Quality Of Information Processing Research Articles

Neuroimaging Correlates of Post-COVID-19 Symptoms: A Functional MRI Approach

Backgrounds and Purpose: Post-COVID syndrome is characterized by persistent symptoms, including fatigue and cognitive impairment. These symptoms may be experienced by up to 80% of patients. We aimed to identify possible patterns of brain activation underlying post-COVID fatigue. Methods: The study used functional MRI (Siemens MAGNETOM Prisma 3T scanner with a specially created protocol) of the brain in 30 patients with post-COVID fatigue syndrome and 20 healthy volunteers. Task functional MRI (fMRI) was performed using a cognitive paradigm (modified Stroop test). Eligible patients included adults aged 18–50 years with a >12 weeks before enrolment (less than 12 months) prior history of documented COVID-19 with symptoms of fatigue not attributable to any other cause, and with MFI-20 score > 30 and MoCA at first visit. Healthy control participants had no prior history of COVID-19 and negative tests for severe acute coronavirus respiratory syndrome with MFI-20 score < 30 and MoCA at first visit. Task fMRI data were processed using the SPM12 software package based on MATLAB R2022a. Results: Cognitive task fMRI analysis showed significantly higher activation in the post-COVID group versus healthy volunteers’ group. Between-group analysis showed significant activation differences. Using a threshold of T > 3 we identified eight clusters of statistically significant activation: supramarginal gyri, posterior cingulate cortex, opercular parts of precentral gyri and cerebellum posterior lobe bilaterally. Conclusions: Post-COVID fatigue syndrome associated with subjective cognitive impairment could show changes in brain functional activity in the areas connected with information processing speed and quality.

The impact of digitalization on the quality and efficiency of information processing in medical institutions

Digitalization of the process of providing medical care in the practice of medical centers has an impact on the quality of services provided, forecasting the workload of medical personnel and diagnostic systems, as well as on the components of efficiency in the healthcare sector. The authors of this article consider the use of industry 4.0 tools in the field of healthcare. Wearable devices for collecting, transmitting and analyzing vital signs of patients in medical institutions were chosen as the object of research.

Этика освоения профессиональных компетенций с помощью нейросетей

Background: The inclusion of neural networks in the work of healthcare institutions and medical education is an urgent problem in bioethics – a discipline that develops issues of personal choice between benefit and harm, between good and evil, between the volume and quality of information processing. The introduction of neural networks into the practice of healing is inevitable and is "the most commonly used analytical tool". The pros and cons of digitalization of medicine are described in detail in the literature, such as acquiring a digital assistant for diagnosis, determining optimal treatment plans and monitoring the health status of patients. Aim: to consid-er the possibility of improving clinical thinking in partnership with neural networks using the example of analyzing clinical situations.Materials and methods: An analytical review of the literature on the problem of integrating artificial intelligence into medical practice was carried out. The empirical base is represented by materials from qualitative sociological research (case study method). Results: Based on the analysis of cases, it is shown that the guidelines for writing out recipes for correcting errors are spelled out abstractly (the neural network is irresponsible, human intelligence must exceed the intelligence of the machine) and concretely (the initial answer of the neural network to the question posed is superficial and requires clarification using questions unexpected for the neural network spe-cific configuration of terms not recognized by artificial intelligence as keywords). The risks of introducing artificial intelligence into the work of medical institutions have been identified: on the one hand, with a high degree of compliance of doctors to the recommenda-tions of neural networks, the doctor is responsible for their errors, and the patient suffers, on the other hand, with a high degree of com-pliance of AI to user requests, training neural networks in dialogues is dangerous multiplication of dubious recommendations from un-differentiated/incompetent users. The doctor’s competence in dialogues training the neural network is invisible, unverified, and essen-tially virtual. Conclusion: Based on the conducted research, the possibility of improving neural networks through their adaptation to regional paradigms of healing, to value systems that are based on the archetypes of domestic healthcare is shown.

