bluuebunny/crossref_metadata_2025 huggingface dataset:
Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations
Authors: Chu Wenhao , Zhang Chunxiao , Li Heng
Published: October 2023

Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM2.5, achieving the Pearson coefficient (r), the coefficient determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m3, and 12.17 µg/m3, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R2: 0.85–0.87, RMSE: 18.13 µg/m3–20.18 µg/m3, and MAE: 11.21 µg/m3–12.52 µg/m3) and temporal-based (r: 0.91–0.98, R2: 0.82–0.96, RMSE: 3.8 µg/m3–21.89 µg/m3, and MAE: 2.71 µg/m3–14.00 µg/m3) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m3, 54.17 µg/m3, 56.24 µg/m3, and 55.00 µg/m3 in Beijing, Tianjin, Hebei, and the BTH region, respectively.
Smart Crop Cultivation System Using Automated Agriculture Monitoring Environment in the Context of Bangladesh Agriculture
Authors: Rahman Md. Bayazid , Chakma Joy Dhon , Momin Abdul , Islam Shahidul , Uddin Md Ashraf , Islam Md Aminul , Aryal Sunil
Published: October 2023

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.
Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
Authors: Liu Zhiguo , Rong Junlin , Jiang Yingru , Zhang Luxi
Published: October 2023

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D–S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module’s training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.
Two-Stage Feature Generator for Handwritten Digit Classification
Authors: Gunler Pirim M. , Tora Hakan , Oztoprak Kasim , Butun İsmail
Published: October 2023

In this paper, a novel feature generator framework is proposed for handwritten digit classification. The proposed framework includes a two-stage cascaded feature generator. The first stage is based on principal component analysis (PCA), which generates projected data on principal components as features. The second one is constructed by a partially trained neural network (PTNN), which uses projected data as inputs and generates hidden layer outputs as features. The features obtained from the PCA and PTNN-based feature generator are tested on the MNIST and USPS datasets designed for handwritten digit sets. Minimum distance classifier (MDC) and support vector machine (SVM) methods are exploited as classifiers for the obtained features in association with this framework. The performance evaluation results show that the proposed framework outperforms the state-of-the-art techniques and achieves accuracies of 99.9815% and 99.9863% on the MNIST and USPS datasets, respectively. The results also show that the proposed framework achieves almost perfect accuracies, even with significantly small training data sizes.
A Route Planning Method for UAV Swarm Inspection of Roads Fusing Distributed Droneport Site Selection
Authors: Zhong Yingchun , Ye Shenwen , Liu Yizhou , Li Jingwen
Published: October 2023

Current methods that use Unmanned Aerial Vehicle (UAV) swarms to inspect roads still have many limitations in practical applications, such as the lack of or difficulty in the route planning, the unbalanced utilization rate of the UAV swarm and the difficulty of the site selection for the distributed droneports. To solve the limitations, firstly, we construct the inspection map and remove the redundant information irrelevant to the road inspection. Secondly, we formulate both the route planning problem and the droneport site selection problem in a unified multi-objective optimization model. Thirdly, we redesign the encoding strategy, the updating rules and the decoding strategy of the particle swarm optimization method to effectively solve both the route planning problem and the droneport site selection problem. Finally, we introduce the comprehensive evaluation indicators to verify the effectiveness of the route planning and the droneport site selection. The experimental results show that (1) with the proposed method, the overlapped part of the optimized inspection routes is less than 7% of the total mileage, and the balanced utilization rate of the UAVs is above 75%; (2) the reuse rate of the distributed droneports is significantly improved after optimization; and (3) the proposed method outperforms the ant colony optimization (ACO) method in all evaluation indicators. To this end, the proposed method can effectively plan the inspection routes, balance the utilization of the UAVs and select the sites for the distributed droneports, which has great significance for a fully autonomous UAV swarm inspection system for road inspection.
Research on Transportation Carbon Emission Peak Prediction and Judgment System in China
Authors: Sun Yanming , Yang Yile , Liu Shixian , Li Qingli
Published: October 2023

