bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Lessons on the Internet of Things in Science, Technology, Engineering, and Medical Education with a Remote Lab
Authors: Amador Nelke Sofia , Kohen-Vacs Dan , Khomyakov Michael , Rosienkiewicz Maria , Helman Joanna , Cholewa Mariusz , Molasy Mateusz , Górecka Anna , Gómez-González José-Francisco , Bourgain Maxime , Sagar Athith , Berselli Giovanni , Blank Daniel , Winokur Michael , Benis Arriel
Published: October 2024

Integrating remote Internet of Things (IoT) laboratories into project-based learning (PBL) in higher education institutions (HEIs) while exploiting the approach of technology-enhanced learning (TEL) is a challenging yet pivotal endeavor. Our proposed approach enables students to interact with an IoT-equipped lab locally and remotely, thereby bridging theoretical knowledge with practical application, creating a more immersive, adaptable, and effective learning experience. This study underscores the significance of combining hardware, software, and coding skills in PBL, emphasizing how IoTRemoteLab (the remote lab we developed) supports a customized educational experience that promotes innovation and safety. Moreover, we explore the potential of IoTRemoteLab as a TEL, facilitating and supporting the understanding and definition of the requirements of remote learning. Furthermore, we demonstrate how we incorporate generative artificial intelligence into IoTRemoteLab’s settings, enabling personalized recommendations for students leveraging the lab locally or remotely. Our approach serves as a model for educators and researchers aiming to equip students with essential skills for the digital age while addressing broader issues related to access, engagement, and sustainability in HEIs. The practical findings following an in-class experiment reinforce the value of IoTRemoteLab and its features in preparing students for future technological demands and fostering a more inclusive, safe, and effective educational environment.
Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review
Authors: Liu Linkun , Pu Yujian , Fan Junzhe , Yan Yu , Liu Wenpeng , Luo Kailong , Wang Yiwen , Zhao Guanlin , Chen Tupei , Puiu Poenar Daniel , Huang Hui
Published: October 2024

Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.
Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals
Authors: Ng Gabriel , Gouda Aliaa , Andrysek Jan
Published: October 2024

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters.
Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I
Authors: Kulyukin Vladimir A. , Coster Daniel , Kulyukin Aleksey V. , Meikle William , Weiss Milagra
Published: October 2024

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.
Development and Field Test of Integrated Electronics Piezoelectric Accelerometer Based on Lead-Free Piezoelectric Ceramic for Centrifugal Pump Monitoring
Authors: Kim Byung-Hoon , Jang Dae-Sic , Lee Jeong-Han , Lee Min-Ku , Lee Gyoung-Ja
Published: October 2024

In this study, an Integrated Electronics Piezoelectric (IEPE)-type accelerometer based on an environmentally friendly lead-free piezoceramic was fabricated, and its field applicability was verified using a cooling pump owned by the Korea Atomic Energy Research Institute (KAERI). As an environmentally friendly piezoelectric material, 0.96(K,Na)NbO3-0.03(Bi,Na,K,Li)ZrO3-0.01BiScO3 (0.96KNN-0.03BNKLZ-0.01BS) piezoceramic with an optimized piezoelectric charge constant (d33) was introduced. It was manufactured in a ring shape using a solid-state reaction method for application to a compression mode accelerometer. The fabricated ceramic ring has a high piezoelectric constant d33 of ~373 pC/N and a Curie temperature TC of ~330 °C. It was found that the electrical and physical characteristics of the 0.96KNN-0.03BNKLZ-0.01BS piezoceramic were comparable to those of a Pb(Zr,Ti)O3 (PZT) ring ceramic. As a result of a vibration test of the IEPE accelerometer fabricated using the lead-free piezoelectric ceramic, the resonant frequency fr = 20.0 kHz and voltage sensitivity Sv = 101.1 mV/g were confirmed. The fabricated IEPE accelerometer sensor showed an excellent performance equivalent to or superior to that of a commercial IEPE accelerometer sensor based on PZT for general industrial use. A field test was carried out to verify the applicability of the fabricated sensor in an actual industrial environment. The test was conducted by simultaneously installing the developed sensor and a commercial PZT-based sensor in the ball bearing housing location of a centrifugal pump. The centrifugal pump was operated at 1180 RPM, and the generated vibration signals were collected and analyzed. The test results confirmed that the developed eco-friendly lead-free sensor has comparable vibration measurement capability to that of commercial PZT-based sensors.
Economic Dynamics as the Main Limitation for Agricultural Sustainability in a Colombian Indigenous Community
Authors: Ojeda Cintya , Romero-Leiton Jhoana P. , Mesa Mónica Jhoana , Zapata Juan , Ceballos Alvaro , Ordoñez Solanyi , Benavides Ivan Felipe
Published: October 2024

