Complex Information Processing Research Articles

Sleep Management: The Use of Continuous Physiological Monitoring to Support Patient Self-Care

Circadian misalignment (CM) is a sleep disruption where an individual’s circadian rhythm and actual sleep-wake cycle are unsynchronized. CM is frequently misdiagnosed and difficult to treat but can adversely impact short-term and long-term health outcomes. CM often interacts bidirectionally with chronic conditions affecting cognitive and physical functionality. Continuous physiological monitoring (CPM) of the hormone melatonin would support real-time, patient-driven interventions to address CM. However, patient-driven self-care tasks involve complex information processing and decision-making. Effective CPM use requires situation awareness regarding current circadian health and projected future status, sufficient background knowledge to identify circadian intervention alternatives, and selection of appropriate alternatives given their current state. Melatonin data has characteristics distinct from biomarkers previously subject to CPM, introducing further challenges around data presentation. Task analysis efforts can identify the cognitive processes involved in five different tasks associated with a continuous melatonin monitor to identify necessary informational elements to include in interface design.

Bounded Rational Decision Networks with Belief Propagation.

Complex information processing systems that are capable of a wide variety of tasks, such as the human brain, are composed of specialized units that collaborate and communicate with each other. An important property of such information processing networks is locality: there is no single global unit controlling the modules, but information is exchanged locally. Here, we consider a decision-theoretic approach to study networks of bounded rational decision makers that are allowed to specialize and communicate with each other. In contrast to previous work that has focused on feedforward communication between decision-making agents, we consider cyclical information processing paths allowing for back-and-forth communication. We adapt message-passing algorithms to suit this purpose, essentially allowing for local information flow between units and thus enabling circular dependency structures. We provide examples that show how repeated communication can increase performance given that each unit's information processing capability is limited and that decision-making systems with too few or too many connections and feedback loops achieve suboptimal utility.

The Impact of Emotional Factors on L2 Learners Enjoyment and Anxiety in Foreign Languages

In light of the expansion of globalization and the intensification of cross-cultural communication, there is an increasing necessity to learn a second language (L2). However, many L2 learners face emotional challenges throughout the learning process, which affect their learning experience and outcomes. This paper explores the emotional experiences of Chinese learners of English as a L2 and their impact on learning outcomes. Theoretical frameworks from positive psychology and educational psychology are then employed to investigate the roles of foreign language enjoyment and foreign language anxiety, as well as the moderating effects of classroom environment and social support. The results show that foreign language enjoyment has a positive effect on learning outcomes, whereas foreign language anxiety has a detrimental effect. In the case of semantic-prefix incongruence, high-level learners exhibited stronger interference effects, which indicates the complexity of affective information processing. Therefore, the paper not only deepens the comprehension of emotional factors in foreign language learning, but also provides valuable insights into foreign language teaching practices, which indicates that teachers should prioritize students affective experiences, optimize classroom environments, and offer positive social support to promote their affective engagement and learning outcomes. Future research should further explore the differences in students affective experiences in different cultural contexts to achieve more comprehensive and personalized foreign language teaching goals.

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 Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

A hierarchy of processing complexity and timescales for natural sounds in human auditory cortex.

Efficient behavior is supported by humans' ability to rapidly recognize acoustically distinct sounds as members of a common category. Within auditory cortex, there are critical unanswered questions regarding the organization and dynamics of sound categorization. Here, we performed intracerebral recordings in the context of epilepsy surgery as 20 patient-participants listened to natural sounds. We built encoding models to predict neural responses using features of these sounds extracted from different layers within a sound-categorization deep neural network (DNN). This approach yielded highly accurate models of neural responses throughout auditory cortex. The complexity of a cortical site's representation (measured by the depth of the DNN layer that produced the best model) was closely related to its anatomical location, with shallow, middle, and deep layers of the DNN associated with core (primary auditory cortex), lateral belt, and parabelt regions, respectively. Smoothly varying gradients of representational complexity also existed within these regions, with complexity increasing along a posteromedial-to-anterolateral direction in core and lateral belt, and along posterior-to-anterior and dorsal-to-ventral dimensions in parabelt. When we estimated the time window over which each recording site integrates information, we found shorter integration windows in core relative to lateral belt and parabelt. Lastly, we found a relationship between the length of the integration window and the complexity of information processing within core (but not lateral belt or parabelt). These findings suggest hierarchies of timescales and processing complexity, and their interrelationship, represent a functional organizational principle of the auditory stream that underlies our perception of complex, abstract auditory information.

DNA-empowered synthetic cells as minimalistic life forms.