The transportation sector is a major contributor to carbon emissions, and managing its carbon peak is essential for China to reach the 2030 carbon peak target. This paper uses the autoregressive integrated moving average model (ARIMA) to design baseline scenarios and “double carbon” scenarios (carbon peak and carbon neutrality) based on the accounting of transportation carbon emissions in 30 provinces and cities in China to facilitate regional differentiation and forecast the development trend of transportation carbon emissions. Using the fuzzy comprehensive evaluation method, a comprehensive transportation carbon emission research and judgment system has been developed based on the forecast results. The research indicates a substantial increase in carbon dioxide (CO2) emissions from transport in China over the past 15 years, with an average growth rate of 5.9%, from 387.42 mt in 2005 to 917.00 mt in 2019. In the scenario prediction analysis, the overall carbon emission of the “two-carbon” scenario exhibits varying levels of reduction compared with the baseline scenario. According to the comprehensive research and judgment system, when the comprehensive evaluation index corresponding to the turning point year of transportation carbon emissions is greater than 0.85, and the index remains above 0.85 after the turning point, it can be judged that a region has achieved the peak of transportation carbon dioxide emissions under 95% possibility. It shows that China’s policies and strategies for carbon and emission reduction have played a significant role in transportation, but the low-carbon transformation and development still face great challenges.
Comprehensive Solutions to Prevent Larvae Breakout in Water Filtration Systems
Authors: Kwon Hyuk Jun , Hidayaturrahman Haerul , Koutavarapu Ravindranadh , Lee Tae Gwan
Published: October 2023

The presence of invertebrates in drinking water distribution systems, particularly Chironomidae larvae, has raised concerns among the general public. This study aimed to comprehensively address the issue of larvae breakout in water filtration systems and provide potential solutions to prevent their escape into the water supply. The research investigated various factors contributing to larvae breakout, including the type of filtration column, sand depth, pretreatment methods, and the effective size and uniformity coefficient of sand media. Experimental results revealed that the GAC column, primarily utilized for adsorption, was ineffective in retaining Chironomidae larvae, leading to their escape within a short period. Similarly, the sand filter column, with a design that is currently widely used with sand specifications of an effective size of 0.7 mm and a uniformity coefficient of 1.7, failed to act as a barrier for larvae. Increasing the height of the sand media and applying a pretreatment method, which was expected to prevent larvae from entering the treated water, yielded unsatisfactory results. Our research results show that reducing the uniformity coefficient to 1.5 while maintaining an effective size of 0.7 mm proved to be important in preventing the release of larvae into treated water. The Sand/GAC and Sand/Anthracite systems, by maintaining adjusted media sand specifications, also succeeded in retaining larvae in the filtration system. Additionally, this study emphasized the importance of following the recommended backwash procedure, consisting of specific steps involving air flow, a combination of air and water flow, and final water flow. This sequence effectively removed contaminants, turbidity, and Chironomidae larvae from the filtration media, ensuring improved water quality and system performance. The findings of this study provide valuable insights and recommendations for water treatment plants to address the issue of larvae breakout and enhance water quality.
Temporal and Spatial Variations of Extreme Climate Events in Northwestern China from 1960 to 2020
Authors: Liang Xiaoyan , Niu Zhenmin , Li Xiaolong
Published: October 2023

In the context of global warming, the frequency and intensity of extreme weather and climate events have been increasing. Characterized by scarce water resources and fragile ecosystems, Northwest China has experienced a climate shift since the 1980s. In this study, spatial and temporal patterns of changes in the indices of climate extremes, based on daily maximum and minimum temperature and precipitation at 172 meteorological stations in Northwest China, were analyzed for the period 1960–2020. A total of 26 indices divided into two categories, 16 extreme temperature indices and 10 extreme precipitation indices, were used. Analysis of these indices revealed a general warming trend in the region, which consistent with global warming. The regional occurrence of summer days, tropical nights, growing season length, warm nights, warm days, and warm spell duration index increased by 0.22, 0.14, 0.29, 0.73, 0.46, and 0.11 days/decade, respectively. Over the same period, the occurrence of frost days, icing days, cool nights, cool days, and cold spell duration index decreased by −0.38, −0.21, −0.93, −0.44, and −0.13 days/decade, respectively. The decreasing trends in cold extremes were greater than the increasing trends in warm extremes. Additionally, many regions have experienced increasing trends in several precipitation indices. The annual total wet-day precipitation increased by 5.3 mm/decade. Increasing trends were also evident in simple daily intensity index, heavy precipitation days, very heavy precipitation days, very wet days, and extremely wet days. Consecutive dry days decreased by −1.5 days/decade, while no significant change was observed in consecutive wet days. In contrast to the remarkable spatial consistency of temperature extremes, precipitation extremes exhibited large and expected spatial variability. Most precipitation indices showed increasing trends in the western region of Northwest China and decreasing trends in the eastern part of Northwest China. These results indicate a transition from cold–dry to warm–wet in Northwestern China. Our findings suggest that Northwest China is experiencing more extreme climate events, which could consequently impact hydrological processes, ecological processes, and human health. These observations increase our understanding of the interactions between climate change and regional climate variability, which is conducive to improving disaster prevention.
The Interplay between School Preparedness and Student’s Individual Protective Actions: The Mediating Role of Disaster Education
Authors: Wang Ziyi , Han Ziqiang , Li Yuhuan
Published: October 2023

