bluuebunny/crossref_metadata_2025 huggingface dataset:
Functionalization of ZnO Nanorods with Au Nanodots via In Situ Reduction for High-Performance Detection of Ethyl Acetate
Authors: Wang Qilin , Wang Wei , Fan Yizhuo , Fang Jian , Chen Yu , Ruan Shengping
Published: October 2024

Ethyl acetate is a critical medical indicator for detecting certain types of cancer. However, at present, available sensitive materials often exhibit drawbacks, such as high operating temperatures and poor responses to low concentrations of ethyl acetate. In this study, a ZnO nanorod sensing material was prepared using high-temperature annealing and a hydrothermally synthesized metal-organic framework (MOF) as a template. Au nanodots (AuNDs) were subsequently modified on the ZnO nanorods using an in situ ion reduction, which provided a better dispersion of Au nanodots compared with that obtained using the common reductant method. A variety of characterization methods indicate that the highly dispersed AuNDs, which possess a high catalytic activity, were loaded onto the surface as active centers, leading to a significant augmentation in the adsorption of oxygen on the surface compared with the original ZnO material. Consequently, the AuND@ZnO material exhibited heightened responsiveness to ethyl acetate at a lower operating temperature. The Au@ZnO-based sensor has a response rate (Ra/Rg) of 41.8 to 20 ppm ethyl acetate gas at 140 °C, marking a 17.4-fold increase compared with that of the original material. Due to its low power consumption and high responsiveness, AuND@ZnO is a promising candidate for the detection of ethyl acetate gas in medical applications.
Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals
Authors: Sun Chengfa , Liu Changchun , Wang Xinpei , Liu Yuanyuan , Zhao Shilong
Published: October 2024

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precision due to suffering from information shortages. To address this issue and capture adequate information, the development of a multi-modal method becomes imperative. In this study, a novel multi-modal learning method is proposed to integrate both ECG and PCG for CAD detection. Along with deconvolution operation, a novel ECG-PCG coupling signal is evaluated initially to enrich the diagnosis information. After constructing a modified recurrence plot, we build a parallel CNN network to encode multi-modal information, involving ECG, PCG and ECG-PCG coupling deep-coding features. To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. The result demonstrates the superiority of the proposed multi-modal method in overcoming information shortages of single-modal signals and outperforming existing models in CAD detection. This study highlights the potential of multi-modal deep-coding information, and offers a wider insight to enhance CAD diagnosis.
RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification
Authors: Wang Yutong , Kou Ziming , Han Cong , Qin Yuchen
Published: October 2024

Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. Specifically, coal gangue image samples were customized in two scenarios: normal lighting and simulated underground lighting with poor lighting conditions. The images were preprocessed using the dim light enhancement algorithm Retinexformer, with YOLOv8 as the backbone network. The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. This indicates that RRBM-YOLO can attain an optimal equilibrium between detection precision and inference velocity, with excellent accuracy, robustness, and industrial application potential.
A Contactless Low-Carbon Steel Magnetostrictive Torquemeter: Numerical Analysis and Experimental Validation
Authors: Clemente Carmine Stefano , Simonelli Claudia , Gori Nicolò , Musolino Antonino , Rizzo Rocco , Raugi Marco , Torri Alessandra , Sani Luca
Published: October 2024

Torque measurement is a key task in several mechanical and structural engineering applications. Most commercial torquemeters require the shaft to be interrupted to place the sensors between the two portions of the shaft where a torque has to be measured. Contactless torquemeters based on the inverse magnetostrictive effect represent an effective alternative to conventional ones. Most known ferromagnetic materials have an inverse magnetostrictive behavior: applied stresses induce variations in their magnetic properties. This paper investigates the possibility of measuring torsional loads applied to a shaft made of ferromagnetic steel S235 through an inverse magnetostrictive torquemeter. It consists of an excitation coil that produces a time-varying electromagnetic field inside the shaft and an array of sensing coils suitably arranged around it, in which voltages are induced. First, the system is analyzed both in unloaded and loaded conditions by a Finite Element Method, investigating the influence of relative positions between the sensor and the shaft. Then, the numerical results are compared with the experimental measurements, confirming a linear characteristic of the sensor (sensitivity about 0.013 mV/Nm for the adopted experimental setup) and revealing the consistency of the model used. Since the system exploits the physical behavior of a large class of structural steel and does not require the introduction of special materials, this torquemeter may represent a reliable, economical, and easy-to-install device.
Lacking Accountability and Effectiveness Measures: Exploring the Implementation of Mentoring Programs for Refugee Youth
Authors: Mairaj Fiza
Published: October 2024

