bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Placement of Electric Vehicle Charging Stations in an Active Distribution Grid with Photovoltaic and Battery Energy Storage System Integration
Authors: Deeum Saksit , Charoenchan Tossaporn , Janjamraj Natin , Romphochai Sillawat , Baum Sergej , Ohgaki Hideagi , Mithulananthan Nadarajah , Bhumkittipich Krischonme
Published: November 2023

This article presents the optimal placement of electric vehicle (EV) charging stations in an active integrated distribution grid with photovoltaic and battery energy storage systems (BESS), respectively. The increase in the population has enabled people to switch to EVs because the market price for gas-powered cars is shrinking. The fast spread of EVs depends solely on the rapid and coordinated growth of electric vehicle charging stations (EVCSs). Since EVCSs can cause power losses and voltage variations outside the permissible limits, their integration into the current distribution grid can be characterized by the growing penetration of randomly dispersed photovoltaic (PV) and battery energy storage (BESS) systems, which is complicated. This study used genetic algorithm (GA) optimization and load flow (accommodation of anticipated rise in the number of electric cars on the road) analysis with a forward and backward sweep methodology (FBSM) to locate, scale and optimize EVCSs from a distribution grid where distributed PV/BESSs are prevalent. Power optimization was demonstrated to be the objective issue, which included minimizing active and reactive power losses. To verify the proposed optimal objective solutions from the active distribution grid, an IEEE 33 bus distribution grid was considered for EVCSs’ optimization under the penetration of photovoltaic and BESS systems. MATLAB simulations for the integrated EVCS-PV-BESS system on the distribution grid for five different zones were performed using detection from zone 1 (ranging from 301.9726 kW to 203.3872 kW), reducing the power losses (accounting for 33%) in the system to a minimum level.
A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids
Authors: Chen Fei , Wang Zhiyang , He Yu
Published: November 2023

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.
Astrocytes: Lessons Learned from the Cuprizone Model
Authors: Kipp Markus
Published: November 2023

A diverse array of neurological and psychiatric disorders, including multiple sclerosis, Alzheimer’s disease, and schizophrenia, exhibit distinct myelin abnormalities at both the molecular and histological levels. These aberrations are closely linked to dysfunction of oligodendrocytes and alterations in myelin structure, which may be pivotal factors contributing to the disconnection of brain regions and the resulting characteristic clinical impairments observed in these conditions. Astrocytes, which significantly outnumber neurons in the central nervous system by a five-to-one ratio, play indispensable roles in the development, maintenance, and overall well-being of neurons and oligodendrocytes. Consequently, they emerge as potential key players in the onset and progression of a myriad of neurological and psychiatric disorders. Furthermore, targeting astrocytes represents a promising avenue for therapeutic intervention in such disorders. To gain deeper insights into the functions of astrocytes in the context of myelin-related disorders, it is imperative to employ appropriate in vivo models that faithfully recapitulate specific aspects of complex human diseases in a reliable and reproducible manner. One such model is the cuprizone model, wherein metabolic dysfunction in oligodendrocytes initiates an early response involving microglia and astrocyte activation, culminating in multifocal demyelination. Remarkably, following the cessation of cuprizone intoxication, a spontaneous process of endogenous remyelination occurs. In this review article, we provide a historical overview of studies investigating the responses and putative functions of astrocytes in the cuprizone model. Following that, we list previously published works that illuminate various aspects of the biology and function of astrocytes in this multiple sclerosis model. Some of the studies are discussed in more detail in the context of astrocyte biology and pathology. Our objective is twofold: to provide an invaluable overview of this burgeoning field, and, more importantly, to inspire fellow researchers to embark on experimental investigations to elucidate the multifaceted functions of this pivotal glial cell subpopulation.
Up Front Unfolded Protein Response Combined with Early Protein Secretion Pathway Engineering in Yarrowia lipolytica to Attenuate ER Stress Caused by Enzyme Overproduction
Authors: Zhu Xingyu , Li Moying , Zhu Rui , Xin Yu , Guo Zitao , Gu Zhenghua , Zhang Liang , Guo Zhongpeng
Published: November 2023