Ancient agroecological farms, or chagras, of the Kamëntšá Biyá and Kamëntšá Inga indigenous communities in the Sibundoy Valley of Colombia offer valuable insights into the environmental challenges of intensive agriculture and promote sustainable food production. Sustainability indices have been developed to assess farm-level sustainability and enhance agroecological practices; however, data limitations hinder monitoring and correlation with external factors. This study evaluated sustainability indices in Sibundoy Valley chagras using the holistic evaluation system for farming intensification (HESOFI) interview system with 800 randomly selected chagras, assessing economic, agro-environmental, and sociopolitical–cultural dimensions. The endogenous factors considered included areas managed with diversified agricultural systems, the percentage of transformed products, inputs generated by the chagra, and products intended for sale. Exogenous factors included distance to rivers, roads, cities, and vegetation index ratio. The results showed that all chagras fell below the minimum sustainability threshold (80%), with the economic dimension scoring the lowest. Based on these findings, three strategies were proposed to improve the economic scores and overall sustainability indices of these chagras.
Comparative Analysis of the Infrastructure of the City of Astana with a Sociological Survey of the Mental Well-Being of Citizens in the Context of the Sustainable Development of the Urban Agglomeration
Authors: Saginov Kairat , Berdenov Zharas , Inkarova Zhansulu , Kakimzhanov Yersin , Mendybayev Erbolat , Ramazanova Nurgul , Assylbekov Kalibek , Safarov Ruslan , Fomin Ivan
Published: October 2024

Rapid urbanization entails complex problems not only in cities, but also within urban agglomerations. In modern landscape science, the greatest problems are primarily related to the ecological state of urban ecosystems. In this context, the most important task of urbanism is the interdisciplinary study of urban infrastructure in relation to the well-being of inhabitants, with a focus on the sustainable development of urban agglomerations. The aim of this study is to conduct a theoretical analysis of interdisciplinary research on the interactions between humans and the urban environment in the context of intensive urbanization, as well as to be an empirical study of the relationship between the real ecological state of the city of Astana, based on the use of geographical, environmental, cartographic, statistical, sociological, and socio-psychological research methods, aimed at identifying the mental well-being of citizens in correlation with their consumed urban ecosystem services using an associative psychological experiment, socio-psychological survey, and GIS mapping. As a result of this study, the authors have determined that the research hypothesis 1 “Spatial representations of the city’s geoecological state are interrelated with the mental well-being and satisfaction with urban ecosystem services of citizens depending on the area of residence” is confirmed. Additionally, a positive correlation has been noted among the key indicators and criteria of geoecological condition, mental well-being, and satisfaction with urban ecosystem services across different districts of the city.
Energy and Exergy Performance Analysis of Solar-Assisted Thermo-Mechanical Vapor Compression Cooling System
Authors: Al Khiro Hussein A. , Boukhanouf Rabah
Published: October 2024

Air conditioning is vital for indoor comfort but traditionally relies on vapor compression systems, which raise electricity demand and carbon emissions. This study presents a novel thermo-mechanical vapor compression system that integrates an ejector with a conventional vapor compression cycle, incorporating a thermally driven second-stage compressor powered by solar energy. The goal is to reduce electricity consumption and enhance sustainability by leveraging renewable energy. A MATLAB® model was developed to analyze the energy and exergy performance using R1234yf refrigerant under steady-state conditions. This study compares four solar collectors—evacuated flat plate (EFPC), evacuated tube (ETC), basic flat plate (FPC), and compound parabolic (CPC) collectors—to identify the optimal configuration based on the collector area and costs. The results show a 31% reduction in mechanical compressor energy use and up to a 44% improvement in the coefficient of performance (COP) compared to conventional systems, with a condenser temperature of 65 °C, a thermal compression ratio of 0.8, and a heat source temperature of 150 °C. The evacuated flat plate collectors performed best, requiring 2 m2/kW of cooling capacity with a maximum exergy efficiency of 15% at 170 °C, while compound parabolic collectors offered the lowest initial costs. Overall, the proposed system shows significant potential for reducing energy costs and carbon emissions, particularly in hot climates.
Factors Influencing Changes in Social Activity under COVID-19 in Polish Rural Areas
Authors: Chrzanowska Mariola , Kłoczko-Gajewska Anna , Danilowska Alina
Published: October 2024

