolition process of a cable truss experimental model as an example, the effectiveness of the proposed method is verified. The research results show that the DT evolution model can accurately map the actual demolition process. The deep learning method analyzes the mechanical properties of the structure and obtains the best construction scheme quickly and accurately. The development and application of the twin platform improves the degree of visualization and realizes the efficient guidance of the virtual model to the actual construction.

The Use of Probability and Edge Analysis to Solve the Multi-Period Degree Constrained Minimum Spanning Tree Problem

The goal of the Multiperiod Degree Constrained Minimum Spanning Tree (MPDCMST) problem is to determine the smallest weight-spanning tree that satisfies the vertex installation criterion for each period and maintains the degree requirement in each vertex. This issue emerges as a network connection problem. The degree requirement indicates the reliability of each vertex, and the vertex connection/installation requirement denotes the priority vertices that must be inserted in the network within a specific time frame. The installation is split up into multiple phases/stages. This is because of various considerations such as severe weather, budgetary limitations, etc. In this research, two algorithms for solving the MPDCMST using probability hybridized with Prim’s modification, and edge analysis are proposed. The algorithms are implemented on the undirected complete graph of orders 10 to 100. The solutions are compared with some heuristics which are already in the literature. The results show that the proposed algorithms perform better.

Feasibility of a Cardiac Magnetic Resonance Protocol for "off-on" Cardiac Resynchronization Therapy Evaluation.

Cardiac resynchronization therapy (CRT) is a standard treatment for patients with heart failure and electrical dyssynchrony. Cardiac magnetic resonance (CMR) is the gold standard for assessing left ventricular (LV) function. However, the feasibility of using CMR with active CRT is still uncertain. To assess the feasibility of a CRT "off-on" protocol during CMR and measure the acute effects of CRT interruption on LV function. Patients underwent CMR before (pre-CRT) and 6 months after (post-CRT) an MR-conditional CRT defibrillator implantation. The post-CRT scan included two complete sets of cine images, one with inactive (post-CRTOFF) and one with active CRT (post-CRTON), maintaining a continuous connection between device and programmer. Out of 29 enrolled patients, 8 (28%) had complete and analyzable post-CRT data. Unsuccessful procedures were attributed to connection problems between the CRT device and the programmer (n = 10), poor image quality (n = 7), and lack of patient cooperation (n = 4). LV ejection fraction significantly increased between pre-CRT scan (28.1%) and both post-CRTOFF (37.9%; p = 0.046) and post-CRTON CMR (35.0%; p = 0.037), with a nonstatistically significant trend toward decreased LV volumes. No adverse events or significant changes in device electrical parameters (including battery level) were detected during the post-CMR scan period. A CRT "off-on" protocol during CMR studies can be safely executed in patients with an MR-conditional CRT defibrillator. However, technical improvements are needed to facilitate high-quality scans during active CRT. Favorable changes in LV function induced by CRT remodeling were not acutely reversed with the interruption of electrical therapy.

The Impact of E-Ticketing System on Bus Transportation Service in Tanzania

This study investigates the impact of the e-ticketing system on bus transportation services in Tanzania. The main objective of the research is to assess how the e-ticketing system influences various aspects of bus transportation in Tanzania. Specific objectives include examining the role of e-ticketing, identifying the challenges it poses, and exploring strategies for its improvement. Primary data were collected through well-structured questionnaires and in-depth interviews, providing both quantitative and qualitative insights into the effectiveness of the e-ticketing system. The findings reveal that the e-ticketing system has significantly enhanced revenue management, passenger convenience, sustainability, and fraud prevention, with significance levels of .02, .01, .02, and .02, respectively. These results highlight the system's valuable contribution to improving service quality. However, the study also identifies several challenges, including technical issues, connectivity problems, resistance to change, and security concerns. To address these challenges, the study proposes strategies such as increasing awareness and educational campaigns, upgrading technology and infrastructure, implementing offline options, and enhancing feedback and support mechanisms. These recommendations aim to optimize the e-ticketing system's effectiveness and address its limitations, ultimately improving the overall bus transportation service in Tanzania.

