bluuebunny/crossref_metadata_2025 huggingface dataset:
Frequency Mixing Magnetic Detection Setup Employing Permanent Ring Magnets as a Static Offset Field Source
Authors: Pourshahidi Ali Mohammad , Achtsnicht Stefan , Offenhäusser Andreas , Krause Hans-Joachim
Published: November 2022

Frequency mixing magnetic detection (FMMD) has been explored for its applications in fields of magnetic biosensing, multiplex detection of magnetic nanoparticles (MNP) and the determination of core size distribution of MNP samples. Such applications rely on the application of a static offset magnetic field, which is generated traditionally with an electromagnet. Such a setup requires a current source, as well as passive or active cooling strategies, which directly sets a limitation based on the portability aspect that is desired for point of care (POC) monitoring applications. In this work, a measurement head is introduced that involves the utilization of two ring-shaped permanent magnets to generate a static offset magnetic field. A steel cylinder in the ring bores homogenizes the field. By variation of the distance between the ring magnets and of the thickness of the steel cylinder, the magnitude of the magnetic field at the sample position can be adjusted. Furthermore, the measurement setup is compared to the electromagnet offset module based on measured signals and temperature behavior.
Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing
Authors: Mir Tahira Sarwar , Liaqat Hannan Bin , Kiren Tayybah , Sana Muhammad Usman , Alvarez Roberto Marcelo , Miró Yini , Pascual Barrera Alina Eugenia , Ashraf Imran
Published: November 2022

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.
Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds
Authors: Nalic Demin , Mihalj Tomislav , Orucevic Faris , Schabauer Martin , Lex Cornelia , Sinz Wolfgang , Eichberger Arno
Published: November 2022

The safety approval and assessment of automated driving systems (ADS) are becoming sophisticated and challenging tasks. Because the number of traffic scenarios is vast, it is essential to assess their criticality and extract the ones that present a safety risk. In this paper, we are proposing a novel method based on the time-to-react (TTR) measurement, which has advantages in considering avoidance possibilities. The method incorporates the concept of fictive vehicles and variable criticality thresholds (VCTs) to assess the overall scenario’s criticality. By introducing variable thresholds, a criticality scale is defined and used for criticality calculation. Based on this scale, the presented method determines the criticality of the lanes adjacent to the ego vehicle. This is performed by placing fictive vehicles in the adjacent lanes, which represent copies of the ego. The effectiveness of the method is demonstrated in two highway scenarios, with and without trailing vehicles. Results show different criticality for the two scenarios. The overall criticality of the scenario with trailing vehicles is higher due to the decrease in avoidance possibilities for the ego vehicle.
A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities
Authors: Yang Jingmin , Lee Trong-Yen , Lee Wen-Ta , Xu Li
Published: November 2022

Information and Communication Technology (ICT) makes cities “smart”, capable of providing advanced municipal services to citizens more efficiently. In the literature, many applications of municipal service platform based on cloud computing and edge computing have been proposed, but the reference model and application instance based on cloud-edge collaboration specially for municipal service platform is rarely studied. In this context, this paper first develops a reference model, including resource collaboration, application collaboration, service collaboration, and security collaboration, and discusses the main contents and challenges of each part. Then, aiming at the problem of computing and communication resources allocation in the cloud-edge collaboration, a game-theory-based dynamic resource allocation model is introduced. Finally, an e-government self-service system based on the cloud-edge collaboration is designed and implemented. The cloud side is a cloud computing server, and the edge side are the self-service terminals integrating various edge computing devices with Artificial Intelligence (AI) embedded. The experimental results show that the designed system combines the advantages of cloud computing and edge computing, and provides a better user experience with lower processing latency, larger bandwidth, and more concurrent tasks. Meanwhile, the findings show that the evolutionary equilibrium and the Nash equilibrium are the optimal solutions, respectively.
An Adaptive Sinusoidal-Disturbance-Strategy Sparrow Search Algorithm and Its Application
Authors: Zheng Feng , Liu Gang
Published: November 2022