A high proportion of refugees coming into the U.S. are under the age of 18, and many continue to struggle to attain basic educational qualifications which may lead to viable economic opportunity. Recently, the Office of Refugee Resettlement (ORR) began issuing youth mentoring (YM) grants to support the integration of refugee youth. This paper explores the decision-making processes of resettlement organizations in a vertically complex network of organizations which implemented the YM programs for refugee youth in one large city of Texas. Through a series of semi-structured interviews with program implementers, findings suggest that resettlement agencies remain powerless in making key programmatic decisions, including implementation of the programs and designing accountability and effectiveness measures, which can truly evaluate the program quality. Other findings suggest that across the network of organizations involved in implementation of the YM programs, compliance with federal regulation is prioritized over accountability. The lack of program effectiveness and accountability measures have implications for one of the most vulnerable population, refugee youth. The main recommendation for policy and practice argues for the lead public agency’s increased involvement in defining key outcomes for the program recipients in a vertically complex network.
Exploring the Influence of Digital Economy Growth on Carbon Emission Intensity Through the Lens of Energy Consumption
Authors: Zhao Rujun , Chen Hai , Liang Xiaoying , Yang Miaomiao , Ma Yuhe , Lu Wenjing
Published: October 2024

Exploring the impact of new economic forms such as the digital economy (DE) on carbon emissions is crucial for China’s “dual carbon” goals. This paper assesses the impact of the DE on carbon emission intensity (CI) from a 2011–2021 perspective on energy consumption in 30 provinces (Hong Kong, Macao, Taiwan, and Tibet are excluded) by using a double fixed-effects model for evaluating the DE. Based on the results from 2011 to 2021, (1) China’s DE and CI differ spatially and temporally. In contrast to CI, DE shows a pattern of low in the northwest and high in the southeast. The changes are similar to Hu Huanyong’s line, with considerable changes in the southeast, especially the coastal region. (2) Chinese DE’s carbon control effects (−0.027) vary by region. A significant negative effect is seen in both the eastern and western regions, with the western region having the greatest negative effect (−0.030), and a positive but insignificant impact in the central region. (3) Total energy consumption (TEC), structure (EC) and efficiency (EI) are all pathways of its influence. Path changes in China are mainly dominated by dual paths (Accounting for over 47%), with fewer single and multiple paths. Among them, the main dual path types are TEC and EC in the east and central regions, TEC and EC, and EC and EI in the western regions; meanwhile, the paths are unchanged in most of the provinces in China, and the changed provinces are mainly in the west. Based on these findings, DE development policies based on regional energy consumption differentiation are crucial to reducing carbon emissions.
Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment
Authors: Kayisu Antoine Kazadi , Mikusova Miroslava , Bokoro Pitshou Ntambu , Kyamakya Kyandoghere
Published: October 2024

