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Abstract Large-scale regular nucleic acid testing (NAT) such as Regular-1/7, played a crucial role in the successful and sustained containment of COVID-19 in China, enabling rapid case detection in low-risk areas. However, Regular-1/7 strategies are extremely costly and time-consuming. To address these challenges and facilitate the broader implementation of large-scale NAT during outbreaks of highly contagious diseases with long incubation periods, we propose a novel sampling strategy called Weighted Hub and Time Sampling (WHTS). This strategy considers the intensity of social activity, the degree of social overlap, and the time interval since the last NAT for each individual. Based on a scale-free contact network model, we simulated the spread of scenario in a community and compared the performance of the WHTS strategy to the Regular-1/7 strategy in terms of cost reduction and shorter alert time. The results show that WHTS-1/14 strategy can save half of the cost, and WHTS-1/7 strategy can alert 1.0 days in advance, compared to Regular-1/7 strategy. Additionally, The WHTS-1/14 strategy also achieves promising results under various scenarios, even though it samples half as many people as the Regular-1/7 strategy. Our study offers a valuable reference for large-scale testing in future efforts to manage emerging infectious diseases.

This article analyses the legal project that points towards an eventual algorithmic technocracy in a future scenario where state decisions are influenced by artificial intelligence (AI), being necessary to highlight the importance of a rigorous control ranging from the self-evaluation of this technological revolution in its decision-making processes to a human audit of the operational programming used in the construction of its algorithms. The analysis focuses on assessing how this transformation might affect democratic state systems, ensuring that a balance is maintained between the efficiency of AI and democratic integrity, thus preserving the fundamental values of society. The methodology followed in the elaboration of this work is based on the application of the historical method (bibliographical), logical analysis (development of the topic), and comparative legal analysis (international), which allowed us to articulate the various approaches to the rights of humanity in the concept of «cybercandidates». As can be seen, there is still a mismatch between the autonomous functioning of AI and existing regulations. This reality is conditioning the eventual trust of citizens in a digital government.

Study Design Retrospective clinical study. Objective The purpose of this study was to establish a novel classification of migrated lumbar disk herniation (LDH) based on magnetic resonance imaging and provide appropriate treatment strategies for each type through algorithms. Summary of Background Data Full‐endoscopic lumbar discectomy is a surgical technique that has been developed rapidly in recent years. For migrated LDH, few surgeons currently classify it with multiplanar positioning, and there is no consensus on the choice of treatment strategy. Therefore, we established a new multiplanar classification criteria that can localize the lesions more accurately than previous studies. Methods A total of 263 eligible patients from March 2017 to March 2022 were included. Protrusions for each patient were located based on our classification and the surgical approach was selected according to our algorithms. The clinical symptoms of all patients before surgery, and at 1 day, 1 month, 3 months, 6 months, and 12 months after surgery were collected. Evaluations were performed using visual analogue scale (VAS), Oswestry Disability Index (ODI) and modified MacNab criteria. We used the chi‐squared test, one‐way analysis of variance (ANOVA), and t‐test to compare perioperative results and postoperative 3‐month, 6‐month, and 12‐month follow‐up results. Results VAS (low back pain) scores were reduced from 5.33 ± 2.67 to 0.73 ± 0.77 (p

The low specificity of Ebola virus disease clinical signs increases the risk for nosocomial transmission to patients and healthcare workers during outbreaks. Reducing this risk requires identifying patients with a high likelihood of Ebola virus infection. Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. External validation of this algorithm on retrospective data revealed the probability of infection continuously increased with the score.

Nowadays, the progress of technology covers, among other things, the development of modern techniques and high technologies used in land surveys. Unmanned aerial vehicles (UAVs), as a good alternative to conventional land survey techniques, have currently played an increasing role. The advantages of using unmanned aerial vehicles in photogrammetric measurements include a relatively short mission time for large-area surveys. In addition, photogrammetric products have a wider range of applications compared with conventional geodetic surveys. Many scientific publications delve into the quality of photogrammetric products, but the accuracy of UAVs in the context of geodetic standards has not been investigated in full. In this paper, we attempt to fill the observed research gap. Our research has analysed the position of objects recorded in geodetic databases referring to their counterparts based on an accurate orthophotomap from a photogrammetry campaign employing an unmanned aerial vehicle. The outcomes were referenced with land survey accuracy standards set out by relevant legislation. To ensure a smooth assessment of the result's accuracy we designed a computing algorithm with a module for selecting comparable points and verifying the results. The tool can be implemented in surveys carried out in any area thanks to open-source GIS software. Our analysis showed that a detailed orthophotomap delivered using UAVs can be a valuable data source on objects recorded in geodetic databases covering selected cadastral and topographic objects and land development components. A general verification of the accuracy and validity of a geodetic numerical map and preliminary detection of areas for potential updates can be a particularly useful application of photogrammetry.

Abstract Background Current digital medical databases record systematically coded diagnoses, but many legacy databases are full of hand-written, free text diagnoses, which can only be meaningfully analysed after mapping them to a coding system. While diagnoses can be extracted from full medical notes with good accuracy, no algorithm using only an unstructured free text diagnosis with no additional data has been published to date. Objectives/methods Therefore, we sought to create an algorithm which maps hand-written German diagnoses from our clinical photography database to ICD-10 diagnosis codes, validate its output manually by dermatologists and analyse diagnosis counts over time as a proof-of-concept of its application. Results Our rule-based algorithm mapped 50,884 unprocessed hand-written German free-text diagnoses covering five decades to ICD-10 codes, while reaching an accuracy of 82% against 817 dermatologist-validated diagnoses. Out of 41,021 data points with the highest algorithm confidence the top 3 identified diagnosis classes were psoriasis, eczema, and non-melanoma skin cancer. The number of ICD-10 codes belonging to chronic inflammatory diseases showed a seasonal pattern with peaks in July, and when analysed aggregated by year, peaks correlated to events such as new therapy classes for these diseases. Conclusion Using the presented algorithm, it is possible to reliably match hand-written free text of German dermatological diagnoses to ICD-10 codes, thus enabling systematic analysis of legacy databases, making past medical knowledge accessible to today’s patient care.