In light of the problems of slow convergence speed, insufficient optimization accuracy and easy falling into local optima in the sparrow search algorithm, this paper proposes an adaptive sinusoidal-disturbance-strategy sparrow search algorithm (ASDSSA) and its mathematical equation. Firstly, the initial population quality of the algorithm is improved by fusing cubic chaos mapping and perturbation compensation factors; secondly, the sinusoidal-disturbance-strategy is introduced to update the mathematical equation of the discoverer’s position to improve the information exchange ability of the population and the global search performance of the algorithm; finally, the adaptive Cauchy mutation strategy is used to improve the ability of the algorithm to jump out of the local optimal solutions. Through the optimization experiments on eight benchmark functions and CEC2017 test functions, as well as the Wilcoxon rank-sum test and time complexity analysis, the results show that the improved algorithm has better optimization performance and convergence efficiency. Further, the improved algorithm was applied to optimize the parameters of the long short term memory network (LSTM) model for passenger flow prediction on selected metro passenger flow datasets. The effectiveness and feasibility of the improved algorithm were verified by experiments.
1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches
Authors: Kamnardsiri Teerawat , Charoenkwan Phasit , Malang Chommaphat , Wudhikarn Ratapol
Published: November 2022

Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model’s performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.
Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction
Authors: Casallas Alejandro , Jiménez-Saenz Camila , Torres Victor , Quirama-Aguilar Miguel , Lizcano Augusto , Lopez-Barrera Ellie Anne , Ferro Camilo , Celis Nathalia , Arenas Ricardo
Published: November 2022

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps.
Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention
Authors: Wang Hao , Liu Juncai , Huang Changhai , Yang Xuewen , Hu Dasha , Chen Liangyin , Xing Xiaoqing , Jiang Yuming
Published: November 2022

In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as “attention” to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.
Φ-OTDR Signal Identification Method Based on Multimodal Fusion
Authors: Zhang Huaizhi , Gao Jianfeng , Hong Bingyuan
Published: November 2022

Distributed fiber optic sensing (DFS) systems are an effective method for long-distance pipeline safety inspections. Highly accurate vibration signal identification is crucial to DFS. In this paper, we propose an end-to-end high-accuracy fiber optic vibration signal detection and identification algorithm by extracting features from the time domain and frequency domain by a one-dimensional convolutional neural network and two-dimensional convolutional neural network, respectively, and introducing a self-attentive mechanism to fuse the features of multiple modes. First, the raw signal is segmented and normalized according to the statistical characteristics of the vibration signal combined with the distribution of noise. Then, the one-dimensional sequence of vibration signal and its two-dimensional image generated by short-time Fourier transform are input to the one-dimensional convolutional neural network and two-dimensional neural network, respectively, for automatic feature extraction, and the features are combined by long and short-time memory. Finally, the multimodal features generated from the time and frequency domains are fused by a multilayer TransformerEncoder structure with a multiheaded self-attentive mechanism and fed into a multilayer perceptron for classification. Experiments were conducted on an urban field database with complex noise and achieved 98.54% accuracy, which demonstrates the effectiveness of the proposed algorithm.
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images Using Images, Normals and Vertices
Authors: Fetzer Torben , Reis Gerd , Stricker Didier
Published: November 2022

This paper presents a novel architecture for simultaneous estimation of highly accurate optical flows and rigid scene transformations for difficult scenarios where the brightness assumption is violated by strong shading changes. In the case of rotating objects or moving light sources, such as those encountered for driving cars in the dark, the scene appearance often changes significantly from one view to the next. Unfortunately, standard methods for calculating optical flows or poses are based on the expectation that the appearance of features in the scene remains constant between views. These methods may fail frequently in the investigated cases. The presented method fuses texture and geometry information by combining image, vertex and normal data to compute an illumination-invariant optical flow. By using a coarse-to-fine strategy, globally anchored optical flows are learned, reducing the impact of erroneous shading-based pseudo-correspondences. Based on the learned optical flows, a second architecture is proposed that predicts robust rigid transformations from the warped vertex and normal maps. Particular attention is paid to situations with strong rotations, which often cause such shading changes. Therefore, a 3-step procedure is proposed that profitably exploits correlations between the normals and vertices. The method has been evaluated on a newly created dataset containing both synthetic and real data with strong rotations and shading effects. These data represent the typical use case in 3D reconstruction, where the object often rotates in large steps between the partial reconstructions. Additionally, we apply the method to the well-known Kitti Odometry dataset. Even if, due to fulfillment of the brightness assumption, this is not the typical use case of the method, the applicability to standard situations and the relation to other methods is therefore established.
Technological Solutions for Social Isolation Monitoring of the Elderly: A Survey of Selected Projects from Academia and Industry
Authors: Bouaziz Ghazi , Brulin Damien , Campo Eric
Published: November 2022