The urban landscape of Kinshasa, Democratic Republic of Congo, faces significant mobility challenges, primarily stemming from rapid urbanization, overpopulation, and outdated infrastructure. These challenges necessitate the exploration of modern smart mobility concepts to improve traffic flow, road safety, and sustainability. This study investigates the potential of solutions such as Mobility-as-a-Service, car sharing, micro-mobility, Vehicle-as-a-Service, and electric vehicles in addressing these challenges. Through a comparative analysis of global implementations, this research identifies key success factors and barriers that inform the feasibility of integrating these solutions into Kinshasa’s unique socio-political and infrastructural context. The study presents a conceptual framework, supported by stakeholder analysis, for adapting these solutions locally. A detailed feasibility analysis considers technological, economic, social, environmental, and regulatory factors, offering a clear roadmap for implementation. Drawing on lessons from cities facing similar urban mobility challenges, the paper concludes with actionable recommendations and insights for policymakers and urban planners in Kinshasa. This research not only highlights the viability of smart mobility solutions in Kinshasa but also contributes to the broader discourse on sustainable urban development in rapidly growing cities. While smart mobility studies have largely focused on cities with developed infrastructure, there is a gap in understanding how these solutions apply to cities like Kinshasa with different infrastructural and socio-political contexts. Previous research has often overlooked the challenges of integrating smart mobility in rapidly urbanizing cities with underdeveloped transportation systems and financial constraints. This study fills that gap by offering a feasibility analysis tailored to Kinshasa, assessing smart mobility solutions for its traffic congestion and road safety issues. The smart mobility solutions studied—Mobility-as-a-Service (MaaS), car sharing, electric vehicles (EVs), and micro-mobility—were chosen for their ability to address Kinshasa’s key mobility challenges. MaaS reduces reliance on private vehicles, easing congestion and improving public transport. Car sharing offers affordable alternatives to vehicle ownership, essential in a city with income inequality. EVs align with sustainability goals by reducing emissions, while micro-mobility (bikes and e-scooters) improves last-mile connectivity, addressing public transit gaps. These solutions are adaptable to Kinshasa’s context and offer scalable, sustainable improvements for urban mobility.
Simulating the Impact of Urban Expansion on Ecological Security Pattern from a Multi-Scenario Perspective: A Case Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China
Authors: Zhang Ran , Chen Taoyi , Su Fei , Liu Yaohui , Zheng Guoqiang
Published: October 2024

Rapid urbanization has further expanded the scale of construction land in urban agglomerations. The encroachment of urban land on ecological land has led to severe ecological problems and threatened the stability of ecological security in urban agglomerations. Analyzing the characteristics of future urban multi-scenario expansion and its impacts on ecological security patterns (ESP) can provide guidance for formulating ecologically sustainable management and control Policies. Our study focuses on Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration as the study area and establishes an ESP. Additionally, a cellular automata (CA) was used to simulate future urban expansion patterns under three scenarios (i.e., natural development scenario, urban development scenario, and ecological conservation scenario). The subsequent analysis evaluates their impact on the ESP. The simulation results indicate that from 2020 to 2030, the CZX urban agglomeration will undergo rapid urban expansion under the natural development scenario and urban development scenario, characterized by outward growth surrounding the existing construction land. In the natural development scenario, urban expansion is primarily concentrated in the northwest and south directions of construction land, the proportion of construction land increased by 2.78%; in the urban development scenario, it is concentrated in the southeast direction of construction land, the proportion of construction land increased by 3.24%. Ecological conflicts in the aforementioned development scenarios primarily arise in the southwestern region of Changsha County, as well as the southern areas of Kaifu District and Furong District. Conversely, under the ecological conservation scenario, the rate of urban expansion has significantly decreased, environmental preservation is upheld at its highest level, and the proportion of construction land only increased by 0.04%. Based on the simulation results, we present targeted recommendations for urban land planning and growth management, as well as the protection, restoration, monitoring, and development of ecological land. These suggestions provide effective guidance for improving the stability of ESP in urban agglomerations and promoting high-quality development in Chinese urban agglomerations.
Spatiotemporal Variations in Gross Ecosystem Product and Its Relationship with Economic Growth in Ecologically Vulnerable Watershed Areas: A Case Study of Yongding River Basin
Authors: Guo Jingyi , Wang Ling
Published: October 2024

