bluuebunny/crossref_metadata_2025 huggingface dataset:
Wedge Resection and Optimal Solutions for Invasive Pulmonary Fungal Infection and Long COVID Syndrome—A Case Report and Brief Literature Review
Authors: Mosteanu Ioana-Madalina , Mahler Beatrice , Parliteanu Oana-Andreea , Stoichita Alexandru , Matache Radu-Serban , Marghescu Angela-Stefania , Filip Petruta-Violeta , Mota Eugen , Vladu Mihaela Ionela , Mota Maria
Published: April 2024

A rise in fungal infections has been observed worldwide among patients with extended hospital stays because of the severe infection caused by the new coronavirus pandemic. A 62-year-old female patient was admitted with a severe form of Coronavirus disease 2019 (COVID-19) and spent four weeks in the intensive care unit (ICU) requiring mechanical ventilation support before being moved to a tertiary hospital for further testing. Aspergillus fumigatus filamentous fungus, Candida spp., and positive bacteriology for multidrug-resistant Klebsiella pneumoniae and Proteus mirabilis were identified by bronchial aspirate cultures. The patient’s progress was gradually encouraging while receiving oral antifungal and broad-spectrum antibiotic therapy along with respiratory physical therapy; but ultimately, thoracic surgery was necessary. Long-lasting tissue damage and severe, persistent inflammatory syndrome were the two main pathophysiological mechanisms that led to significant outcomes regarding lung lesions that were rapidly colonized by fungi and resistant flora, cardiac damage with sinus tachycardia at the slightest effort, and chronic inflammatory syndrome, which was characterized by marked asthenia, myalgias, and exercise intolerance.
Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data
Authors: Yu Haiying , Yang Qianhua , Jiang Shouzheng , Zhan Bao , Zhan Cun
Published: April 2024

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.
Generative Adversarial Network and Mutual-Point Learning Algorithm for Few-Shot Open-Set Classification of Hyperspectral Images
Authors: Xu Tuo , Wang Ying , Li Jie , Du Yuefan
Published: April 2024

Existing approaches addressing the few-shot open-set recognition (FSOSR) challenge in hyperspectral images (HSIs) often encounter limitations stemming from sparse labels, restricted category numbers, and low openness. These limitations compromise stability and adaptability. In response, an open-set HSI classification algorithm based on data wandering (DW) is introduced in this research. Firstly, a K-class classifier suitable for a closed set is trained, and its internal encoder is leveraged to extract features and estimate the distribution of known categories. Subsequently, the classifier is fine-tuned based on feature distribution. To address the scarcity of samples, a sample density constraint based on the generative adversarial network (GAN) is employed to generate synthetic samples near the decision boundary. Simultaneously, a mutual-point learning method is incorporated to widen the class distance between known and unknown categories. In addition, a dynamic threshold method based on DW is devised to enhance the open-set performance. By categorizing drifting synthetic samples into known and unknown classes and retraining them together with the known samples, the closed-set classifier is optimized, and a (K + 1)-class open-set classifier is trained. The experimental results in this research demonstrate the superior FSOSR performance of the proposed method across three benchmark HSI datasets.
Time-Series Cross-Radiometric Calibration and Validation of GF-6/WFV Using Multi-Site
Authors: Wang Yingxian , Liu Yaokai , Zhao Weiwei , Zeng Jian , Wang Huixian , Wang Renfei , Xu Zhaopeng , Han Qijin
Published: April 2024