Оценка качества технической воды в горно-промышленном производстве на основе методов машинного обучения

Introduction. Mining requires water treatment and wastewater processing, abstraction and discharge during mining increases consumption several times. Since water consumption in mining and processing is usually associated with domestic, industrial and technical needs, the need for water supply systems required for water treatment increases. Water from different sources can be used for treatment: incoming water, process and reused water, and wastewater. But the water obtained from any of the sources must meet all the norms and requirements. Water quality is determined by physical, chemical and bacteriological properties. The main directions for improving water consumption by mining enterprises are to reduce the consumption of drinking water from rivers, lakes and municipal water supply, as well as to expand the use of mine and quarry water for domestic and technical needs. Materials and methods. As training data for training the neural network, a dataset that includes water quality data obtained from fresh water sources was selected for the methods work, and using machine learning, develops a model that predicts whether the water is suitable for technical use in mines. This dataset includes 2293 values (samples) as well as 9 attributes. Correlation, neural network, and decision tree methods were used to build the models in this study. Results. Various machine learning methods (neural network and decision trees) were used to build a predictive model to assess the quality of water that would be suitable for use in the mining industry for technical purposes. With the help of the built models were processed data obtained from public sources, when analyzing which it was found that the method of decision trees was more accurate. The constructed model, for determining dependencies, thus, has high accuracy (small error). To increase the practical significance of the study, a number of transformations of the initial data set were carried out, in particular, an experiment with the division of attributes into groups of importance, in relation to the data, taking into account the subject area. The results obtained made it clear that checking only for hazardous impurities does not guarantee the suitability of water, but almost completely excludes (low significance factor) samples with impurities that do not meet the requirements, and the model can have practical significance. Allocation of the group for rapid quality determination, showed that for the express test, in an emergency situation or under time constraints, the possibility of practical use of the obtained model, has a justification, due to the small error. In general, the conducted experiments have shown that when taking into account the costs (total) for data collection, it makes sense to use models, taking into account the reduction of collected data, on the parameters (factors) of technical water. Discussion. In general, on the basis of the conducted research, we can talk about the successful application of machine learning methods in determining the suitability of technical water in the mining industry. During the experiments, the decision tree method performed particularly well, with the lowest error values. In addition, further work can be carried out to reduce the error in the models, in particular, by possibly increasing the number of attributes, as well as more fine-tuning of the applied machine learning methods. Conclusions. The authors conclude that machine learning techniques can be successfully integrated to determine the quality and suitability of process water in the mining industry in today’s world. Resume. The paper compares machine learning methods such as decision trees and neural network method. The comparative analysis of these methods and their quality of information processing is shown on the example of a set of data on water quality in the mining industry. With the help of built models were processed data obtained from open sources, when analyzing which it was found that the method of decision trees was more accurate. The constructed model for determining dependencies has high accuracy (small error). Suggestions for practical applications and future research directions. This study can form the basis for research in this or related fields to conduct further studies on the reliability and accuracy of using machine learning to predict the quality of water used in the mining industry. Continued work in the above direction may be the rationale for wider use of the above methods to improve various meaningful production performance in this or related areas.

Contribution of the Magnetic Subsystem to the Generation of Longitudinal Acoustic Waves during Domain Wall Motion in Yttrium Orthoferrite

Purpose. Modern technologies in their development are increasingly in need of increasing the speed of processing and recording information. An alternative to semiconductor methods is magnetic memory based on changing magnetic moments. The speed of magnetization reversal is determined by the speed of motion of domain walls. The speed is maximum in weak ferromagnets. The purpose of this work is to study the effect of a magnetic wave generated by a moving domain wall on a longitudinal acoustic wave, taking into account the absorption of a magnetic wave in yttrium orthoferrite.Methods. The equation describing the effect of a magnetic wave generated by a moving domain wall on a longitudinal acoustic wave is solved using the following methods: the slowly varying amplitude method, the perturbation theory method, and the Lagrange method.Results. For an orthoferrite crystal, the contribution of the magnetic wave accompanying the motion of the domain wall in yttrium orthoferrite to the deformation of the amplitude of the longitudinal acoustic wave is calculated. This contribution in a bounded crystal, without taking into account the absorption of magnetic waves in the absence of the influence of acoustic waves on them, is of the order of 10-6 cm. In an unbounded crystal, the corresponding contribution, taking into account the absorption of magnetic waves, into the generation of longitudinal acoustic waves is of the order of magnitude 10-10 cm with a theoretical thickness of the domain wall D3 ≈ 10-6 cm.Conclusion. To study the mechanisms of the influence of a magnetic wave generated by a moving DW on a longitudinal acoustic wave, yttrium orthoferrite was taken. Since a weak ferromagnet is characterized by a significant increase in the magnetoelastic coupling when it overcomes the sound barrier. It is this circumstance that made it possible to experimentally observe the generation of elastic displacements by a moving domain wall. To develop devices for recording and processing information based on weak ferromagnets, it is necessary to fully investigate the mechanisms that affect the dynamics of a domain wall during its motion. Such a factor is the interaction of magnetic and acoustic waves with the domain wall, which can affect the quality of information processing. The evaluation of the contributions of these mechanisms can be used to develop the element base of devices for recording and processing information based on the magnetic memory of weak ferromagnets.