Engineering the yeast Yarrowia lipolytica as an efficient host to produce recombinant proteins remains a longstanding goal for applied biocatalysis. During the protein overproduction, the accumulation of unfolded and misfolded proteins causes ER stress and cell dysfunction in Y. lipolytica. In this study, we evaluated the effects of several potential ER chaperones and translocation components on relieving ER stress by debottlenecking the protein synthetic machinery during the production of the endogenous lipase 2 and the E. coli β-galactosidase. Our results showed that improving the activities of the non-dominant translocation pathway (SRP-independent) boosted the production of the two proteins. While the impact of ER chaperones is protein dependent, the nucleotide exchange factor Sls1p for protein folding catalyst Kar2p is recognized as a common contributor enhancing the secretion of the two enzymes. With the identified protein translocation components and ER chaperones, we then exemplified how these components can act synergistically with Hac1p to enhance recombinant protein production and relieve the ER stress on cell growth. Specifically, the yeast overexpressing Sls1p and cytosolic heat shock protein Ssa8p and Ssb1p yielded a two-fold increase in Lip2p secretion compared with the control, while co-overexpressing Ssa6p, Ssb1p, Sls1p and Hac1p resulted in a 90% increase in extracellular β-galp activity. More importantly, the cells sustained a maximum specific growth rate (μmax) of 0.38 h−1 and a biomass yield of 0.95 g-DCW/g-glucose, only slightly lower than that was obtained by the wild type strain. This work demonstrated engineering ER chaperones and translocation as useful strategies to facilitate the development of Y. lipolytica as an efficient protein-manufacturing platform.
DNA Damage, Repair, and Cancer Metabolism
Authors: Kang Tae-Hong
Published: November 2023

The intricate interplay between DNA damage response (DDR) and metabolism unveils a profound insight into the fundamental mechanisms governing the maintenance of genomic integrity [...]
Sex Drives Functional Changes in the Progression and Regression of Liver Fibrosis
Authors: Sayaf Katia , Zanotto Ilaria , Gabbia Daniela , Alberti Dafne , Pasqual Giulia , Zaramella Alice , Fantin Alberto , De Martin Sara , Russo Francesco Paolo
Published: November 2023

Liver fibrosis is a common and reversible feature of liver damage associated with many chronic liver diseases, and its onset is influenced by sex. In this study, we investigated the mechanisms of liver fibrosis and regeneration, focusing on understanding the mechanistic gaps between females and males. We injected increasing doses of carbon tetrachloride into female and male mice and maintained them for a washout period of eight weeks to allow for liver regeneration. We found that male mice were more prone to developing severe liver fibrosis as a consequence of early chronic liver damage, supported by the recruitment of a large number of Ly6Chigh MoMφs and neutrophils. Although prolonged liver damage exacerbated the fibrosis in mice of both sexes, activated HSCs and Ly6Chigh MoMφs were more numerous and active in the livers of female mice than those of male mice. After eight weeks of washout, only fibrotic females reported no activated HSCs, and a phenotype switching of Ly6Chigh MoMφs to anti-fibrogenic Ly6Clow MoMφs. The early stages of liver fibrosis mostly affected males rather than females, while long-term chronic liver damage was not influenced by sex, at least for liver fibrosis. Liver repair and regeneration were more efficient in females than in males.
Association Analysis of Transcriptome and Targeted Metabolites Identifies Key Genes Involved in Iris germanica Anthocyanin Biosynthesis
Authors: Zhao Xiaojie , Wu Yumeng , Zhang Xiaoyu , Tian Feng , Yu Fang , Li Xue , Huang Dazhuang
Published: November 2023

The anthocyanin biosynthetic pathway is the main pathway regulating floral coloration in Iris germanica, a well-known ornamental plant. We investigated the transcriptome profiles and targeted metabolites to elucidate the relationship between genes and metabolites in anthocyanin biosynthesis in the bitone flower cultivar ‘Clarence’, which has a deep blue outer perianth and nearly white inner perianth. In this study, delphinidin-, pelargonidin-, and cyanidin-based anthocyanins were detected in the flowers. The content of delphinidin-based anthocyanins increased with the development of the flower. At full bloom (stage 3), delphinidin-based anthocyanins accounted for most of the total anthocyanin metabolites, whereas the content of pelargonidin- and cyanidin-based anthocyanins was relatively low. Based on functional annotations, a number of novel genes in the anthocyanin pathway were identified, which included early biosynthetic genes IgCHS, IgCHI, and IgF3H and late biosynthetic genes Ig F3′5′H, IgANS, and IgDFR. The expression of key structural genes encoding enzymes, such as IgF3H, Ig F3′5′H, IgANS, and IgDFR, was significantly upregulated in the outer perianth compared to the inner perianth. In addition, most structural genes exhibited their highest expression at the half-color stage rather than at the full-bloom stage, which indicates that these genes function ahead of anthocyanins synthesis. Moreover, transcription factors (TFs) of plant R2R3-myeloblastosis (R2R3-MYB) related to the regulation of anthocyanin biosynthesis were identified. Among 56 R2R3-MYB genes, 2 members belonged to subgroup 4, with them regulating the expression of late biosynthetic genes in the anthocyanin biosynthetic pathway, and 4 members belonged to subgroup 7, with them regulating the expression of early biosynthetic genes in the anthocyanin biosynthetic pathway. Quantitative real-time PCR (qRT-PCR) analysis was used to validate the data of RNA sequencing (RNA-Seq). The relative expression profiles of most candidate genes were consistent with the FPKM of RNA-seq. This study identified the key structural genes encoding enzymes and TFs that affect anthocyanin biosynthesis, which provides a basis and reference for the regulation of plant anthocyanin biosynthesis in I. germanica.
Improved Reversion of Calcifications in Porcine Aortic Heart Valves Using Elastin-Targeted Nanoparticles
Authors: Feldmann Anja , Nitschke Yvonne , Linß Franziska , Mulac Dennis , Stücker Sina , Bertrand Jessica , Buers Insa , Langer Klaus , Rutsch Frank
Published: November 2023