The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these quantitative applications. Currently, the absolute radiometric calibration of GF-6/WFV relies primarily on vicarious calibration conducted by the China Center for Resources Satellite Data and Application (CRESDA). However, annual vicarious calibration may not adequately capture the radiometric performance of GF-6/WFV due to performance degradation. Therefore, increasing the frequency of on-orbit radiometric calibration throughout the lifetime of GF-6/WFV is essential. This study proposes a method for conducting long-term cross-radiometric calibrations of GF-6/WFV by taking the multispectral imager (MSI) onboard the Sentinel-2 satellite as a reliable reference sensor and the sites from RadCalNet as reference ground targets. Firstly, we conducted 62 on-orbit cross-radiometric calibrations of GF-6/WFV since its launch by tracking with the Sentinel-2/MSI sensor after correcting the discrepancy spectrum and solar zenith angle. Then, validation of cross-radiometric calibration results against RadCalNet products indicated an average absolute relative error between 3.55% and 4.64%. Cross-validation with additional reference sensors, including Landsat-8/OLI and MODIS, confirmed the reliability of calibration, demonstrating relative differences from GF-6/WFV of less than 5%. Furthermore, the overall uncertainty of the cross-radiometric calibration was estimated to be from 4.08% to 4.89%. Finally, trend analysis of the time-series radiometric performance was also conducted and revealed an annual degradation rate ranging from 0.57% to 2.31%. This degradation affects surface reflectance retrieval, introducing a bias of approximately 0.0073 to 0.0084. Our findings highlight the operational effectiveness of the proposed method in achieving long-time-series on-orbit radiometric calibration and degradation monitoring of GF-6/WFV. The study also demonstrates that the radiometric performance of GF-6/WFV is relatively stable and suitable for further quantitative applications, especially for long-term monitoring applications.
GPS Data and Machine Learning Tools, a Practical and Cost-Effective Combination for Estimating Light Vehicle Emissions
Authors: Rivera-Campoverde Néstor Diego , Arenas-Ramírez Blanca , Muñoz Sanz José Luis , Jiménez Edisson
Published: April 2024

This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling vehicles in Ecuador are acquired using a data logger with GPS included, and emissions are measured using a PEMS in six RDE tests with two standardized routes for each vehicle. The data obtained on Route 1 are used to estimate the gears used during driving using the K-means algorithm and classification trees. Then, the relative importance of driving variables is estimated using random forest techniques, followed by the training of ANNs to estimate CO2, CO, NOX, and HC. The data generated on Route 2 are used to validate the obtained ANNs. These models are fed with a dataset generated from 324, 300, and 316 km of random driving for each type of vehicle. The results of the model were compared with the IVE model and an OBD-based model, showing similar results without the need to mount the PEMS on the vehicles for long test drives. The generated model is robust to different traffic conditions as a result of its training and validation using a large amount of data obtained under completely random driving conditions.
Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm
Authors: Hassan Sunzid , Wang Lingxiao , Mahmud Khan Raqib
Published: April 2024

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively.
Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models
Authors: Zaylaa Amira J. , Kourtian Sylva
Published: April 2024

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.
A Comprehensive Review of Vision-Based 3D Reconstruction Methods
Authors: Zhou Linglong , Wu Guoxin , Zuo Yunbo , Chen Xuanyu , Hu Hongle
Published: April 2024

With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medicine. The rapid development of 3D reconstruction is becoming inevitable. This survey categorizes the various methods and technologies used in 3D reconstruction. It explores and classifies them based on three aspects: traditional static, dynamic, and machine learning. Furthermore, it compares and discusses these methods. At the end of the survey, which includes a detailed analysis of the trends and challenges in 3D reconstruction development, we aim to provide a comprehensive introduction for individuals who are currently engaged in or planning to conduct research on 3D reconstruction. Our goal is to help them gain a comprehensive understanding of the relevant knowledge related to 3D reconstruction.
Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC
Authors: Tan Wei , Wang Mengfei , Ma Ke
Published: April 2024

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.
Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space
Authors: Chiu Chien-Ching , Lee Yang-Han , Chen Po-Hsiang , Shih Ying-Chen , Hao Jiang
Published: April 2024

In this paper, we introduce a novel artificial intelligence technique with an attention mechanism for half-space electromagnetic imaging. A dielectric object in half-space is illuminated by TM (transverse magnetic) waves. Since measurements can only be made in the upper space, the measurement angle will be limited. As a result, we apply a back-propagation scheme (BPS) to generate an initial guessed image from the measured scattered fields for scatterer buried in the lower half-space. This process can effectively reduce the high nonlinearity of the inverse scattering problem. We further input the guessed images into the generative adversarial network (GAN) and the self-attention generative adversarial network (SAGAN), respectively, to compare the reconstruction performance. Numerical results prove that both SAGAN and GAN can reconstruct dielectric objects and the MNIST dataset under same measurement conditions. Our analysis also reveals that SAGAN is able to reconstruct electromagnetic images more accurately and efficiently than GAN.
A Pilot Study into the Use of Qualitative Methods to Improve the Awareness of Barriers to Sustainable Medical Waste Segregation within the United Kingdom’s National Health Service
Authors: Webb Christina , Anguilano Lorna , Schmidt Rivera Ximena
Published: April 2024