Ecosystem service value is crucial for balancing economic growth and ecological preservation in ecologically vulnerable watershed areas. Although Gross Ecosystem Product (GEP) has received significant attention, most existing studies have focused on how to measure it. Few studies have explored spatiotemporal variations in GEP and how land-use changes affect these variations regarding ecological restoration at the river basin level. Additionally, while many studies have examined the relationship between ecosystem service value and economic growth, there is little research on how components of GEP influence economic growth. Analyzing the spatiotemporal structure of GEP and its components could offer new insights into optimizing ecological restoration strategies and promoting sustainable development in vulnerable watershed regions. In this study, we used ArcGIS, InVEST, SPSS, and Python to analyze spatiotemporal variations in GEP in the Yongding River Basin within the Beijing–Tianjin–Hebei Economic Region from 1995 to 2020. Moran’s Index and variance decomposition were applied to analyze the spatiotemporal structure. The grey prediction model forecasted GEP trends from 2025 to 2035. The random forest model was used to assess land-use changes’ impacts on GEP. Paired T-tests were used to compare GEP and GDP, and a dynamic panel model was used to examine how ecosystem service value factors influenced economic growth. The results show the following: (1) Regarding values, GEP accounting and variance decomposition results indicated that ecosystem cultural service value (ECV) and ecosystem regulating service value (ERV) each contributed about half of the total GEP. Ecosystem provisioning service value (EPV) showed an upward trend with fluctuations. Regarding the spatial distribution, Moran’s I analysis showed significant positive spatial correlations for EPV and ERV. The grey prediction model results indicated significant growth in GEP from 2025 to 2035 under current ecological restoration policies, especially for ERV and ECV. (2) In terms of the influence of land-use changes, random forest analysis showed that the forest land area was consistently the most influential factor across GEP, EPV, and ERV. Unused land area was identified as the most significant factor for ECV. (3) Before 2010, GEP was larger than GDP, with significant differences between 1995 and 2000. From 2010 onwards, GDP surpassed GEP, but the differences were not statistically significant. Dynamic panel regression further showed that the water conservation value significantly boosted GDP, whereas the water purification value significantly reduced it. This study highlights the importance of integrating GEP into ecological restoration and economic development to ensure the sustainability of ecologically vulnerable watershed areas.
The Design of a PV System of a Multi-Location Large Consumer: A Case Study from Al-Balqa Applied University
Authors: Al-Mahrouk Akram , Awada Emad , Al-Rousan Wasseem , Al-Mahrouk Dana
Published: October 2024

This research applies the suggested photovoltaic solar system design to Jordan’s Al-Balqa Applied University. Twelve of the twenty-four college buildings are located on the main campus, while the remaining twelve remote colleges are dispersed throughout Jordan’s many geographic locations, ranging from the far north to the far south. Azimuth angle, irradiation effect, building size, location, power consumption, and student population are all taken into account in the suggested design. Additionally, in-site generation and remote-wheeling generation are two types of solar system installation designs that are taken into consideration. A method for calculating the optimal location of remote-wheeling generation is proposed based on the centroid mass theory. The proposed method is applied in four scenarios. The first three scenarios are single-point, two-point, and three-point centroid locations. The fourth scenario is based on three single points, considering the power distribution regions in Jordan, which cover three regions. The results of the four scenarios will be discussed and demonstrated along with a comprehensive comparison between in-site generation, single-centroid-point remote-wheeling generation, and three-centroid-point remote-wheeling generation.
Integrating Lean Construction with Sustainable Construction: Drivers, Dilemmas and Countermeasures
Authors: Chen Yunxin , Qiu Dong , Chen Xiaomin
Published: October 2024

Sustainable construction (SC) has become a primary objective of the building and construction industry due to the growing demand for reduced environmental impacts and more efficient resource use. Simultaneously, lean construction (LC), which focuses on minimizing waste and maximizing productivity, offers an opportunity to achieve greater sustainability in the construction process. This study explores the integration of LC and SC through a systematic literature review, employing VOSviewer software for quantitative analysis alongside qualitative analysis in conjunction with a comprehensive literature review. The findings reveal substantial synergies between LC and SC, including cost savings, waste reduction, and enhanced safety. However, challenges such as cost conflicts, environmental trade-offs, and worker welfare concerns act as barriers to effective integration. To address these challenges, this study proposes leveraging dynamic capabilities (DCs)—such as change management, organizational learning, organizational flexibility, and continuous improvement and innovation—as strategies for the successful integration of LC and SC. These insights contribute to academic research and industry practice, providing a practical framework for integrating LC and SC to improve sustainability and efficiency in construction projects.
Trends in Vegetation Seasonality in the Iberian Peninsula: Spatiotemporal Analysis Using AVHRR-NDVI Data (1982–2023)
Authors: Gutiérrez-Hernández Oliver , García Luis V.
Published: October 2024

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change.
Intelligent Control Framework for Improving Energy System Stability Through Deep Learning-Based Modal Optimization Scheme
Authors: Fathollahi Arman
Published: October 2024