Artificial Intelligence and Its Impact to the Operations of Enterprises: Basis for Strategic Plan

Artificial intelligence has had a profound impact on the production and operation management of enterprises, including human resource management, financial management, production management, and decision-making assistance. Artificial intelligence can improve production and management efficiency, and reduce costs. It can improve the quality and specialization level of financial information processing, achieve intelligent financial management, and improve the efficiency and accuracy of financial management. The development of artificial intelligence may have an impact and crowding out effect on jobs with primary repetitive standardized processes, leading to a reduction and adjustment of some positions, while also providing new employment opportunities. The application of artificial intelligence in production and operation management can strengthen enterprise supply chain management, improve quality inspection efficiency, reduce operating costs, improve production efficiency, enhance the scientificity of enterprise management, thereby improving management efficiency and economic benefits, and promoting enterprise development and competitiveness. At the same time, it is necessary to pay attention to data security management and privacy protection, reduce the cost of using and maintaining artificial intelligence, continuously improve and perfect artificial intelligence tools, effectively enhance the use of artificial intelligence in accordance with regulations, and pay attention to the risk of personnel unemployment caused by the application of artificial intelligence. Therefore, it is suggested that senior leaders of enterprises should attach great importance to the application of artificial intelligence, increase funding, provide budget support and resource investment, strengthen data quality management and customized analysis capabilities, reduce usage and maintenance costs, promote human-machine cooperation, strengthen the application of artificial intelligence in business management, attach importance to employee training and transformation support, and pay attention to technological development trends, optimize and expand application scenarios, Actively explore and apply artificial intelligence tools to enhance the competitiveness of enterprises. The government should formulate relevant policies and regulations, strengthen education and training support, promote the development of the artificial intelligence industry, strengthen international cooperation and exchanges, formulate policies to promote employment and social security systems, encourage innovation and entrepreneurship, strengthen supervision and regulation, and promote the comprehensive application of artificial intelligence in enterprise management.

Extracellular molecular signals shaping dendrite architecture during brain development.

Proper growth and branching of dendrites are crucial for adequate central nervous system (CNS) functioning. The neuronal dendritic geometry determines the mode and quality of information processing. Any defects in dendrite development will disrupt neuronal circuit formation, affecting brain function. Besides cell-intrinsic programmes, extrinsic factors regulate various aspects of dendritic development. Among these extrinsic factors are extracellular molecular signals which can shape the dendrite architecture during early development. This review will focus on extrinsic factors regulating dendritic growth during early neuronal development, including neurotransmitters, neurotrophins, extracellular matrix proteins, contact-mediated ligands, and secreted and diffusible cues. How these extracellular molecular signals contribute to dendritic growth has been investigated in developing nervous systems using different species, different areas within the CNS, and different neuronal types. The response of the dendritic tree to these extracellular molecular signals can result in growth-promoting or growth-limiting effects, and it depends on the receptor subtype, receptor quantity, receptor efficiency, the animal model used, the developmental time windows, and finally, the targeted signal cascade. This article reviews our current understanding of the role of various extracellular signals in the establishment of the architecture of the dendrites.

Comparative descriptive analysis of socio-demographic status in immunocompetent and immunocompromised patients with mycoses of the skin, hair and nails