The COVID-19 pandemic, due to unprecedented countermeasures aimed at limiting social contact, affected many spheres of life, including the forms and scope of social activity. This paper aims to identify the importance of the size and economic type of village, as well as the existence of rural organizations before the pandemic to identify the changes in different forms of social activity among rural area residents during the first and second year of the COVID-19 pandemic in Poland. This research was based on phone interviews (CATI) carried out in December 2022 with village administrators. It was concluded that the village size, economic profile, and the activities of various types of social organizations had very limited influence on changes in social contact during the pandemic. The economic type of the village, both low and high levels of agrarisation, showed a significant relationship with change of only one form of social activity: joint work on a farm. The activities of organizations popular in rural areas such as Volunteer Firefighters’ Brigades or Rural Housewives’ Clubs played an important role in the changes (decrease) in meetings at home and after mass in church.
Research on Path Planning for Intelligent Mobile Robots Based on Improved A* Algorithm
Authors: Wang Dexian , Liu Qilong , Yang Jinghui , Huang Delin
Published: October 2024

Intelligent mobile robots have been gradually used in various fields, including logistics, healthcare, service, and maintenance. Path planning is a crucial aspect of intelligent mobile robot research, which aims to empower robots to create optimal trajectories within complex and dynamic environments autonomously. This study introduces an improved A* algorithm to address the challenges faced by the preliminary A* pathfinding algorithm, which include limited efficiency, inadequate robustness, and excessive node traversal. Firstly, the node storage structure is optimized using a minimum heap to decrease node traversal time. In addition, the heuristic function is improved by adding an adaptive weight function and a turn penalty function. The original 8-neighbor is expanded to a 16-neighbor within the search strategy, followed by the elimination of invalid search neighbor to refine it into a new 8-neighbor according to the principle of symmetry, thereby enhancing the directionality of the A* algorithm and improving search efficiency. Furthermore, a bidirectional search mechanism is implemented to further reduce search time. Finally, trajectory optimization is performed on the planned paths using path node elimination and cubic Bezier curves, which aligns the optimized paths more closely with the kinematic constraints of the robot derivable trajectories. In simulation experiments on grid maps of different sizes, it was demonstrated that the proposed improved A* algorithm outperforms the preliminary A* Algorithm in various metrics, such as search efficiency, node traversal count, path length, and inflection points. The improved algorithm provides substantial value for practical applications by efficiently planning optimal paths in complex environments and ensuring robot drivability.
Semi-Supervised Learning with Close-Form Label Propagation Using a Bipartite Graph
Authors: Peng Zhongxing , Zheng Gengzhong , Huang Wei
Published: October 2024

In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw data by capturing their underlying manifold structures. This bipartite graph, with a sparse and anti-diagonal affinity matrix which is symmetrical, serves as a low-rank approximation of the original graph. Consequently, our algorithm accelerates both the graph construction and label propagation steps. In particular, on the one hand, our algorithm computes the label propagation in closed-form, reducing its computational complexity from cubic to approximately linear with respect to the number of data points; on the other hand, our algorithm calculates the soft label matrix for unlabeled data using a closed-form solution, thereby gaining additional acceleration. Comprehensive experiments performed on six real-world datasets demonstrate the efficiency and effectiveness of our algorithm in comparison to five state-of-the-art algorithms.
A Bibliometric Analysis of Research on the Metaverse for Smart Cities: The Dimensions of Technology, People, and Institutions
Authors: Zhou Lele , Suh Woojong
Published: October 2024

The “Metaverse” is evaluated as having significant potential in a “Smart city” design and operation. Despite growing interest, there is still a lack of comprehensive quantitative analysis on the “Metaverse”, particularly in the context of smart cities. This study conducts a bibliometric analysis of 604 articles selected from the “WoS” database and employs three dimensions of technology, people, and institutions as a balanced perspective on smart cities, providing a comprehensive understanding of research trends on the “Metaverse” in the context of smart cities. This study identifies the “Metaverse” as a Virtual reality technology, popular since 2021, and provides information on the active years, countries, fields, journals, authors, and institutions involved in “Metaverse” research on smart cities. This study also identifies three stages of research development as follows: Stage 1 (2007–2013) to Stage 2 (2014–2020) and Stage 3 (2021–20 October 2023), revealing the research focus evolution from basic “urban planning” to complex “urban governance” and “Smart city” construction with consideration of multi-stakeholders’ perspectives. Additionally, this study reveals that “Metaverse” research studies on the “technology” dimension have consistently outnumbered that on “institutions” and “people” across all stages in the “Smart city” domain. These findings address current theoretical gaps and offer a foundation for future research.
Influence of Graphene, Carbon Nanotubes, and Carbon Black Incorporated into Polyamide Yarn on Fabric Properties
Authors: Arumugam Veerakumar , Góra Aleksander , Lipik Vitali
Published: October 2024