Disaster preparedness serves as a pivotal strategy to achieve the 2030 Sustainable Development Goals. Empowering children and youth in disaster risk reduction through the education system not only enhances present resilience, but also augments future sustainability. This study investigates the mediating role of disaster education between school preparedness and student preparedness, using data from a survey conducted in China, encompassing a substantial sample size of 3675. Employing multiple linear regressions and the Sobel–Goodman tests, the study estimates correlations while controlling for essential confounding variables, such as socioeconomic and demographic characteristics. The results reveal that both school preparedness and disaster education are positively correlated with higher levels of student preparedness. Notably, disaster education plays a mediating role between school preparedness and student preparedness. These findings underscore the paramount importance of seamlessly integrating effective school preparedness and disaster education initiatives, equipping students to confidently navigate potential disasters and emergencies.
Soil Water Retention and Pore Characteristics of Intact Loess Buried at Different Depths
Authors: Hou Xiaokun , Qi Shengwen , Liu Fangcui
Published: October 2023

Surface water infiltration is a primary factor responsible for engineering challenges and geological disasters on the Loess Plateau of China (LPC). Due to the extensive groundwater in this region, surface water must pass through thick unsaturated zones to recharge the groundwater reservoirs. Exploring the unsaturated hydraulic characteristics of loess, especially at varying depths, may significantly contribute to disaster prevention and mitigation and the pursuit of sustainable development in the Loess Plateau. The soil-water characteristic curve (SWCC), intricately linked to the soil’s pore structure, is a critical hydraulic parameter of loess. An exploration well with a depth of 30 m was excavated in the LPC to obtain intact specimens at depths of 5 m, 15 m, and 25 m. Basic physical property tests, SWCC measurements, and particle size distribution (PSD) analyses were conducted. Additionally, the relationship between PSD and SWCC is discussed in this paper. The findings highlight the influence of depth on the dominant pore size and distribution density, both of which decreased with increasing depth. The air occlusion value of the SWCC experienced an increase, and the slope of the SWCC in the transition zone exhibited consistency. These observations underscore the pivotal role played by pore structure in shaping the soil’s water-retention behavior. Furthermore, predictions based on PSD data demonstrated excellent accuracy in replicating the wetting SWCC of loess over a wide suction range (e.g., 10–104 kPa).
A Simulation-Based Experimental Design for Analyzing Energy Consumption and Order Tardiness in Warehousing Systems
Authors: Jeon Hyun-woo , Ebrahimi Ahmad , Lee Ga-hyun
Published: October 2023

For warehouses to be more sustainable and cost-effective, it is essential to consider energy consumption (EC) and order tardiness (OT) together in evaluating warehouse activities since improving both EC and OT at the same time is very demanding. While existing studies try to improve EC and OT, the current studies consider only either a reserve area or a forward area between the two major warehouse areas. Thus, this study proposes a simulation-based approach to assessing EC and OT when reserve and forward areas are considered together in one framework for different configurations of five important warehousing parameters: (i) number of forklifts, (ii) number of storage/retrieval (S/R) machines, (iii) number of automated storage/retrieval systems (AS/RS) input/output (I/O) points, (iv) order size, and (v) proportions of order flows through a reserve or forward area. In particular, we use real forklift movement and energy data for our simulation models to provide a more realistic analysis. By building the simulation model with the 25 full factorial experimental design, we analyze the results with analysis of variance (ANOVA). The resulting Pareto-optimal solutions show that less traffic flows through a reserve area can help improve both EC and OT while other factors have smaller or limited effects on the two responses. Also, the order flow factor has the largest effect on EC while order size has the largest effect on OT. The results from this study can help warehouse operators make informed decisions in considering and finding a trade-off between sustainability and customer satisfaction.
A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles
Authors: Bozhi  , Mohamed Mahmoud , Gilani Vahid Najafi Moghaddam , Amjad Ayesha , Majid Mohammed Sh. , Yahya Khalid , Salem Mohamed
Published: October 2023