The article shows the necessity of institutional regulation of the process of involving unused agricultural land into the economic turnover of the agrarian sphere of a region. It presents quantitative and qualitative characteristics of previously withdrawn agricultural lands, their territorial distribution by soil and climatic microzones and administrative districts of a region, unused period duration, residual fertility level (in terms of humus content), and land plots degradation degree. A calculation-analytical complex such as an algorithm representing a four-stage mechanism of analysis, preparation and decision-making on the choice of the land plots restoration method and their further use option has been developed and proposed for use. It is recommended to use three options for land plots restoration: direct return to economic turnover in case of a short period of non-use; carrying out cultural and technical measures with subsequent implementation into the turnover; transfer to another category of use in case of a high degree of degradation and low fertility level. The developed algorithm of unused agricultural land involvement is aimed at preserving the land potential of a region, strengthening the rational land use system. The presented stages provide not only theoretical and methodological, but also practical significance. Application scope is scientific organizations, agroindustrial complex management bodies at the municipal and regional levels, and agricultural producers.

Background: This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity. Methods: We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals. Results: Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January–June 2021), 90.4% and 87.5% for moderate or higher status (July–December 2021), and 92.3% and 85.7% for severe status (July–December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%. Conclusion: The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included, and 13 470 infrared pupil images were collected for the study. All infrared images for pupil segmentation were labeled using the Labelme software. The computation of pupil diameter is divided into four steps: image pre-processing, pupil identification and localization, pupil segmentation, and diameter calculation. Two major models are used in the computation process: the modified YoloV3 and Deeplabv3+ models, which must be trained beforehand.RESULTS:The test dataset included 1 348 infrared pupil images. On the test dataset, the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils. The DeeplabV3+ model achieved a background intersection over union(IOU)of 99.23%, a pupil IOU of 93.81%, and a mean IOU of 96.52%. The pupil diameters in the test dataset ranged from 20 to 56 pixels, with a mean of 36.06±6.85 pixels. The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels, with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm, proven to be highly accurate and reliable for clinical application.

Introduction: the right to receive qualified legal assistance is enshrined in Article 48 of the Constitution of the Russian Federation. It is the right of everyone who is subject to criminal prosecution, regardless of the financial situation of the person. In the system of criminal law and criminal procedural relations, this right is imperative, and violation of this right entails negative consequences for the results of the criminal case investigation and its consideration on the merits. At the same time, in practice, situations occur when a lawyer participating in the process, for one reason or another, disrupts the production of pre-planned investigative actions or court hearings, thereby delaying the investigation and consideration of the criminal case on the merits. In this situation, the investigation and the court can appoint another defense attorney despite objections of a defendant and his/her lawyer, thereby giving rise to a “double defense situation”. At the same time, the legal community recognizes the participation of an appointed defense lawyer in the “double defense” situation as a violation of the Code of Professional Ethics of a Lawyer. In this article, the author continues the previously started research on this topic, and, taking into account the established disciplinary practice, proposes an algorithm of actions that will allow the lawyer to act within the framework established by the criminal procedure legislation and internal corporate regulatory legal acts in order to avoid possible violations, including preventing the emergence of a “double defense situation”. Purpose: to continue the study of controversial issues arising at the current stage of the implementation of the constitutional right to defense in criminal proceedings, affecting the problems of interaction between the defense lawyer and the defendant and other lawyers participating in the case, and to propose an algorithm of actions for lawyers entering into criminal proceedings by appointment of the inquiry officer, investigator or court in order to avoid possible violations of the Code of Professional Ethics of a Lawyer and the situation of “double defense”. The task of the study is to develop an algorithm for the entry of a appointed lawyer into criminal proceedings in order to prevent violation of the right to defense of a person brought to criminal liability and, at the same time, to prevent violations of internal corporate regulatory legal acts and avoid the emergence of a situation of “double defense”. Methods: general dialectical method of cognition of sociolegal phenomena, general scientific research methods (analysis and synthesis, deduction and induction, system-structural and others), private scientific methods of cognition (logical-formal and system analysis). The results of the study are both theoretical and applied in nature and have elements of scientific novelty. The author studies issues of entry of an appointed defense lawyer into criminal proceedings, proposes an algorithm of the lawyer’s actions in order to avoid possible violations and emergence of “a double defense situation”. At the same time, the author suggests considering “double defense” not as an institution of criminal procedural law, but as a significant violation of the norms of advocacy in criminal proceedings. Conclusion: discussion of the identified problems will draw attention of the scientific community and the legislator to the development of solutions to eliminate the identified gaps in the implementation of defense in criminal proceedings and will protect appointed defense lawyers from possible violation and subsequent accusation of violating internal corporate regulatory legal acts and creating “a double defense” situation.