Ensuring the stability of power systems is essential to promote energy sustainability. The integrated operation of these systems is critical in sustaining modern societies and economies, responding to the increasing demand for electricity and curbing environmental consequences. This study focuses on the optimization of energy system stability through the coordination of power system stabilizers (PSSs) and power oscillation dampers (PODs) in a single-machine infinite bus energy grid configuration that has flexible AC alternating current transmission system (FACTS) devices. Intelligent control strategies using PSS and POD techniques are suggested to increase power system stability and generate supplementary control signals for both the generator excitation system and FACTS device switching control. An intelligent optimal modal control framework equipped with deep learning methods is introduced to control the generator excitation system and thyristor-controlled series capacitor (TCSC). By optimally choosing the weighting matrix Q and implementing close-loop pole shifting, an optimal modal control approach is formulated. To harness its adaptive potential in fine-tuning controller parameters, an auxiliary deep learning-based optimization algorithm with actor–critic architecture is implemented. This comprehensive technique provides a promising path to effectively reduce electromechanical oscillations, thereby enhancing voltage regulation and transient stability in power systems.
Research on the Synergistic Development of China’s Pension Industry and Pension Career
Authors: Wang Qian , Jian Lingxiang , Zhang Lanyin , Zhang Yiping
Published: October 2024

To adapt to the new demographic trends and foster high-quality and sustainable population growth, this study analyzes the synergistic development of the pension industry and pension career based on the data of 31 provinces in China from 2016 to 2022, using a distance coordination model, the latest Vague set similarity measure method, new grey relational analysis, kernel density estimation, and Markov chain. The findings indicate that (1) the development levels of China’s pension industry and pension career have shown a steady upward trend, with the gap between them gradually narrowing, though the overall development of the pension career remains higher than that of the pension industry. (2) The synergistic degree of China’s pension industry and pension career is high, with the degree of synergy fluctuating but generally increasing. (3) The synergistic development degree of the pension industry and pension career has continued to rise. The increase in the synergistic development degree shows a spatial distribution pattern of “Central > East > West”. (4) The spatial distribution of synergistic development of China’s pension industry and pension career is uneven, and in the long term, regions with higher synergistic development levels tend to positively influence less developed regions.
Analysis of Mechanisms and Environmental Sustainability in In Situ Shale Oil Conversion Using Steam Heating: A Multiphase Flow Simulation Perspective
Authors: Zhang Zhaobin , Xie Zhuoran , Montilla Maryelin Josefina Briceño , Li Yuxuan , Xu Tao , Li Shouding , Li Xiao
Published: October 2024

Shale oil as an unconventional energy source holds significant extraction value. However, traditional extraction techniques often entail significant environmental impacts, emphasizing the need for more sustainable and environmentally friendly methods. In situ conversion of shale oil using superheated steam fits this bill. Based on this, we used a new TFC coupling simulator to build a geological model, providing a comprehensive depiction of the evolution process of various elements during in situ conversion by steam, thereby investigating the feasibility of this method. The results show that based on the temperature distribution within the shale oil reservoir during the heating stage, the area between the heating well and the production well can be divided into five regions. In addition, the steam injected contributes to driving the oil. However, due to the relatively low energy density of the steam, a large amount of steam needs to be injected into the reservoir in order to attain the intended heating outcome, resulting in a high ratio of liquid water in the produced products. Meanwhile, the evolution of components during in situ conversion is influenced by factors such as the injection rate of steam and soaking time. A slow injection rate and prolonged soaking time are both adverse to extraction of shale oil. On this basis, the in situ conversion heating strategy can be refined.
Rice Leaf Disease Classification—A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet-V2 Architectures
Authors: Dutta Monoronjon , Islam Sujan Md Rashedul , Mojumdar Mayen Uddin , Chakraborty Narayan Ranjan , Marouf Ahmed Al , Rokne Jon G. , Alhajj Reda
Published: October 2024

Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices.
Spatial–Temporal Characteristics, Source Apportionment, and Health Risks of Atmospheric Volatile Organic Compounds in China: A Comprehensive Review
Authors: Wei Yangbing , Jing Xuexue , Chen Yaping , Sun Wenxin , Zhang Yuzhe , Zhu Rencheng
Published: October 2024