The proliferation of electric vehicles (EVs) hinges upon the availability of robust and efficient charging infrastructure, notably encompassing swift and convenient solutions. Among these, dynamic wireless charging systems have garnered substantial attention for their potential to revolutionize EV charging experiences. Inductive power transfer (IPT) systems, in particular, exhibit a promising avenue, enabling seamless wireless charging through integrated pavements for EVs. This review engages in an in-depth exploration of pertinent parameters that influence the inductivity and conductivity performance of pavements, alongside the assessment of potential damage inflicted by IPT pads. Moreover, the study delves into the realm of additive materials as a strategic approach to augment conductivity and pavement performance. In essence, the review consolidates a diverse array of studies that scrutinize IPT pad materials, coil dimensions, pavement characteristics (both static and dynamic), and adhesive properties. These studies collectively illuminate the intricate dynamics of power transfer to EVs while considering potential repercussions on pavement integrity. Furthermore, the review sheds light on the efficacy of various additive materials, including metal and nanocomposite additives with an SBS base, in amplifying both conductivity and pavement performance. The culmination of these findings underscores the pivotal role of geometry optimization for IPT pads and the strategic adaptation of aggregate and bitumen characteristics to unlock enhanced performance within wireless pavements.
New Results on the Oscillation of Solutions of Third-Order Differential Equations with Multiple Delays
Authors: Omar Najiyah , Moaaz Osama , AlNemer Ghada , Elabbasy Elmetwally M.
Published: October 2023

This study aims to examine the oscillatory behavior of third-order differential equations involving various delays within the context of functional differential equations of the neutral type. The oscillation criteria for the solutions of our equation have been obtained in this study to extend and supplement existing findings in the literature. In this study, a technique that relies on repeatedly improving monotonic properties was used in order to exclude positive solutions to the studied equation. Negative solutions are excluded based on the symmetry between the positive and negative solutions. Our results are important because they become sharper when applied to a Euler-type equation as compared to previous studies of the same equation. The significance of the findings was illustrated through the application of these findings to specific cases of the investigated equation.
Adaptive Supply Chain System Design for Fruit Crops under Climate Change
Authors: Jeong Hoyoung , Choi ChulHun
Published: October 2023

There is an urgent need for adaptive supply chain strategies that can mitigate the effects of climate change on the agricultural system. This study introduces a new mathematical model, Capacitated Facility Location Problems with Multi-Period (CFLP-MP), to respond to this challenge. Unlike existing models, the CFLP-MP framework is designed to optimize the supply chain system design of facility locations and transportation routes by accommodating changes in production and demand for multiple time periods. The proposed model can be extended to other time-sensitive agricultural foods, but in this research, it is applied to the U.S. apple industry as a case study. Despite varying conditions, the model demonstrates resilience in maintaining its operational efficiency, thereby offering a solution to the initial challenge of creating an adaptive agricultural supply chain system. In conclusion, this research offers a versatile toolset for enhancing resilience and sustainability in fruit crop production, precisely addressing the climate-induced challenges identified above.
Exploring the Impact of Heterogeneous Environmental Regulations on Green Innovation Efficiency: Evidence from China’s Yangtze River Economic Belt
Authors: Xiong Ruochan , Gao Da
Published: October 2023