Calcified aortic valve disease in its final stage leads to aortic valve stenosis, limiting cardiac function. To date, surgical intervention is the only option for treating calcific aortic valve stenosis. This study combined controlled drug delivery by nanoparticles (NPs) and active targeting by antibody conjugation. The chelating agent diethylenetriaminepentaacetic acid (DTPA) was covalently bound to human serum albumin (HSA)-based NP, and the NP surface was modified using conjugating antibodies (anti-elastin or isotype IgG control). Calcification was induced ex vivo in porcine aortic valves by preincubation in an osteogenic medium containing 2.5 mM sodium phosphate for five days. Valve calcifications mainly consisted of basic calcium phosphate crystals. Calcifications were effectively resolved by adding 1–5 mg DTPA/mL medium. Incubation with pure DTPA, however, was associated with a loss of cellular viability. Reversal of calcifications was also achieved with DTPA-coupled anti-elastin-targeted NPs containing 1 mg DTPA equivalent. The addition of these NPs to the conditioned media resulted in significant regression of the valve calcifications compared to that in the IgG-NP control without affecting cellular viability. These results represent a step further toward the development of targeted nanoparticular formulations to dissolve aortic valve calcifications.
Phase Equilibria of the Ag-Al-Au Ternary System and Interfacial Reactions in the Au-xAg/Al Couples at 450 °C
Authors: Ramadhani Mavindra , Laksono Andromeda Dwi , Liang Chien-Lung , Yang Chiao-Yi , Chen Kuo-Jung , Yen Yee-Wen , Hsiao Hsien-Ming
Published: November 2023

The phase equilibria of the Ag-Al-Au ternary system and the solid-state reaction couple for the Au-xAg/Al system were investigated isothermally at 450 °C. By investigating the Ag-Al-Au ternary system and its isothermal section, this study aims to provide a clearer understanding of the phase stability and interfacial reactions between different phases. This information is crucial for designing materials and processes in electronic packaging, with the potential to reduce costs and improve reliability. There were seven single-phase regions, thirteen two-phase regions, and six three-phase regions, with no ternary intermetallic compound (IMC) formed in the isothermal section of the Ag-Al-Au ternary system. When the Au-25 wt.% Ag/Al couple was aged at 450 °C for 240–1500 h, the AuAl2, Au2Al, and Au4Al phases formed at the interface. When the Ag contents increased to 50 and 75 wt.%, the Ag2Al, AuAl2, and Au4Al phases formed at the interface. When the aging time increased from 240 h to 1500 h, the total IMC thickness in all Au-xAg/Al couples became thicker, but the types of IMCs formed at the interface did not change. The total IMC thickness also increased with the increase in the Ag content. When the Ag content was greater than 25 wt.%, the Au2Al phase was converted into the Ag2Al phase. The IMC growth mechanism in all of the couples followed a reaction-controlled process.
Temperature-Controlled Switchable Photonic Nanojet Generated by Truncated Cylindrical Structure
Authors: Su Ning , Zhang Weiming , Zeng Xintao , Wu Pinghui , Cui Lina , Chen Xiaohui
Published: November 2023

We propose a novel micro-nano structure that can realize a photonic nanojet (PNJ) switch by adjusting the temperature, which is composed of a truncated cylinder coated with a thin vanadium dioxide (VO2) film. The influence of temperature on the maximum strength, full width at half maximum (FWHM), working distance, and focal length of the PNJ were studied by finite-difference time-domain (FDTD) method. The results demonstrate that the structure can adjust the open and close state of the PNJ by changing the temperature. A PNJ with varying characteristics can be obtained at both high and low temperatures, and the maximum intensity ratio of the PNJ can reach up to 7.25. This discovery provides a new way of optical manipulation, sensing and detection, microscopy imaging, optoelectronic devices, and other fields.
Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations
Authors: Zhang Tianyu , Jin Xin , Bai Song , Peng Yuxin , Li Ye , Zhang Jun
Published: November 2023