Digital Skills Improvement for MSMEs in Rural Areas for Online Marketing

This Community Service (PKM) aims to improve the digital skills of MSME actors in rural areas, especially in online marketing, through online training using the Zoom platform. This activity is motivated by the low digital skills of MSMEs in rural areas, which limits their access to the digital market. The method used is participatory action research (PAR), involving 30 MSME actors who are selected by purposive sampling. The training lasts four weeks and includes two weekly sessions, covering material on digital marketing, the use of social media, e-commerce, and marketing performance monitoring. The results showed a significant improvement in participants' digital skills, with an average pre-test score of 45 increasing to 80 on the post-test. Implementing the skills learned also resulted in an increase in engagement on social media by 40% and an increase in online transactions by 20%-30%. Participants gave positive feedback regarding the relevance of the training and the application of the skills taught. This activity fills the gap in previous research by focusing on practical strategies adapted to the conditions of MSMEs in rural areas. The main obstacle faced is the problem of internet connectivity, which needs to be considered in the next training. Overall, this activity has succeeded in increasing the competitiveness of MSMEs in the digital market

Resilience and Adversities: The Case of HEI Faculty and Students

The goal of this study was to look into the Higher Education Institution’s (HEI) faculty and students’ resilience and adversity amidst challenges in life. A descriptive correlational research design was employed in this study, with data drawn from the faculty respondents having a normal level of resilience while the students have low resilience in facing adversities. Furthermore, there was no significant relationship between gender, age, and marital status with the resilience level of the faculty. However, there is a negative correlation with gender and marital status to the level of students’ resilience. Thus, it is recommended to have interventions on some factors such as promoting the well-being of the graduate faculty and students, internet connectivity problems, and Information and Communications Technology (ICT) literacy in using the new learning modality.

The Telegraphic Body: Dyspepsia, Modern Life, and 'Gastric Time' in Nineteenth-Century Medicine and Culture.

From Italian physician Hieronymus Mercurialis's contention that the stomach was 'the king of the belly', to its promotion by the end of the nineteenth century to the 'monarch of humanity' in patent medicine, to Byron Robinson's discovery of the enteric nervous system in 1907 (a mesh of neural connectivity that led him to dub the gut 'the second brain'), there has historically been a longstanding awareness of the expansive reach of the gut in the functions of the body. In the nineteenth century, the authority of the gut and its allyship with the brain became a focus for writers thinking about the intersections of illness and 'modern life'. In medical texts, domestic health manuals, patent medicine, and fiction, the electric telegraph in particular became a way of envisaging what we would now call the 'gut-brain axis'. The telegraphic metaphor enabled a view of digestion as not simply a mechanical or chemical process, but one that was understood in terms of time, space, and communication. However, such a framework also suggested problems of connection that were common to both systems, emphasising not only the healthy body's quasi-telegraphic networks but also its vulnerability to delay, disruption, and pathological incoherence. This article will explore the use of telegraphic technologies as proxies for theorising gastric connection and more broadly the concept of 'gastric time' as a key conceit for understanding digestion as a process that was and is subject to the idiosyncrasies of bodily rhythms.

Association Model-Based Intermittent Connection Fault Diagnosis for Controller Area Networks

Controller Area Networks (CANs) play an important role in many safety-critical industrial systems, which places high demands on their reliability performance. However, the intermittent connection (IC) of network cables, a random and transient connectivity problem, is a common but hard troubleshooting fault that can cause network performance degradation, system-level failures, and even safety issues. Therefore, to ensure the reliability of CANs, a fault symptom association model-based IC fault diagnosis method is proposed. Firstly, the symptoms are defined by examining the error records, and the domains of the symptoms are derived to represent the causal relationship between the fault locations and the symptoms. Secondly, the fault probability for each location is calculated by minimizing the difference between the symptom probabilities calculated from the count information and those fitted by the total probability formula. Then, the fault symptom association model is designed to synthesize the causal and the probabilistic diagnostic information. Finally, a model-based maximal contribution diagnosis algorithm is developed to locate the IC faults. Experimental results of three case studies show that the proposed method can accurately and efficiently identify various IC fault location scenarios in networks.

Correction to: A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles

Correction to: A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles

Automatic device arrangement and wiring for fire alarm systems in metro stations: A case study

Fire alarm systems in metro stations play an important role in safeguarding lives and properties. The design process for these systems is complex and time-consuming due to the diversity of device types, wide distribution of device locations, and numerous compulsory design codes. This paper proposes an automatic design framework for fire alarm systems aimed at enhancing design efficiency. The framework includes five automated processes: information preprocessing, device arrangement, system loop generation, device wiring, and result generation. By converting the computer-aided design (CAD) results into building information models (BIM), building information and related professional device information are obtained, addressing the need for fire alarm system (FAS) design to integrate with multiple disciplines. The process can also automatically arrange FAS devices according to design specifications. Optimization algorithms, such as Ant Colony Algorithm and Prim Algorithm, are used to determine the device connection order and solve the problems of obstacle avoidance and orthogonal connections between two points. Compared to traditional design methods, the proposed automatic design framework for fire alarm systems in metro stations can not only satisfy the mandatory design requirements but also avoid the cumbersome and repetitive design process, providing new solutions for designers. Additionally, it can reduce wire length and save on construction costs. Using a real metro station as a case study, it is calculated that the automatic design can save between 8 % and 48 % of the wire length.