Enhancing environmental regulation (ER) is an effective way to improve the green innovation efficiency (GIE) of the Yangtze River Economic Belt (YREB) and the key to achieving the target of carbon peaking and neutrality. Using the data of 103 cities in YREB in 2013–2019, this paper explores the effect of heterogeneous ER on GIE. The results of the study are as follows: first, we categorize environmental regulations into formal and informal environmental regulations and explore their impact on green innovation efficiency separately. The results show that both formal and informal environmental regulations can effectively promote GIE in the YREB urban agglomeration. Second, the Spatial Durbin Model (SDM) model is used to analyze the spatial effect. The results show that there are spatial effects between heterogeneous environmental regulations and GIE, and environmental regulation can significantly strengthen GIE through positive spatial effects. Third, this paper uses the threshold model to explore the non-linear relationship between environmental regulation and green innovation efficiency. The results show that as ER increases, the positive effect on GIE increases, and this interesting finding holds for both formal and informal environmental regulations. Fourth, the mediating effect model is used to examine whether green technological innovation and industrial structure upgrading exert mediation effects on how environmental regulations affect GIE. The research results can provide effective policy recommendations to promote the green development of the cities in YREB.
Assessment of the Level of Practical and Theoretical Knowledge as a Starting Point for the Implementation of Lean Hospital Concept
Authors: Chmielarz Anna , Wieczorek-Wojcik Beata , Zdęba-Mozoła Agnieszka , Kozłowski Remigiusz , Kilańska Dorota , Solarz Jan Krzysztof , Czapla Tomasz , Marczak Michał
Published: October 2023

Background: The implementation of the Lean Hospitals concept can contribute to the improvement of internal processes in healthcare organizations. The level of a management team’s knowledge is an important part of effective implementation of Lean Hospital elements in hospitals. The purpose of this article is to determine the degree of theoretical and practical knowledge of Lean Hospitals (defined for the purposes of the study as a set of lean tools) among the management teams of Polish hospitals. The authors focused on examining the discrepancy between practical and theoretical knowledge to determine which of them is less prevalent in hospitals in order to correctly establish elements of the implementation procedures, which must be improved and perfected to more effectively implement the lean concept in healthcare. Methods: The research methods used to achieve the study objectives included, respectively, an analysis of the literature on the subject and gathering of data using the Qualtrics Platform with a CAWI survey. Respondents rated their level of knowledge regarding Lean Hospital tools on a Likert scale. Basic descriptive statistics and radar diagrams were used to analyze and present the data. Statistical analysis was performed using Excel spreadsheets. Results: It was established that the vast majority of management teams in the studied hospitals had limited basic knowledge about Lean Hospitals, if any. The greatest lack of knowledge was found in the field of practical (implementation) knowledge of Lean Hospital tools. The research found no significant discrepancy between the level of theoretical and practical knowledge at the level of general knowledge and detailed knowledge relating to the knowledge of individual lean tools. The standardized work tool was rated best in terms of self-assessing practical knowledge. The worst rated tools in terms of both theoretical and practical knowledge self-assessment were Kaizen, Kanban and TPM. Conclusions: The results of the conducted studies indicate a low level of knowledge and advancement in the implementation of the Lean Hospitals concept in selected Polish hospitals. Limited knowledge of the Lean Hospital concept was established for entire management teams. A low level of knowledge was noted in both theoretical and practical knowledge. Supplementing knowledge only at the theoretical level without taking care of the practical knowledge aspect may prolong the implementation procedure or stop it completely. Therefore, based on the result of the research, it can be concluded that the first stage of lean implementation in hospitals should focus on supplementing the knowledge and preparing the employees for work in a lean culture, diverting particular attention to the practical part of the training.
Recent Advances in the 3D Printing of Pure Copper Functional Structures for Thermal Management Devices
Authors: Choong Yue Hao , Krishnan Manickavasagam , Gupta Manoj
Published: October 2023

Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices.
Vaccine Effectiveness of Cell-Based Quadrivalent Influenza Vaccine in Children: A Narrative Review
Authors: Mould-Quevedo Joaquin F. , Pelton Stephen I. , Nguyen Van Hung
Published: October 2023

Cell-based manufacturing of seasonal influenza vaccines eliminates the risk of egg-adaptation of candidate vaccine viruses, potentially increasing vaccine effectiveness (VE). We present an overview of published data reporting the VE and cost-effectiveness of a cell-based quadrivalent influenza vaccine (QIVc) in preventing influenza-related outcomes in the pediatric population. We identified 16 clinical studies that included data on the VE of a QIVc or the relative VE (rVE) of a QIVc versus an egg-based QIV (QIVe) in children and/or adolescents, 11 of which presented estimates specifically for the pediatric age group. Of these, two studies reported rVE against hospitalizations. Point estimates of rVE varied from 2.1% to 33.0%, with studies reporting significant benefits of using a QIVc against influenza-related, pneumonia, asthma, and all-cause hospitalization. Four studies reported rVE against influenza-related medical encounters, with point estimates against non-strain specific encounters ranging from 3.9% to 18.8% across seasons. One study evaluated rVE against any influenza, with variable results by strain. The other four studies presented VE data against laboratory-confirmed influenza. Three health economics studies focusing on a pediatric population also found the use of QIVc to be cost-effective or cost-saving. Overall, using a QIVc is effective in pediatric patients, with evidence of incremental benefits over using a QIVe in preventing hospitalizations and influenza-related medical encounters in nearly all published studies.
Hesitance and Misconceptions about the Annual Influenza Vaccine among the Saudi Population Post-COVID-19
Authors: Alghalyini Baraa , Garatli Tala , Hamoor Reela , Ibrahim Linda , Elmehallawy Yara , Hamze Dima , Abbara Zain , Zaidi Abdul Rehman Zia
Published: October 2023

