Conflict Detection Research Articles

Image Diagnosis of Trigeminal Neuralgia

Classic trigeminal neuralgia is mainly caused by arterial compression; most cases involve the superior cerebellar artery, followed by the anterior cerebellar, basilar, and vertebral arteries. The detection of neurovascular conflicts in trigeminal neuralgia requires special magnetic resonance imaging(MRI)modalities, including high-resolution three-dimensional(3D)-T2 sequence, 3D-time of flight angiography, 3D-T1 sequencing with gadolinium injection, and merged images of these sequences. The conflicting sites are not necessarily restricted to the root entry zone of the trigeminal nerve root and can be located more distally, proximal to the Meckel's cavum. Arterial compression and its severity, including displacement, angulation, distortion, and atrophy of the trigeminal root, are good predictors of the long-term efficacy of decompression surgery. Veins, primarily the transverse pontine vein, comprise 10%-20% of all causative vessels in trigeminal neuralgia. Gadolinium-enhanced 3D-T1 MRI and high-resolution 3D-T2 MRI merged with computed tomographic angiography are useful for detecting venous compression.

The Impact of Problematic Social Media Use on Inhibitory Control and the Role of Fear of Missing Out: Evidence from Event-Related Potentials.

The general deficit in inhibitory control of problematic social media users has received widespread attention. However, the neural correlates of problematic social media use (PSMU) and inhibitory control remain unclear. Additionally, the co-occurrence of the fear of missing out (FoMO) with social media use is common, yet its role in the relationship between PSMU and inhibitory control has not been investigated. This study aimed to examine the electrophysiological correlates of PSMU and inhibitory control using a modified two-choice oddball task combined with event-related potentials (ERPs), and to explore the role of FoMO in this relationship. A total of 66 participants with varying degrees of PSMU were included in the analysis based on the Problematic Mobile Social Media Usage Questionnaire. The study found that PSMU could impact inhibitory control. Specifically, as the PSMU score increases, the N2 amplitude is greater for social media-related pictures, and the P3 amplitude is smaller, while no significant differences are observed for neutral pictures. This suggests that PSMU affects inhibitory control by consuming more cognitive resources in the early conflict detection stage and leading to insufficient cognitive resources in the later stages of the inhibitory process. Furthermore, FoMO played a mediating role between PSMU and inhibitory control. PSMU could further impact inhibitory control through FoMO. This study provides electrophysiological evidence for deficits in inhibitory control in PSMU and suggests that FoMO may further reduce inhibitory control in PSMU individuals.

THE ROLE OF SOCIAL MEDIA ALGORITHMS IN SHAPING AND CONFLICT RESOLUTION

This research aims to explore the dual role of social media algorithms in conflict formation and resolution. Using a systematic literature review method, this research analyses how algorithms can amplify polarization and spread misinformation, as well as their potential to be leveraged in mitigating conflict and promoting constructive dialogue. The results show that algorithms designed to maximise user engagement often contribute to conflict escalation through the formation of "filter bubbles" and the spread of misinformation. However, recent research has also revealed the potential of algorithms, if designed with ethical and social principles in mind, to be instrumental in early conflict detection and the promotion of dialogue across groups. This study highlights the implications of these findings for technology companies, policymakers, and civil society, and emphasises the need for an interdisciplinary approach, proactive regulation, and increased digital literacy in addressing algorithm challenges. In conclusion, social media algorithms are flexible tools, and their impact depends on the values, principles, and goals embedded in their design. A holistic and collaborative approach is needed to harness the potential of algorithms in mitigating conflict while minimising their role in deepening social divisions.

Dynamic AGV Conflict Detection Under Speed Uncertainty Considerations

Dynamic AGV Conflict Detection Under Speed Uncertainty Considerations

Research on Industry-Education Integration Model of Vocational Education Applying Automatic Coding

Abstract Many applied undergraduate institutions in China have recognized that the integration of industry and education is essential for the advancement and innovation of educational practices. To expedite the development of this integration within vocational education, this paper introduces the Genetic Automatic Coding Assisted Integration of Industry and Education Teaching Mode. This approach begins with the cultivation of vocational education talents in higher education institutions, offering a comprehensive analysis of the industry-education fusion mode and delineating its implementation strategies and guiding principles. Employing the principles of automatic coding, the curriculum content of this integration mode is represented through genetic algorithm gene coding. The model for genetic automatic coding in industry-education integration for vocational education is established by assessing the compatibility of the integration mode and incorporating mechanisms for conflict detection and mutation crossover. The empirical analysis reveals that in genetic automatic coding, the eigenvalues stabilize after approximately 200 iterations. This suggests that this method enhances scheduling effectiveness in the vocational education-oriented industry-education fusion mode. Moreover, the results indicate a significant improvement in educational outcomes: students in the experimental group, where this model was applied, achieved an average assessment score of 88.38, compared to 78.47 in the control group. This substantial improvement confirms that the industry-education fusion mode, underpinned by genetic automatic coding, not only enhances the quality of education but also boosts students’ mastery of professional courses.