Social isolation is likely to be one of the most serious health outcomes for the elderly due to the COVID-19 pandemic, especially for seniors living alone at home. In fact, two approaches have been used to assess social isolation. The first is a self-reported survey designed for research purposes. The second approach is the use of monitoring technology. The objective of this paper is to provide some illustrative publications, works and examples of the current status and future prospects in the field of monitoring systems that focused on two main activities of daily living: meal-taking activity (shopping, cooking, eating and washing dishes) and mobility (inside the home and the act of going out). These two activities combined seem relevant to a potential risk of social isolation in the elderly. Although current research focuses on identifying only ADLs, we propose to use them as a first step to extract daily habits and risk level of social isolation. Moreover, since activity recognition is a recent field, we raise specific problems as well as needed contributions and we propose directions and research opportunities to accelerate advances in this field.
Cross-Modal Contrastive Hashing Retrieval for Infrared Video and EEG
Authors: Han Jianan , Zhang Shaoxing , Men Aidong , Chen Qingchao
Published: November 2022

It is essential to estimate the sleep quality and diagnose the clinical stages in time and at home, because they are closely related to and important causes of chronic diseases and daily life dysfunctions. However, the existing “gold-standard” sensing machine for diagnosis (Polysomnography (PSG) with Electroencephalogram (EEG) measurements) is almost infeasible to deploy at home in a “ubiquitous” manner. In addition, it is costly to train clinicians for the diagnosis of sleep conditions. In this paper, we proposed a novel technical and systematic attempt to tackle the previous barriers: first, we proposed to monitor and sense the sleep conditions using the infrared (IR) camera videos synchronized with the EEG signal; second, we proposed a novel cross-modal retrieval system termed as Cross-modal Contrastive Hashing Retrieval (CCHR) to build the relationship between EEG and IR videos, retrieving the most relevant EEG signal given an infrared video. Specifically, the CCHR is novel in the following two perspectives. Firstly, to eliminate the large cross-modal semantic gap between EEG and IR data, we designed a novel joint cross-modal representation learning strategy using a memory-enhanced hard-negative mining design under the framework of contrastive learning. Secondly, as the sleep monitoring data are large-scale (8 h long for each subject), a novel contrastive hashing module is proposed to transform the joint cross-modal features to the discriminative binary hash codes, enabling the efficient storage and inference. Extensive experiments on our collected cross-modal sleep condition dataset validated that the proposed CCHR achieves superior performances compared with existing cross-modal hashing methods.
Pleasantness Recognition Induced by Different Odor Concentrations Using Olfactory Electroencephalogram Signals
Authors: Hou Hui-Rang , Han Rui-Xue , Zhang Xiao-Nei , Meng Qing-Hao
Published: November 2022

Olfactory-induced emotion plays an important role in communication, decision-making, multimedia, and disorder treatment. Using electroencephalogram (EEG) technology, this paper focuses on (1) exploring the possibility of recognizing pleasantness induced by different concentrations of odors, (2) finding the EEG rhythm wave that is most suitable for the recognition of different odor concentrations, (3) analyzing recognition accuracies with concentration changes, and (4) selecting a suitable classifier for this classification task. To explore these issues, first, emotions induced by five different concentrations of rose or rotten odors are divided into five kinds of pleasantness by averaging subjective evaluation scores. Then, the power spectral density features of EEG signals and support vector machine (SVM) are used for classification tasks. Classification results on the EEG signals collected from 13 participants show that for pleasantness recognition induced by pleasant or disgusting odor concentrations, considerable average classification accuracies of 93.5% or 92.2% are obtained, respectively. The results indicate that (1) using EEG technology, pleasantness recognition induced by different odor concentrations is possible; (2) gamma frequency band outperformed other EEG rhythm-based frequency bands in terms of classification accuracy, and as the maximum frequency of the EEG spectrum increases, the pleasantness classification accuracy gradually increases; (3) for both rose and rotten odors, the highest concentration obtains the best classification accuracy, followed by the lowest concentration.
Review and Evaluation of Eye Movement Event Detection Algorithms
Authors: Birawo Birtukan , Kasprowski Pawel
Published: November 2022

Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods.
Displacement Estimation Using 3D-Printed RFID Arrays for Structural Health Monitoring
Authors: Pekgor Metin , Arablouei Reza , Nikzad Mostafa , Masood Syed
Published: November 2022

Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using 3D printable embedded passive RFID tags within uniform linear arrays together with the multiple signal classification algorithm to estimate the direction of arrival using only the phase of the backscattered signals. We validate our proposed approach via data collected from real-world experiments using a unipolar RFID reader antenna and both narrowband and wideband measurements.
Fast THz-TDS Reflection Imaging with ECOPS—Point-by-Point versus Line-by-Line Scanning
Authors: Pałka Norbert , Maciejewski Marcin , Kamiński Kamil , Piszczek Marek , Zagrajek Przemysław , Czerwińska Elżbieta , Walczakowski Michał , Dragan Krzysztof , Synaszko Piotr , Świderski Waldemar
Published: November 2022

We built a high-speed TDS setup with the use of electronically controlled optical sampling (ECOPS), which can measure up to 1600 terahertz pulses per second. The movement of the sample was provided by two fast-speed motorized linear stages constituting the gantry. We developed a flat-bar-based metal marker approach for the synchronization of continuous line-by-line scans. We carefully compared the performance of the terahertz reflection time-domain spectroscopy (TDS) scanner operating in a slow point-by-point and a one-hundred-times faster line-by-line imaging scheme. We analyzed images obtained for both schemes for a uniform metallic breadboard with holes, as well as a glass composite sample with defects. Although the measurement time was reduced by 100 times in terms of the line-by-line scheme, the overall performance in both schemes was almost identical in terms of the defects’ sizes, shapes and locations. The results proved that the proposed ECOPS TDS system can provide uniform and extremely fast scanning without any deterioration in image quality.
Metal-Chelating Peptides Separation Using Immobilized Metal Ion Affinity Chromatography: Experimental Methodology and Simulation
Authors: Irankunda Rachel , Camaño Echavarría Jairo Andrés , Paris Cédric , Stefan Loïc , Desobry Stéphane , Selmeczi Katalin , Muhr Laurence , Canabady-Rochelle Laetitia
Published: November 2022

Metal-Chelating Peptides (MCPs), obtained from protein hydrolysates, present various applications in the field of nutrition, pharmacy, cosmetic etc. The separation of MCPs from hydrolysates mixture is challenging, yet, techniques based on peptide-metal ion interactions such as Immobilized Metal Ion Affinity Chromatography (IMAC) seem to be efficient. However, separation processes are time consuming and expensive, therefore separation prediction using chromatography modelling and simulation should be necessary. Meanwhile, the obtention of sorption isotherm for chromatography modelling is a crucial step. Thus, Surface Plasmon Resonance (SPR), a biosensor method efficient to screen MCPs in hydrolysates and with similarities to IMAC might be a good option to acquire sorption isotherm. This review highlights IMAC experimental methodology to separate MCPs and how, IMAC chromatography can be modelled using transport dispersive model and input data obtained from SPR for peptides separation simulation.
Intergenerational Conflict-Initiating Factors and Management Styles: U.S. Older Adults’ Report
Authors: Zhang Yan Bing , Wiebe Weston T.
Published: November 2022

Guided by the theoretical frameworks of communication accommodation theory and conflict management, this study examines U.S. older participants’ (65 or older) written conflict scenario either with a grandchild or a nonfamily young adult. Using content analysis approach, we analyzed these written conflict scenarios to uncover major conflict initiating factors and conflict management styles. Results revealed that intergenerational conflict initiated by old-to-young criticism (more frequently reported in conflicts with nonfamily young adults) or disagreement/generation gap (more frequently reported in conflicts with grandchildren) was reported most frequently followed by young-to-old rebuff, cumulative annoyance, and young-to-old criticism. Additionally, results indicated that older adults used the problem-solving style most frequently when disagreement/generation gap initiated the conflict, especially in the family contexts; both young and older adults used the competing style most frequently when old-to-young criticism initiated the conflict, especially in nonfamily contexts. Furthermore, the use of the competing and problem-solving styles by young adults was significantly associated with the use of the same styles by older adults and vice versa, indicating both positive and negative reciprocation in intergenerational conflict. Results in general show that young and older adults manage intergenerational conflicts in different ways in family versus nonfamily contexts.
Muscle Asymmetries in the Lower Limbs of Male Soccer Players: Preliminary Findings on the Association between Countermovement Jump and Tensiomyography
Authors: Buoite Stella Alex , Galimi Annalisa , Martini Miriam , Di Lenarda Luca , Murena Luigi , Deodato Manuela
Published: November 2022