Carbon nanomaterials are increasingly being integrated into modern research, particularly within the textile industry, to significantly boost performance and broaden application possibilities. This study investigates the impact of incorporating three distinct carbon-based nanofillers—carbon nanotubes (CNTs), carbon black (CB), and graphene (Gn)—into polyamide 6 (PA6) multifilament yarns. It explores how these nanofillers affect the physical, mechanical, and thermal properties of PA6 yarns and fabrics. By utilizing melt extrusion, the nanomaterials were uniformly distributed in the yarns, and knitted fabrics were subsequently produced for detailed analysis. The research offers critical insights into how each nanofiller improves the thermal behavior of PA6-based textiles, enabling the customization of their applications. FTIR spectroscopy revealed significant chemical interactions between polyamide and carbon additives, while DSC analysis showed enhanced thermal stability, particularly with the inclusion of graphene. The introduction of these nanomaterials led to increased absorbance and decreased transmittance in the UV-Vis-NIR spectrum. Additionally, Far-Infrared (FIR) emissivity and thermal effusivity varied with different concentrations, with optimal improvements observed at specific levels. Although thermal conductivity decreased with the addition of these nanomaterials, heat management experiments demonstrated varied effects on heat accumulation and cooling times, underscoring potential applications in insulation and cooling technologies. These findings enrich the existing knowledge on nanomaterial-enhanced textiles, providing valuable guidance for optimizing PA6 yarns and fabrics for use in protective clothing, sportswear, and technical textiles. The comparative analysis offers a thorough understanding of the relationship between carbon nanomaterials and thermal properties, paving the way for innovative advancements in functional textile materials.
Under-Reporting of Human Leptospirosis Cases in Cities of Triângulo Mineiro, Minas Gerais, Brazil
Authors: Franco Mariani Borges , Gomes Lara Reis , Rostkwoska Cristina , Pajuaba Ana Cláudia Arantes Marquez , Mineo José Roberto , Lima Anna Monteiro Correia , Oliveira Stefan Vilges de
Published: October 2024

Leptospirosis is an infectious disease caused by the pathogenic Leptospira species through direct or indirect contact with infected animals. Due to protean clinical manifestation in the early stages, leptospirosis is often difficult to distinguish from other common acute febrile illnesses, such as dengue. Thus, this study aimed to investigate the prevalence of leptospirosis in suspected dengue patients whose serological diagnosis was negative. A total of 449 serum samples from patients (negative IgM-ELISA dengue) with fever, headache, myalgia, and nausea were tested. The Dual-Path Platform (DPP) rapid test developed by the Instituto de Tecnologia em Imunobiológicos Bio-Manguinhos in the city of Rio de Janeiro, Brazil was used for screening IgM antibodies against Leptospira in blood serum, and the microscopic agglutination test (MAT) was performed on samples positive in the DPP for leptospirosis, as well as on an equal number of negative samples. Results: The data obtained from the samples analyzed with the DPP assay showed 26 positive results (5.79%), of which 38.46% were male and 61.54% female, with a mean age of 41 years. We tested 52 samples using the MAT, including 26 reactive for IgM and 26 non-reactive in the DPP assay. Nine samples (17.31%) were reactive, and among them, six also showed reactivity in the DPP assay. Of the six samples reactive in both tests, 66.67% were female, living in urban areas in the city of Uberlândia, with a mean age of 50 years, being 50% white, 33.33% brown, and 16.67% black. The findings demonstrated that leptospirosis cases are underdiagnosed and undertreated in the study population and more attention needs to be paid for ruling out leptospirosis and other pathogens causing acute febrile illness in dengue-endemic areas.
Generational Factors Associated with SARS-CoV-2 Vaccine Completion for Americans of Mexican Decent Living along the United States–Mexico Border Region
Authors: Soto Francisco , Servin Argentina E. , Smith Davey M. , Muñoz Fatima , Aldous Jeannette L. , Stockman Jamila K. , Ramirez Daniel , Skaathun Britt
Published: October 2024

Background: SARS-CoV-2 vaccine uptake variation remains a significant barrier to overcoming the spread of COVID-19. Individual beliefs/attitudes about the SARS-CoV-2 vaccine vary significantly across generations due to personal experiences, access to accurate information, education levels, political beliefs, and trust in healthcare systems. Methods: This analysis used data from the baseline visit of Project 2VIDA!, a cohort of Americans of Mexican descent (AoDM) and African American individuals (n = 1052) in San Diego, CA, along the U.S.–Mexico border region. The survey assessed sociodemographics, healthcare access, socioeconomic factors, and trust in public health information/SARS-CoV-2 prevention. We conducted a logistic regression involving AoDM individuals to identify generational factors associated with completing the SARS-CoV-2 vaccine series. Results: The results of the logistic regression analysis revealed that Generation X (OR = 0.52, 95% CI = 0.33–0.82), Millennials (OR = 0.24, 95% CI = 0.14–0.41), and Generation Z (OR = 0.10, 95% CI = 0.05–0.22) were less likely to complete the SARS-CoV-2 vaccine series when compared to Baby Boomers and the Silent Generation. Conclusions: Participants with a history of SARS-CoV-2 testing and trust in the SARS-CoV-2 vaccine were significantly more likely to complete the SARS-CoV-2 vaccine series. Efforts to address vaccine series completion should be tailored to the specific concerns and motivations of different age groups.
Efficacy and Safety in Dogs Following Administration of an Alphavirus RNA Particle Canine Influenza H3N2 Vaccine
Authors: Classe Haley M. , Dant Jennifer C. , Mogler Mark , Stachura Kenneth A. , LaFleur Rhonda L. , Xu Zach , Tarpey Ian
Published: October 2024