BACKGROUND: Despite the achievements of modern medicine, people's susceptibility to fungal infections is only increasing. In this regard, it is extremely important to understand which social factors are most characteristic of people suffering from mycoses of the skin, hair and nails.
 AIM: to study the socio-demographic status of immunocompetent and immunocompromised patients with mycoses of skin, hair and nails.
 MATERIALS AND METHODS: A multicenter prospective cohort observational study of the social and demographic characteristics of immunocompetent and immunocompromised individuals with mycoses of the skin of hair and nails was conducted. Four groups of patients were formed: immunocompetent persons, persons with diabetes mellitus, patients with autoimmune diseases while taking immunosuppressants, patients with infection human immunodeficiency virus (HIV). A total of 646 people were examined. To ensure the quality of research and information processing, questionnaires were developed to be filled in for each examined patient. At the time of inclusion in the study, data concerning the place of residence and living conditions of patients, their lifestyle, marital status, bad habits and profession were entered into the questionnaires.
 RESULTS: Significant differences (p 0.0001) were revealed between the studied socio- demographic characteristics in the study groups. The age of immunocompetent patients with mycoses of skin, hair and nails was 914 years lower than the age of immunocompromised. Female persons prevailed in the case of autoimmune diseases ― 71.1%. Harmful habits were registered in 60% of patients with superficial mycoses and HIV. 83.33% of autoimmune patients were married. Urban residents prevailed over rural residents in the case of diabetes mellitus and autoimmune pathology. Persons of manual work professions in 48.48% of cases were identified among patients with HIV infection, which was more than in other study groups.
 CONCLUSION: When studying the socio- demographic characteristics of patients with mycoses of skin, hair and nails, significant differences were revealed in all the factors studied. The obtained results indicate the need to develop a personalized approach to the diagnosis, prevention and treatment of these infections.

Mechanisms for successful management of enterprise resource planning from user information processing and system quality perspective

Enterprise resource planning (ERP) systems are now ubiquitous in modern organizations. A number of previous studies have focused only on system factors and perceptions, there is a noticeable shortfall in research that concurrently addresses technological factors and human roles in explaining user satisfaction. This study aimed to identify these variables from the perspectives of information systems, technology, and human participation, thereby addressing this knowledge gap. The focus of the study was a large shipbuilding and marine company utilizing an ERP system. The participants, a sample of 234 ERP users, were carefully selected by the company’s executives and practitioners, and data was collected through online questionnaires. They were selected through purposive sampling from among employees who use ERP systems in large-scale shipbuilding and marine engineering companies. The study aimed to clarify the relationships between user satisfaction and perceived ease of use, perceived usefulness, system quality, service quality, participation, and information quality. A partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data. The results indicated that perceived ease of use, system quality, service quality, and participation positively influenced user satisfaction, whereas perceived usefulness did not have a significant impact. Interestingly, participation was found to lessen the effects of perceived usefulness on satisfaction. The findings of this study suggest that to enhance ERP user satisfaction, managers should strive to make the ERP system easy-to-use and stable, encourage employee participation in the decision-making process, and bolster the role of the support team. It should be noted, however, that the study has limitations as it did not consider all possible factors, such as training and support. Future research should take a broader view of the variables involved in the operation of an enterprise-wide information system.

DIGITAL TRANSLATION OF ABBREVIATIONS IN THE FIELD OF PUBLIC RELATIONS

This article focuses on various features of digital translation of public relations (PR) abbreviations in a computer-assisted environment. The relevance of this research is determined by the new digital era. Digitalization of professional activity of specialists in various fields of knowledge leads to the creation of new conditions and revision of approaches to professionally-oriented translation. Digital translation plays a special role in information exchange and its dissemination. The aim of this research is to identify the advantages and disadvantages of using digital tools to meet the needs of PR specialists from the standpoint of linguistics. PR abbreviations functioning in the professional PR discourse are taken as material for analysis. The use of digital translation technologies within PR activities enables the improvement of the quality of information collection and processing, its translation, abstracting and dissemination. PR abbreviations acquire a highly specialized meaning within the target language (TL), demanding a particular algorithm for translation into Russian, which is due to the insufficient PR terminology development, especially in terms of translating abbreviations. To achieve the object in view, the method of continuous sampling, the method of comparative analysis and the method of linguistic description have been used. As a result of the investigation, it has been established that together with digital translation gaining momentum, as it makes it possible to transfer a large amount of information into different languages in a short time, a number of issues remain unresolved and need to be researched.