Within the United Kingdom, most medical waste is incorrectly classified as hazardous and disposed of via incineration or alternative treatment. Currently, no research has been conducted on why such a large quantity of medical waste is erroneously segregated. This pilot study explores the barriers to correct segregation with the aim to decrease the volume of incinerated waste by investigating why medical waste is wrongly identified as hazardous. No previous data are available to compare results, and so this study demonstrates the significance of using qualitative methods (questionnaires and focus groups) to bring awareness to issues faced within medical facilities when segregating waste. The low availability of different bins as well as lack of space and the healthcare workers’ busy schedules were identified as main reasons for poor segregation. Bins were sparsely placed, and staff lacked time to find the appropriate one leading to incorrect segregation of non-hazardous waste. Lack of information around whether a material was recyclable or not led to less recycled waste. When ways to engage with this issue were discussed, most medical staff favoured quick forms of information provision, such as posters, whereas a participant proclaimed longer hands-on style sessions as more effective. The findings of this study provide evidence that governmental strategies focused on sustainable medical waste management should direct their attention to the placement and availability of bins, whilst including ‘on-the-ground’ personnel in their decision making. This pilot study showed the value in using qualitative methods when current data are lacking and can be repeated by other healthcare facilities to collectively grow a greater awareness of the sustainability issues faced by the UK healthcare waste management system.
Youth Perspectives on Collaborative Consumption: A Study on the Attitudes and Behaviors of the Romanian Generation Z
Authors: Bulin Daniel , Gheorghe Georgică , Kanovici Adrian Lucian , Curteanu Adrian Bogdan , Curteanu Oana-Diana , Dobre Robert-Ionuţ
Published: April 2024

With the emergence of the sharing economy, a significant change in consumer behavior can be observed worldwide, which has a considerable impact on various industries. The rise of the sharing economy has changed the way people experience transport services, with ridesharing being a catalyst for change. In Romania, the debut of Uber in 2015 sparked controversy and led to legal regulations that were adapted to local specificities, highlighting the adaptability of ridesharing platforms to different legal frameworks. In the context of this development, the views and perceptions of Generation Z will be crucial in determining the direction in which this conflict between disruptive models and traditional players in the transport sector develops. The article deals with business models based on collaborative consumption, with a focus on ridesharing, and examines the attitudes, perceptions, and behavior of Romanian youths (aged 18–26) towards these models. The aim of the study is to determine the opinion of young Romanians on collaborative consumption in transport services—ridesharing (Uber case)—and their attitude towards the ethical controversies related to Uber’s business model. A quantitative research approach was chosen, and an exploratory study was conducted using a questionnaire, with the non-probabilistic sample consisting of relevant observation units aged 18–26 years. The results show that almost 90% of the young Romanians surveyed use Uber and are satisfied with the quality, convenience, and speed of the service. Despite the positive attitude, there is a paradoxical tendency among respondents to regulate ridesharing services in a similar way to traditional taxis. Ethical considerations show that respondents tend to neutralize perceptions and justify the emergence of new models as normal and beneficial for competition and consumers.
Assessment of the Hydrochemical Characteristics of the Carbon Observational Site ‘Carbon-Sakhalin’ (Aniva Bay, Sea of Okhotsk)
Authors: Pishchalnik Vladimir , Myslenkov Stanislav , Latkovskaya Elena , Arkhipkin Victor
Published: April 2024