(1) Background: Vaccination is a cornerstone of public health strategy for mitigating the morbidity and mortality associated with seasonal influenza. However, vaccine hesitancy and misconceptions pose significant barriers to this effort, particularly in the context of the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to investigate the transfer of COVID-19 vaccine hesitancy to the influenza vaccine and to identify misconceptions about the influenza vaccine among the Saudi population in the post-COVID-19 era. (2) Methods: A web-based, cross-sectional study was conducted between February and June 2023 using a questionnaire adapted from the Adult Vaccine Hesitancy Scale (aVHS). The questionnaire was disseminated to 589 Saudi residents, aged 18 and above, with access to digital devices. Data were analyzed via logistic regression analysis to determine the associations between vaccine hesitancy, knowledge of influenza, and baseline characteristics. (3) Results: This study found that 37.7% of respondents exhibited vaccine hesitancy, while 56.7% demonstrated good knowledge about influenza. There was a significant relationship between nationality and vaccine hesitancy (p-value > 0.05), with non-Saudi respondents exhibiting higher hesitancy. Logistic regression analysis revealed significant associations between vaccine hesitancy, age, and nationality. Meanwhile, participants with higher educational qualifications showed greater knowledge about influenza. (4) Conclusions: The findings highlight an important crossover of COVID-19 vaccine hesitancy to influenza vaccines. This study underscores the need for targeted public health interventions to address misconceptions about the influenza vaccine, particularly among certain demographic groups, in order to improve influenza vaccine uptake in the post-COVID era.
Synthesis and Characterization of Nanometal Oxide-Biochar Derived from Date Palm Waste for Adsorption of Manganese and Iron from Contaminated Water
Authors: Alrowais Raid , Bashir Muhammad Tariq , Sikandar Muhammad Ali , Hayet Khan Md. Munir , Alwushayh Bandar , Ghazy Ahmed , Uddin Md. Alhaz , Iqbal Javed
Published: October 2023

Groundwater is a predominant stream of potable water in numerous areas and frequently harbors pollutant removal, notably iron, and manganese. The present work explored synthesizing and conducting a thorough analysis of a composite material termed nanometal oxide-biochar (NMO) and biochar that is prepared from date palm waste. The application of Fourier Transform Infrared (FTIR) spectroscopy analysis, SEM/EDX, XRD, and BET facilitated the identification of unique molecule characteristics inside the composite material. This research also investigated the kinetics of manganese and iron adsorption, and the results suggested that both first- and second-order models are applicable, with a slight preference for the pseudo-second-order model. The mechanisms of adsorption in the NMO were further clarified by the Langmuir and Freundlich adsorption isotherm models, which emphasized that the NMO predominantly undergoes monolayer adsorption. In short, composite materials exhibited an adsorption capacity of 3.169 mg/g and 4.151 mg/g for manganese and iron on biochar as well as 4.33 mg/g and 4.859 mg/g on NMO, respectively. In addition, values for R2 provide goodness of fit for the Adams–Bohart and Thomas models. The adsorption capacity for manganese and iron are observed as 31.97 mg/g and 32.28 mg/g on NMO as well as 26.6 mg·L−1 and 29.54 mg·L−1 on biochar, respectively, at a flow rate of 7 mL/min. In conclusion, this study highlights the potential of the NMO-BC composite for monitoring water pollution, sustainably obtained from date palm waste, as a viable approach for eliminating manganese and iron from polluted water.
A Framework for Assessing Nature-Based Urban Stormwater Management Solutions: A Preliminary Spatial Analysis Approach Applied to Southeast Serbia
Authors: Blagojević Borislava , Vasilevska Ljiljana , Anđelković Dimitra , Bogojević Aleksandar , Lousada Sérgio
Published: October 2023