Abstract Introduction Hidradenitis suppurativa (HS) is a painful, inflammatory skin disease associated with a high disease burden and long diagnostic delay. Prevalence estimates of HS vary widely in the literature owing to differing estimation methodologies. This study aimed to apply stepwise algorithms to estimate the prevalence of possible/diagnosed cases of HS in the US. Methods This was a retrospective cohort study in adult and pediatric patients with HS which utilized data from four US databases (MarketScan [Medicare and Medicaid] and Optum [electronic health record (EHR) and Clinformatics Data Mart (CDM)]). Patients with possible/diagnosed HS were identified using two algorithms (termed Algorithm 1 and Algorithm 2), which assessed symptoms such as multiple skin boils in site-specific areas based on international classification of disease (ICD) codes. Patients with diagnosed HS were defined as having ≥ 2 outpatient or ≥ 1 inpatient diagnosis codes of HS. In each database, patients with continuous medical and pharmacy benefits in the 365 days pre-index and 0–365 days post-index periods were eligible for inclusion. Results Across all databases, Algorithm 2 (MarketScan Medicare [N = 309,916]; MarketScan Medicaid [N = 188,783]; Optum EHR [N = 366,158]; Optum CDM [N = 173,812]) identified more patients with possible/diagnosed HS than Algorithm 1 (MarketScan Medicare [N = 194,353]; MarketScan Medicaid [N = 99,276]; Optum EHR [N = 177,957]; Optum CDM [N = 112,244]). Based on ICD-9/10 codes, the 5-year period prevalence of HS ranged from 0.06% to 0.12% across all databases, while for Algorithm 1 and Algorithm 2, this ranged from 0.27% to 0.41% and 0.49% to 0.78%, respectively. Adults and females generally had a higher 5-year period prevalence versus pediatric patients and males, respectively. Conclusion This real-world study highlights that HS diagnosis codes alone may be insufficient to estimate the prevalence of HS, demonstrating the value of employing algorithms in practice which assess for parameters such as multiple skin boils in site-specific areas. Integrating robust methods to identify the prevalence of HS may improve the diagnostic delay observed in HS and improve treatment outcomes.

The aim was to analyze the prognostic value of ventilation/ gas exchange dissociation markers to predict the treatment length in patients with the acute phase of COVID-19.Materials and methods: The analysis was performed using a database of 384 randomly selected patients from December 2021 to May 2022 with a confirmed diagnosis of the acute phase of COVID-19, aged 61±16 years. Spirometry of maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP) was performed on 2,2±0,2 day of hospitalization with a portable MicroRPM device (CareFusion, UK) and measurement of surrogate oxygenation index (SpO2/FiO2), ROX index (SpO2/ FiO2)/respiratory rate) was carried out in all patients. Log-regression models were used (STATISTICA 10) to determine cutoff values for these functional variables and their ability to predict the patients length of hospital stay from the date of examination (≤ 7 vs > 7 days).Results: The lowest sensitivity (SE) and specificity (SP) were shown in models using only the respiratory muscle strength markers MIP (SE=54%, SP=70%) and MEP (SE=73%, SP=47%), that were the same in the combination of MIP and MEP (SE=65%, SP=58%). The areas under ROC were equal to 0.6 in all three models. The models based on hypoxia markers had a higher classification power (AUC 0,7) compared to the previous three, and the sensitivity value was higher in the model using the ROX index (SE = 58% and SP = 78%), but the specificity was better in the surrogate oxygenation index model (SE = 48% and SP = 88%). A complex model based on a combination of the two integral hypoxia indices, as well as a diaphragm strength marker (SpO2/FiO2+MIP*ROX+MIP) had the best sensitivity (67%) and specificity (84%), and the area under ROC reached 0.8.Conclusion: Indicators of respiratory muscle strength and oxygenation are suitable markers for combined models and algorithms for predicting hospital length of stay in COVID-19 patients.

Background and Purpose: Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients’ unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE. Materials and Methods: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient’s size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM’s estimations (manual) using Bland–Altman’s agreement and Pearson’s correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived. Results: The results demonstrated good agreement and accuracy between the automated and AAPM’s patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r > 0.9771) and phantom study (r > 0.9999). Conclusion: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation.

The aim of this article is a critical analysis of the logocentric representation of brain as a conceptual basis for artificial neural networks (ANNs). Neuro-logocentrism turns possible mostly because of some similarities of brain activity to logical reasoning. They are discrete mode of action, semblance of realization of logical connectives and inferences, presence of two “values” (“all-or-none” principle). It is demonstrated that they are only loose analogies. The idea that neurons are net nodes that realize logical and computational operations as quite simple automata is based on the concept of connectionism that appears a new special type of hylemorphism. On the contrary, brain activity demonstrates the primacy of the whole and its irreducibility to the aggregative sum of elements and parts. It appears continual totality, where everything is reciprocally interconnected, so that it is impossible to establish any unified formalism for it. Brain essentially is a part of the living, not “bio-logic” but fully biological reality, which has an emergent mode of existence with its unique properties. Such processes as neuroplasticity and synaptic pruning are responsible for adaptive and teleonomic re‑shaping of the brain. Usual conventional logic is suitable for computing machines and their nets, but it is incapable of reproducing such autopoietic phenomena. As a result, there is a fatal ontological gap between natural networks of neurons and ANNs. The second ones are not able to implement complex cognitive functions as they are product of the logocentric hypertrophy of algorithms which is fully devoid of flexibility of the living brain.

Subject and Purpose. The subject of the research is the statistical characteristics of the signal, noise, and interference and their distribution functions. The emphasis is on exploring the properties of these elements and assessing their impact on algorithms designed to detect and identify the manifestations of human breathing and heartbeat during rescue operations. The work seeks comprehensive descriptions of broadband structural noise to develop optimum digital signal processing algorithms and ensure quicker and more reliable detection and identification of information signals during rescue missions. Method and Methodology. The analysis is grounded on the mathematical modeling method. The distribution function of correlation function peaks for a pseudo-noise signal is synthesized considering the first moments. The estimates derived from this distribution are used to assess the influence of broadband structural noise on the performance of algorithms for detecting and identifying radar signals. Results. In the most important band, where signal spectral components bear information on human breathing and heartbeat, estimates of the first four moments of a random process have been made to contribute to an appropriate model of fluctuating broadband structural noise. Analytical expressions of the function of structural interference distribution have been derived. A specific case focused on the interference represented by a phase-shift keyed signal with randomly alternating ones and zeros has been examined. Estimates of probabilities of false alarms and target misses have been calculated across various signal-to-noise ratios. Furthermore, a procedure to determine an optimal signal-detection threshold has been proposed. Conclusions. Analytical expressions of the distribution density of broadband structural interference have been derived. Quantitative estimates have been calculated to assess the impact this interference exerts on algorithms designed for detecting and recognizing radar information signals for rescuers. An adaptive procedure adjusting a target detection threshold as interference varies during the radar operation has been proposed.

Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review’s identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement.

Abstract Background This study aims to identify a morphological indicator of aortic dissection (AD) based on the geometrical characteristics of the thoracic aorta. Methods We evaluated computed tomographic angiograms of 63 samples with AD (22 with type A AD, 41 with type B AD) and 71 healthy samples. Via centerline extraction and spatial transformation, the spatial entanglement of the aorta was minimized, and the expanded 2D aortic morphology was obtained. The 2D morphology of the thoracic aorta was fit to a circle. The applicability of the fitting circle method for identifying aortic dissection was verified by multivariable logistic regression analysis. Results Via the 3D coordinate transformation algorithm, the optimal aortic view was obtained. On this view, the geometrical characteristics of the thoracic aortas of the healthy controls were similar to a portion of a circle (sum of residuals: 3502.45 ± 2566.71, variance: 86.23 ± 56.60), while that of AD samples had poorer similarity to the circle (sum of residuals: 5404.78 ± 3891.69, variance: 129.90 ± 90.09). This difference was significant (p

Abstract Background Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy. Aim To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database. Methods Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text. Results A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days. Conclusions The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide.

In this study, a multipoint boundary value problem for Volterra-Fredholm integro-differential equations is considered. The addition of a new function converts the system of Volterra-Fredholm integro-differential equations to a system of Fredholm integro-differential equations. In contrast to the original problem, the dimension of a Fredholm integro-differential equation is determined by the number of matrices in the degenerate kernel of the Volterra integral. A numerical algorithm of Dzhumabaev parameterization method for addressing a multipoint boundary value problem for Volterra-Fredholm integro-differential equations is proposed. The main advantage of the proposed method is splitting the problem into auxiliary Cauchy problems for ordinary differential equations and a system of algebraic equations with respect to the parameters. The conditions for the unique solvability of the multipoint boundary value problem for Fredholm integro-differential equations are established. Finally, various numerical examples are provided to demonstrate the efficiency and correctness of the suggested technique.

The treatment of head and neck cancer is dominated by surgery and radiochemotherapy. For elderly patients, however, the rationality of invasive treatment is questioned in clinical practice. Although the proportion of geriatric patients with head and neck cancer is increasing, there are few consistent standards in the literature and guidelines. From the perspective of evidence-based medicine, this paper presents current evidence for treatment so as to provide specific recommendations for treating head and neck cancer in the elderly. Furthermore, based on a systematic literature review, a clinical algorithm is proposed to evaluate the treatment risk of elderly patients with head and neck cancer.

Summary: Background: The globally rising prevalence of obesity highlights the importance of implementing effective methods for prevention and treatment. In this context, personalized nutrition (PN) is being discussed as a promising approach. The Food4Me study, a clinical trial investigating the effectiveness of PN interventions, demonstrated that tailoring dietary advice to individual needs is more likely to initiate behavior changes than standard dietary guidelines. However, the accessibility of PN remains a challenge due to financial and structural barriers. ChatGPT, a freely available, natural language processing model published in 2022, might provide a solution to increase the availability of personalized nutrition to a broader population. Methods: To investigate ChatGPT's potential to provide accurate and reliable personalized dietary recommendations, we conducted a comparative analysis, comparing its recommendations with those of the Food4Me algorithm. We selected 20 obese subjects of the German Food4Me sub-cohort and entered their baseline data into ChatGPT (version 3.5). For each subject, a separate chat space was set up, maintaining consistent wording and prompt order throughout all chats to ensure comparability. The prompts were written from a first-person perspective, simulating an authentic scenario of average users asking for dietary recommendations. ChatGPT's responses were compared to the Food4Me feedback reports that had been provided to the selected subjects within the study. Results: ChatGPT may provide suitable personalized dietary advice and holds some noteworthy advantages over the Food4Me algorithm, but still tends to produce recommendations with inconsistent and unpredictable errors, for example, regarding intakes of macro- or micronutrients. Conclusion: Currently, it is not advisable for individuals without nutritional expertise to rely on ChatGPT for personalized dietary recommendations. Nevertheless, ChatGPT remains a promising approach and may develop into a reliable tool for PN with further updates and fine-tuning in the future.

An intelligent technique is used to control a novel design solar air cooler, where, a solar air cooler has two sections, one is the solar photovoltaic system and the second is an air cooler. A photovoltaic system has non-linear characteristics of active power versus voltage graph that depends on solar irradiation (Watt/Area). Thus, here, an intelligent technique is developed using Arduino IED to extract maximum power from the solar panel. At maximum power buck converter is pulsed to charge the 12V battery. Whereas a second section has a novel approach to circulate unpleasant humid air to pleasant cool air and it completes its duty in three stages. In the first stage, hot or humid air comes from the atmosphere and enters into the tank where 20 W DC pump circulates water inside the evaporator which is made of circular copper coils. The circular coil maintains the cool temperature, whenever, hot air comes into contact with the cool copper coils. In the second stage, a DC fan 60W is installed in between the coils which takes cool less humid air and exhaust through the narrow gap and in the third stage, air takes another path to circulate pleasant air. These approaches reduce 10% of relative humidity and maintain 260C temperature inside the 12ft-by-12ft room. Moreover, relative humidity and temperature are fully controlled under the hybrid condition of the proposed air cooler. Hybrid condition is the condition when the proposed air cooler runs with a split-phase air conditioner.

