bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Concentric Differential Model with Geographic Information-Driven Weights Allocation for Enhanced Infrared Small Target Detection
Authors: Peng Lingbing , Lu Zhi , Lei Tao , Jiang Ping
Published: January 2025

Infrared small target detection technology has received extensive attention due to its advantages in long-distance monitoring. However, there is much room for improvement in its performance due to complex backgrounds and the lack of distinct features in small targets. Many specific scenarios can lead to target loss, such as edge-adjacent targets, intersecting targets, low contrast caused by locally bright backgrounds, and false alarms induced by globally bright backgrounds. To address these issues, we have identified the positional correlation differences between the local background location and whether the target can be perceived by the human eye, thereby introducing geographic information weights to represent this correlation difference. We first constructed a non-concentric Gaussian difference structure to prevent the central target energy loss caused by traditional concentric filters. Based on this, we introduced Gabor filters, which have the capability of directional feature extraction and position correlation representation, into the non-concentric differential structure. By adjusting the relative position of the Gabor filter center and configuring frequency parameters based on geographic information, we optimized the filter weights to handle complex situations, such as targets being close to background clutter or other targets. Subsequently, an improved logarithmic function was applied to adjust the overall saliency of candidate targets, preventing the loss of low-contrast targets and the residual high-energy background clutter. Extensive experiments show that our method exhibits effective detection performance and robustness in four application scenes and three challenging image distribution scenes.
Validation of DSDs of GPM DPR with Ground-Based Disdrometers over the Tianshan Region, China
Authors: Lu Xinyu , Wang Xiuqin , Li Cheng , Liu Yan , Zeng Yong , Huo Hong
Published: January 2025

The Tianshan Mountains are known as the “Water Tower of Central Asia” and are of significant strategic importance for Xinjiang as well as the Central Asian region. Accurately monitoring the spatiotemporal distribution of precipitation in the Tianshan Mountains is crucial for understanding global water cycles and climate change. Raindrop Size Distribution (DSD) parameters play an important role in improving quantitative precipitation estimation with radar and understanding microphysical precipitation processes. In this study, DSD parameters in the Tianshan Mountains were evaluated on the basis of Global Precipitation Measurement mission (GPM) dual-frequency radar data (DPR) and ground-based laser disdrometer observations from 2019 to 2024. With the disdrometer observations as the true values, we performed spatiotemporal matching between the satellite radar and laser disdrometer data. The droplet spectrum parameters retrieved with the GPM dual-frequency radar system were compared with those calculated from the laser disdrometer observations. The reflectivity observations from the GPM DPR in both the Ku and Ka bands (ZKu and ZKa) were greater than the actual observations, with ZKa displaying a greater degree of overestimation than ZKu. In the applied single-frequency retrieval algorithm (SFA), the rainfall parameters retrieved from the Ka band outperformed those retrieved from the Ku band, indicating that the Ka band has stronger detection capability in the Tianshan Mountains area, where light rain predominates. The dual-frequency ratio (DFR), i.e., the differences in the reflectivity of the raindrop spectra obtained from both the Ku and Ka bands, fluctuated more greatly than those of the GPM DPR. DFR is a monotonically increasing function of the mass-weighted mean drop diameter (Dm). Rainfall rate (R) and Dm exhibited a strong positive correlation, and the fitted curve followed a power function distribution.
Long Time Series Spatiotemporal Variations in NPP Based on the CASA Model in the Eco-Urban Agglomeration Around Poyang Lake, China
Authors: Du Tianmeng , Yang Fei , Li Jun , Zhang Chengye , Cui Kuankuan , Zheng Junxi
Published: January 2025

The ecological urban agglomeration around Poyang Lake represents a critical development area in the Yangtze River basin. The spatiotemporal characteristics of the net primary productivity (NPP) of vegetation are explored from the perspective of the city’s functional position, providing important insights for the city to achieve the dual-carbon target and green development. The study evaluates the spatiotemporal variations in NPP from 2003 to 2022 in the eco-urban agglomeration around Poyang Lake, using the CASA model. Its variation characteristics were explored in detail from a completely new perspective and scope using indicators such as cycle amplitudes, CV coefficients, Hurst indices, and others. Results indicate seasonal fluctuations and significant variations between urban areas and vegetation, with implications for sustainable development. The annual NPP ranged from 200 to 800 gC/(m2·a), with a change rate of 0.58 gC/(m2·a) and evident seasonal fluctuations in the study area. Notably, urban core cities like Jiujiang and Nanchang exhibit lower NPP and decreasing trends. Scenic areas showed high forest cover and vigorous NPP changes, highlighting the need for targeted urban ecological management to enhance green development. Additionally, the seasonal fluctuations in NPP were notably influenced by specific land use types and local economic conditions. In areas with high vegetation cover, the seasonal characteristics of NPP are pronounced, while they are less evident in regions with strong urban economic conditions.
Physics-Constrained Three-Dimensional Swin Transformer for Gravity Data Inversion
Authors: Yu Ping , Zhou Longran , Zhou Shuai , Jiao Jian , Huang Guanlin , Lu Pengyu
Published: January 2025