Strength and power asymmetries have been observed in different sports, including soccer. Such asymmetries, as well as the bilateral deficit (BLD), can be assessed during different tasks, static or dynamic, and with different methods and devices, in order to detect the possible different aspects, as well as the association with physical performance and injuries. The aim of this study was to investigate the association between muscle asymmetries and BLD during a countermovement jump (CMJ), and tensiomyography (TMG) parameters and asymmetries, in the lower limbs of male soccer players. A total of 23 male soccer players (18 ± 4 years) were recruited. Bilateral and unilateral CMJs were performed, and peak power (W) and height (cm) were obtained. TMG was performed on different muscles of the lower limbs, and lateral and functional symmetries were obtained. Playing position and history of injuries were collected. CMJ inter-limb symmetry was found to significantly correlate with biceps femoris (r = 0.574, p = 0.004) and soleus (r = 0.437, p = 0.037) lateral symmetry. Players in central roles presented significantly worse functional symmetry scores of the knee than defense players (−17.5%, 95% CI −31.2–−3.9; p = 0.10). Participants reporting a history of injury at the ankle were characterized by significantly lower functional symmetry in both the dominant (43%, 39.5–48.0 vs. 74.5%, 46.5–89.3, p = 0.019) and non-dominant (45%, 42.5–46.0 vs. 81.0%, 45.8–90.3, p = 0.024) ankle. Findings from this preliminary study suggest an association between lower-limb muscle asymmetries during a dynamic task, such as jumping, and muscle contractile properties evaluated with TMG; moreover, functional asymmetries may be present after ankle injuries. Future studies in larger samples should evaluate the presence of such asymmetries as predictors or characteristics of different muscular and joint injuries.
Hepatitis B, C, and D Virus Infection among Population Aged 10–64 Years in Mongolia: Baseline Survey Data of a Nationwide Cancer Cohort Study
Authors: Dambadarjaa Davaalkham , Radnaa Otgonbayar , Khuyag Ser-Od , Shagdarsuren Oyu-Erdene , Enkhbayar Uranbaigali , Mukhtar Yerkyebulan , Tsogzolbaatar Enkh-Oyun , Nyam Gunchmaa , Shaarii Shatar , Singh Pramil , Takahashi Masaharu , Namdag Bira , Okamoto Hiroaki
Published: November 2022

Hepatitis B, C, and D virus infections are a major public health problem, and Mongolia has one of the highest prevalences of dual and triple infections in the world. We aimed to determine the seroprevalence of hepatitis infection and dual or triple hepatitis infections among 10–64-year-olds. A questionnaire was used to identify risk factors for hepatitis infection, and seromarkers were measured by the fully automated immunologic analyzer HISCL-5000. Among a total of 10,040 participants, 8.1% of the population aged 10–64 was infected with HBV, 9.4% with HCV, and 0.4% with HBV and HCV, and the prevalence of the disease varied by age, sex, and the area of residence. Young people were particularly unaware of their hepatitis infection status. A small proportion of children aged 10 to 19 years and the majority of adults younger than 30 years were unaware of their HBV and HCV infection. Men were also more likely to be unaware of their HBV and HCV infection status than women. The results suggested that the prevalence of infection in the general population is high and that most people are unaware that they are infected or have become chronic carriers. Identifying mono-, co-, or triple-infection status is critical to prevent the rapid progression of liver disease among the Mongolian population.
Review of modern gonadotropins to increase the period of functional longevity in pre- and postmenopausal women
Authors:   FSBEI HE Penza State University, Penza, Russian Federation, Moiseeva I. Ya. , Sergeeva-Kondrachenko M. Yu. ,   Penza Institute for Postgraduate Medical Education — branch of the FSBEI CPE RMAPE of the Ministry of Health, Penza, Russian Federation, Strukov V. I. ,   Penza Institute for Postgraduate Medical Education — branch of the FSBEI CPE RMAPE of the Ministry of Health, Penza, Russian Federation, Vinogradova O. P. ,   Penza Institute for Postgraduate Medical Education — branch of the FSBEI CPE RMAPE of the Ministry of Health, Penza, Russian Federation, Petrova E. V. ,   FSBEI HE Penza State University, Penza, Russian Federation, Panina E. S. ,   FSBEI HE Penza State University, Penza, Russian Federation, Rodina O. P. ,   FSBEI HE Penza State University, Penza, Russian Federation, Poluboyarinov P. A. ,   FSBEI HE Penza State University, Penza, Russian Federation, Elistratov D. G. ,   OOO Parapharm, Penza, Russian Federation
Published: November 2022