Obstetric hemorrhages (OH) are the main preventable cause of morbidity, mortality and cases of "near miss" among obstetric complications worldwide. Early preventive measures based on the OH prediction allow to profoundly reduce the rate of female mortality and morbidity as well as prevent the economic costs of patient intensive care, blood transfusion, surgical treatment and long-term hospitalization. Postpartum haemorrhage (PPH) is the most frequent obstetric haemorrhage determined by one of the four causes: a uterine tonus disorder, maternal birth trauma, retention of placenta parts and blood-clotting disorder. There is still a need for the continued search for an accurate and reliable prediction method despite multiple attempts to develop an effective system for predicting OH. The solution to this may be reasonably considered an innovative method such as artificial intelligence (AI) including computer technologies capable of obtaining conclusions similar to human thinking. One of the particular AI variants is presented by machine learning (ML), which develops accurate predictive models using computer analysis. Machine learning is based on computer algorithms, the most common among them in medicine are the decision tree (DT), naive Bayes classifier (NBC), random forest (RF), support vector machine (SVM), artificial neural network (ANNs), deep neural network (DNN) or deep learning (DL) and convolutional neural network (CNN). Here, we review the main stages of ML, the principles of algorithms action, and the prospects for using AI to predict OH in real-life clinical practice.

Objective. The article presents the results of a study of tracking algorithms for analyzing a basketball game. The purpose of the work is to determine the optimal method for using athlete tracking when used online.Method. The research is based on methods and algorithms for solving management problems in organizational systems.Result. Algorithms with object re-identification are considered, taking into account both motion dynamics and appearance. The most popular tracking algorithms, BYTE, taken from the Bytetrack algorithm, and the Deepsort algorithm, which showed high results in the task of tracking pedestrians in a crowd, were selected as candidates. The algorithms were compared using the MOTA and MOTP tracking assessment quality metrics, as well as the operating time of the algorithms. The experiments were carried out on a general and sports dataset - MOT20 и SportMot.Conclusion. The study showed that the best result in online frame processing is achieved by the ByteTrack algorithm. It showed comparable quality metrics with fast turnaround times. The authors used open implementations of the algorithms and wrote a convenient interface for conducting experiments on different datasets and detection sources.

Objective. The purpose of the study is to construct an algorithm for the functioning of a software package that automates the process of analyzing and assessing the security of the software used and selecting its most secure version for use at informatization facilities of internal affairs bodies.Method. During the study, we used: a method of a systematic approach to determining software security indicators, a method of mathematical formalization and algorithmization of the process of analyzing and assessing software security for developing program code.Result. An algorithm for the functioning of a software complex is proposed that allows for analysis and quantitative assessment of the security of software of automated systems of internal affairs bodies in relation to current vulnerabilities in real time. The algorithm is complex in nature, including five component algorithms. The operation of the main blocks of the algorithm is described.Conclusion. Conclusions are drawn about the importance of the practical implementation of the developed algorithm in the form of a software package that selects the optimal (most secure) version of software for operation at informatization facilities of internal affairs bodies in order to increase the actual security of limited-distribution official information.

We present the Gaussian Mixture Periodicity Detection Algorithm (GMPDA), a novel method for detecting periodicity in the binary time series of event onsets. The GMPDA addresses the periodicity detection problem by inferring parameters of a generative model. We introduce two models, the Clock Model and the Random Walk Model, which describe distinct periodic phenomena and provide a comprehensive generative framework. The GMPDA demonstrates robust performance in test cases involving single and multiple periodicities, as well as varying noise levels. Additionally, we evaluate the GMPDA on real-world data from recorded leg movements during sleep, where it successfully identifies expected periodicities despite high noise levels. The primary contributions of this paper include the development of two new models for generating periodic event behavior and the GMPDA, which exhibits high accuracy in detecting multiple periodicities even in noisy environments.

Abstract Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

Jie Jin,1 Jie Lu,1 Xinyang Su,2 Yinhuan Xiong,3 Shasha Ma,4 Yang Kong,5 Hongmei Xu1 1School of Nursing, Binzhou Medical University, Binzhou, People’s Republic of China; 2Department of Spine Surgery, Binzhou Medical University Hospital, Binzhou, People’s Republic of China; 3Department of Nursing, Binzhou People’s Hospital, Binzhou, People’s Republic of China; 4Department of Neurosurgery, Binzhou Medical University Hospital, Binzhou, People’s Republic of China; 5School of Health Management, Binzhou Medical University, Yantai, People’s Republic of ChinaCorrespondence: Hongmei Xu, School of Nursing, Binzhou Medical University, No. 525 Huanghe 3rd Road, Binzhou, 256600, People’s Republic of China, Tel +86-13754689536, Email hmx58@163.com Yang Kong, School of Health Management, Binzhou Medical University, No. 346 Guanhai Road, Yantai, 264003, People’s Republic of China, Tel +86-13773576163, Email kongyang@bzmc.edu.cnPurpose: The purpose of this study was to establish and validate machine learning-based models for predicting the risk of venous thromboembolism (VTE) in intensive care unit (ICU) patients.Patients and Methods: The clinical data of 1494 ICU patients who underwent Doppler ultrasonography or venography between December 2020 and March 2023 were extracted from three tertiary hospitals. The Boruta algorithm was used to screen the essential variables associated with VTE. Five machine learning algorithms were employed: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and Logistic Regression (LR). Hyperparameter optimization was conducted on the predictive model of the training dataset. The performance in the validation dataset was measured using indicators, including the area under curve (AUC) of the receiver operating characteristic (ROC) curve, specificity, and F1 score. Finally, the optimal model was interpreted using the SHapley Additive exPlanation (SHAP) package.Results: The incidence of VTE among the ICU patients in this study was 26.04%. We screened 19 crucial features for the risk prediction model development. Among the five models, the RF model performed best, with an AUC of 0.788 (95% CI: 0.738– 0.838), an accuracy of 0.759 (95% CI: 0.709– 0.809), a sensitivity of 0.633, and a Brier score of 0.166.Conclusion: A machine learning-based model for prediction of VTE in ICU patients were successfully developed, which could assist clinical medical staff in identifying high-risk populations for VTE in the early stages so that prevention measures can be implemented to reduce the burden on the ICU patients.Keywords: venous thromboembolism, machine learning, algorithm, prediction model, intensive care unit