Canine influenza virus (CIV) H3N2 causes a highly contagious respiratory disease in dogs and has been the source of outbreaks across North America since 2015. An injectable RNA Particle (RP)-CIV H3N2 vaccine has been developed to protect dogs against this disease. To demonstrate efficacy, dogs were randomized into two treatment groups, then vaccinated subcutaneously twice, 21 days apart, with a placebo vaccine (n = 20) or an RP-CIV H3N2 vaccine (n = 20). Three weeks later, dogs were challenged intranasally with virulent CIV H3N2 and observed daily for 10 days for clinical signs of disease. Nasal swabs were also collected daily to evaluate the shedding of the challenge virus. Ten days post-challenge, the dogs were euthanized, and the lungs were examined for consolidation. RP-CIV H3N2 vaccination demonstrated a significant reduction in the duration of clinical signs, duration and amount of virus shed, lung consolidation, and the incidence of suppurative pneumonia. To evaluate safety, dogs from multiple geographic regions were vaccinated subcutaneously, 3–4 weeks apart, with an RP-CIV H3N2 vaccine and observed for adverse events for 14 days after each administration. The RP-CIV H3N2 vaccine was deemed safe, with lethargy being the most reported adverse event at a rate of 1.6%.
Truncated VZV gE Induces High-Titer Neutralizing Antibodies in Mice
Authors: Wu Jiehui , Li Hai , Yuan Yanping , Wang Ruichen , Shi Tianxin , Li Ziyi , Cui Qianqian , Fu Shihong , Nie Kai , Li Fan , Yin Qikai , Du Jiayi , Wang Huanyu , Xu Songtao
Published: October 2024

Backgrounds: A contemporary public health challenge is the increase in the prevalence rates of herpes zoster (HZ) worldwide. Methods: In this work, the gE gene structure was analyzed using bioinformatics techniques, and three plasmids of varying lengths, tgE537, tgE200, and tgE350, were expressed in Chinese hamster ovary (CHO) cells. These proteins were used to immunize BALB/c mice with Al/CpG adjuvant; ELISPOT and FCM were used to evaluate cellular immunity; and ELISA, VZV microneutralization, and FAMA assays were performed to detect antibody titers. Results: Target protein concentrations of 1.8 mg/mL for tgE537, 0.15 mg/mL for tgE200 and 0.65 mg/mL for tgE350 were effectively produced. The ability of the three protein segments to stimulate CD4+ and CD8+ T cells, as well as to cause lymphocytes to secrete IFN-γ and IL-4, did not significantly differ from one another. Both tgE537 and tgE350 were capable of generating VZV-specific antibodies and neutralizing antibodies, while tgE350 had the highest neutralizing antibody titer (4388). There was no equivalent humoral immune response induced by tgE200. Conclusions: The results of this investigation provide the groundwork for the creation of HZ recombinant vaccines using truncated proteins as antigens.
Utilizing UAVs in Wireless Networks: Advantages, Challenges, Objectives, and Solution Methods
Authors: Sobouti Mohammad Javad , Mohajerzadeh Amirhossein , Adarbah Haitham Y. , Rahimi Zahra , Ahmadi Hamed
Published: October 2024

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance the performance and functionality of mobile networks. UAVs can act as flying base stations, relays, or users to provide wireless services to ground users or devices. However, the optimal placement and trajectory design of UAVs in mobile networks is a challenging problem, as it involves multiple objectives, constraints, and uncertainties. In this paper, we provide a comprehensive survey of the state-of-the-art research on UAV placement and trajectory optimization in cellular networks. We first introduce the main objectives and challenges of UAV placement and trajectory optimization, such as maximizing coverage, throughput, energy efficiency, or reliability, while minimizing interference, delay, or cost. We also examine the primary models and assumptions employed for UAV placement and trajectory optimization, including channel models, mobility models, network architectures, and constraints. Additionally, we discuss the main methods and algorithms employed for UAV placement and trajectory optimization. These include optimization techniques, heuristic algorithms, machine learning approaches, and distributed solutions. Analytical results, numerical simulations, or experimental tests are further discussed as the main performance metrics and evaluation methods used for UAV placement and trajectory optimization. We also highlight the main applications and scenarios of UAV placement and trajectory optimization, such as cellular offloading, emergency communications, or aerial base stations. Finally, we identify some open problems and future research directions on UAV placement and trajectory optimization in cellular networks.
Occurrence and Speciation of Pollutants in Guilin Huixian Wetland: Nutrients, Microplastics, Heavy Metals, and Emerging Contaminants
Authors: Gao Hang , Chen Hao , Jin Yue , Gao Ruoting , Wei Chunzhong , Zhang Chunfang , Zhang Wenjie
Published: October 2024