Comparison of performance between PMS and trombone arrival route topologies in terminal maneuvering area

The contradiction between air traffic capacity and demand exerts congestion and delay, resulting in airspace operating at or above its capacity. This paper proposes a performance comparison of two arrival route topologies in Terminal Maneuvering Area (TMA) with a tailored optimization algorithm, including Point Merge System (PMS) and trombone paradigms. We aim at enhancing the capacity of arrival topologies and efficiently landing more aircraft through identifying which topological route is better suitable for current traffic demands. The problem involves integrating and sequencing arrival operations into the TMA. A mathematical model is developed for which entry times, entry speeds and flight routes are regarded as decision variables. The operation constraints of this optimization modelling correspond to detection of potential conflicts between consecutive aircraft at merging waypoints and on the same link segments, based on the safety separation standards. The objective function is set to minimize the delay and speed deviation for all flights in the optimized system. Because of the highly combinatorial nature of this problem and the non-deterministic polynomial hard (NP-hard), a tailored selective simulated annealing algorithm is proposed to optimally make decisions for the arrival traffic flow. Finally, the validation of our approach has been performed for Ataturk Airport in Turkey, by using 11 different operation scenarios of arrival demands. For lower demands, all aircraft can be positively scheduled, while for more than 80% extra scenarios based on the regular traffic demand, there exist some remaining conflicts for the situations. The results illustrate that, compared with the trombone procedure, PMS paradigm could enhance the arrival capacity and has better performance throughout the arrival procedure. Additionally, more flexible sequence position shift is possible in PMS, and it has the ability to accommodate high-density flight operating environment.

Modeling and detection of low-altitude flight conflict network based on SVM

With the continuous increase in low altitude flight density, the issue of low altitude navigation safety has attracted widespread attention. Due to the complex low altitude environment, low altitude flight is more susceptible to ground obstacles and weather effects than commercial aviation. In order to ensure the flight safety of helicopters in low altitude airspace, this paper proposes an improved support vector machine based flight conflict detection model. By modeling the conflict network in low altitude flight areas and utilizing Support Vector Machine (SVM) classification features, the safety discrimination of low altitude flight was achieved, ultimately achieving the safety of aircraft in low altitude flight. This article adopts a protected area model that considers the shape of the aircraft as a conflict zone. In order to reduce the complexity of the conflict detection model, an improved ID3 decision tree algorithm and random forest are used to reduce the complexity of the classifier. The study solved the saturation problem of S-type functions in conflict detection models by using more sensitive functions for probability mapping. And use intelligent optimization algorithms to pre train key parameters, achieving efficient conflict detection suitable for low altitude flight.

Real-time railway traffic management under moving-block signalling: A literature review and research agenda

Railway traffic management is responsible for the detection and resolution of conflicts in case of disturbed operations. To minimise delay propagation, rescheduling decisions are taken by human dispatchers, possibly supported by mathematical models. Existing conflict detection and resolution (CDR) models mostly refer to conventional fixed-block multi-aspect signalling systems, in which minimum train headways are determined based on a preset number of blocks considering worst-case braking distances and number of signal aspects. In moving-block signalling systems, minimum headways are based on absolute braking distances. This paper reviews literature on CDR with the aim to identify gaps and to propose next steps in the research on CDR under moving-block signalling. A research agenda presents various modelling options, for which modelling approaches are proposed based on a comparative analysis.

Probabilistic modeling and reasoning of conflict detection effectiveness by tracking systems towards safe urban air mobility operations

This study targets the scenario where centralized tracking systems provide tactical conflict detection for urban air mobility (UAM) flights. In this scenario, the interaction between airspace design and tracking system performances, and its impact on the effectiveness of conflict detection have not been addressed in the literature. To overcome this gap, this study aims at achieving probabilistic modeling and reasoning analysis, to provide references for stakeholders in the design of urban airspace and the deployment of flight tracking systems. A framework integrating multiple probabilistic methods is established. We formulate the event tree of pair-wise aircraft encounters with conflict detection provided by the tracking system. Then Monte Carlo simulation is performed by using an agent-based tool that we develop, to quantify the probability of event occurrences. Finally, Bayesian Networks models are built to infer the dependencies of conflict detection effectiveness on the airspace design and tracking system performances. The outcomes of this study demonstrate improvement in conflict detection that better tracking system performances can provide, and the impact of airspace design under different tracking configurations. The results can be used in the UAM traffic network planning, and systems standardization for UAM flight tracking, towards safe urban air traffic.

Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC

The steady evolution of the Open RAN concept sheds light on xApps and their potential use cases in O-RANcompliant deployments. There are several areas where xApps can be used that are being widely investigated, but the issue of mitigating conflicts between xApp decisions requires further in-depth investigation. This article defines a conflict mitigation framework (CMF) built into the existing O-RAN architecture; it enables the Conflict Mitigation component in O-RAN's Near- Real-Time RAN Intelligent Controller (Near-RT RIC) to detect and resolve all conflict types defined in the O-RAN Alliance's technical specifications. Methods for detecting each type of conflict are defined, including message flows between Near-RT RIC components. The suitability of the proposed CMF is proven with a simulation of an O-RAN network. Results of the simulation show that enabling the CMF allows balancing the network control capabilities of conflicting xApps to significantly improve network performance, with a small negative impact on its reliability. It is concluded that defining a unified CMF in Near-RT RIC is the first step towards providing a standardized method of conflict detection and resolution in O-RAN environments.

Automated decision aids: When are they advisors and when do they take control of human decision making?

We applied a computational model to examine the extent to which participants used an automated decision aid as an advisor, as compared to a more autonomous trigger of responding, at varying levels of decision aid reliability. In an air traffic control conflict detection task, we found higher accuracy when the decision aid was correct, and more errors when the decision aid was incorrect, as compared to a manual condition (no decision aid). Responses that were correct despite incorrect automated advice were slower than matched manual responses. Decision aids set at lower reliability (75%) had smaller effects on choices and response times, and were subjectively trusted less, than decision aids set at higher reliability (95%). We fitted an evidence accumulation model to choices and response times to measure how information processing was affected by decision aid inputs. Participants primarily treated low-reliability decision aids as an advisor rather than directly accumulating evidence based on its advice. Participants directly accumulated evidence based upon the advice of high-reliability decision aids, consistent with granting decision aids more autonomous influence over decisions. Individual differences in the level of direct accumulation correlated with subjective trust, suggesting a cognitive mechanism by which trust impacts human decisions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Survey on Conflict Detection in IoT-based Smart Homes

As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user satisfaction. In this survey, we introduce a novel conflict taxonomy, complete with formal definitions of each conflict type that may arise within the smart home environment. We design an advanced conflict model to effectively categorize these conflicts, setting the stage for our in-depth review of recent research in the field. By employing our proposed model, we systematically classify conflicts and present a comprehensive overview of cutting-edge conflict detection approaches. This extensive analysis allows us to highlight similarities, clarify significant differences, and uncover prevailing trends in conflict detection techniques. In conclusion, we shed light on open issues and suggest promising avenues for future research to foster accelerated development and deployment of IoT-based smart homes, ultimately enhancing their overall performance and user experience.

Modeling and application of ship traffic conflicts in nearshore waters with heavy traffic

Ensuring the safety of ship navigation is essential to support the sustainable development of the shipping industry, and maritime safety analysis has thus received much attention in the field of shipping. Traditionally, the safety evaluation is mostly performed with the help of accident data, which is thus limited by the insufficient amount of accident data and affects the evaluation effect. In order to overcome this limitation, a ship traffic conflict model is established, which consists of three sub-models: multi-risk ship domain model, ship traffic conflict preliminary screening model and ship traffic conflict detection model. The multi-risk ship domain model is the key foundation, combining the traditional ship domain model with the water traffic conflict severity model to fully consider the severity of ship traffic conflict. To validate the proposed model, three ships in the study waters are selected as the own ships, and the severity of traffic conflicts formed with their target ships within a certain conflict time range are analyzed separately. Then, the conflict datasets obtained from the training of the ship traffic conflict model are used to analyze the traffic conflict situation in the heavily trafficked waters near Tianjin Port. Specifically, the spatial distribution characteristics are obtained using the kernel density estimation (KDE) method and the water grid method, and the temporal distribution characteristics are verified with the help of the accident statistics method. The proposed ship conflict model can provide technical support for further research in the field of ship traffic conflict, and the experimental analysis results are of great significance for strengthening maritime traffic supervision.

A conceptual framework for automatic modelling and conflict detection of 3D land-use regulation restrictions to support issuing planning permits

There are a wide variety of Land-use Regulation (LuR) restrictions, ranging from those that need simple 2D spatial analyses to be verified (e.g., area calculation), to those that need complex 3D analyses (e.g., overshadowing and overlooking). Assessing LuR restrictions to automatically detect LuRs’ potential conflicts with physical objects, such as building elements, could be a challenging task in 2D land administration systems relying on 2D drawings and 2D representations, especially when 3D/vertical analyses are required. Land administration systems can include 3D representations of LuR restrictions to identify their impacts on land parcels and vice versa since the LuRs contain legally binding restrictions for all parties including governments and citizens. As part of the transition from the 2D representation of LuR restrictions and manual conflict detection between LuR restrictions and physical objects such as building elements towards a 3D digital representation and automatic conflict detection, this paper proposes a three-stage conceptual framework for automatic 3D modelling and conflict detection of 3D LuR restrictions. The three-stage conceptual framework, as a generic approach, proposes required principles and procedures to automatically (1) model 3D LuR restrictions (called 3D CityLuR), (2) combine them with other sources of information (e.g., zoning maps, 3D city models, and BIM), and (3) detect LuRs’ potential conflicts between the modelled LuRs and physical objects (i.e., proposed/existing building elements). For modelling and verifying 3D LuRs automatically, the framework organises all the required information for IFC (Industry Foundation Classes) as the proposed building, CityGML as the existing buildings, and most importantly, the planning and zoning requirements. It proposes two categories of required information as “level of information need” for (1) modelling and combing 3D CityLuR with other sources of information automatically and (2) automatically detecting potential spatio-semantic conflicts between LuR restrictions and physical objects such as proposed/existing building elements. Based on a case study in Victoria, Australia, 3D LuR restrictions subject to planning approvals and building subdivisions are investigated. The paper concludes with a discussion arguing the feasibility of automating the verification of LuR restrictions to support decision-making in planning permit applications and building subdivisions.

Inhibitory Control in Young Healthy Adults - a tDCS Study.

Inhibitory control plays a role in the behavior selection and detection of conflicts. Defects in inhibitory control are an integral part of many neuropsychiatric disorders and the possibilities of influencing it are the subject of active study. Studies have shown and confirmed the activation of the dorsolateral prefrontal cortex (DLPFC) during the Stroop task and other tests involving response inhibition. Non-invasive brain stimulation is an emerging and actively developing group of methods used in cognitive research. In the present study, we used non-invasive, painless, and delicate transcranial direct stimulation (tDCS) for the study of inhibitory control, and to explore the effect of impulsivity on response inhibition ability in young healthy participants. We conducted a cross-over study with cross-hemispheric application of 2 mA tDCS with electrodes placed on the right - cathode, and left - anode - DLPFC. Participants performed a classic Stroop test before and after stimulation. Impulsivity was measured via the personal impulsiveness questionnaire. There was no significant difference in interference score alteration between active and sham stimulations, anodal and sham tDCS both induced slight improvement in Stroop test results. Individual impulsivity in healthy participants showed no influence on their results. Our study adds to the picture and helps to deepen knowledge about the impact of different stimulation parameters on cognitive functions.

Resting-State Functional Connectivity of the Anterior Cingulate Cortex Among Persons With Mood Disorders and Suicidal Behaviors.

To assess whether anterior cingulate cortex (ACC) abnormalities contribute to suicide risk in major depressive disorder and bipolar disorder, the investigators compared resting-state functional connectivity (rsFC) of ACC subdivisions between individuals with major depressive or bipolar disorder with and without a lifetime history of suicidal behavior. Forty-two inpatients with and 26 inpatients without a history of suicidal behavior (SB+ and SB-, respectively) associated with major depressive or bipolar disorder and 40 healthy control (HC) participants underwent rsFC neuroimaging. RsFC of the subgenual, perigenual, rostral, dorsal, and caudal subdivisions of the ACC was calculated. Possible confounders, such as psychosis and severity of depression, were controlled for, seed-to-voxel and post hoc region of interest (ROI)-to-ROI analyses were performed, and the accuracy of rsFC in classifying suicidal behavior was studied. Compared with individuals in the SB- and HC groups, patients in the SB+ group had higher rsFC between the left rostral and right dorsal ACC seeds and visual cortex clusters. Conversely, rsFC between the left rostral and right dorsal ACC seeds and cingulate and frontal clusters was lower in the SB+ group than in the HC group. Left rostral ACC to left Brodmann's area 18 connectivity showed up to 75% discriminative accuracy in distinguishing SB+ from SB- patients. A history of suicidal behavior among individuals with major depressive disorder or bipolar disorder was associated with altered rsFC of the rostral and caudal ACC, regions involved in conflict detection and error monitoring. Replication of these findings is needed to further explore the involvement of the ACC in the neurobiology of suicidal behavior and suicidal ideation.

Effects of fear of missing out on inhibitory control in social media context: evidence from event-related potentials.

The present study aimed to investigate the impact of fear of missing out (FoMO) on inhibitory control in social media context. The present study used a two-choice oddball task combined with event-related potentials (ERPs) technology to measure inhibitory control. Based on the Fear of Missing Out Scale, participants with varying degrees of FoMO were recruited to complete two studies. A total of 78 participants in Study 1 completed a two-choice oddball task (stimuli "W" or "M"). The results showed that FoMO did not have a significant impact on general inhibitory control at both the behavioral and electrophysiological levels. To further examine the effect of FoMO in social media context. In Study 2, 72 participants completed a modified two-choice oddball task with three types of pictures (high and low social media-related and neutral). The behavioral results revealed that as FoMO scores increased, inhibitory control decreased. ERP analysis revealed that with higher FoMO scores, social media-related pictures elicited larger N2 amplitude and smaller P3 amplitude, but not for neutral pictures. This suggests that FoMO undermines inhibitory control by consuming more cognitive resources in the early conflict detection stage and leading to insufficient cognitive resources in the later stages of the inhibitory process. These findings suggest that FoMO can undermine inhibitory control in the social media context. Considering the indispensable use of social media in the digital age, addressing and understanding the influence of FoMO on inhibitory control could be essential for promoting healthy digital behaviors and cognitive functions.

SLOW ADOPTION OF BUILDING INFORMATION MODELING: COMPARING VIEWPOINTS OF DESIGNERS AND CONTRACTORS

The slow adoption of Building Information Modeling (BIM) technology poses significant problems for the construction industry in the Middle East. The root causes of this issue are purported to include a lack of understanding of its benefits and the existence of barriers to its adoption. This study investigates the slow adoption of BIM technology in the Middle East to identify solutions to facilitate BIM adoption and approaches by which stakeholders can transform to utilizing BIM. The main objectives of this research are to: (1) examine the attitudes and readiness of construction stakeholders for BIM, (2) investigate the extent to which there is a lack of understanding of BIM benefits, (3) explore the barriers that hinder BIM adoption, and (4) identify the solutions recommended by the industry experts to promote BIM utilization. The study also compares the viewpoints of designers and contractors to identify potential differences. A questionnaire survey has been devised to gather data from stakeholder firms and experienced BIM professionals. The results revealed that the viewpoints of designers and contractors generally align, except when it comes to recognizing a few benefits such as the generation of accurate and consistent 2D drawings, achieving greater predictability in project time, enhancing communication among project stakeholders, minimizing change orders, and mitigating redesign issues. Similarly, there was a difference in recognizing a few barriers, namely, the lack of skilled personnel and the cost of training existing staff. Furthermore, the analysis led to the conclusion that the most notable advantage of BIM is improved conflict detection, and the most significant barrier is the lack of BIM training. The research culminated in offering recommendations for addressing the industry's BIM-related challenges. These suggestions encompassed raising awareness of BIM as a robust design and construction tool, supporting construction firms for adopting BIM techniques, and identifying methods to streamline the implementation process within these firms.

Utilizing Knowledge Graph to Build a Scientific and Technological Knowledge Management System for Grid Industry

Abstract Based on knowledge mapping, this paper designs the application architecture of the scientific and technological knowledge management system for the power grid industry and proposes the design of key modules such as knowledge extraction, knowledge fusion and cross-checking. In terms of knowledge fusion, this paper proposes the functional design of conflict detection, entity alignment based on deep learning, data fusion based on linking and cross-validation based on confidence assessment. In terms of cross-truth, this paper investigates the credibility calculation of candidate message sets, the representation of candidate message sets, and the construction of logic rules for cross-truth models. Finally, this paper evaluates the system’s term extraction, concept recognition, and performance. The results show that the average elapsed time for each function of graph visualization, keyword search, keyword search, advanced search, and advanced search in the knowledge management system to be executed 20 times is 1.33s, 1.27s, 3.14s, 1.47s, and 3.26s, respectively, and the average response time is only 2.1s. Therefore, the knowledge management system for grid industry science and technology presented in this paper is effective.

Conflict detection and elimination integrating agent’s influence and conflict coefficient with incomplete trust relationship

Conflict detection and elimination integrating agent’s influence and conflict coefficient with incomplete trust relationship