EXPLORING STUDENT ENGAGEMENT WITH FORMATIVE ASSESSMENT AND ICT TOOLS

This study investigates the potential of Information and Communication Technology (ICT) tools to enhance formative assessment (FA) practices in English as a Foreign Language (EFL) classrooms. While previous research has focused primarily on teacher feedback in writing courses, this study emphasizes a broader scope of FA. Utilizing a cross-sectional, quantitative analysis, the research examines how ICT tools support FA's goal of facilitating the learning process from students' perspectives, thereby informing effective teaching practices and improving learning outcomes. Participants included 173 university EFL students in Japan, divided into English majors and non-English majors. The study employed a revision of an existing survey for language learning with digital tools and a self-developed survey based on an existing framework for FA. Correlational analysis indicated that participants generally perceived ICT tools as supportive of all five FA principles. Despite issues like connectivity problems, students found the tools valuable for making the learning progress visible and enhancing peer communication. The study underscores the importance of understanding and practicing FA beyond feedback, such as clarifying learning goals and fostering peer learning, to enhance learning. These findings suggest that integrating ICT tools into FA practices can significantly benefit EFL instruction by promoting student engagement and self-regulated learning.

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA–disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA–disease associations. Our experimental results on the Human microRNA Disease Database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers.

Affective disorders accompanied by cognitive impairment in patients with a cardiological profile: prevalence, multimorbidity, medical and social risk factors

Clinical and medico-social issues of affective pathology combined with cognitive impairment in cardiac patients are a serious problem of modern medicine. This multimorbidity is characterized by higher risks of social maladjustment, disability, concomitant health problems and mortality. Cognitive dysfunctions in affective pathology are of great clinical and social importance, but their initial manifestations in cardiac patients are still insufficiently diagnosed. This leads to missed opportunities for the prevention of cognitive deficits. This review presents epidemiological data, pathological mechanisms, biological and psychosocial problems of multimorbid connections of cardiovascular, affective and cognitive pathology. Full-text articles, fragments of monographs selected by keywords in the databases Scopus, Web of Science, Medline, RSCI, eLibrary.ru, disserCat.ru, Psychiatrist, Sciencedirect were used.

On q-Painlevé VI and the geometry of Segre surfaces

In the context of q-Painlevé VI with generic parameter values, the Riemann–Hilbert correspondence induces a one-to-one mapping between solutions of the nonlinear equation and points on an affine Segre surface. Upon fixing a generic point on the surface, we give formulae for the function values of the corresponding solution near the critical points, in the form of complete, convergent, asymptotic expansions. These lead in particular to the solution of the nonlinear connection problem for the general solution of q-Painlevé VI. We further show that, when the point on the Segre surface is moved to one of the sixteen lines on the surface, one of the asymptotic expansions near the critical points truncates, under suitable parameter assumptions. At intersection points of lines, this then yields doubly truncated asymptotics at one of the critical points or simultaneous truncation at both.

A genetic algorithm method for improving suboptimal sensor arrangements in coverage and connectivity problems

This paper is concerned with the solution of a Coverage and Connectivity Problem (CCP). The proposed method performs the placement of sensors forming a connected Wireless Sensor Network (WSN) to fully cover a Field of Interest (FoI). Full coverage is attained despite the presence of opaque obstacles that impair both sensing and communication. To this end, we leverage set operations involving polygons to tessellate the free space. Moreover, we propose the concept of CR-visibility to assess the total area coverage of a polygon by a sensor. The deployment of the minimal number of sensors to completely cover the FoI is cast into the form of an Integer Linear Program (ILP). Two different formulations of connectivity constraints are appended to the ILP. The first one is necessary and sufficient for connectivity, whereas the second alternative is only sufficient. While the number of inequalities in the former grows combinatorially with the number of sensors, the growth is linear in the latter, rendering it more computationally appealing for real-sized FoI. Lastly, we formulate an unconstrained version of the CCP, which is solved by a Genetic Algorithm (GA) with integer variables. We present a small simulation scenario for initial illustration of the proposed method, and a larger, real-life scenario based on the map of an actual urban setting. The results obtained with the real-life scenario show that: (i) high-quality solutions can be obtained in short computation times by imposing the sufficient constraints for connectivity; (ii) the large number of inequalities associated to the necessary and sufficient constraints render the numerical solution impractical; (iii) by using random initialization, the GA solution of the unconstrained problem requires more sensors than the ILP solution with the sufficient connectivity constraint; (iv) including that ILP solution in the initial population of the GA enables it to find a sensor placement that requires fewer sensors.

Adaptive Imputation of Irregular Truncated Signals with Machine Learning

In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue.