Complex Information Research Articles

Adrenergic C1 neurons enhance anxiety via projections to PAG.

Anxiety is an emotional state precipitated by the anticipation of real or potential threats. Anxiety disorders are the most prevalent psychiatric illnesses globally and increase the risk of developing comorbid conditions that negatively impact the brain and body. The etiology of anxiety disorders remains unresolved, limiting improvement of therapeutic strategies to alleviate anxiety-related symptoms with increased specificity and efficacy. Here, we applied novel intersectional tools to identify a discrete population of brainstem adrenergic neurons, named C1 cells, that promote aversion and anxiety-related behaviors via projections to the periaqueductal gray matter (PAG). While C1 cells have traditionally been implicated in modulation of autonomic processes, rabies tracing revealed that they receive input from brain areas with diverse functions. Calcium-based in vivo imaging showed that activation of C1 cells enhances excitatory responses in vlPAG, activity that is exacerbated in times of heightened stress. Furthermore, inhibition of C1 cells impedes the development of anxiety-like behaviors in response to stressful situations. Overall, these findings suggest that C1 neurons are positioned to integrate complex information from the brain and periphery for the promotion of anxiety-like behaviors.

Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing.

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.

Evading Cyber-Attacks on Hadoop Ecosystem: A Novel Machine Learning-Based Security-Centric Approach towards Big Data Cloud

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface “Ambari” was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection.

A game of snakes and ladders: the world of complex health and social care data linkage

Objective and ApproachResearch on social care services requires large, comprehensive, routine datasets. Launched in November 2019, Developing resources And minimum data set for Care Homes’ Adoption (DACHA) study aims to develop a prototype minimum dataset as proof of concept and to propose implementation. We describe our experience since 2021 of identifying, applying for, and linking care home, local, integrated care system (ICS) and national datasets, including direct-care software-provider data, GP and NHS England (NHSE) datasets. ResultsOur key challenges: Risk aversion and fragmented information governance within and between organisations. Complex information governance structures must be understood before participants are recruited. Shifts in the data landscape: data ownership moved from clinical commissioning groups to ICSs; NHSE merged with NHS Digital. Research funding: research is required of ICSs, but it is not a system priority, so processes and funding aren’t in place, and research burdens an already strained system. Our key lessons: Formalising agreements early on and obtaining, and maintaining, senior, committed buy-in. Studies should value the data sharing process through the inclusion of data controllers and information governance staff in the system in research project budgets. The facilitation of GP and social care data collection nationally must be balanced with its administrative burden. ImplicationsThe DACHA study overlapped with the COVID-19 pandemic and could only engage a limited number (n=3) of ICSs within project resources. Nevertheless, the complexity of the system and the plurality of actors delay or even block legitimate research interests.

Determinants of gamification effectiveness: perspectives of technology affordances and coping responses in the context of team-based gamified training

ABSTRACT Firms face significant challenges in effectively training employees in adopting complex information systems, such as enterprise resource planning (ERP). Gamified training has emerged as a promising means to address these challenges. Despite this, prior research has paid little attention to the factors affecting the effective use of gamified training. This study aims to identify the determinants of the effective use of gamified training. Based on technology affordances, coping, and gamification literature, the study proposes that team-based gamification affordances (e.g. collaboration and competition affordances) affect coping responses (e.g. task-focused, emotion-focused, and avoidance coping), leading to effective use. We utilized the ERP simulation game to empirically test the research model, employing a multi-study approach comprising two studies (Study 1 = 255 participants, Study 2 = 219 participants). Structural equation modelling was used for data analysis. Our results indicate that collaboration affordance significantly affects task-focused, emotion-focused, and avoidance coping. However, competition affordance influenced only task-focused coping. We also found that task-focused and emotion-focused coping affected effective use. This study contributes to IS literature by highlighting gamification affordances and coping responses as important predictors of effective use in gamified training, leading organizations and IS scholars to design impactful and engaging gamified training programmes.

Examining the Care Experiences of People Experiencing Homelessness in Kingston

Background Across Canada, approximately 235,000 individuals face housing instability, and homelessness has been associated with an increased use of emergency department (ED) care. EDs are a safety net for people experiencing homelessness (PEH), when faced with limitations to primary care access, such as limited service hours and locations. PEH have reported negative ED care experiences, including feeling judged, not having health concerns taken seriously, and experiencing stigmatizing behaviour. Prior negative experiences contribute to the avoidance of care services, exacerbating issues for PEH. Objective This study examines the experiences of unhoused populations accessing ED services, to better understand the adversity they face. Methods The study is participatory and cross-sectional, using the sensemaking Spryng.io platform to survey patients at the Kingston Health Sciences Centre EDs. Sensemaking methodology (SM) uses a mixed-methods research approach that collects data through storytelling. This enables individuals to naturally convey complex information and make sense of their experiences. Within the sensemaking survey, individuals who self-identified as a PEH answered an additional subset of questions about their experiences accessing care. Follow-up focus group discussions (FGDs) will take place at St. Vincent de Paul, an organization that supports people in Kingston with resources such as food, clothes, and housing. FGDs will be used to validate the sensemaking results and co-create care improvement strategies. Conclusion By engaging PEH, our results can lead to actionable care improvement strategies. The study is also a foundation for future research collaborating with PEH and community partners, to mitigate negative care experiences.

A developmental model of audio-visual attention (MAVA) for bimodal language learning in infants and robots

A social individual needs to effectively manage the amount of complex information in his or her environment relative to his or her own purpose to obtain relevant information. This paper presents a neural architecture aiming to reproduce attention mechanisms (alerting/orienting/selecting) that are efficient in humans during audiovisual tasks in robots. We evaluated the system based on its ability to identify relevant sources of information on faces of subjects emitting vowels. We propose a developmental model of audio-visual attention (MAVA) combining Hebbian learning and a competition between saliency maps based on visual movement and audio energy. MAVA effectively combines bottom-up and top-down information to orient the system toward pertinent areas. The system has several advantages, including online and autonomous learning abilities, low computation time and robustness to environmental noise. MAVA outperforms other artificial models for detecting speech sources under various noise conditions.

Optical tomography and coherence of a cavity interacting with two time-dependent position qubits

Optical tomography is a widely used method for estimating complex information. It provides a monotonic relation between the coherent field states density and their corresponding probability distributions. This approach is critical for validating any quantum information processing system’s implementation. This paper explores the optical tomography and coherence dynamics for a cavity interacting with two two-level atoms having time-dependent locations. We analyze the dynamics of the photon-field states, as two moving atoms enter a cavity filled with two superposed coherent states. The von-Neumann entropy dynamics illustrates how interaction couplings between the two atoms and cavity can give rise to entangled states under the effects of the atom-field couplings and the time-dependent atomic location parameter. Aside from coherence, the interactions between the cavity and atoms are essential for producing nonclassical proprieties in optical tomography. Furthermore, we investigate the dynamics of optical tomography densities with respect to the couplings between atoms and photons for time-dependent atomic location. Our results show that the couplings between atoms and cavity not only accelerate but also improve the processes involved in generating nonclassical optical tomography and coherence dynamics.

Centrality of Information Sources in Agricultural Input Networks: Insights from Social Network Analysis

Input dealers being credible localite sources of information for farmers have huge potential of aiding faster diffusion of innovations. Under this context, understanding how the input dealers’ access and exchange information is essential for enriching them with updated information and improving the agricultural value chain. This study employs Social Network Analysis (SNA) to explore the centrality measures—degree centrality, closeness centrality, and betweenness centrality—of various information sources utilized by input dealers in Nagar Kurnool and Wanaparthy districts of Telangana during 2020. The analysis reveals that traditional sources, such as extension agents and other input dealers, are highly central to the network, with both scoring the highest in degree and closeness centrality (0.733 and 0.698, respectively). These sources are not only frequently accessed but also serve as key bridges within the network, facilitating the flow of information. Digital platforms like smartphones show growing importance, with significant degree centrality (0.667), yet they play a less critical bridging role. In contrast, social media and print media exhibit lower centrality scores, indicating their peripheral influence. The findings suggest that while traditional information sources remain dominant, digital platforms are emerging as valuable tools for information dissemination highlighting the need for strategies that strengthen traditional channels while integrating digital tools to enhance the efficiency and reach of agricultural information networks. This research contributes to a deeper understanding of the complex information ecosystem in agriculture, offering insights for policymakers and stakeholders aiming to optimize information flows within the sector.

Weighing up the options: experiences in applying decision science from a large-scale conservation program

ABSTRACT The need to make evidence-based decisions in conservation planning for threatened species in the face of limited resources and knowledge is widely recognised as a growing challenge. Increasingly sophisticated decision-support tools and approaches are available to conservation programs. The ability of conservation planners to effectively implement these tools will be key to incorporating complex information into threatened species management. The development of effective decision science approaches does not end when they are made available to planners. Planner and practitioner input into their use and outputs is an important part of incorporating these tools into on-ground conservation. The New South Wales Saving our Species program is a large-scale conservation program with jurisdiction over more than 1100 threatened species, ecological communities and populations. We discuss why co-design is key to successful implementation of decision science in program-level planning; this approach has supported the Saving our Species program to account for forms of knowledge that may otherwise be ignored by data driven optimisation. This paper focuses on the role of conservation planners in developing and applying decision tools. We present three case studies that deployed tools co-developed for the Saving our Species program. Through these case studies, we suggest that effective conservation planning can be best achieved through (1) narrowing down the number of options under consideration, by eliminating sub-optimal choices (2) supporting decision-makers to understand the relative advantages and disadvantages of the choices under consideration and (3) enhancing the effectiveness of decision-support tools by integrating practitioner expertise into their application.

FSCME: A Feature Selection Method Combining Copula Correlation and Maximal Information Coefficient by Entropy Weights.

Feature selection is a critical component of data mining and has garnered significant attention in recent years. However, feature selection methods based on information entropy often introduce complex mutual information forms to measure features, leading to increased redundancy and potential errors. To address this issue, we propose FSCME, a feature selection method combining Copula correlation (Ccor) and the maximum information coefficient (MIC) by entropy weights. The FSCME takes into consideration the relevance between features and labels, as well as the redundancy among candidate features and selected features. Therefore, the FSCME utilizes Ccor to measure the redundancy between features, while also estimating the relevance between features and labels. Meanwhile, the FSCME employs MIC to enhance the credibility of the correlation between features and labels. Moreover, this study employs the Entropy Weight Method (EWM) to evaluate and assign weights to the Ccor and MIC. The experimental results demonstrate that FSCME yields a more effective feature subset for subsequent clustering processes, significantly improving the classification performance compared to the other six feature selection methods.

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction.

Pedestrian trajectory prediction is an important technique of autonomous driving. In order to accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider social interactions among pedestrians and the influence of surrounding scene simultaneously, which can fully represent the complex behavior information and ensure the rationality of predicted trajectories obeyed realistic rules. In this article, we propose one new prediction model named social soft attention graph convolution network (SSAGCN), which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. In detail, when modeling social interaction, we propose a new social soft attention function, which fully considers various interaction factors among pedestrians. Also, it can distinguish the influence of pedestrians around the agent based on different factors under various situations. For the scene interaction, we propose one new sequential scene sharing mechanism. The influence of the scene on one agent at each moment can be shared with other neighbors through social soft attention; therefore, the influence of the scene is expanded both in spatial and temporal dimensions. With the help of these improvements, we successfully obtain socially and physically acceptable predicted trajectories. The experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results. The project code is available at https://github.com/WW-Tong/ssagcn_for_path_prediction.

Assessing the quality of AI information from ChatGPT regarding oral surgery, preventive dentistry, and oral cancer: An exploration study

AimEvaluation of the quality of dental information produced by the ChatGPT artificial intelligence language model within the context of oral surgery, preventive dentistry, and oral cancer. MethodologyThis study adopted quantitative methods approach. The experts prepared 50 questions (including dimensions of, risk factors, preventive measures, diagnostic methods, and treatment options) that would be presented to ChatGPT, and its responses were rated for their accuracy, completeness, relevance, clarity or comprehensibility, and possible risks using a standardized rubric. To carry out the assessment of the responses by ChatGPT, a standardized scoring rubric was used. Evaluation process included feedback concerning the strengths, weaknesses, and potential areas of improvement in the responses provided by ChatGPT. ResultsWhile achieving the highest score for preventive dentistry at 4.3/5 and being able to communicate the complex information coherently, the tool showed lower accuracy for oral surgery and oral cancer, scoring 3.9/5 and 3.6/5, respectively, with several gaps for post-operative instructions, personalized risk assessments, and specialized diagnostic methods. Potential risks, such as a lack of individualized advice, were shown in 53% of the oral cancer and in 40% of the oral surgery. While showing promise in some domains, ChatGPT had important limitations in specialized areas that require nuanced expertise. ConclusionThe findings point to the need for professional supervision while using AI-generated information and ongoing evaluation as capabilities evolve, for the assurance of responsible implementation in the best interest of patient care.

Data-driven visual model development and 3D visual analytics framework for underground mining

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations.

Enhancing Sustainable Traffic Monitoring: Leveraging NanoSight–YOLO for Precision Detection of Micro-Vehicle Targets in Satellite Imagery

Satellite remote sensing technology significantly aids road traffic monitoring through its broad observational scope and data richness. However, accurately detecting micro-vehicle targets in satellite imagery is challenging due to complex backgrounds and limited semantic information hindering traditional object detection models. To overcome these issues, this paper presents the NanoSight–YOLO model, a specialized adaptation of YOLOv8, to boost micro-vehicle detection. This model features an advanced feature extraction network, incorporates a transformer-based attention mechanism to emphasize critical features, and improves the loss function and BBox regression for enhanced accuracy. A unique micro-target detection layer tailored for satellite imagery granularity is also introduced. Empirical evaluations show improvements of 12.4% in precision and 11.5% in both recall and mean average precision (mAP) in standard tests. Further validation of the DOTA dataset highlights the model’s adaptability and generalization across various satellite scenarios, with increases of 3.6% in precision, 6.5% in recall, and 4.3% in mAP. These enhancements confirm NanoSight–YOLO’s efficacy in complex satellite imaging environments, representing a significant leap in satellite-based traffic monitoring.