The trend towards an increase in human life expectancy that emerged in the 21st century poses new challenges for scientists. The main one of these tasks is to improve the quality of life of people who have crossed a certain age limit, and especially women who have entered the menopause period. The purpose of this review is to present the most promising developments of Russian medical science and pharmaceutical industry in ensuring the functional longevity of citizens.
Prospects for the use of osteobiotics in antibiotic therapy
Authors:   FSBEI HE Penza State University, Penza, Russian Federation, Moiseeva I. Ya. , Sergeeva-Kondrachenko M. Yu. ,   Penza Institute for Postgraduate Medical Education — branch of the FSBEI CPE RMAPE of the Ministry of Health, Penza, Russian Federation, Strukov V. I. ,   Penza Institute for Postgraduate Medical Education — branch of the FSBEI CPE RMAPE of the Ministry of Health, Penza, Russian Federation, Vinogradova O. P. ,   Penza Institute for Postgraduate Medical Education — branch of the FSBEI CPE RMAPE of the Ministry of Health, Penza, Russian Federation, Petrova E. V. ,   FSBEI HE Penza State University, Penza, Russian Federation, Panina E. S. ,   FSBEI HE Penza State University, Penza, Russian Federation, Rodina O. P. ,   FSBEI HE Penza State University, Penza, Russian Federation, Poluboyarinov P. A. ,   FSBEI HE Penza State University, Penza, Russian Federation, Elistratov D. G. ,   OOO Parapharm, Penza, Russian Federation
Published: November 2022

The recent trend towards the frequent prescription of antibiotics has indicated the need for additional therapy to offset the harm done to the body in general and to the musculo skeletal system in particular. This review examines the specifics and effects of the new class of drugs called osteobiotics. The efficacy of the first Russian drug of this category and the possibility of its use in antibiotic therapy are evaluated.
Predicting the number of hunting animals in the State complex “Zavidovo” based on a mathematical model
Authors:   Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University– Moscow Timiryazev Agricultural Academy”, Russia, Moscow, Kaledin A. P. , Yuldashbaev Yu. A. ,   Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University – Moscow Timiryazev Agricultural Academy”, Russia, Moscow, Filatov A. I. ,   Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University– Moscow Timiryazev Agricultural Academy”, Russia, Moscow, Ostapchuk A. M. ,   Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University– Moscow Timiryazev Agricultural Academy”, Russia, Moscow, Zhukov D. V. ,   State complex “Zavidovo”, Russia, Tver region, Konakovsky area, Kozlovo, Makeeva V. M. ,   Federal State Budgetary Educational Institution of Higher Education “Lomonosov Moscow State University”, Moscow, Russia, Golubeva O. N. ,   Museum of Hunting and Fishing of Association “Rosokhotrybolovsoyuz”, Russia, Moscow
Published: November 2022