Does enterprise social media use in the post-acceptance stage improve employee autonomy? An information processing perspective

PurposeWith the prevalence of enterprise social media (ESM), this study examines how the innovative and routine ESM use behaviors in the post-acceptance stage influence the quality and quantity dimensions of information processing (i.e. information equality and information overload), subsequently affecting employee autonomy grounded in the digital information management theoretical (DIMT) framework. This study further explores the moderating effects of communication visibility on the relationships between employee autonomy and each of the two information processing factors.Design/methodology/approachThe proposed theoretical model was empirically validated using survey data of 266 knowledge workers that use ESM in a hybrid working context.FindingsThe results demonstrate that innovative and routine ESM use have different impacts on the quality and quantity dimensions of information processing, and information equality (quality dimension) improves employee autonomy as expected, but, surprisingly, information overloads (quantity dimension) as well. ESM communication visibility positively moderates the relationship between employee autonomy and each dimension of information processing.Originality/valuePrior studies on ESM use widely focused on ESM's positive and negative outcomes and the mechanisms from the perspectives of employees' psychological and cognitive reactions. How ESM use behaviors in the post-acceptance stage empower employee autonomy and allow organizations to leverage the organizations' information technology (IT) investments remains unclear. Drawing on the information processing perspective, this study provides a novel angle to examine how to leverage IT values that stem from ESM use by considering the complementary roles of active and passive information processing.

Mechanisms in Emotional Information Processing in Individuals with Major Depressive Disorder: An Event-Related Potential Study of an Information Processing Model.

Individuals with major depressive disorder have a cognitive bias toward emotional stimuli, which influences the quality and speed of emotional information processing. This study aimed to understand the factors underlying this bias by identifying when it occurs during information processing using an information processing model. A total of 57 participants-19 each [ (16 (84.21%) females and 3 (15.79%) males per group)], for the first-episode MDD (FMDD), recurrent episodes MDD (RMDD), and healthy controls (HCs) - matched for sex and hand preference, completed event-related potentials (ERP) to perform psychological function and a choice response time task. Results revealed that recurrent episodes major depressive disorder participants had decreased N2b and P3b amplitudes but increased contingent negative variation during the processing of happy and neutral facial stimuli, relative to their counterparts. Both recurrent episodes major depressive disorder and first-episode major depressive disorder participants used a parallel information processing strategy for happy information at P3a latency, while healthy controls used a linear information processing strategy. The use of a parallel processing strategy among individuals with major depressive disorder may have led to impaired "happy" information processes, possibly explaining why individuals with major depressive disorder are less efficient than healthy controls. The results suggest the possibility that biases related to the processing of "happy" information among individuals with major depressive disorder may be related to a tendency for these individuals to engage in superficial decision-making. Future research is needed to examine the processes contributing to people with major depressive disorder having challenges with inhibition-facilitation of emotional stimuli.

Hospital Management Information System Implementation Assessment Using HOT-FIT Model in Langsa General Hospital Aceh, Indonesia

In providing the best health care to the community, hospitals as health care facilities utilize technologies that are influenced by developments and advances in medical sciences and technologies. One of such technologies is the information management technology. Since the quality of information processing is an essential factor for succesful medical care of patients, Regulation of the Minister of Health of the Republic of Indonesia Number 82 of 2013 requires all hospitals to implement a hospital information management system (HIMS). To assess the successful implementation of HIMS, various models and frameworks have been developed, including the HOT-FIT model. This study aimed to analyze the implementation of the hospital management information system at Langsa General Hospital, Aceh, Indonesia, using the HOT-FIT model which applied the SEM-PLS method. This quantitative study was performed in approximately three months using the HOT-FIT (company, technology-FIT human,) framework that includes nine variables of system quality, information quality, service quality, organization structure, facility situations, support from leadership, system usage, user satisfaction, and net benefits. Data analysis was performed using the SEM-PLS analysis in SmartPLS application (V.3.2.9). Results showed that human, organization, and technology supports were factors that influence the successful implementation of HIMS. To conclude, the HOT-FIT model can be used to identify the factors that influence the successful implementation of HIMS to inform the HIMS improvement in the hospital that will eventually improve the hospital’s quality system, information, service quality, and user satisfaction.