The Huixian Wetland is a natural ecosystem of immense ecological value, providing crucial ecosystem services such as water purification, water regulation, and a habitat for the region’s flora and fauna. Its karst peak forest landforms and surrounding environment also possess unique ecological and landscape value. However, with the ongoing socioeconomic development, including the rise of industrial, agricultural, and aquaculture activities in the wetland area, the nutrient composition of the Huixian Wetland has been altered. This paper reviews the current status of nitrogen, phosphorus, heavy metals, emerging pollutants, and biodiversity in various environmental media of the Huixian Wetland. It synthesizes the literature to identify the factors influencing these changes and projects future research directions for the wetland. This work is of significant practical importance, providing scientific foundations for the restoration and protection of the Huixian Wetland.
Achieving Responsible Reclaimed Water Reuse for Vineyard Irrigation: Lessons from Napa Valley, California and Valle de Guadalupe, Baja California
Authors: Beutel Marc , Mendoza-Espinosa Leopoldo , Medina Clara , Morandé Jorge Andrés , Harmon Thomas C. , Medellín-Azuara Josué
Published: October 2024

Here we report on preliminary efforts to assess the potential to use reclaimed water from municipal wastewater treatment plants for irrigation of vineyards in Napa Valley, California, USA and Valle de Guadalupe, Baja California, Mexico. Vineyards in Napa Valley use a range of source waters including 70 L/s of reclaimed water during the summertime irrigation season. Reclaimed water is secondary effluent that undergoes filtration and disinfection and meets stringent total coliform (<240 MPN/100 mL) and turbidity (10 NTU) requirements. Vineyards in Valle de Guadalupe currently use regional groundwater supplies of marginal quality, and there is interest in expanding source waters to include reclaimed water from nearby Ensenada or the more remote Tijuana. Valle de Guadalupe is drier than Napa Valley and has ongoing salinity management challenges, making the region more sensitive to using reclaimed water for irrigation. Several social and economic factors facilitated the implementation of reclaimed water reuse in Napa Valley for vineyard irrigation, including (1) formation of an assessment district by interested growers to help finance pipeline construction, (2) a long-term reclaimed water vineyard irrigation study by agricultural experts that confirmed the reclaimed water was safe, and (3) a well-defined and relatively low unit cost of reclaimed water. In Valle de Guadalupe, the federal government has approved a project to transport 1000 L/s of reclaimed water over 100 km from Tijuana to Valle de Guadalupe. Questions remain including financing of the project, reclaimed water quality, grower interest in using reclaimed water, and community concerns for such a large-scale program. In considering reclaimed water reuse in vineyards, a key issue is implementation of long-term studies showing that reclaimed water is effectively treated and is safe for irrigation, especially from the standpoint of salt content. In addition, the cost of reclaimed water needs to be comparable with traditional water sources. Finally, in addition to assessing economic constraints, social constraints and water user concerns should be comprehensively addressed in the context of a regional integrated water management framework.
Enhanced TDS Modeling Using an AI Framework Integrating Grey Wolf Optimization with Kernel Extreme Learning Machine
Authors: Sayadi Maryam , Hessari Behzad , Montaseri Majid , Naghibi Amir
Published: October 2024

Predictions of total dissolved solids (TDS) in water bodies including rivers and lakes are challenging but essential for the effective management of water resources in agricultural and drinking water sectors. This study developed a hybrid model combining Grey Wolf Optimization (GWO) and Kernel Extreme Learning Machine (KELM) called GWO-KELM to model TDS in water bodies. Time series data for TDS and its driving factors, such as chloride, temperature, and total hardness, were collected from 1975 to 2016 to train and test machine learning models. The study aimed to assess the performance of the GWO-KELM model in comparison to other state-of-the-art machine learning algorithms. Results showed that the GWO-KELM model outperformed all other models (such as Artificial Neural Network, Gaussian Process Regression, Support Vector Machine, Linear Regression, Classification and Regression Tree, and Boosted Regression Trees), achieving the highest coefficient of determination (R2) value of 0.974, indicating excellent predictive accuracy. It also recorded the lowest root mean square error (RMSE) of 55.75 and the lowest mean absolute error (MAE) of 34.40, reflecting the smallest differences between predicted and actual values. The values of R2, RMSE, and MAE for other machine learning models were in the ranges of 0.969–0.895, 60.13–108.939, and 38.25–53.828, respectively. Thus, it can be concluded that the modeling approaches in this study were in close competition with each other and, finally, the GWO-KELM model had the best performance.
Recovering Nitrogen from Anaerobic Membrane Bioreactor Permeate Using a Natural Zeolite Ion Exchange Column
Authors: Godifredo Jesús , Ruiz Laura , Hernández Silvia , Serralta Joaquín , Barat Ramón
Published: October 2024