The development of the stormwater management strategies, e.g., low-impact development (LID), water-sensitive urban design (WSUD), and sustainable drainage solutions (SUDS), was initiated in the mid-1980s as a set of engineering approaches and technologies to reduce the harmful effects of stormwater. Over time, all of them evolved in the holistic, multidisciplinary approaches and, today, they are increasingly viewed and implemented under the umbrella term “Nature-based Solutions” (NbS). The technical elements and measures of these NbS represent various technical solutions, implemented i.a., according to the suitability of the site to achieve their maximum efficiency. Currently, there are no standards or procedures for the application of NbS technologies in Serbia. To overpass this and encourage implementation, we carried out preliminary assessment of NbS elements suitability for application in eight urban settlements in the Region of Southern and Eastern Serbia. The assessment is based on publicly available data and performed according to the existing recommendations in the field of spatial planning and rainwater management for WSUD. The analyses were conducted by GIS tools that involved spatial analyses of various terrain characteristics and provided an insight into the criteria, i.e., constraints that are key to the placement of various technical elements, including bioretention, rain garden, and permeable pavement. Research findings point out that creation of the thematic maps with area suitability ratings for individual NbS stormwater elements might represent a good starting point for further investigation, planning, and design. The proposed framework for preliminary assessment is potentially useful for the countries and regions without regulations in the field of NbS for stormwater management.
A Machine Learning Framework for Enhancing Short-Term Water Demand Forecasting Using Attention-BiLSTM Networks Integrated with XGBoost Residual Correction
Authors: Shan Shihao , Ni Hongzhen , Chen Genfa , Lin Xichen , Li Jinyue
Published: October 2023

Accurate short-term water demand forecasting assumes a pivotal role in optimizing water supply control strategies, constituting a cornerstone of effective water management. In recent times, the rise of machine learning technologies has ushered in hybrid models that exhibit superior performance in this domain. Given the intrinsic non-linear fluctuations and variations in short-term water demand sequences, achieving precise forecasts presents a formidable challenge. Against this backdrop, this study introduces an innovative machine learning framework for short-term water demand prediction. The maximal information coefficient (MIC) is employed to select high-quality input features. A deep learning architecture is devised, featuring an Attention-BiLSTM network. This design leverages attention weights and the bidirectional information in historical sequences to highlight influential factors and enhance predictive capabilities. The integration of the XGBoost algorithm as a residual correction module further bolsters the model’s performance by refining predicted results through error simulation. Hyper-parameter configurations are fine-tuned using the Keras Tuner and random parameter search. Through rigorous performance comparison with benchmark models, the superiority and stability of this method are conclusively demonstrated. The attained results unequivocally establish that this approach outperforms other models in terms of predictive accuracy, stability, and generalization capabilities, with MAE, RMSE, MAPE, and NSE values of 544 m3/h, 915 m3/h, 1.00%, and 0.99, respectively. The study reveals that the incorporation of important features selected by the MIC, followed by their integration into the attention mechanism, essentially subjects these features to a secondary filtration. While this enhances model performance, the potential for improvement remains limited. Our proposed forecasting framework offers a fresh perspective and contribution to the short-term water resource scheduling in smart water management systems.
Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network
Authors: Cachumba-Suquillo Santiago J. , Alfaro-Ponce Mariel , Torres-Cedillo Sergio G. , Cortés-Pérez Jacinto , Jimenez-Martinez Moises
Published: October 2023

Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.
Comparative analysis of chlorhexidine-containing antiseptic agents used in periodontics and implantology
Authors: Bogatyreva R. M. , Slazhneva E. S. , Blashkova Yu. V. , Atrushkevich V. G.
Published: October 2023

Relevance. Despite the variety of antibacterial drugs, including multicomponent ones, chlorhexidine-containing antiseptics are nowadays the gold standard of antiseptic products relevant for periodontal therapy. At the same time, adverse side effects and new data about unfavourable chlorhexidine effects on the microbiome determine it necessary to search for a new optimal chlorhexidine-containing product that combines high effectiveness and relative safety. Purpose. The study aimed to compare and analyze the effectiveness of chlorhexidine-containing antiseptics used in dental practice.