КРИТЕРІАЛЬНІ ПОКАЗНИКИ ЯКОСТІ ОБРОБКИ МУЛЬТИСЕНСОРНОЇ ІНФОРМАЦІЇ В УМОВАХ ДЕСТАБІЛІЗУЮЧИХ ВПЛИВІВ

The approach to determining the criterion indicators of the quality of digital information processing in the multi-sensor information-controlled system of the car under the influence of destabilizing factors is considered. It was established that the process of processing information that enters the information-controlled system in digital form from various sensors involves the calculation of discrete convolutions of matrices of relevant operators and arrays of initial data. This process includes procedures for forming an array of responses to the observed information flow, an array of responses to the restored (restored) information flow, and forming conditions for restoration (restoration) of the observed information flow. The approach to assessing the quality of digital information processing in terms of determining subjective and objective assessments and methods of obtaining them is analyzed. The basis of these methods is the procedure of direct inversion of the estimated correlation matrix of random realizations of the observed multidimensional information process. Systems with an inverse operator for processing a multidimensional information array are quite sensitive to destabilizing influences: to random perturbations of the coefficients of the recovery (processing) operator and internal noise of the system along with internal system perturbations. The need to take into account destabilizing influences in the process of processing digital information in the car's information-controlled system necessitates the development of a general methodology for studying the influence of destabilizing factors on the quality of image processing. Based on the application of inverse synthesis methods, analytical expressions for criterion quality indicators of digital image processing are proposed, which are not limited to the upper limit of quality assessment and allow analytical evaluation and quantitative determination of the quality level of discrete images restored by direct inversion at the stage of system synthesis. Keywords: car, information-controlled system, image, restoration, restoration operator, criterion indicators, multisensory information, quality of information processing. internal noise, destabilizing factors.

ПОВЫШЕНИЕ КАЧЕСТВЕННЫХ ПОКАЗАТЕЛЕЙ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ ПРИ ОБРАБОТКЕ СЕТЕВОГО ТРАФИКА НА ОСНОВЕ СЕГМЕНТИРОВАНИЯ ВЫБОРКИ ДАННЫХ

The development of artificial intelligence systems necessitates the creation of methods to improve the quality of information processing. The values of target variables and predictors are formed under the influence of external and internal factors that affect their ranges. Phenomena such as concept drift lead to the fact that the model may lose completeness and accuracy of results over time. A method of automatic data segmentation based on clustering is proposed to improve the quality of machine learning algorithms. Classification models are trained on a variety of examples in which there may be outliers, noisy data, an imbalance of observed objects, which affect the qualitative indicators of the results. In many systems, the time intervals of the action of factors can be determined and formalized in advance. The sample generated for machine learning methods usually consists of tuples whose values are obtained under different conditions. The influence of influencing factors can be determined empirically, or automatically, using, for example, clustering methods. The main (difference) feature of the proposed method is that, based on information about the factors influencing the ranges of values of these factors, the sample is divided into subsamples. The selection of individual impacts makes it possible to determine the data areas in which the factor was influenced, evaluate their properties and assign a classifying algorithm that has the best characteristics of qualitative indicators. The results of experiments on a number of data sets are presented. They show that the proposed solution makes it possible to improve the quality of processing. The model can be considered as an improvement of ensemble methods of processing information flows and data samples. In the case of transformation of data properties, training a separate algorithm on a local segment makes it possible to reduce computational costs, compared with training a complex data processing model.

Vigilance, arousal, and acetylcholine: Optimal control of attention in a simple detection task.

Paying attention to particular aspects of the world or being more vigilant in general can be interpreted as forms of 'internal' action. Such arousal-related choices come with the benefit of increasing the quality and situational appropriateness of information acquisition and processing, but incur potentially expensive energetic and opportunity costs. One implementational route for these choices is widespread ascending neuromodulation, including by acetylcholine (ACh). The key computational question that elective attention poses for sensory processing is when it is worthwhile paying these costs, and this includes consideration of whether sufficient information has yet been collected to justify the higher signal-to-noise ratio afforded by greater attention and, particularly if a change in attentional state is more expensive than its maintenance, when states of heightened attention ought to persist. We offer a partially observable Markov decision-process treatment of optional attention in a detection task, and use it to provide a qualitative model of the results of studies using modern techniques to measure and manipulate ACh in rodents performing a similar task.

A Time-Division Position-Sensitive Detector Image System for High-Speed Multitarget Trajectory Tracking.