In the framework of a circular economy, wastewater treatment should be oriented toward processes that allow the recovery of the resources present in the wastewater while ensuring good effluent quality. Nitrogen recovery is usually carried out in streams concentrated in this nutrient because these high concentrations facilitate nitrogen valorization. On the other hand, the mainstream of a wastewater treatment plant (WWTP) has a high potential for nitrogen recovery, but it is not usually considered because it is hard to manage due to its low nitrogen concentration. To solve this problem and facilitate the recovery of nitrogen in the mainstream, this work proposes ion exchange with zeolites as a stage of ammonium concentration, to provide a nitrogen-concentrated stream that could be valorized by another technology, while obtaining a nitrogen-free effluent. The working stream, the permeate of an AnMBR process in the mainstream, has suitable characteristics to be treated in an ion exchange column (free of suspended solids and with very low organic matter content). To this end, the effect of the working flow rate (17.5 to 4.4 BV/h) and the ammonium concentration (54 to 17 mg NH4-N/L) on the adsorption capacity of the zeolite in the loading phase was evaluated. The adsorption curves were fitted to three mathematical models: Thomas, Bohart–Adams, and Yoon–Nelson. The effect of the regeneration flow rate (from 8.7 to 2.2 BV/h) and the regenerant concentration (NaOH at 0.2, 0.1, and 0.05 M) on regeneration capacity and efficiency were also studied. A novel control strategy based on effluent conductivity was used in both phases to control the duration of the adsorption and regeneration phases.
Assessing the Synergies of Photo-Fenton at Natural pH and Granular Activated Carbon as a Quaternary Treatment
Authors: Núñez-Tafalla Paula , Salmerón Irene , Venditti Silvia , Hansen Joachim
Published: October 2024

The challenge of microcontaminants (MCs) in wastewater effluent has been addressed by using different technologies, including advanced oxidation processes (AOPs) and adsorption. This work evaluates the benefits and synergies of combining these two processes. The AOPs were photo-Fenton and UV/H2O2 operated under natural pH but with different reagents dosages, lamps, and chelating agents. Chelating agents were used at analytical (ethylenediamine-N,N-disuccinic acid and citric acid) and technical grade (citric acid) to simulate scaling-up conditions. The adsorption process was studied via granular activated carbon (GAC) filtration using fresh and regenerated GAC. Four AOP scenarios were selected and coupled with GAC filtration, showing benefits for both processes. AOP treatment time decreased from 10–15 min to 5 min, resulting in a reduction in energy consumption of between 50 and 66%. In the photo-Fenton process, it was possible to work with low reagent dosages (1.5 mg L−1 iron and 20 mg L−1 of H2O2). However, the use of UV/H2O2 showed close removal, highlighting it as a real alternative. An extension of the GAC lifetime by up to 11 times was obtained in all the scenarios, being higher for regenerated than for fresh GAC. Furthermore, the toxicity and phytotoxicity of the treated wastewater were evaluated, and no acute toxicity or slight variation in the phytotoxicity was observed in the combination of these processes.
Quantifying the Impact of Rain-on-Snow Induced Flooding in the Western United States
Authors: Bean Brennan Lynn , Watts Emma
Published: October 2024

The potentially destructive flooding resulting from rain-on-snow (ROS) events motivates efforts to better incorporate these events and their residual effects into flood-related infrastructure design. This paper examines relationships between measured streamflow surges at streamgages across the Western United States and the meteorological conditions preceding them at SNOTEL stations within the same water catchment. Relevant stream surges are identified using a peak detection algorithm via time series analysis, which are then labeled ROS- or non-ROS-induced based on the preceding meteorological conditions. Both empirical and model-derived differences between ROS- and non-ROS-induced stream surges are then explored, which suggest that ROS-induced stream surges are 3–20 percent larger than non-ROS-induced stream surges. Quantifying the difference between ROS and non-ROS-induced stream surges promises to aid the improvement of flood-related infrastructure design (such as culverts) to better guard against extreme flooding events in locations subject to ROS.
Rapid Screening for Retired Batteries Based on Lithium-Ion Battery IC Curve Prediction
Authors: Duan Shuangming , Yu Zhiyu , Li Junhui , Zhao Zhiqiang , Liu Haojun
Published: October 2024