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data.
Design and Demonstration of a Microelectromechanical System Single-Ring Resonator with Inner Ring-Shaped Spring Supports for Inertial Sensors
Authors: Khan Imran , Ranji Ahmad Rahbar , Nagesh Gnanesh , Ting David S.-K. , Ahamed Mohammed Jalal
Published: November 2023

This paper presents a novel single-ring resonator design and experimentally demonstrates its dynamic behavior. The proposed ring resonator design is simple and has a solid anchor at its center connected to an outside ring via inner ring-shaped springs. The mode shapes and frequency of the ring resonator were determined numerically and compared with analytical approaches, and the minimum split frequency was observed for the n = 3 mode of vibration. Numerical and analytical methods were used to determine the resonance frequencies, pull-in voltage, resonance frequency shift and harmonic response of the ring resonator for different silicon orientations. The split frequency in the n = 3 mode of vibration increases by the applied DC bias voltage almost by the same amount for all types of silicon. When an AC voltage with a 180-degree phase is applied to two opposite electrodes, the ring has two resonance frequencies in mode n = 2, and when the AC voltage applied to two opposite electrodes is in the same phase, the ring has one resonance frequency regardless of the crystal orientation of silicon. Prototypes were fabricated using a double silicon-on-insulator-based wafer fabrication technique and were tested to verify the resonator performance.
Design of a Soft Sensor Based on Long Short-Term Memory Artificial Neural Network (LSTM) for Wastewater Treatment Plants
Authors: Recio-Colmenares Roxana , León Becerril Elizabeth , Gurubel Tun Kelly Joel , Conchas Robin F.
Published: November 2023

Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble X1 and solid X2 substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for X1, and 9.74, 0.93, and 1.89 for X2, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems.
Improved UAV-to-Ground Multi-Target Tracking Algorithm Based on StrongSORT
Authors: Cao Xinyu , Wang Zhuo , Zheng Bowen , Tan Yajie
Published: November 2023

Unmanned aerial vehicles (UAV) are essential for aerial reconnaissance and monitoring. One of the greatest challenges facing UAVs is vision-based multi-target tracking. Multi-target tracking algorithms that depend on visual data are utilized in a variety of fields. In this study, we present a comprehensive framework for real-time tracking of ground robots in forest and grassland environments. This framework utilizes the YOLOv5n detection algorithm and a multi-target tracking algorithm for monitoring ground robot activities in real-time video streams. We optimized both detection and re-identification networks to enhance real-time target detection. The StrongSORT tracking algorithm was selected carefully to alleviate the loss of tracked objects due to factors like camera jitter, intersecting and overlapping targets, and smaller target sizes. The YOLOv5n algorithm was used to train the dataset, and the StrongSORT tracking algorithm incorporated the best-trained model weights. The algorithm’s performance has greatly improved, as demonstrated by experimental results. The number of ID switches (IDSW) has decreased by sixfold, IDF1 has increased by 7.93%, and false positives (FP) have decreased by 30.28%. Additionally, the tracking speed has reached 38 frames per second. These findings validate our algorithm’s ability to fulfill real-time tracking requisites on UAV platforms, delivering dependable resolutions for dynamic multi-target tracking on land.
A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN
Authors: Zhou Wenhao , Zhou Zhanyang , Niu Yingtao , Zhou Quan , Ding Huihui
Published: November 2023

Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies.
Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems
Authors: Küttel Dániel , Kovács László , Szölgyén Ákos , Paulik Róbert , Jónás Viktor , Kozlovszky Miklós , Molnár Béla
Published: November 2023

As the field of routine pathology transitions into the digital realm, there is a surging demand for the full automation of microscope scanners, aiming to expedite the process of digitizing tissue samples, and consequently, enhancing the efficiency of case diagnoses. The key to achieving seamless automatic imaging lies in the precise detection and segmentation of tissue sample regions on the glass slides. State-of-the-art approaches for this task lean heavily on deep learning techniques, particularly U-Net convolutional neural networks. However, since samples can be highly diverse and prepared in various ways, it is almost impossible to be fully prepared for and cover every scenario with training data. We propose a data augmentation step that allows artificially modifying the training data by extending some artifact features of the available data to the rest of the dataset. This procedure can be used to generate images that can be considered synthetic. These artifacts could include felt pen markings, speckles of dirt, residual bubbles in covering glue, or stains. The proposed approach achieved a 1–6% improvement for these samples according to the F1 Score metric.
Towards the Augmentation of Digital Twin Performance
Authors: Charrier Quentin , Hakam Nisar , Benfriha Khaled , Meyrueis Vincent , Liotard Cyril , Bouzid Abdel-Hakim , Aoussat Améziane
Published: November 2023