It is necessary to predict the number of hunting animals under the conditions of the impact of endogenous and exogenous factors the habitat and economic use, for rational and inexhaustible their use. Understanding changes in the number of hunting animals is an important tool for managing hunting resources. Predicting of the number of hunting animals in the region as arule, is based on trend models of population dynamics. In practice, the calculation and constructive method and the method of mathematical modeling have become the most widespread in predicting the population of hunting animals in dynamics. The computational and constructive method is based on the use of correlation and regression models, but it does not take into account the main indicators of population size formation: the fertility rate, which is formed on the basis of the specic weight of males, the percentage of yeldness and the number of calves (young animals) in the calving (litter), and the livability depending on the percentage of natural death and losses from poaching. The method of mathematical modeling involves the compilation of mathematical models and conducting model experiments to predict the number of species of hunting animals in dynamics. The relevance of this mathematical analysis lies in its multifactorial nature and rather high reliability. In the eld of predicting the number of hunting animals the modied P. H.Leslie model is of particular interest. The purpose of the work was to predict the number of wild ungulates (roe deer, elk, maral and spotted deer) in the Zavidovo State Complex in the Tver region until 2035. The use of the modied P. H.Leslie model makes it possible to predict the development of hunting animal populations both in the Zavidovo State Complex as awhole and in individual hunting areas with precise adjustment of models according to area parameters.
Evaluation of biological characteristics and working traits of dogs of service breeds according to the IGP standard
Authors:   Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University– Moscow. Timiryazev Agricultural Academy”, Russia, Moscow, A. V. Dikov A. V. Dikov , Kostomakhin N. M. ,   Federal State Budgetary Educational Institution of Higher Education “Russian State Agrarian University– Moscow. Timiryazev Agricultural Academy”, Russia, Moscow
Published: November 2022

IGP is an international test standard that serves to identify and test the traits necessary for acommercial dog to successfully use it. Competitions with the use of IGP can be used as the basis of selection to consolidate in service dogs such important traits as excellent physical tness, teamwork, rmness of character, manageability and trail study. The purpose of the work was to evaluate the biological characteristics and working traits of dogs of service breeds according to the IGP standard. The material for the research was the data of dog performances at the IGP World Championship in 2019. The object of the research was dogs of Belgian and German shepherd breeds. The total number of dogs participating in the study was 105 animals. It was found as aresult of the conducted studies that the age of Belgian shepherd breed males was signicantly lower than that of German shepherd breed males (P>0,95). Statistically signicant dierences were revealed in the results of performances according to the IGP-3 standard between males of Belgian and German shepherds: in sections A(trail work) and B(obedience) (P>0,99), in section C(protection) the reliability was (P>0,95). Obviously, the wider use of inbreeding in the breeding of Belgian shepherd dogs causes their greater uniformity and high results when evaluated by IGP standard.
Normative regulation in the field of electronic agriculture, digital and other innovative technologies in the Agro industrial complex of Belarus
Authors:   Institute of System Research in Agro-industrial Complex of the National Academy of Sciences of Belarus, Republic of Belarus, Minsk, Kovalyev I. L. , Kostomakhin M. N. ,   Federal State Budgetary Scientic Institution “Federal Research Center Agro Engineering All Russian Institute of Mechanization”, Russia, Moscow
Published: November 2022

Currently, the most pressing problems of the Belarusian agro-industrial complex remain: extremely costly agricultural production and its low eciency. Thus, as of the end of 2020 the level of protability excluding state support of all agricultural organizations included in the system of the Ministry of Agriculture and Food of the Republic of Belarus amounted to only 1,7 %. The most important direction of the state innovation policy of the Republic of Belarus for the current ve-year period is the digital transformation of traditional sectors of the national economy. The current level of domestic regulatory and legal regulation of dierent digitalization processes in the agricultural sector signicantly lags behind the widely implemented information, digital and other innovative technologies that are continuously developing and improving. The purpose of the work was to study he normative regulation in the eld of electronic agriculture, digital and other innovative technologies in the Agro–industrial complex of Belarus. Anumber of highly promising directions in the digital transformation of agriculture today was described in the article. Legislative support at the level of timely and continuous updating and modernization of the basic regulations governing them is extremely necessary for all measures for the eective implementation of the latest innovative technologies. In order to strengthen condence in the latest technologies and their successful eective implementation in Belarusian agriculture and to create a legal basis for international cooperation and above all within the framework of the EAEU, the most important task today is the formation of an up-to-date regulatory framework. The formation of a system of legal regulation aimed at ensuring the transition to the digital transformation of the national economy is one of the important tools for implementing the directions of the modern state innovation policy of the country.
RAISING HOLSTEIN HEIFERS ON TECHNOLOGICAL SCHEMES IN THE CONTINENTAL CLIMATE OF KAZAKHSTAN
Authors: САЙЛАУБЕК П.Ж. , СИВКИН Н.В. , БАЙСАБЫРОВА А.А.
Published: November 2022