Volatile organic compounds (VOCs) are ubiquitous in the atmosphere, posing significant adverse impacts on air quality and human health. However, current research on atmospheric VOCs mainly focuses on specific regions or industries, without comprehensive national-level analysis. In this study, a total of 99 articles on atmospheric VOCs in China published from 2015 to 2024 were screened, and data on their concentrations, source apportionment, and health risks were extracted and summarized. The results revealed that the annual average concentrations of TVOCs and their groups in China generally increased and then decreased between 2011 and 2022, peaking in 2018–2019. A distinct seasonal pattern was observed, with the highest concentrations occurring in winter, followed by autumn, spring, and summer. TVOC emissions were highly concentrated in northern and eastern China, mainly contributed by alkanes and alkenes. Source apportionment of VOCs indicated that vehicle sources (32.9% ± 14.3%), industrial emissions (18.0% ± 12.8%), and other combustion sources (13.0% ± 13.0%) were the primary sources of VOCs in China. There was a significant positive correlation (p < 0.05) between the annual mean VOC concentration and population size, and a notable negative correlation (p < 0.05) with GDP per capita. Atmospheric VOCs had no non-carcinogenic risk (HI = 0.5) but exhibited a probable carcinogenic risk (7.5 × 10−5), with relatively high values for 1,2-dibromoethane, 1,2-dichloroethane, and naphthalene. The health risk was predominantly driven by halocarbons. These findings are essential for a better understanding of atmospheric VOCs and for developing more targeted VOC control measures.
Into the Cauldron of the Variant Soup: Insights into the Molecular Epidemiology and Transition to Endemicity of SARS-CoV-2 in Cyprus (November 2022–February 2024)
Authors: Chrysostomou Andreas C. ,   The COMESSAR Network, Kostrikis Leondios G.
Published: October 2024

The coronavirus disease 2019 (COVID-19) pandemic, driven by the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been characterized by the virus’s ongoing evolution, leading to the appearance of more transmissible variants that have often triggered infection surges. In this study, we analyzed the SARS-CoV-2 epidemic in Cyprus, utilizing 1627 viral sequences from infected individuals between November 2022 and February 2024. Over this period, 251 distinct lineages and sublineages were identified, predominantly categorized into three groups: Omicron 5, XBB, and JN.1 (parental lineage BA.2.86), all of which harbor S protein mutations linked to enhanced transmissibility and immune escape. Despite the relatively low numbers of new infections during this period, and the lack of any major waves, unlike earlier phases of the pandemic, these lineages demonstrated varying periods of dominance, with Omicron 5 prevailing from November 2022 to February 2023, XBB variants leading from March to November 2023, and JN.1 generating a wavelet from December 2023 to February 2024. These findings suggest that the SARS-CoV-2 epidemic in Cyprus has reached endemicity, with new variants gradually replacing previously circulating variants irrespective of seasonal patterns. This study highlights the critical importance of ongoing surveillance of SARS-CoV-2 evolution in Cyprus and emphasizes the role of preventive measures in limiting virus transmission, providing valuable insights for safeguarding public health.
Beyond Awareness: Hope for a CMV Vaccine! An Introduction to the Conference, “CMV Vaccine Development—How Close Are We?” (27–28 September 2023)
Authors: Pesch Megan H. , Schleiss Mark R. , Plotkin Stanley A. , Permar Sallie R. , Gautam Rajeev
Published: October 2024

Congenital cytomegalovirus (cCMV) is the most common infectious cause of disability in children. The major theme of this National Institute of Allergy and Infectious Diseases (NIAID) workshop, “CMV Vaccine Development—How Close Are We?”, was to report progress on the development of a pre-conception vaccine that could confer protective immunity for women of child-bearing age. Such a vaccine could result in a reduced cCMV disease burden, although other populations, including solid organ transplant and hematopoietic stem cell transplant patients, could benefit as well. To frame the compelling need for a cCMV vaccine, a keynote lecture by Dr. Megan Pesch, immediate past-president of the National CMV Foundation and a leading cCMV researcher from the University of Michigan, was given. This manuscript provides a summary of Dr. Pesch’s presentation from this workshop, which was written as the introductory conference report for the meeting.
Trihalomethane Formation Potential at the Barekese Water Treatment Plant and the Related Cancer Risk to Consumers in the Kumasi Metropolis of Ghana
Authors: Fellalaga Nathaniel Kabral Wezenamo , Mensah Lawson , Agbekey Bright Kwaku , Bentil Ethel , Waaley Lilian , Anornu Geophery Kwame
Published: October 2024