Data Infrastructure for Improvement in Perioperative Medicine: Experience at a London Tertiary Hospital.

Adoption of electronic health record systems offers an opportunity to collate massive volumes of complex information about patient care. Healthcare data can inform performance management, enable predictive analytics and enhance strategic decision making. A data-driven approach to improving patient care is vital to address the growing burden of morbidity and mortality associated with major surgery. We describe our methodology for transforming and utilising process of care data in an electronic health record system to develop a registry for quality improvement purposes in patients undergoing major surgery at a single UK hospital. We highlight development of our data-driven vision, technical aspects of processing raw data into metrics relevant to clinical decision making, alongside challenges encountered. Finally, we outline how our data infrastructure supports clinical governance, quality improvement and research. In sharing our experiences, we hope to enable others to embed and access the transformative clinical insights that healthcare data can yield.

Negotiating grey areas: an interview-based analysis of paramedic uncertainty and decision-making in cardiac arrest events

BackgroundParamedics are responsible for critical resuscitation decisions when attending Out of Hospital Cardiac Arrests (OHCA). Existing research indicates that a range of clinical and non-clinical factors moderate their decision-making. Within the United Kingdom (UK), there is little evidence on how and why paramedics make their decisions at actual OHCA events.MethodsWe explored the experiences of UK paramedics using individually recalled OHCA events as catalysts for discussion. Pen portraits developed from semi-structured interviews with 31 paramedics across two UK ambulance services were thematically analysed, enabling cross-participant comparisons whilst retaining depth and context.ResultsWe identified four themes: uncertainties encountered in resuscitation guidelines, influences on decision-making, holistic perspectives, and indirect moderators. We found that paramedics experienced uncertainty at all stages of the resuscitation process. Uncertainties arose from indeterminate, ambiguous or complex information and were described as having both clinical and ethical dimensions. Whilst guidelines drove paramedics’ decisions, non-clinical personal, practical and relational factors moderated their assessments of survivability and decision-making, with attitudes to interactions between patient age, frailty and quality of life playing a substantial role. Coping strategies such as uncertainty reduction, assumption-based reasoning and weighing pros and cons were evident from interviews.ConclusionsThe complexity of interactions between clinical and non-clinical factors points to an element of variability in paramedics’ responses to uncertainty. Exploring UK paramedics’ uncertainties and decision-making during specific OHCA events can help acknowledge and address uncertainties in resuscitation guidelines and paramedic training, providing paramedics with the tools to manage uncertainty in a consistent and transparent way.

Real-Time Detection and Counting of Wheat Spikes Based on Improved YOLOv10

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model’s feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research.

A novel link prediction model for interval-valued crude oil prices based on complex network and multi-source information

Accurate crude oil price forecasting is crucial for maintaining energy economy stability and enhancing investment and operational decision-making. Nowadays, the link prediction model for crude oil prices based on a complex network model has become an emerging and promising approach. However, existing link prediction methods are unable to extract complex information about interval-valued crude oil prices, and fail to consider the impact of multi-source information. Therefore, this paper proposes the link prediction model for interval-valued crude oil prices using complex network and multi-source information, which converts crude oil price interval series into complex networks. Through link prediction, it takes into account both the impact of past interval-crude oil prices on future interval time series and the interference of external real-time information on interval-valued prices. First, news headlines and geopolitical risk indices related to crude oil prices are captured, and search index data and news sentiment scores associated with interval data are analyzed. Subsequently, the interval sequence data and related influencing factors are decomposed and reconstructed respectively. The reconstructed interval center and radius sequence are then converted into a directed network, and node similarity in the network is calculated to generate similar nodes of the interval subsequence as feature values. Finally, multiple machine learning models are employed to acquire the optimal weighted combination prediction with interval prediction values. According to the experimental data, this approach outperforms other benchmark methods in terms of prediction accuracy.

Spatial-Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model.

Most current methods use spatial-temporal graph neural networks (STGNNs) to analyze complex spatial-temporal information from traffic data collected from hundreds of sensors. STGNNs combine graph neural networks (GNNs) and sequence models to create hybrid structures that allow for the two networks to collaborate. However, this collaboration has made the model increasingly complex. This study proposes a framework that relies solely on original Transformer architecture and carefully designs embeddings to efficiently extract spatial-temporal dependencies in traffic flow. Additionally, we used pre-trained language models to enhance forecasting performance. We compared our new framework with current state-of-the-art STGNNs and Transformer-based models using four real-world traffic datasets: PEMS04, PEMS08, METR-LA, and PEMS-BAY. The experimental results demonstrate that our framework outperforms the other models in most metrics.