Abstract Background Fabry disease (FD), an X-linked lysosomal storage disorder, is caused by mutations in the gene encoding α-galactosidase A, resulting in lysosomal accumulation of globotriaosylceramide and other glycosphingolipids. Early detection of FD is challenging, accounting for delayed diagnosis and treatment initiation. This study aimed to develop an algorithm using a logistic regression model to facilitate early identification of patients based on ICD-10-GM coding using a German Sickness Fund Database. Methods The logistic regression model was fitted on a binary outcome variable based on either a treated FD cohort or a control cohort (without FD). Comorbidities specific to the involved organs were used as covariates to identify potential FD patients with ICD-10-GM E75.2 diagnosis but without any FD-specific medication. Specificity and sensitivity of the model were optimized to determine a likely threshold. The cut-point with the largest values for the Youden index and concordance probability method and the lowest value for closest to (0,1) was identified as 0.08 for each respective value. The sensitivity and specificity for this cut-point were 80.4% and 79.8%, respectively. Additionally, a sensitivity analysis of the potential FD patients with at least two codes of E75.2 diagnoses was performed. Results A total of 284 patients were identified in the potential FD cohort using the logistic regression model. Most potential FD patients were

The aim of the study was to develop an algorithm for intraspecific differentiation of tularemia agent strains using a set of approaches based on amplification and sequencing technologies.Materials and methods. 97 strains of Francisella tularensis of various subspecies, biovars and subpopulations from the State Collection of Pathogenic Bacteria of the Russian Research Anti-Plague Institute “Microbe” were used in the work. The intraspecific identification of tularemia agent strains was carried out using the “F. tularensis-4c” system; analysis of the variability of the RD1 differentiation region, the sdhA gene, by applying the disk diffusion method using disks with erythromycin. Fragment Sanger sequencing was performed on a 3500 XL genetic analyzer (Applied Biosystems, USA) taking into account the manufacturer’s recommendations. Sequence homology assessment was conducted using the BLAST algorithm, the GenBank NCBI database, MEGA11 v11.0.13 and Unipro UGENE v50.0 software.Results and discussion. Subspecies- and biovarspecific mutations have been detected in the 23S rRNA gene. Promising regions of this gene for further investigation have been identified using fragment sequencing. A comprehensive scheme for intraspecific differentiation of tularemia microbe strains has been put forward, where at the first stage the subspecies and biovar japonica are determined, and at the second stage, the results are verified based on the determination of mutations in the 23S rRNA gene. The effectiveness of the proposed integrated approach has been confirmed in a study of 97 collection strains of tularemia agent. The conducted research allows for rapid identification of tularemia agent strains of different subspecies and verification of their taxonomic appurtenance using molecular-genetic methods, expanding data on the circulation of various subspecies, biovars and subpopulations of the pathogen in Europe, Asia and other regions of the world.

Integration of distributed generators in micro grid causes bidirectional current flow as a result of the need for readjustment of the setting of the overcurrent protective relays in the electrical network. Adaptive coordination is one of the solutions that adjust the setting of protective relays according to the dynamics of the distribution network. Various algorithms had been reported on the adaptive coordination of time overcurrent relays. This paper reports a survey about the coordination of time overcurrent relays in micro grid electrical distribution network. The review found that there is limited research on distributed control rules of the multi agent coordination of protection relays. Furthermore, it was found that the trend of coordination algorithms focused on the nature inspired algorithms.

【Objective】 Change in soil water content is not only an indicator of water stresses used for irrigation management but also controls biogeochemical processes in soil. In this paper, we study the feasibility of using thermal infrared imaging to estimate soil moisture dynamics. 【Method】 The experiment was conducted in July-August 2023 in a walnut orchard in Xinjiang. Thermal infrared images of the walnut canopy were measured continuously using a thermal infrared camera. Based on the HSV color space of the images, an improved K-means segmentation algorithm was proposed to analyze the change in canopy temperature. We also measured air temperature and humidity, illuminance, wind speed, atmospheric CO2, and soil water content in the 0-80 cm soil layer, from which we proposed an inversion model to estimate soil water dynamics. 【Result】 The improved K-means algorithm increased the accuracy from 82.34% to 94.55%, and the errors between the canopy temperature acquired from the images and the measured canopy temperature were in the range of 0 to 1.0. The infrared imaging method was most accurate between14:00 pm to 16:00 pm. Our results showed that the walnut roots were most active in taking up water from the 40-60 cm soil layer 50-60 cm away horizons from the tree truck. Canopy temperature, air temperature and relative humidity, and atmospheric CO2 concentration were correlated with soil water content at significant levels; they can thus be used to estimate soil water dynamics, with a coefficient of determination of R2=0.86 and p

This article investigates the economic consequences of EU membership for the countries that established the EU in 1992. The Synthetic Control method is used in the frame of an algorithmic methodology that aims to guide the choice of donor pool countries and predictors by testing different combinations. The algorithm allows to judge the appropriateness of the research design by computing a set of synthetic countries able to improve the performance of the benchmark case (i.e., a synthetic country computed using an initial large basket of donor pool units and predictors). According to the results, the algorithm has been able to improve the counterfactual scenarios' precision and robustness for all tested countries. It shows that the economic effect of the EU membership has significantly varied among countries. Results suggest that the European integration process has not prevented persistent divergence and heterogeneity of growth paths among member countries.

The last decade has seen a significant number of remarkable expansions in machine ‎learning research. The field has achieved unprecedented popularity by developing new areas and ‎increased momentum on the existing sites. Whereas the symbolic methods have been exceedingly ‎active, the sub-symbolic approaches, especially the neural networks, have had tremendous ‎growth. Unlike the efforts whose main concentrations were on the single learning strategies, the ‎new trends have been to integrate various methods and for the development of cognitive learning ‎architectures. Besides, there is an increased interest in the experimental comparisons of several ‎methods and learning algorithms' theoretical analysis. The same data sets have often been shared ‎amongst the researchers who have led to applying the same techniques in dealing with the same ‎problems to understand the relative merits of various methods. There are new insights that have ‎been brought into the complexity of the learning process through theoretical investigations. This ‎research paper seeks to transmit the knowledge of machine learning, which has become very ‎popular and the researchers' applications of the experience. ‎KEYWORDS: Algorithm, Machine Learning, Supervised learning, unsupervised learning, ‎

Health care providers like physiotherapists should address the needs of performing artists for a specific health care derived from physical and psychosocial (performance-related) complaints. The aims of this study were (1) to compose an algorithm including subjective outcome measures in order to suggest how to evaluate the physical and psychosocial health status of performing artists in its complexity in a manner as targeted and detailed as possible, and (2) to test the feasibility of applying this algorithm.

An algorithm for analyzing double-crystal rocking curves from the near-surface layers of monocrystals has been proposed, and corresponding software has been developed. It is taken into account that to obtain correct results, both coherent and diffuse components of X-ray scattering need to be considered. The possibility of simultaneous analysis of rocking curves from several reflections is provided. To approximate experimental rocking curves with theoretical ones, an approach that simultaneously uses three different approximation methods has been utilized. The effectiveness of the proposed approach is confirmed by verifying the uniqueness of the obtained results.

In this work, we show that if F is a positive integer, then Sat(F)={S∣S is a saturated numerical semigroup with Frobenius number F} is a covariety. As a consequence, we present two algorithms: one that computes Sat(F), and another which computes all the elements of Sat(F) with a fixed genus. If X⊆S\Δ(F) for some S∈Sat(F), then we see that there exists the least element of Sat(F) containing X. This element is denoted by Sat(F)[X]. If S∈Sat(F), then we define the Sat(F)-rank of S as the minimum of {cardinality(X)∣S=Sat(F)[X]}. In this paper, we present an algorithm to compute all the elements of Sat(F) with a given Sat(F)-rank.

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers’ favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power.

It has been demonstrated that technologies and methods of intelligent data analysis (IDA) in the educational domain, particularly based on the analysis of digital traces (DT) of students, offer substantial opportunities for analyzing student activities. Notably, the DT of students are generated both during remote learning sessions and during blended learning modes. By applying IDA methods to DT, one can obtain information that is beneficial for both the educator in a specific discipline and for the educational institution's management. Such information might pertain to various aspects of the functioning of the digital educational environment (DEE) of the institution, such as: the student's learning style; individual preferences; the amount of time dedicated to a specific task, among others. An algorithm has been proposed for constructing a process model in the DEE based on log analysis within the DEE. This algorithm facilitates the description of a specific process in the DEE as a hierarchy of foundational process elements. Additionally, a model based on cluster analysis methods has been proposed, which may prove beneficial for analyzing the registration logs of systemic processes within the university's DEE. Such an analysis can potentially aid in detecting anomalous behavior of students and other individuals within the university's DEE. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.

Aim. Create and justify the algorithm of physical therapy application, which will be used at the early stage of treatment of critically ill patients treated in the transferred anesthesiology and intensive care unit. Materials and methods. The study was conducted during five months of 2023 in two medical and diagnostic centers in Kyiv, Ukraine, in the conditions of the department of anesthesiology and intensive care unit. 80 critically ill patients took part in the study, who were divided into main and control groups of 40 people each. Participants in the main group had physical therapy included in the treatment process, while the control group did not receive such treatment. The sample of patients was based on selection on the basis of indicators of critical condition and the need for treatment in the conditions of the anesthesiology and intensive care unit. Participation in the study varied from 1 to 3 weeks, with the first assessment at the meeting with the physical therapist and the last before discharge or transfer of the patient. Assessment included manual muscle testing and the Richmond Agitation/Sedation Scale, as well as observation for comorbidities. During the research, a developed algorithm for the use of physical therapy for critically ill patients was applied. Results. In the course of the study, a complex of physical therapy methods was used to influence the dynamics of the patients' state of consciousness, the functional strength of the muscles of the upper and lower limbs, as well as to study the frequency and severity of the development of accompanying complications (bedsores, contractures, swellings, deformations, wounds). Data collected from the main and control groups were subjected to comparative statistical analysis. The results of the analysis confirm that the use of the developed algorithm of physical therapy contributed to a significant improvement in the cognitive state of the participants, an increase in functional strength in the muscles of the limbs, and also effectively reduced the risk and severity of comorbidities. This indicates the importance of integrating physical therapy into a comprehensive approach to the treatment of critically ill patients. Conclusions. The proposed approach to the use of physical therapy for patients who are in critical condition and treated in the conditions of the department of anesthesiology and intensive care confirms its high efficiency in the early stages of treatment. Studies show that the use of physical therapy improves the functional state of muscles, improves the cognitive functions of patients, and also helps to prevent the development of concomitant complications that may occur during a stay in the conditions of the anesthesiology and intensive care units.

The game problem of two persons (players) is considered. The two players alternately choose their strategies from the appropriate sets. First, the first player chooses his strategy, then, knowing the strategy of the first player, the second player chooses his strategy. The set of strategies of the second player depends on the strategy of the first player. It is required to determine the following: for any strategy of the first player, does there exist a corresponding strategy of the second player? This problem is solved using a special linear maximin problem with connected variables, the solution of which is reduced to determining the maximum value of the objective function of the problem’s dual to it on special strategies. The algorithm for solving the problem considered is given. Two examples that illustrate the algorithm and the results of numerical experiment is given.

This article on The Good Place (NBC, 2016-2020) explores the series’ reflexivity and meta-discourse on contemporary seriality, by comparing it to other metatextual TV series with similar themes. The Good Place and Westworld (HBO, 2016-2022) display the same infernal simulacra based on meaningless reboots, as their protagonists constantly forget what happened to them. Person of Interest (CBS, 2011-2016) shares with The Good Place an in-depth discourse on the value of human life against inhuman and dehumanizing algorithms and mass surveillance, the irony being that The Good Place is distributed internationally on SVoD platform Netflix. Last but not least, the link between The Good Place and Lost (ABC, 2004-2010) allows us to read the former as a commentary on “promising closure” and on what may be a “good” ending for TV series.