The prevalence of disinfection by-products in drinking water supplies is a global concern due to their carcinogenicity. However, the monitoring of DBPs such as trihalomethanes (THMs) and haloacetic acids (HAAs) in drinking water supplies is non-existent in many developing Asian, South American, and African countries. The formation of THMs during disinfection arises from a reaction between the disinfectant and natural organic matter in the water, particularly, dissolved organic carbon (DOC). This reaction is hastened by increases in temperature, high levels of disinfectant doses or residual, elevated water pH, long disinfection contact times, and high DOC concentrations. However, the inclusion of a granular activated carbon adsorption process in the water treatment process is the most effective method for the removal of the main precursor (DOC) for the formation of THMs in treated water. The Barekese WTP, which disinfects with chlorine, has no adsorption process for DOC removal, and supplies over 80% of pipe-borne water to the city of Kumasi in Ghana, was assessed for the THM formation potential (THMFP). A THM predictive model was used to determine the potential THM concentration in the final water. The THMFP at the Barekese WTP ranged between 22.42 and 38.94 µg/L, which was below the 100 µg/L threshold set by the WHO. The lifetime average daily doses were 3.9494 × 10−4 µg/Kg/d and 3.9294 × 10−4 µg/Kg/d for male and female consumers, respectively. The lifetime integrative cancer risks associated with consumption of the water were 1.817 × 10−5 and 1.808 × 10−5 for males and females, respectively. The cancer risk posed was acceptably low. However, direct measurement of DBPs is required to corroborate these findings and verify the cancer risk posed to the consumers of treated water from the Barekese WTP to inform policies, regulations, public health interventions, and investment.
Inversion Model for Permeability Coefficient Based on Random Forest–Secretary Bird Optimization Algorithm: Case Study of Lower Reservoir of C-Pumped Storage Power Station
Authors: Ma Zekai , Shen Zhenzhong , Yang Jiangyin
Published: October 2024

The geological complexity of the karst regions presents significant challenges, with the permeability coefficient being a critical parameter for accurately analyzing seepage behavior in hydraulic engineering projects. To overcome the limitations of traditional inversion methods, which often exhibit low computational efficiency, poor accuracy, and instability, this study utilizes a finite-element forward model and orthogonal experimental design to establish a sample set for permeability-coefficient inversion. A surrogate model for seepage calculation based on the Random Forest (RF) algorithm is subsequently developed. Furthermore, the Secretary Bird Optimization Algorithm (SBOA) is incorporated to propose an intelligent RF–SBOA inversion method for permeability-coefficient estimation, which is validated through a case study of the C-pumped storage power station. The results demonstrate that the RF model’s predictions for water levels at four boreholes closely align with the measured data, outperforming models such as CART, BP, and SVR. The SBOA effectively identifies the optimal geological permeability coefficient, with the borehole water-level inversion achieving a maximum relative error of only 0.128%, which meets the accuracy requirements for engineering applications. Additionally, the computed distribution of the natural seepage field is consistent with the typical distribution patterns observed in mountain seepage systems. During the normal water-storage phase, both the calculated seepage flow and gradient comply with engineering standards, while the seepage-field distribution aligns with empirical observations. This inversion model provides a rapid and accurate method for estimating the permeability coefficient of strata in the project area, with potential applicability to permeability inversion in other engineering geology contexts, thus demonstrating considerable practical value for engineering applications.
Resilience Adaptation Through Risk Analysis for Wastewater Treatment Plant Operators in the Context of the European Union Resilience Directive
Authors: Pankow Nora , Krause Steffen , Schaum Christian
Published: October 2024