High-speed trajectory tracking with real-time processing capability is particularly important in the fields of pilotless automobiles, guidance systems, robotics, and filmmaking. The conventional optical approach to high-speed trajectory tracking involves charge coupled device (CCD) or complementary metal-oxide-semiconductor(CMOS) image sensors, which suffer from trade-offs between resolution and framerates, complexity of the system, and enormous data-analysis processes. Here, a high-speed trajectory tracking system is designed by using a time-division position-sensitive detector (TD-PSD) based on a graphene-silicon Schottky heterojunction. Benefiting from the high-speed optoelectronic response and sub-micrometer positional accuracy of the TD-PSD, multitarget real-time trajectory tracking is realized, with a maximum image output framerate of up to 62000 framesper second. Moreover, multichannel trajectory tracking and image-distortion correction functionalities are realized by TD-PSD systems through frequency-related image preprocessing, which significantly improves the capacity of real-time information processing and image quality in complicated light environments.

Processing of information from networks of airspace surveillance radar systems

The presented paper examines the principles and methods for processing of information from airspace surveillance radar networks. Information technologies allow implementing automatic collection, processing, storage, transmission and issuance of radar information to users. In this work, the synthesis and analysis of the optimal structure of the interstage processing of signal data and radar information of primary processing in the network of airspace surveillance radar systems is carried out. The quality of information from radar networks of airspace surveillance systems affects almost all indicators of the quality of the radar systems network functioning. The stages of radar information processing in radar systems networks are analyzed. The importance of specifying the above stages for creating a complete picture of the air situation in the area of responsibility is shown. To improve the quality of information support for consumers, a network of radar systems requires information processing at all stages. At each stage of information processing, information processing quality indicators were analyzed. This made it possible to show that the staged implementation of information processing, on the one hand, simplified the optimization of processing within each processing stage, but on the other hand, it made it difficult to carry out compatible optimization of both the detection of an aerial object and the measurement of the coordinates of an aerial object. The synthesized structure of processing radar information of the network of radar systems of airspace surveillance, which in its turn made it possible to carry out interstage optimization of processing of both signal data and primary processing information. Calculations have shown that the method of information processing, in which the combination of information is carried out at the level of decision-making on the detection of airborne objects in each signal data processing channel, has some advantages in the quality of information processing of the network of radar systems compared to the one currently used. time option combination information at the stage of signal processing. At the same time, for the method of combining information at the level of decision-making about the detection of aerial objects, the flow of transmitted information to the joint processing point is significantly reduced. All this allows improving the quality of information processing in the airspace control system.

Nanopore Detection Assisted DNA Information Processing.

The deoxyribonucleotide (DNA) molecule is a stable carrier for large amounts of genetic information and provides an ideal storage medium for next-generation information processing technologies. Technologies that process DNA information, representing a cross-disciplinary integration of biology and computer techniques, have become attractive substitutes for technologies that process electronic information alone. The detailed applications of DNA technologies can be divided into three components: storage, computing, and self-assembly. The quality of DNA information processing relies on the accuracy of DNA reading. Nanopore detection allows researchers to accurately sequence nucleotides and is thus widely used to read DNA. In this paper, we introduce the principles and development history of nanopore detection and conduct a systematic review of recent developments and specific applications in DNA information processing involving nanopore detection and nanopore-based storage. We also discuss the potential of artificial intelligence in nanopore detection and DNA information processing. This work not only provides new avenues for future nanopore detection development, but also offers a foundation for the construction of more advanced DNA information processing technologies.

CEO–CFO gender congruence and stock price crash risk in energy companies

We propose and test a novel determinant of crash risk: gender congruence with the chief executive officer (CEO) or chief finance officer (CFO). To ensure the homogeneity of the analyzed companies, our study chooses a sample of 116 energy companies from 2007–2017, we find that the CEO–CFO gender congruence improve the quality of corporate information processing and corporate governance, which in turn inhibits corporate executives from hoarding bad news and ultimately reduces crash risk in energy companies. Further, we observe a significant negative impact of CEO–CFO gender congruence on crash risk in both traditional and new energy companies; while this effect is significant only in state-owned energy companies. Additionally, we explore two potential mechanisms for the impact of CEO–CFO gender congruence on the reduction of crash risk in energy companies. The results reveal that the effect of CEO–CFO gender congruence on reducing energy companies’ crash risk is stronger in companies with low information asymmetry and at weak level of external monitoring. Overall, this paper proposes an ”information processing” mechanism for same-sex CEO–CFOs and demonstrates the effect of CEO–CFO​ gender congruence on crash risk in energy companies. Our results highlight the importance of CEO–CFO association indicators on corporate decision-making and crash risk.