В статье приводятся результаты научного опыта по эффективности увеличения норм выпойки молока телятам голштинской породы в условиях резкоконтинентального климата юго-востока Казахстана. Интродукция голштинского скота в агроклиматические условия Республики Казахстан требует совершенствования технологий выращивания телят в направлении повышения адаптационных способностей и эффективности отрасли скотоводства. Опыт проведен в условиях сельхозпредприятия Ордабасинского района Туркестанской области при содержании телят молочного периода в индивидуальных домиках на крытой площадке. В опытной и контрольных группах, сформированных по принципу пар-аналого, было по 10 телочек. За первые 3 мес жизни им скармливали по 440 л и 360 л молока соответственно. В течение первых 2—3 дней молодняк выпаивали только материнским молозивом и молоком, а затем — сборным от всех коров родильного отделения. Телята, рожденные летом, проявляли высокую интенсивность прироста живой массы и характеризовались стабильными биохимическими показателями крови. Скармливание им цельного молока, со 2-й декады жизни по 8 л, с 3-й и 4-й — по 9 л и 5-й — по 7 л, при раннем приучении к концентратам и сену способствовало увеличению среднесуточных приростов телок до 3 мес на 155,3 г и до 10 мес — на 57 г (P≤0,001), но при этом повышались и затраты корма на прирост 1 ц живой массы, в рублях — на 3,8%.

The article presents the results of scientific experience on the effectiveness of increasing of milk feeding levels for Holstein calves in the continental climate of the south-east of Kazakhstan. The introduction of Holstein cattle into the Republic of Kazakhstan requires the improvement of calf raising technologies in the direction of increasing the adaptive abilities and efficiency of the cattle breeding. The experiment was conducted in Ordabasinsky district of Turkestan region when housing calves of the dairy period in hutches on a covered area. In the experimental and control groups formed according to the principle of pairs of analogues, there were 10 chicks. For the first 3 months of life, they were fed 440 liters and 360 liters of milk, respectively. During the first 2-3 days, the calves were fed only with maternal colostrum and milk, and then with a collection from all cows. Calves born in the summer showed a high intensity of live weight gain and were characterized by stable biochemical blood parameters. Feeding them whole milk, from the 2nd decade of life for 8 liters, the 3rd and 4th — for 9 liters and the 5th — for 7 liters, with early offered concentrates and hay, contributed to an increase in the average daily gains of heifers up to 3 months by 155.3 g and up to 10 months — by 57 g (P≤0.001), but at the same time, feed costs for an increase of 1 kg of live weight increased, in rubles — by 3.8%.
Health and socio-demographic characteristics associated with uptake of seasonal influenza vaccination amongst pregnant women: Retrospective cohort study
Authors: Woodcock Thomas , Novov Vesselin , Skirrow Helen , Butler James , Lovett Derryn , Adeleke Yewande , Blair Mitch , Saxena Sonia , Majeed Azeem , Aylin Paul
Published: November 2022

Pregnant women are at increased risk from influenza, yet maternal influenza vaccination levels remain suboptimal. This study aimed to estimate associations between socio-demographic and health characteristics and seasonal influenza vaccination uptake among pregnant women and understand trends over time to inform interventions to improve vaccine coverage. A retrospective cohort study using linked electronic health records of women in North West London with at least one pregnancy overlapping with an influenza season between September 2010 and February 2020. We used a multivariable mixed-effects logistic regression model to identify associations between characteristics of interest and primary outcome of influenza vaccination. 451,954 pregnancies, among 260,744 women, were included. In 85,376 (18.9%) pregnancies women were vaccinated against seasonal influenza. Uptake increased from 8.4% in 2010/11 to 26.3% in 2018/19, dropping again to 21.1% in 2019/20. Uptake was lowest among women: aged 15-19 years (12%) or over 40 years (15%; OR 1.17, 95% CI 1.10 to 1.24); of Black ethnicity (14.1%; OR 0.55, 95% CI 0.53 to 0.57), or unknown ethnicity (9.9%; OR 0.42, 95% CI 0.39 to 0.46), lived in more deprived areas (OR least vs most deprived 1.16, 95% CI 1.11 to 1.21), or with no known risk factors for severe influenza. Seasonal influenza vaccine uptake in pregnant women increased in the past decade, prior to the COVID-19 pandemic, but remained suboptimal. We recommend approaches to reducing health inequalities should focus on women of Black ethnicity, younger and older women, and women living in areas of greater socio-economic deprivation.