Following a tendency of many economies to shift towards carbon neutrality, there came the necessity for certain regions to be assessed in terms of their greenhouse gas emissions from the ocean. A carbon polygon was created in Sakhalin Oblast in order to evaluate the carbon balance of this marine ecosystem in a sub-arctic region, with the possibility of deploying carbon farms for additional CO2 absorption. To obtain such an assessment, it seems crucial to analyze hydrochemical parameters that reflect the situation of the marine environment in Aniva Bay as a basis of the carbon polygon. The article presents the results of the analysis of hydrochemical parameters in Aniva Bay waters and their spatial and seasonal variability. This research was based on available published sources and measurement databases for the period of 1948–1994. Additionally, the review uses hydrochemical data for Aniva Bay in 2001–2013 weather station data for the period of 2008–2023 and weather station data for 2008–2023. Some tendencies were discovered for spatial and temporal distributions of oxygen, pH, and biogenic matter (inorganic phosphorus, inorganic nitrogen, silicon). In surface layers, the mean oxygen year maximum (9.1 mg/L) is registered with the beginning of photosynthesis, i.e., immediately after the ice melting in April. The highest pH values 8.26 are registered in the euphotic layer in May. The lowest pH values was in August (7.96) in the near-bottom layer. The maximum annual P-PO4 registered on the surface (>18 µg/L) immediately after ice melting, with a minimum (7.17 µg/L) at the end of July. Si-SiO3 concentrations have two maximums: at the end of June and at the beginning of October. N-NO2 concentration on the surface is >2 µg/L in mid-July and on the 50 m depth it is >3.5 µg/L in mid-September. Some spatial patterns of hydrochemical parameters were shown based on the analysis of maps.
How to Improve an Enterprise’s Innovation Capability from the Perspective of High- and Low-Level Enterprises Using Fuzzy-Set Qualitative Comparative Analysis
Authors: Bai Xiaoyu , Xiong Shengxu , Zhou Zhe , Liu Xin
Published: April 2024

An enterprise’s capability is based on the quantity and collocation pattern of the heterogeneous resources it possesses. Innovation resources are the source of enterprise innovation capability. However, there is still a “black box” problem of the impact of the intensity of government support on enterprise innovation. Therefore, it is necessary to study high- or low-level enterprise innovation capability by combining the internal and external factors of the enterprise—the background characteristics of R&D personnel and the degree of government support. Based on the configuration perspective, this study uses the qualitative comparative analysis (fsQCA) method to conduct a comparative analysis of the innovation capabilities of enterprises in five time windows over six years with the longitudinal database of China’s industrial enterprises whose operating income exceeds RMB 20 million from 2010 to 2015. This paper summarizes two ways of realizing high-level enterprise innovation capability: female and highly educated R&D personnel type, and highly educated R&D personnel and high government investment type. The enterprise innovation capability is affected simultaneously by multiple conditional variables, and the impact of each conditional variable on enterprise innovation capability has a trend. Further, it analyzes the impact of every antecedent variable comparing high- to low-level enterprise innovation.
Sustainable Aviation Fuel Production through Catalytic Processing of Lignocellulosic Biomass Residues: A Perspective
Authors: Ribeiro Lucília Sousa , Pereira Manuel Fernando Ribeiro
Published: April 2024

Currently, the transportation sector represents about one third of the total energy consumed in the world, most of this energy being obtained almost exclusively from oil. However, the world is changing, as well as the aviation industry. Since lignocellulosic biomass is a low-cost feedstock that does not compete with food, it has drawn great attention as one of the most attractive alternatives to replace fossil feedstocks for the production of fuels. Renewable jet fuels could have a significant impact on lowering greenhouse gas emissions and providing a long-term sustainable alternative to petroleum-derived fuels. However, the catalytic upgrading of lignocellulosic residues in industry still remains a big challenge and the development of highly integrated systems that allow the direct conversion of lignocellulosic wastes is essential to achieve that goal. The importance of renewable jet fuels and the potential of lignocellulosic biomass have already been extensively reviewed. However, this work presents a new perspective on the main catalytic routes and challenges for the sustainable production of aviation fuels from biomass wastes.
Effects of Orange Waste Extract Produced by Hydrodynamic Cavitation on the Germination of Chenopodium album L. and Lactuca sativa L.
Authors: Ugolini Francesca , Crisci Alfonso , Baronti Silvia , Cencetti Gabriele , Dal Prà Aldo , Albanese Lorenzo , Michelozzi Marco , Zabini Federica , Meneguzzo Francesco
Published: April 2024

A byproduct from orange juice processing known as pastazzo represents a significant organic waste stream. Rich in essential oils and known for its inhibitory effect on plant germination, pastazzo could serve as a valuable input for agricultural purposes. This study assesses the effects of a 40% w/v orange pastazzo water extract (OPWE) produced by hydrodynamic cavitation on the germination of two species, one of economic interest (Lactuca sativa L.) and one common weed (Chenopodium album L.). Three dilutions of OPWE in water (25%; 50%; 75%) were compared to a control treatment in four experiments, using (i) seeds in Petri dishes; (ii) seeds in commercial substrate; (iii) C. album seeds and transplanted L. sativa in commercial substrate; and (iv) other weeds in an open-field plantation of L. sativa. Highly rich in limonene, OPWE applied at higher concentrations in Petri dishes caused the effective inhibition of germination in C. album and a germination delay in L. sativa. Similar results were observed in the germination of the two species in commercial substrate, with none of the dilutions affecting L. sativa biomass. In the field experiment, despite a relatively low number of weeds in the control treatment, higher OPWE concentrations reduced the number of grasses and forbs, largely confirming the inhibitory effects. We conclude that OPWE produced with hydrodynamic cavitation, an efficient and affordable method of extraction, represents an effective crop treatment due to the species-specific effects of its constituent limonene on plant germination. Further tests are essential to understand the extent to which OPWE interacts with other species and types of substrate.
Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study
Authors: Oyucu Saadin , Dümen Sezer , Duru İremnur , Aksöz Ahmet , Biçer Emre
Published: April 2024

Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.
Numerical Analysis of the Discrete MRLW Equation for a Nonlinear System Using the Cubic B-Spline Collocation Method
Authors: Liu Xingxia , Zhang Lijun , Sun Jianan
Published: April 2024

By employing the cubic B-spline functions, a collocation approach was devised in this study to address the Modified Regularized Long Wave (MRLW) equation. Then, we derived the corresponding nonlinear system and easily solved it using Newton’s iterative approach. It was established that the cubic B-spline collocation technique exhibits unconditional stability. The dynamics of solitary waves, including their pairwise and triadic interactions, were meticulously investigated utilizing the proposed numerical method. Additionally, the transformation of the Maxwellian initial condition into solitary wave formations is presented. To validate the current work, three distinct scenarios were compared against the analytical solution and outcomes from alternative methods under both L2- and L∞-error norms. Primarily, the key strength of the suggested scheme lies in its capacity to yield enhanced numerical resolutions when employed to solve the MRLW equation, and these conservation laws show that the solitary waves have time and space translational symmetry in the propagation process. Finally, this paper concludes with a summary of our findings.
VP4 Mutation Boosts Replication of Recombinant Human/Simian Rotavirus in Cell Culture
Authors: Valusenko-Mehrkens Roman , Schilling-Loeffler Katja , Johne Reimar , Falkenhagen Alexander
Published: April 2024

Rotavirus A (RVA) is the leading cause of diarrhea requiring hospitalization in children and causes over 100,000 annual deaths in Sub-Saharan Africa. In order to generate next-generation vaccines against African RVA genotypes, a reverse genetics system based on a simian rotavirus strain was utilized here to exchange the antigenic capsid proteins VP4, VP7 and VP6 with those of African human rotavirus field strains. One VP4/VP7/VP6 (genotypes G9-P[6]-I2) triple-reassortant was successfully rescued, but it replicated poorly in the first cell culture passages. However, the viral titer was enhanced upon further passaging. Whole genome sequencing of the passaged virus revealed a single point mutation (A797G), resulting in an amino acid exchange (E263G) in VP4. After introducing this mutation into the VP4-encoding plasmid, a VP4 mono-reassortant as well as the VP4/VP7/VP6 triple-reassortant replicated to high titers already in the first cell culture passage. However, the introduction of the same mutation into the VP4 of other human RVA strains did not improve the rescue of those reassortants, indicating strain specificity. The results show that specific point mutations in VP4 can substantially improve the rescue and replication of recombinant RVA reassortants in cell culture, which may be useful for the development of novel vaccine strains.
Structure-Based Optimization of One Neutralizing Antibody against SARS-CoV-2 Variants Bearing the L452R Mutation
Authors: Chen Yamin , Zha Jialu , Xu Shiqi , Shao Jiang , Liu Xiaoshan , Li Dianfan , Zhang Xiaoming
Published: April 2024

Neutralizing antibodies (nAbs) play an important role against SARS-CoV-2 infections. Previously, we have reported one potent receptor binding domain (RBD)-binding nAb Ab08 against the SARS-CoV-2 prototype and a panel of variants, but Ab08 showed much less efficacy against the variants harboring the L452R mutation. To overcome the antibody escape caused by the L452R mutation, we generated several structure-based Ab08 derivatives. One derivative, Ab08-K99E, displayed the mostly enhanced neutralizing potency against the Delta pseudovirus bearing the L452R mutation compared to the Ab08 and other derivatives. Ab08-K99E also showed improved neutralizing effects against the prototype, Omicron BA.1, and Omicron BA.4/5 pseudoviruses. In addition, compared to the original Ab08, Ab08-K99E exhibited high binding properties and affinities to the RBDs of the prototype, Delta, and Omicron BA.4/5 variants. Altogether, our findings report an optimized nAb, Ab08-K99E, against SARS-CoV-2 variants and demonstrate structure-based optimization as an effective way for antibody development against pathogens.
Oncolytic Adenovirus for the Targeting of Paclitaxel-Resistant Breast Cancer Stem Cells
Authors: Robert Sacha , Roman Ortiz Natasha Ivelisse , LaRocca Christopher J. , Ostrander Julie Hanson , Davydova Julia
Published: April 2024

Adjuvant systemic therapies effectively reduce the risk of breast cancer recurrence and metastasis, but therapy resistance can develop in some patients due to breast cancer stem cells (BCSCs). Oncolytic adenovirus (OAd) represents a promising therapeutic approach as it can specifically target cancer cells. However, its potential to target BCSCs remains unclear. Here, we evaluated a Cox-2 promoter-controlled, Ad5/3 fiber-modified OAd designed to encode the human sodium iodide symporter (hNIS) in breast cancer models. To confirm the potential of OAds to target BCSCs, we employed BCSC-enriched estrogen receptor-positive (ER+) paclitaxel-resistant (TaxR) cells and tumorsphere assays. OAd-hNIS demonstrated significantly enhanced binding and superior oncolysis in breast cancer cells, including ER+ cells, while exhibiting no activity in normal mammary epithelial cells. We observed improved NIS expression as the result of adenovirus death protein deletion. OAd-hNIS demonstrated efficacy in targeting TaxR BCSCs, exhibiting superior killing and hNIS expression compared to the parental cells. Our vector was capable of inhibiting tumorsphere formation upon early infection and reversing paclitaxel resistance in TaxR cells. Importantly, OAd-hNIS also destroyed already formed tumorspheres seven days after their initiation. Overall, our findings highlight the promise of OAd-hNIS as a potential tool for studying and targeting ER+ breast cancer recurrence and metastasis.
Changes in Drought Characteristics in the Yellow River Basin during the Carbon-Neutral Period under Low-Emission Scenarios
Authors: Li Xunyu , Jiao Yang , Liu Jieyu
Published: April 2024

Droughts have a severe impact on the environment and social economy, and predicting their future changes is challenging due to significant uncertainties in climate change and human activities. Many countries have pledged to achieve carbon neutrality to limit global warming; however, few studies have focused on drought changes during the carbon-neutral period. Here, we analyzed the variations in drought characteristics across the Yellow River Basin (YRB) during the carbon-neutral period under two low-emission scenarios from 7 CMIP6 model outputs. The results show that the temperature and precipitation will increase significantly during the 2015–2100 period under both SSP1-1.9 and SSP1-2.6 scenarios. Compared to the historical period (1979–2014), the hydrological drought frequency is projected to decrease by 15.5% (13.0–18.1%), while drought severity is expected to increase by 14.4% (13.2–15.7%) during the carbon-neutral period. Meteorological droughts exhibit a similar changing trend, although the results vary between different regions. In general, more severe hydrological droughts may occur in the southern YRB in the carbon-neutral period under low-emission scenarios. This study has implications for future drought mitigation within the Yellow River Basin.
Hydrodynamic Characterization of Carbonate Aquifers Using Atypical Pumping Tests without the Interruption of the Drinking Water Supply
Authors: Rusi Sergio , Di Curzio Diego , Di Giovanni Alessia
Published: April 2024

The Gran Sasso carbonate aquifer is the largest and most productive in the Apennines. Its hydrogeological structure has been studied since the middle of the last century for the springs’ characterization for drinking purposes and for a motorway tunnel. Meanwhile, its hydrodynamic parametrization is less developed and has been limited to monitoring the discharge and chemical and isotopic parameters. Secondary porosity characterizes the aquifer, and an underlying impermeable marly complex represents the basal aquiclude. It might appear inappropriate to characterize the hydraulic properties via pumping tests, as their reliability has been proven in homogeneous and isotropic media. However, the high extent of the aquifer, the wells’ location, the scarcity of information available and the lack of alternatives has forced the estimation of hydrodynamic parameters as in porous aquifers and the experimental testing of the aquifer, especially in maximum pumping conditions, for a possible exploitation increase. Since aquifer testing was performed during the normal well field’s activities, it was not possible to perform typical tests. Therefore, the step-drawdown test was conducted by turning on an increasing number of wells over time and keeping the observation points fixed. As results, a mean hydraulic conductivity of 5 × 10−3 m/s and a mean transmissivity of 0.3 m2/s were established without interrupting the water supply; meanwhile, the influence radius and flow directions were also estimated.
Coastal Groundwater Bodies Modelling Using Geophysical Surveys: The Reconstruction of the Geometry of Alluvial Plains in the North-Eastern Sicily (Italy)
Authors: Capizzi Patrizia , Martorana Raffaele , Canzoneri Alessandro , Bonfardeci Alessandro , Favara Rocco
Published: April 2024

The integration of various geophysical methodologies is considered a fundamental tool for accurately reconstructing the extent and shape of a groundwater body and for estimating the physical parameters that characterize it. This is often essential for the management of water resources in areas affected by geological and environmental hazards. This work aims to reconstruct the pattern and extent of two groundwater bodies, located in the coastal sectors of the North-Eastern Sicily, through the integrated analysis and interpretation of several geoelectrical, seismic and geological data. These are the Sant’Agata-Capo D’Orlando (SCGWB) and the Barcelona-Milazzo (BMGWB) Groundwater Bodies, located at the two ends of the northern sector of the Peloritani geological complex. These two studied coastal plains represent densely populated and industrialized areas, in which the quantity and quality of the groundwater bodies are under constant threat. At first, the resistivity models of the two groundwater bodies were realized through the inversion of a dataset of Vertical Electrical Soundings (VES), constrained by stratigraphic well logs data and other geophysical data. The 3D resistivity models obtained by spatially interpolating 1D inverse VES models have allowed for an initial recognition of the distribution of groundwater, as well as a rough geological framework of the subsoil. Subsequently, these models were implemented by integrating results from active and passive seismic data to determine the seismic P and S wave velocities of the main lithotypes. Simultaneous acquisition and interpretation of seismic and electrical tomographies along identical profiles allowed to determine the specific values of seismic velocity, electrical resistivity and chargeability of the alluvial sediments, and to use these values to constrain the HVSR inversion. All this allowed us to recognize the areal extension and thickness of the various lithotypes in the two investigated areas and, finally, to define the depth and the morphology of the base of the groundwater bodies and the thickness of the filling deposits.
Coupled DSSAT and HYDRUS-1D Simulation of the Farmland–Crop Water Cycling Process in the Dengkouyangshui Irrigation District
Authors: Zhou Jie , Tian Delong , Shi Haibin , Xu Bing , Zheng Zhonghou , Wang Fan , Wang Guoshuai , Miao Xiangyang
Published: April 2024