Digital Twin (DT) aims to provide industrial companies with an interface to visualize, analyze, and simulate the production process, improving overall performance. This paper proposes to extend existing DT by adding a complementary methodology to make it suitable for process supervision. To implement our methodology, we introduce a novel framework that identifies, collects, and analyses data from the production system, enhancing DT functionalities. In our case study, we implemented Key Performance Indicators (KPIs) in the immersive environment to monitor physical processes through cyber representation. First, a review of the Digital Twin (DT) allows us to understand the status of the existing methodologies as well as the problem of data contextualization in recent years. Based on this review, performance data in Cyber–Physical Systems (CPS) are identified, localized, and processed to generate indicators for monitoring machine and production line performance through DT. Finally, a discussion reveals the difficulties of integration and the possibilities to respond to other major industrial challenges, like predictive maintenance.
Improved Methods for Fourier-Based Microwave Imaging
Authors: Alvarez López Yuri , Las-Heras Andrés Fernando
Published: November 2023

Fourier-based imaging has been widely adopted for microwave imaging thanks to its efficiency in terms of computational complexity without compromising image resolution. Together with other backpropagation imaging algorithms like delay-and-sum (DAS), they are based on a far-field approach to the electromagnetic expression relating to fields and sources. To improve the accuracy of these techniques, this contribution presents a modified version of the well-known Fourier-based algorithm by taking into account the field radiated by the Tx/Rx antennas of the microwave imaging system. The impact on the imaged targets is discussed, providing a quantitative and qualitative analysis. The performance of the proposed method for subsampled microwave imaging scenarios is compared against other well-known aliasing mitigation methods.
Toward Optimal Irrigation Management at the Plot Level: Evaluation of Commercial Water Potential Sensors
Authors: Aldaz-Lusarreta Alaitz , Campo-Bescós Miguel Ángel , Virto Iñigo , Giménez Rafael
Published: November 2023

Proper irrigation practice consists of applying the optimum amount of water to the soil at the right time. The porous characteristics of the soil determine the capacity of the soil to absorb, infiltrate, and store water. In irrigation, it is not sufficient to only determine the water content of the soil; it is also necessary to determine the availability of water for plants: water potential. In this paper, a comprehensive laboratory evaluation—accuracy and variability—of the world’s leading commercial water potential sensors is carried out. No such comprehensive and exhaustive comparative evaluation of these devices has been carried out to date. Ten pairs of representative commercial sensors from four different families were selected according to their principle of operation (tensiometers, capacitive sensors, heat dissipation sensors, and resistance blocks). The accuracy of the readings (0 kPa–200 kPa) was determined in two soils of contrasting textures. The variability in the recordings—repeatability and reproducibility—was carried out in a homogeneous and inert material (sand) in the same suction range. The response in terms of accuracy and value dispersion of the different sensor families was different according to the suction range considered. In the suction range of agronomic interest (0–100 kPa), the heat dissipation sensor and the capacitive sensors were the most accurate. In both families, registrations could be extended up to 150–200 kPa. The scatter in the readings across the different sensors was due to approximately 80% of the repeatability or intrinsic variability in the sensor unit and 20% of the reproducibility. Some sensors would significantly improve their performance with ad hoc calibrations.
Predicting Functional Outcome After Ischemic Stroke Using Logistic Regression and Machine Learning Models
Authors: Okoye Grace Chinwe , Umeh Edith Uzoma
Published: November 2023