In order to solve the issue of low efficiency in retired battery clustering, a method for quickly obtaining a charging curve and Incremental Capacity (IC) curve based on Convolutional Neural Networks (CNN) is proposed. By training a CNN model, the method enables accurate prediction of complete IC curves and V-Q curves from local charging curves starting at any beginning. The prediction accuracy was validated using the Oxford battery degradation dataset, and transfer learning was conducted by fine-tuning the model trained on LCO batteries for use with LFP batteries, which reduced the RMSE of the estimation and validated the generalizability of the model. Peak parameters were extracted from both the original and predicted IC curves for clustering, and the t-test was applied to eliminate outliers, which significantly reduced the time required to obtain clustering features and improved clustering efficiency.
New era of information security in industry: challenges, threats, solutions
Authors:   AKB «Barrier», Russia, Voronezh, Parshin R.G.
Published: October 2024

The article examines the key trends in the transformation of the information security landscape of industrial enterprises. The reasons for the aggravation of cyber threats, including the digitalization of production, geopolitical factors and changes in the regulatory environment are analyzed. A conceptual model for the transition to a proactive paradigm of information protection in the face of new challenges is proposed. Practical recommendations for upgrading information security systems are provided, taking into account the specifi cs of the industrial sector.
Identification of Typical Risks of an Industrial Enterprise at the Stages of the Product Life Cycle
Authors:   Russian State Agrarian University — Moscow Timiryazev Agricultural, Moscow, Russia, Ariskin M.R.
Published: October 2024

The purpose of the study is to identify typical risks of an industrial enterprise at the stages of the product life cycle. The risk management process at industrial enterprises was chosen as the object of the study. The subject of the study is risks and factors aff ecting the likelihood of their occurrence. The article discusses the main stages and elements of the risk management process at an industrial enterprise. By the method of analytical research and synthesis, the relationship between the main stages and elements of the risk management process of a manufacturing enterprise has been established. The main elements of the risk management process are defi ned. A context diagram has been compiled in IDEF0 notation. The passport of the risk management process is presented, which includes a description of such elements of the risk management process as: planned process goals; inputs and outputs; process manager; responsible executors and participants in the process; main process resources; monitoring method; observed parameters during process monitoring; measurement method; process indicator measured for process management; records when process monitoring; responsible for recording and evaluating the eff ectiveness of the process. Based on the analysis, the objectives of such stages of the product life cycle of a manufacturing enterprise are formulated as: analysis and conclusion of contracts; design and development; logistics; production preparation; production; product control and testing; packaging and storage. The risks and factors aff ecting the probability of their occurrence for each of the analyzed stages are determined.
Efectividad de tres dosis de nitrógeno en dos densidades de siembra de maíz amiláceo (Zea mays L.)
Authors: García Juárez Hugo Daniel , Silva Mejías Erick Smith , Rodríguez Abraham Antonio Rafael , López Córdova Jenny Jeanette , Piñarreta Olivares Rony Alexander
Published: October 2024

La fertilización adecuada con Nitrógeno (N) permite un mejor retorno económico que se evidencia en la calidad y producción de los cultivos. Los agricultores de maíz necesitan ajustar las dosis de N a las densidades de la siembra para obtener la mayor productividad de la cosecha y menores efectos adversos al medio ambiente; por consiguiente, esta investigación se planteó como objetivo determinar la efectividad de tres dosis de N en dos densidades de siembras de maíz amiláceo (Zea mays L.), en Cieneguillo Centro Sullana, región de Piura, en Perú. Se realizó una investigación evaluativa, con un diseño experimental de Bloques Completos al Azar (DBCA) con arreglo factorial de 3x2, con cuatro repeticiones, para un total de 24 tratamientos. Las variables independientes o factores, estuvieron constituidas por la concentración de Nitrógeno en forma de Urea (N1:100, N2:150 y N3:200 kg/ha) y la densidad de la siembra, D1: 75000 y D2: 93750, plantas de maíz por hectárea (p/ha). La variable dependiente o respuesta, se consideró la productividad del cultivo la cual se midió a través de las características del rendimiento de la cosecha: longitud y diámetro de la mazorca, peso de 100 granos y altura de planta. Se puede destacar como resultado que la combinación de los factores de concentración de nitrógeno de 200kg/ha y la densidad de 93750 p/ha obtuvo el mayor rendimiento en cuanto a longitud y diámetro de mazorca, peso de 100 granos y altura de la planta.