(1) Background: Effective water management in agricultural systems poses a significant challenge, particularly in the Dengkouyangshui irrigation district. Inefficiencies and insufficient detail in water usage across crop growth stages have resulted in suboptimal water cycling. Recent infrastructure improvements and technological interventions necessitate a reevaluation of water usage, especially concerning changes in irrigation and seepage dynamics. (2) Methods: This study addresses these concerns by employing an integrated modeling approach that combines the DSSAT with the HYDRUS-1D soil hydrology model to simulate complex interactions among soil, crop growth, and irrigation practices within the district. Observational data were used to calibrate and validate the integrated model, including soil moisture, LAI, and crop yields from the 2022 and 2023 agricultural seasons. (3) Results: The simulation results strongly align with the empirical data, highlighting the ability of the model to capture the intricate dynamics of soil–water–atmosphere–plant interactions. (4) Conclusions: The soil’s retention and moisture-holding characteristics exhibited resilience during periods without water supplementation, with measurable declines in soil moisture at various depths, indicating the soil’s capacity to support crops in water-limited conditions. This study delineates water consumption by maize crops throughout their growth cycle, providing insights into evapotranspiration partitioning and quantifying seepage losses. An in-depth analysis of water balances at different growth stages informs irrigation strategies, suggesting optimal volumes to enhance efficiency during critical crop development phases. This integrative modeling approach is valuable for providing actionable data to optimize the water cycling process and improve agricultural sustainability in the Dengkouyangshui irrigation district.
Mentoring as an effective tool of pedagogical activity in the training of mid-level medical specialists
Authors:   Federal Scientific and Clinical Center for Medical Radiology and Oncology of the Federal Medical and Biological Agency, Ulyanovsk region, Dimitrovgrad, Russia, Katkasova L. G. , Zaitseva A. V. ,   Dimitrovgrad Institute of Engineering and Technology — branch of the Federal State Autonomous Educational Institution of Higher Education «National Research Nuclear University «MIFI», Ulyanovsk region, Dimitrovgrad, Russia
Published: April 2024

The article highlights the system of organization of mentoring in the technical school of the MIFI on the basis of the social and humanitarian department. The main directions in the training of medical students are presented, mentoring models that are implemented during the educational process are considered.
Analysis of practical experience in the development and implementation of standard operating procedures in the activities of nursing medical staff on the base of the FSBHI MRC «Sergievsk mineral waters» FMBA of Russia
Authors:   Federal State Budgetary Healthcare Institution MRC «Sergievsk Mineral Waters» FMBA of Russia, Samara region, Sernovodsk, Russia, Chebotaeva I. N. , Novikova S. V. ,   Federal State Budgetary Healthcare Institution MRC «Sergievsk Mineral Waters» FMBA of Russia, Samara region, Sernovodsk, Russia, Gulyaeva N. V. ,   Federal State Budgetary Healthcare Institution MRC «Sergievsk Mineral Waters» FMBA of Russia, Samara region, Sernovodsk, Russia
Published: April 2024

The use of innovative technologies in the daily practice of a nurse makes her work more professional, ensures safety and convenience when performing basic professional tasks, reduces labor costs, and allows for the implementation of the diagnostic and treatment process quickly and efficiently. Target. To analyze the experience of developing and implementing standard operating procedures for nurses for 2022–2023. Results. The successful development and further implementation of SOPs for various types of activities of nursing staff was completed; solved problems that arose during the development and implementation of SOPs by creating corrective actions with mutual analysis, taking into account the feedback of the team of specialists; the proposed technologies and standard documents can be used as a model by most medical organizations. Conclusion. In the Federal State Budgetary Healthcare Institution MRC «Sergievsk Mineral Waters» FMBA of Russia, as of January 2024, the following have been developed: 121 SOPs, 30 SOPs have been revised. In 2024, 4 SOPs were introduced in medical organizations and continuous work is underway to further develop them.
How HR processes are transformed during remote employment
Authors:   сonsulting agency HR KEYS., Russia, Samara region, Mordyashova A.M.
Published: April 2024

In this article we consider how HR processes in companies have changed or are being transformed when employees are employed remotely in key HR blocks: personnel selection (changed actions of HR specialists, managers and candidates); adaptation and training of personnel; remote support, team communication of remote employees and new forms of feedback; modern online tools for personnel management