This research employed binary logistic regression and machine learning techniques; Decision Tree, Random Forest, and Support Vector Machine (SVM), to predict functional outcomes following ischemic stroke. The main goal was to determine the most suitable model for the dataset through a comprehensive performance evaluation. Four models were examined for predicting post-ischemic stroke functional outcomes: Decision Tree, Random Forest, Logistic Regression, and SVM. The evaluation involved metrics such as Accuracy, Precision, F1-Score, and Recall. The Logistic Regression model achieved the highest accuracy at 90%, accurately predicting outcomes in 90% of cases. However, it had lower precision (50%), indicating an increased rate of false positive predictions. On the other hand, the SVM model displayed the highest precision (71.3%), implying fewer false positive predictions. It also attained the highest F1-Score (77.5%), indicating a strong balance between precision and Recall compared to the other models. Notably, the Logistic Regression model achieved perfect Recall (100%), correctly identifying all positive outcomes, while the Random Forest model showed significant recall performance (93.2%). Conversely, the Decision Tree model exhibited moderate accuracy (66.11%) but lower precision (66%), F1-Score (6.15%), and recall (3.2%), suggesting challenges with false positives and false negatives. Choosing the best model depends on analysis priorities. For accurate identification of positive outcomes, the Logistic Regression model's perfect recall is advantageous. For balanced performance, the SVM model's high F1-Score makes it a compelling option.
Molecular Predictors of Fetus Growth Delay
Authors: Ганчар Е.П.
Published: November 2023

Цель. На основании изучения метаболического профиля свободных аминокислот у женщин в 1-м триместре беременности создать метод прогнозирования задержки роста плода. Материалы и методы. На 1-м этапе произведен забор плазмы крови у 518 женщин в сроке беременности 11–13 недель. Плазма криоконсервирована в условиях умеренно низкой температуры (–80 °С). Произведен анализ исходов беременности и родов. На 2-м этапе в исследование включено 50 пациенток: 1-я группа (основная) – 29 пациенток с подтвержденным диагнозом задержки роста плода; 2-я группа (контрольная) – 21 женщина с беременностью без осложнений. В плазме крови методом высокоэффективной жидкостной хроматографии определено содержание 40 свободных аминокислот, их производных и метаболитов. Результаты. Методом логистической регрессии получена формула, включающая определение концентрации таурина и аспарагина в плазме крови женщин в 1-м триместре беременности, при расчете индекса p>0,55 прогнозируют задержку роста плода, а при p<0,55 делают заключение о низком риске развития данного осложнения. Заключение. Предложен метод прогнозирования задержки роста плода с чувствительностью 93%, специфичностью 90%.

Purpose. Based on the study of the metabolic profile of free amino acids in women in the 1st trimester of pregnancy, create a method for predicting fetal growth retardation. Materials and methods. At the 1st stage, blood plasma was taken from 518 women at 11–13 weeks of gestation. The plasma is cryopreserved at moderately low temperatures (–80 °C). An analysis of the outcomes of pregnancy and childbirth was made. At stage 2, 50 patients were included in the study: group 1 (main) – 29 patients with a confirmed diagnosis of fetal growth retardation; group 2 (control) – 21 women with pregnancy without complications. In blood plasma, the content of 40 free amino acids, their derivatives and metabolites were determined by high-performance liquid chromatography. Results. Using the method of logistic regression, a formula was obtained, including the determination of the concentration of taurine and asparagine in blood plasma in the 1st trimester of pregnancy, when calculating the prognostic index p>0.55, fetal growth retardation is predicted, and at p<0.55, a conclusion is made about the low risk of developing this complication. Conclusion. A method for predicting fetal growth retardation with a sensitivity of 93% and a specificity of 90% has been proposed.
Mathematical modelling of ocular epithelial transport: a review
Authors: Dvoriashyna Mariia , Foss Alexander , Gaffney Eamonn , Repetto Rodolfo
Published: November 2023

Purpose: Ocular epithelial layers are fundamental for the physiology of the eye as they regulate water transport. The purpose of this review is to discuss the existing mathematical models of water transport across these layers.
Methods: We detail the physical mechanisms that can induce water transport across epithelial layers and describe how they can be mathematically modelled.
Results: We consider 3 ocular epithelial layers. The first is the epithelium of the ciliary processes, which is responsible for aqueous humour production. The second is the corneal endothelium (functionally an epithelium), which plays a key role in maintaining the delicate hydration state of the cornea. The third is the retinal pigment epithelium, which actively removes water from the retina by pumping it into the choroid.
Conclusion: Owing to the difficulty of obtaining direct measurements of water fluxes across epithelial layers, mathematical models can significantly improve our understanding of this field. For instance, they can help develop insight and predictive capability concerning the role of different ion channels, transporters, exchangers, and pumps, as well as carbon dioxide hydrolysis, in ocular water transport processes. Likewise, they can elucidate the importance of the various mechanisms and associated parameters that are involved.
Application of the averages and ranges method for the evaluation of the measurement system of an automotive component manufacturing company
Authors: GONZÁLEZ-SÓBAL Martín , FLORES-BÁEZ Dulce María , SOLÍS-JIMÉNEZ Miguel Ángel
Published: November 2023

Objective: To evaluate the degree of reliability of the measurement system of a company that manufactures automotive components, guaranteeing the quality of the products supplied to its customers.

Methodology: The development of the research is based on four phases: diagnostic evaluation, definition of methods and resources, development of the R&R and analysis of results. These studies were developed based on the MSA applying the method of means and ranges.

Contribution: The results of the research determine that the current measurement system is adequate with respect to the processes analyzed, however, improvements are required to achieve the quality standards demanded by the automotive industry. Within the measurement system, 21.48% corresponds to the repeatability component, which is attributable to the measuring instrument; the reproducibility component has 5.818% and the GR&R interaction component represents 22.258%, which is within the standard recommended by the AIAG; however, this will serve as a basis for the company to orient its processes towards an improvement that allows a percentage of less than 10%.
НОРМАТИВНО-ПРАВОВЕ ЗАБЕЗПЕЧЕННЯ ПУБЛІЧНОЇ ПОЛІТИКИ У СФЕРІ ЗБЕРЕЖЕННЯ, РОЗВИТКУ ТА ВИКОРИСТАННЯ КУЛЬТУРНОЇ СПАДЩИНИ
Authors: Гаврашенко Aлiнa
Published: November 2023

Статтю присвячено проблематиці нормативно-правового забезпечення публічної політики у сфері, розвитку та використання культурної спадщини. Досліджено сучасний стан законодавства у сфері збереження, розвитку та використання культурної спадщини. Наведено огляд відповідних напрацювань, що здійснюються у зазначеному напрямку. Досліджено європейський досвід та запропоновано рекомендації для практичного використання в контексті нормативно-правового забезпечення щодо збереження, розвитку та використання культурної спадщини. Визначено, що наразі Україна не має єдиного комплексного нормативно-правового акту, який враховуватиме всі ключові правові питання, пов'язані з культурною спадщиною, а багаторічне зволікання із приєднанням України до базових міжнародних конвенцій, стало наслідком помітного відставання від загальноєвропейських нормативно-правових тенденцій щодо спадщини.

В контексті інтеграції українського культурного простору в загальноєвропейський культурний ландшафт надзвичайної актуальності набуває культура та все, що з нею пов’язано. Досліджено та доведено, що зміцнення культурного потенціалу та реформування нормативно-правового забезпечення сфери культури сприятиме підвищенню інвестиційної привабливості України та становлять основу для стійкого соціально-економічного розвитку в країні. Правові основи суспільних відносин у цій сфері як об’єкта публічного управління в цілому характеризуються повнотою та системністю, однак, в той же час потребують подальшого удосконалення, як необхідної передумови підвищення рівня ефективної діяльності органів влади.
Geoinformation mapping in educational programs of the Department of Geography and Cartography of Kazan University
Authors: Panasyuk Mikhail , Pudovik Elena , Malganova Irina
Published: November 2023

The article describes educational programs of Geography and Cartography Department of Kazan (Volga Region) Federal University — bachelor’s and master’s degree programs in the field of “Cartography and Geoinformatics”. The most important professional competencies in the field of geoinformatics mapping are enlightened, including those aimed at improving methodological skills and abilities related to the processing and analysis of spatial data, obtained from various sources. The main feature of the developed system of courses on geoinformation mapping is the variety of modern educational and geoinformation technologies. Due to them, each course provides an opportunity to master a wide range of problem-solving skills in the environment of various geoinformation systems, and can be used for both full-time and online learning forms of education. The effectiveness of educational programs is revealed, inter alia, through interaction with employers. Among partners of Geography and Cartography Department of Kazan Federal University are State Budgetary Institution “Spatial Data Foundation of the Republic of Tatarstan”, Innopolis University, Municipal Budgetary Institution “Institute of City Development” (Kazan), State Budgetary Institution “Institute of Spatial Planning of the Republic of Tatarstan”, Limited Liability Company “GC-group”, etc.; companies providing consulting services and performing expertise—Limited Liability Company “Geoconsulting”, Limited Liability Company “Expert Consulting Center ‘Industrial Safety’ ”, Limited Liability Company “Multifunctional Engineering Center”, large companies working in the field of technical inventory of real estate, surveying, land management, such as Joint-Stock company “Bureau of Technical Inventory and Cadastral Works of the Republic of Tatarstan”; Ministries of Construction, Agriculture and Food, Forestry of the Republic of Tatarstan and a number of other organizations and enterprises.
Quantitative assessment of erosional soil loss in various areas of the arable slope (Upper Oka Basin)
Authors: Trofimetz Lyubov , Panidi Evgeny , Kochurov Boris , Chaadaeva Natalya , Tyapkina Angela , Saraeva Anna , Tarasov Arkady , Barkalov Aleksandr , Petelko Anatoly
Published: November 2023