Background. In modern conditions the economic success of regional development is determined by the speed of managerial responses to changes and challenges in the external environment. The purpose of the study was to develop an algorithm for classifying socio-economic systems according to the level of management of innovation potential of the regions, as part of the long-term objectives of building a competitive economy, which is an important factor in achieving technological sovereignty of the country. Materials and methods. When compiling the dataset of actual values of the integral level of performance of the tools of management of innovation potential of socio-economic mesosystems the analytics of Leontief Centre – AV Group Consortium (LC-AV), based on the data of Rosstat version 2022 on the data of 2020–2021 was used. The article applied methods of univariate clustering based on the Fisher algorithm, hierarchical cluster analysis, k-means clustering, fuzzy clustering. Euclidean distance was used to assess dissimilarity, Ward's method of agglomeration was used, centring was not performed, truncation was determined based on the adapted Hartigan index. Results. An 8-stage process of selecting an effective data clustering algorithm is proposed for application. Identification of 85 socioeconomic mesosystems of the Russian Federation using the author's methodology of classification by the level of innovation potential management is carried out. Conclusions. In the matrix of balanced scientific and technological development the influence of innovation potential management factors on five groups of socio-economic mesosystems clusters (strong, medium-strong, medium, medium-weak and weak innovators) was investigated, which will give further opportunity to develop managerial decisions regarding the development of differentiated strategies and policies adapted to the needs and characteristics of each mesosystem in order to improve competitiveness, technological sovereignty and quality of life.

Abstract Objective(s) Pediatric epistaxis is a common, often non‐operative condition encountered by Otolaryngologists. The present study seeks to (1) describe our outcomes of epistaxis management, (2) estimate the associated healthcare burden, and (3) propose a clinical model to optimize care coordination with primary care and advanced practice providers. Methods Retrospective case series of pediatric patients treated outpatient for epistaxis by a single otolaryngologist from 2021 to 2022. The primary outcome after treatment with nasal lubricants was defined as (1) refractory epistaxis, (2) improvement, or (3) complete resolution. Cost data for office versus operative nasal cautery were analyzed. Results In total, 122 patients were included for analysis. Over a follow‐up duration of 20.5 months (IQR 8–36), 24.6% of patients experienced refractory epistaxis, 41.8% of patients found improvement, and 33.6% had complete resolution (n = 122). Refractory epistaxis was associated with a family history of coagulopathy (p = .007), daily epistaxis episodes (p = .043), and anemia (p

Abstract Little is known about the catastrophic bushfire from a micro‐pollution point of view, and there is also very limited understanding of the emerging contamination of microplastics and nanoplastics. Upon exposure to fire, plastic items, such as water tanks, may release a substantial quantity of microplastics and nanoplastics, as characterized in this study through the analysis of residual debris. Using Raman imaging with the scanning pixel size down to 100 nm × 100 nm, we over‐scan the sample surface to collect a hyperspectral matrix. In order to map and convert the scanning hyperspectral matrix to an image, we compare and advance the chemometrics of algorithms, including logic and principal component analysis (PCA), to extract the weak signal of microplastics and particularly nanoplastics, which enables us to directly visualize the different degrees of burning. By doing so, we can identify the microplastics and nanoplastics down to ˜100 nm, which means that we can break through the diffraction limit of the laser which is ˜296 nm (λ/2NA) to capture nanoplastics. Using statistical analysis, we estimate that 1.4–4.7 million micro‐ and nanoplastics per cm2 can be left behind by the mimicked‐bushfire‐burned plastic tank. This study suggests that bushfire can accelerate the release of micro‐ and nanoplastics in the environment. This study not only contributes essential insights into the micro‐pollution consequences of fire burning but also underscores the urgency of addressing this understudied aspect to inform environmental conservation strategies and public health measures.

Background. Research into the formation of algorithms for obtaining MRI images is highly relevant. The purpose of the work is to analyze advances in the development of a software algorithm for obtaining MRI images of the brain. Materials and methods. Found 17 articles in specialized databases Cyberleninka, eLibrary, PubMed, ScienceDirect. Methods of analysis, synthesis and induction were used. Results. The best way to suppress noise in brain MRI images is the Gaussian Filter, the improvement of which has been achieved through the evolution of neural networks. Automatic segmentation achieved performance comparable to manual segmentation by using a combined system with integrated modules to eliminate the influence of noise and background, to identify image features and edge information. The Sobel operator allows the bright edges of an MRI image to be more clearly identified for removal. For volumetric visualization of brain images, due to its labor-intensive nature, the use of layer-by-layer representation of MRI data is proposed. Watershed segmentation and the K-nearest neighbor classification algorithm resulted in an MRI image accuracy of only 89 %; the wavelet transform was performed without calculating the accuracy. Support Vector Machine (SVM) using the GLCM algorithm showed an accuracy of up to 93 %, but only 36 images were used for training. Based on 150 MRI images of the brain, their classification was performed using the MATLAB 2018a software package (Matrix Laboratory) and a testing accuracy of 96,7 % was achieved. Conclusions. Improved algorithms for removing noise and bright edges from MRI brain images, segmenting them, and creating them with volumetric visualization are being created, including effective software modules for automatic segmentation based on convolutional neural networks.

Despite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual “fit”-based model to: (1) summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings.

After the arrival of the post-internet era we cannot distinguish any more between the life we develop outside and inside the Internet, offline and online. We are learning how to live between the body we inhabit and the images which represents us in the web. In this context, images have started to develop new meanings for the viewer. These meanings are determined by the new circuits in which we insert them: the social networks. In this way, we first see how the image transforms itself into an image-object and then into a digital product.