Cells, the fundamental units of life, orchestrate intricate functions - motility, adaptation, replication, communication, and self-organization within tissues. Originating from spatiotemporally organized structures and machinery, coupled with information processing in signalling networks, cells embody the 'sensor-processor-actuator' paradigm. Can we glean insights from these processes to construct primitive artificial systems with life-like properties? Using de novo design approaches, what can we uncover about the evolutionary path of life? This Review discusses the strides made in crafting synthetic cells, utilizing the powerful toolbox of structural and dynamic DNA nanoscience. We describe how DNA can serve as a versatile tool for engineering entire synthetic cells or subcellular entities, and how DNA enables complex behaviour, including motility and information processing for adaptive and interactive processes. We chart future directions for DNA-empowered synthetic cells, envisioning interactive systems wherein synthetic cells communicate within communities and with living cells.

Rationale for the creation of multispectral data-processing system shock reconnaissance UAVs for use in modern warfare

In recent years, in the technically developed countries of the world, special attention has been paid to the development of the theory and practice of warfare in a single information space, changing views on the training and use of armed forces in modern wars and conflicts. An important aspect in the combat management chain is the distribution and transmission of information to support the decisions of the command and control of the armed forces. The purpose of the work is to identify typical tasks solved using a multispectral information and computing system of a reconnaissance and strike unmanned aerial vehicle (UAV). The analysis of tasks and the target load of UAVs in modern wars is carried out. The data on modern UAVs are systematized, in particular, on the payload used, the features of combat use in military conflicts, the main disadvantages in the functioning of control systems to achieve specific combat tasks. The modern world concepts and technologies for the development of integrated UAV information systems are presented. The hierarchical principle of obtaining information in a multispectral system and its complex processing are considered. The result of the functioning of a set of sensors in complex information processing will be the possibility of forming solutions that reduce the amount of information transmitted through data transmission channels and simplify the operator's work.

The interaction of concepts and laws of system development in the economic analysis of business entities' activities

Subject. The increasing complexity of business operation processes and rising expectations of stakeholders require rethinking the content of economic analysis of business entities’ activities. Objectives. The study aims at assessing the impact of interaction of modern concepts and laws of system development on the economic analysis of business entities’ activities. Methods. The study employs logical and systems approaches, methods of grouping, comparison, generalization, analysis, and synthesis. We apply the matrix method in the economic analysis to identify the results of interaction of the concepts under consideration and the laws of system development. Results. The analysis demonstrated that the methodology of economic analysis should be geared to solving theoretical and practical issues of the activities of business entities that take into account the transformation of business processes under the influence of digitalization, the emergence of new forms of business organization and management technologies with a focus on harmonious development, the increase in information flows and the complexity of economic information processing, systematization, and transmission. Conclusions. The economic analysis has gradually transformed into an integrated and interdisciplinary scientific discipline aimed at disclosing the problems of business processes and developing possible solutions, which ultimately boils down to developing the most rational courses of action that meet the requirements of stakeholders. The methodology of economic analysis itself is directly dependent on the laws of development and the concepts under consideration. This requires considering the manifestations of their interaction in the development of information and analytical support for the management of economic entities.

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots.

Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways

With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory + Gaussian mixture model) lane change decision and trajectory prediction model, which includes a lane-changing intention decision module. The model comprises a lane change decision module responsible for determining three lane change intentions: left lane change, right lane change, and car-following. Subsequently, a multi-head attention mechanism processes complex vehicle interaction information to enhance modeling accuracy and intelligence. In addition, uncertainty in trajectory prediction is considered by using multimodal trajectory prediction and Gaussian mixture model, and diversity and uncertainty are combined by combining trajectory prediction from several different modalities through probabilistic combinatorial sampling patterns. Test results indicate that the MCLG model, based on the multi-head attention module, outperforms existing methods in trajectory prediction. The decision module, which takes interactive information into account, exhibits higher predictability and accuracy. Furthermore, the MCLG model, considering the lane-changing decision module, significantly enhances trajectory prediction accuracy, providing robust decision-making support for autonomous driving systems.

브랜드 스토리텔링에 관한 고찰 및 새로운 연구 방향

Nowadays, brand has become the most important intangible asset of a company, and the competition in the market has become a competition of brands. Narrative is considered to be an effective means of branding. Storytelling is an effective way for companies to communicate with consumers. Since stories do not require complex information processing by consumers, the content conveyed in the form of stories makes it easier for consumers to understand and be persuaded. Through storytelling, companies communicate their values to consumers, allowing them to empathize with them and build relationships with them. Brand storytelling has become one of the hot topics in the marketing field. This study collates and analyzes the research results of brand storytelling from five aspects: definition, types, components, roles, and research trends, and proposes suggestions for future research directions in view of the shortcomings of current research.