The paper presents the results of detailed erosional study of soil loss observed during the post-Chernobyl period. Radiocaesium method was applied in the experimental area located in Oryol district of the Oryol region (Upper Oka basin). The humidification conditions analysis was conducted for recent decades (2021–2022), an increase in the groundwater supply of rivers and lakes was observed. It was found that intense April precipitation has a strong erosional impact onto the soil cover, despite the decrease in the volume of meltwater in recent years. The analysis of temperature conditions confirmed the warming of the winter season in the last decade and in 2022, erosional potential of meltwater has almost halved. Intense precipitation in September and October of 2021, a warm winter and intense rains in April 2022 caused intensive soil washing in spring. The inter-aggregate adhesion is disrupted in the soil after freezing and consequent thawing, so the intense spring precipitation forms a soil washout. Soil runoff in the studied area is carried out by a complex system of furrows and erosional ravines. The article describes the main approaches applied to develop a semi-empirical methodology for erosional soil loss estimation, and the equations obtained on its basis for six zones of studied area differing in the structure of the surface. The methodology is based upon the in-situ data collected by authors in 2014–2017. The dependences of caesium-137 specific activity on morphometric variables (namely catchment area and profile curvature) are estimated. The reference value of caesium-137 specific activity was establish reducing the results of layer-by-layer in-depth measurements within three block elevations on the watershed. The equations used for the calculation of soil runoff amount were applied to produce a grid map of the soil runoff intensity for the sloping surface of the southern exposure. Estimation of the soil runoff (provided under the conditions of 25 cm arable layer depth and 1110 g/m³ soil material density) shows that in the studied area of gray forest heavy loam soils, the soil runoff value varies from 5 t/ha/year to more than 20 t/ha/year. The proposed methodology can be refined by enlarging the (spatial) scale of research.
GIS of hydrological and environmental safety of the Selenga River Basin: structure, content and functionality
Authors: Pyankov Sergey , Garmaev Endon , Abdullin Rinat , Shikhov Andrey , Aurzhanaev Alexander , Chernykh Vladimir , Sudnitsyna Tatyana
Published: November 2023

The transboundary basin of the Selenga river (the main tributary of Lake Baikal) is characterized by widespread and frequent hazardous events of climatic, hydrological and geomorphological nature. To integrate, display and analyze various data on these events, as well as provide them to the scientific community, a web GIS “Hydrological and environmental safety of the Selenga river basin” has been developed. The structure of the web GIS includes layers and databases that characterize the average long-term and extreme climatic and hydrological conditions in the basin, hazardous processes of hydrological and geomorphological nature, as well as their negative impact on landscapes, settlements and infrastructure. Both external databases and services (long-term weather and hydrological data series, WorldClim 2.0 database, ERA5-Land reanalysis and other) and previously unpublished result of the authors’ studies were used for web GIS content. The web-GIS development is based on open source software and libraries. A feature of the web GIS in comparison with previously published analogues for other river’s basins is an information rich climate section, which allows to assess various manifestations of the observed climate change in the Selenga river basin. Also, the data on several potentially hazardous natural processes like mudflows or aufeis formation are published on the service for the first time. Prospects for the development and improvement of the web GIS are associated with the publication of new GIS layers and databases compiled by the authors, with the development of hazard and risk assessment maps for various types of hazardous hydrological events. In addition, climatic characteristics based on modern ERA5 reanalysis and future climate projections for the 21st century are planned to be published.
Embarazo ectópico en cicatriz de cesárea: reporte de un caso
Authors: Fernandez-Navarro Manuel , Pinillos-Peralta Mia , Mires-Montoro Vanessa , Urbina-Gordillo Cesar
Published: November 2023

La gestación extrauterina se denomina embarazo ectópico, esta es una emergencia obstétrica del primer trimestre, que cada vez está teniendo una incidencia mayor. Una de las localizaciones en las que se pueden generar estas gestaciones es la cicatriz de cesárea previa, lo cual supone un reto para el ginecólogo tratante debido a su dificultad diagnóstica y opciones terapéuticas. Se reporta el caso de una paciente de 37 años que ingresó por el servicio de emergencia con 6 semanas de amenorrea, y con el antecedente de 2 cesáreas. Se le realizó un legrado uterino que se complicó y terminó en la realización de una histerectomía. El embarazo ectópico en cicatriz de cesárea es raro, sin embargo, es importante pensar en esta opción diagnóstica en gestantes con sangrado en el primer trimestre con antecedente de cesárea para poder buscar signos ecográficos en la evaluación.