To combat new threats to critical infrastructure, the European Union enacted new regulations for their member states. With the directive on measures for a high common level of cybersecurity (NIS2) and the directive on critical infrastructure resilience (EU RCE), EU member states must identify critical infrastructure (CIs) and enable measures to reduce the risk of default in stress situations. The topic of resilience in urban water systems has already been of interest in previous research. However, there are still open questions. As it is a multidisciplinary term, understanding resilience and its adaptation into management systems is not an easy task for practitioners. This study will provide an overview of resilience within the framework of municipal wastewater treatment plants (WWTP) and show the current situation of existing implementation of safety and security regulations, taking Germany as an example. One of the main requirements of the EU RCE is a risk assessment (RA) for CIs. Until now, risk analysis for WWTP in research was mostly carried out for individual WWTP. By applying guidelines from the drinking water sector, this paper shows a possible methodology for a risk analysis. This paper aims to support practitioners by forming a common understanding of resilience and risk as well as providing an example for a risk analysis.
Irrigation Water in Northwest Syria: Impact of the Recent Crisis and Drought
Authors: Atik Omar , Kadour Anas , Mahmoud Ibrahim , Al Hasan Khalid , Al Nabhan Ahmad , Jazieh Hani , Nijhawan Anisha , Pianosi Francesca
Published: October 2024

This study examines how Northwest Syria’s arable and irrigated areas are changing, with a particular emphasis on processes that have affected cultivation and water availability since 2011. A substantial decline in cultivated areas is revealed by the analysis of 502 samples from various stakeholders, including farmers, well owners, well-digging companies, agricultural pharmacies, and service offices. District-specific variations show a decrease of 15.3% and 8.5% in total cultivated land and irrigated areas, respectively. Particularly in areas known for their summer vegetables and winter crops, such Al-Bab and Jebel Saman, agricultural productivity has drastically decreased since 2011. Today, 67% of irrigation comes from groundwater wells, especially in Jabal Saman and Idleb, whereas Jisr-Ash-Shugur depends on the Orontes River. Due to drought and a lack of regulatory oversight, the number of irrigation wells has risen since 2011, severely reducing water supplies and driving up irrigation prices. High fuel and electricity costs, uncontrolled well drilling, broken machinery, and insufficient irrigation systems are some of the main obstacles. A lot of well owners would rather sell drinking water, which would cut down on irrigated land and crop cultivation. The needs of the stakeholders include subsidies for fuel and electricity, the development of alternative energy, well repairs, improvements to irrigation systems, the provision of agricultural inputs, and better local water management. This study emphasizes the critical need for focused interventions to support agriculture and livelihoods in Northwest Syria and offers significant insights for sustainable water management solutions.
How Wetting and Drainage Cycles and Wetting Angle Affect Capillary Air Trapping and Hydraulic Conductivity: A Pore Network Modeling of Experiments on Sand
Authors: Princ Tomas , Koestel John , Snehota Michal
Published: October 2024

Entrapped air in porous media can significantly affect water flow but simulations of air entrapment are still challenging. We developed a pore-network model using quasi-static algorithms to simulate air entrapment during spontaneous wetting and subsequent drainage processes. The model, implemented in OpenPNM, was tailored to replicate an experiment conducted on a medium-sized unconsolidated sand sample. We started building the model with three types of relatively small networks formed by 54,000 pore bodies which we used to calibrate basic network topological parameters by fitting the model to the water retention curve and the saturated hydraulic conductivity of the sand sample. Using these parameters, along with X-ray image data (µCT), a larger network formed by over 250,000 pore bodies was introduced in the form of stacked sub-networks where topological parameters were scaled along the z-axis. We investigated the impact of two different contact angles on air entrapment. For a contact angle of 0, the model showed good agreement with the experimental data, accurately predicting the amount of entrapped air and the saturated hydraulic conductivity. On the contrary, for a contact angle of π/4, the model provided reasonable accuracy for saturated hydraulic conductivity but overestimated the amount of entrapped air. Overall, this approach demonstrated that a reasonable match between simulated and experimental data can be achieved with minimal computational costs.
A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration
Authors: Rojo-Yepes Miguel Ángel , Zuluaga-Ríos Carlos D. , Saldarriaga-Zuluaga Sergio D. , López-Lezama Jesús M. , Muñoz-Galeano Nicolas
Published: October 2024

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.
Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction
Authors: Cheng Yunqi , Xu Meng , Wang Kezhi , Chen Zonghai , Wang Jikai
Published: October 2024

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.