Research Status and Development Trend of EEG Technology for Deep Learning in Emotion Recognition

EEG technology has made significant progress as people's attention to their emotions increases. This has also led to significant progress in various brain sciences, as well as their correlation with emotions. Nevertheless, based on the development of contemporary brain science, it is not possible to provide detailed cause analysis or draw accurate conclusions based on EEG information for a certain symptom or representation. The brain, as a complex organ of multiple entities, processes complex information by connecting multiple brain regions and clarifying the division of labor for each region. This creates a significant obstacle for the research method of brain computer interface, which uses computer algorithms to read information in the brain. Recently, many solutions and research methods have emerged to address this issue, such as electroencephalography (EEG), Positive Positron Emission Computed Tomography (PET) and Functional Magnetic Resolution Imaging (fMRI), etc. However, with such a large number of technologies, a method that can provide precise data from both temporal and spatial angles has not yet been derived. So in-depth research in biology, continuous exploration of computer algorithms can make outstanding contributions to the refinement of brain computer data. In summary, this article aims to provide an overview of the relationship between emotions and EEG analysis, as well as the principles of machine recognition and other applied technologies.

Leaving the Cold Behind: The Role of Emotions and Cognitive Biases in Business Adaptation to Climate Change

This article develops the argument that the interplay between emotions and cognitive biases influences corporate decision-making on climate change adaptation. Our theoretical analysis examines how emotions can change the effect of cognitive biases on adaptation decisions by influencing how firms select, access, and process complex and uncertain climatic information. We draw on research on climate adaptation, social psychology, and managerial cognition and focus on three forms of bias: availability heuristic, framing, and anchoring. We explain how each bias shapes the decision-making process on adaptation and theorize how emotions of different valence and arousal affect this process. We shed light on the underlying mechanisms that explain how emotions influence the effect of cognitive biases as a source of inaction on adaptation in firms. Our analysis provides a new perspective on how firms approach the strategic decision to adapt to climate change by considering both cognitive and emotional aspects.

Impressions from Faces, Personality Judgment, and Relevant Factors

The phrase "facial impression" describes how someone's first impression of another person's personality, state of mind, attitude, and other traits are dependent on how others express themselves facially. One's facial impression is mostly determined by the visual cues of one's emotions, such as the eyes, grin, and other facial features. Personality judgment is the in-depth assessment of a person's personality using indications from their behavior and social interactions, as well as visual cues from their face and other relevant aspects. Personality evaluation requires more complex cognitive and information processing that carefully considers all other non-facial variables in addition to analyzing face impressions. The relationship between facial impressions and personality evaluations is reciprocal. There is evidence that a person's personality qualities may be inferred from their facial features, however the precision of this association varies depending on the context. Previous research also discovered that face characteristics have an impact on people's social cognition and conduct. Therefore, face characteristics are crucial in creating initial impressions and assessing personality. People routinely assess the emotions and personality traits of others by analyzing their facial expressions, which is an essential part of human social interaction. This review can provide some guidance to prevention and intervention programs that aim to reduce social bias and discrimination.

Functional neuroimaging related to lower urinary tract sensations: Future directions for study designs and selection of patient groups: ICI-RS 2023.

Neuroimaging studies have advanced our understanding of the intricate central nervous system control network governing lower urinary tract (LUT) function, shedding light on mechanisms for urine storage and voiding. However, a lack of consensus in methodological approaches hinders the comparability of results among research groups and limits the translation of this knowledge to clinical applications, emphasizing the need for standardized methodologies and clinical utilization guidelines. This paper reports the discussions of a workshop at the 2023 meeting of the International Consultation on Incontinence Research Society, which reviewed uncertainties and research priorities to progress the field of neuroimaging in LUT control and dysfunction. Neuroimaging holds great potential for improving our understanding of LUT control and pathophysiological conditions. To date, functional neuroimaging techniques have not yet achieved sufficient strength to make a direct clinical impact. Potential approaches that can improve the clinical utilization of neuroimaging were discussed and research questions proposed. Neuroimaging offers a valuable tool for investigating LUT control, but it's essential to acknowledge the potential for oversimplification when interpreting brain activity due to the complex neural processing and filtering of sensory information. Moreover, technical limitations pose challenges in assessing key brain stem and spinal cord centers, particularly in cases of neurological dysfunction, highlighting the need for more reliable imaging of these centers to advance our understanding of LUT function and dysfunction.