Material and Methods. The study analyzed the results of clinical studies published from 2018 to 2023 and devoted to the investigation of chlorhexidine effectiveness in the treatment of periodontal diseases. The publications were searched in the Pub Med database by the keywords 'chlorhexidine', 'periodontal disease', and 'periodontitis'. A total of eighty-four publications corresponded to the search criteria. After the primary analysis of all available publications corresponding to the inclusion and exclusion criteria, we studied and analyzed 32 publications.

Results. The analysis of the publications for the past five years extracted a trend for a more frequent increase of chlorhexidine concentrations to 0.12% and 0.2% in periodontal therapy medications.

Conclusion. Prescribing chlorhexidine-containing medications should be reasonable and respond to the characteristics of the clinical picture. At that, a thorough selection of chlorhexidine active agent concentration, a form of presentation and duration of use should correspond to the clinical situation and goal.
Autoestima y autocontrol en el rendimiento académico de estudiantes universitarios
Authors: Ponce Pardo John Eloy , Alvarado Acuña Georgina Rosa , Fernández Avila Miguel Luis , Chiri Saravia Paulo Cesar
Published: October 2023

La autoestima, el autocontrol en el rendimiento académico son factores importantes en el proceso de enseñanza y aprendizaje y en el desarrollo integral de las personas. Por ende, el objetivo de este estudio es relacionar la influencia de la autoestima y el autocontrol en el rendimiento académico de estudiantes universitarios de la especialidad de inglés. Se desarrolló un estudio bajo enfoque cuantitativo, de tipo descriptivo, y análisis correlacional-causal de corte transversal. Se aplicó una encuesta a 120 estudiantes usando como instrumento el cuestionario. Se hallo que la autoestima de los estudiantes en su mayoría se presentó en un nivel regular con un 43.3%, similar al aérea personal, familiar, académica y social con un 41.7%, 43.3%, 44.2% y 45.0% respectivamente. Se concluyó que la autoestima y el autocontrol del estudiante influye significativamente en el rendimiento académico del estudiante
Aprendizaje autónomo en el desarrollo de las competencias comunicativas en estudiantes de educación básica
Authors: Quispe Marleni Mercedes
Published: October 2023

En la educación, el reforzamiento de las competencias comunicativas y el aprendizaje autónomo son elementos fundamentales para el desarrollo de los educandos, ya que apoya a los niños y jóvenes a mejorar el nivel intelectual y personal de los estudiantes. Por tanto, el objetivo de la investigación fue determinar la relación del aprendizaje autónomo y el desarrollo de las competencias comunicativas en estudiantes de educación básica. El estudio fue cuantitativo, diseño es no experimental, nivel correlacional, descriptiva aplicado a una población censal de 121 estudiantes de una institución básica. Los resultados demuestran que el 76,9% de los estudiantes, infieren que tienen un desarrollo regular sobre el aprendizaje autónomo, asimismo, el 77,7% de las competencias comunicativas. Se concluye que el aprendizaje autónomo se relaciona con la competencias comunicativas, debido que fortalece y mejora el aprendizaje del estudiante.
Enseñanza remota como viabilizador de la educación superior pública en tiempos de pandemia
Authors: Ramos-Mendoza Julio Cesar , Tasayco-Jala Abel Alejandro , Pachas-Barrientos Luz Milagros , Valdez-Arango Ada
Published: October 2023

El brindar una educación de calidad desde la perspectiva digital, denota contar con un sistema online que permita generar un entorno de comunicación en línea, a través del trabajo colaborativo entre los estudiantes y su relación con el docente. Es así, que el propósito de este estudio fue analizar la enseñanza remota como viabilizador de la educación superior pública en tiempos de pandemia. El estudio se realizó bajo el enfoque cualitativo, de tipo descriptivo-analítico. Se usó como técnica la entrevista; como instrumento el cuestionario con preguntas semiestructurada a 5 docentes y estudiantes de pregrado de una universidad privada, como herramienta de análisis se usó Atlas.ti 9. Los hallazgos fueron que la enseñanza virtual se vincula con la adaptabilidad tecnológica y accesibilidad tecnológica mediante la interacción de nuevas tecnologías, dominio de las plataformas enriqueciendo el conocimiento. Concluyendo que la enseñanza remota es el puente para el uso de los entornos virtuales, las redes sociales y la alfabetización digital fortaleciéndose mediante las herramientas tecnológicas.