Enteroendocrine cell regulation of the gut-brain axis.

Enteroendocrine cells (EECs) are an essential interface between the gut and brain that communicate signals about nutrients, pain, and even information from our microbiome. EECs are hormone-producing cells expressed throughout the gastrointestinal epithelium and have been leveraged by pharmaceuticals like semaglutide (Ozempic, Wegovy), terzepatide (Mounjaro), and retatrutide (Phase 2) for diabetes and weight control, and linaclotide (Linzess) to treat irritable bowel syndrome (IBS) and visceral pain. This review focuses on role of intestinal EECs to communicate signals from the gut lumen to the brain. Canonically, EECs communicate information about the intestinal environment through a variety of hormones, dividing EECs into separate classes based on the hormone each cell type secretes. Recent studies have revealed more diverse hormone profiles and communication modalities for EECs including direct synaptic communication with peripheral neurons. EECs known as neuropod cells rapidly relay signals from gut to brain via a direct communication with vagal and primary sensory neurons. Further, this review discusses the complex information processing machinery within EECs, including receptors that transduce intraluminal signals and the ion channel complement that govern initiation and propagation of these signals. Deeper understanding of EEC physiology is necessary to safely treat devastating and pervasive conditions like irritable bowel syndrome and obesity.

HIA as a standard tool for effective decision-making on NCD policies

Abstract Background Increasing rates of NCD are observed within EU countries. This paper aims to highlight the potential of Health Impact Assessment (HIA) in informing projects, plans, and programs related to health improvement, thus enhancing its role in the decision-making process. Methods The preliminary analysis focuses on HIA's involvement in the decision-making process regarding NCD in the EU, particularly in CEE countries over the past decade. The study examines various aspects of the identified HIAs, including their general information, the time frame of the HIA, proposed preventive measures, methods used, the description of the determinants of health affected by the decision, the type of document assessed, the affected population, the impact of the HIA on subsequent decisions, the source and cost of funding, the limitations of the study, HIA as a part of EIA or not, the use of artificial intelligence algorithms in the possibility of HIA implementation, etc. The impact of each HIA on subsequent decisions was categorized using an adaptation of a classification system developed by Wismar et al. for use in evaluating European HIAs (Wismar et al. 2007). Results The findings suggest that HIA is crucial in addressing the public and providing transparent information to entities with decision-making authority. It is recommended that the HIA process be a standard tool used to clarify information on health risk factors, implement new health policies, and monitor their effectiveness. The study also indicates that a completely digital approach to HIA is an open question that requires further research. AI algorithms can potentially aid in improving HIA's efficiency and processing complex information and data. Conclusions HIA has excellent potential to improve the decision-making process related to non-communicable diseases, and its incorporation as a standard tool is essential. The use of AI algorithms in HIA implementation is a promising area for future research. Key messages • HIA is a crucial tool in addressing the public and providing transparent information to entities with decision-making authority. • The use of AI algorithms in HIA implementation is a promising area for future research.

Accelerating the integration of the metaverse into urban transportation using fuzzy trigonometric based decision making

Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.

Fast Prediction for Criminal Suspects through Neighbor Mutual Information-Based Latent Network

One of the interesting characteristics of crime data is that criminal cases are often interrelated. Criminal acts may be similar, and similar incidents may occur consecutively by the same offender or by the same criminal group. Among many machine learning algorithms, network-based approaches are well-suited to reflect these associative characteristics. Applying machine learning to criminal networks composed of cases and their associates can predict potential suspects. This narrows the scope of an investigation, saving time and cost. However, inference from criminal networks is not straightforward as it requires being able to process complex information entangled with case-to-case, person-to-person, and case-to-person connections. Besides, being useful at a crime scene requires urgency. However, predictions from network-based machine learning algorithms are generally slow when the data is large and complex in structure. These limitations are an immediate barrier to any practical use of the criminal network geared by machine learning. In this study, we propose a criminal network-based suspect prediction framework. The network we designed has a unique structure, such as a sandwich panel, in which one side is a network of crime cases and the other side is a network of people such as victims, criminals, and witnesses. The two networks are connected by relationships between the case and the persons involved in the case. The proposed method is then further developed into a fast inference algorithm for large-scale criminal networks. Experiments on benchmark data showed that the fast inference algorithm significantly reduced execution time while still being competitive in performance comparisons of the original algorithm and other existing approaches. Based on actual crime data provided by the Korean National Police, several examples of how the proposed method is applied are shown.