This paper proposes a physics-constrained 3D Swin Transformer (ST) for gravity inversion. By leveraging the self-attention mechanism in 3D ST, the method effectively models global dependencies within gravity data, enabling the network to reweight features globally and focus on critical anomalous regions. Additionally, prior gradient information is integrated into the loss function, and a hierarchical weight allocation strategy is adopted to guide the model in learning boundary information of density structures and deep-seated features more effectively. Synthetic experiments demonstrate that the proposed method achieves lower model errors, better boundary alignment, and higher inversion accuracy. The approach is further validated using gravity anomaly observations from the Gonghe Basin in Qinghai, yielding reliable and precise inversion results.
Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI
Authors: Khan Sheheryar , Wang Huiliang , Nauman Umer , Dars Rabia , Boota Muhammad Waseem , Wu Zening
Published: January 2025

Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 to 2020, with forecasts extended to 2030. Advanced data preprocessing techniques, including Yeo-Johnson and Box-Cox transformations, Savitzky–Golay smoothing, and outlier elimination, were applied to improve data quality. Datasets from MODIS, TRMM, GLDAS, and ERA5 were utilized to enhance model accuracy. The predictive performance of various time series forecasting models, including Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, and ETS, was systematically evaluated. This study also introduces novel algorithms for Explainable AI (XAI) and SHAP (SHapley Additive exPlanations), enhancing the interpretability of model predictions and improving understanding of how climate variables affect ET. This comprehensive methodology not only accurately forecasts ET but also offers a transparent approach to understanding climatic effects on ET. The results indicate that Prophet and ETS models demonstrate superior prediction accuracy compared to other models. The ETS model achieved the lowest Mean Absolute Error (MAE) values of 0.60 for precipitation, 0.51 for wind speed, and 0.48 for solar radiation. Prophet excelled with the lowest Root Mean Squared Error (RMSE) values of 0.62 for solar radiation, 0.67 for wind speed, and 0.74 for precipitation. SHAP analysis indicates that temperature has the strongest impact on ET predictions, with SHAP values ranging from −1.5 to 1.0, followed by wind speed (−0.75 to 0.75) and solar radiation (−0.5 to 0.5).
Characterization of 77 GHz Radar Backscattering from Sea Surfaces at Low Incidence Angles: Preliminary Results
Authors: Xu Qinghui , Zhao Chen , Chen Zezong , Wu Sitao , Wang Xiao , Fan Lingang
Published: January 2025

Millimeter-wave (MMW) radar is capable of providing high temporal–spatial measurements of the ocean surface. Some topics, such as the characterization of the radar echo, have attracted widespread attention from researchers. However, most existing research studies focus on the backscatter of the ocean surface at low microwave bands, while the sea surface backscattering mechanism in the 77 GHz frequency band remains not well interpreted. To address this issue, in this paper, the investigation of the scattering mechanism is carried out for the 77 GHz frequency band ocean surface at small incidence angles. The backscattering coefficient is first simulated by applying the quasi-specular scattering model and the corrected scattering model of geometric optics (GO4), using two different ocean wave spectrum models (the Hwang spectrum and the Kudryavtsev spectrum). Then, the dependence of the sea surface normalized radar cross section (NRCS) on incidence angles, azimuth angles, and sea states are investigated. Finally, by comparison between model simulations and the radar-measured data, the 77 GHz frequency band scattering characterization of sea surfaces at the near-nadir incidence is verified. In addition, experimental results from the wave tank are shown, and the difference in the scattering mechanism is further discussed between water surfaces and oceans. The obtained results seem promising for a better understanding of the ocean surface backscattering mechanism in the MMW frequency band. It provides a new method for fostering the usage of radar technologies for real-time ocean observations.
A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions
Authors: Wang Fengling , Jiang Yiyue , Zhang Rongjie , Wei Aimin , Xie Jingming , Pang Xiongwen
Published: January 2025

Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity. MTSAD requires simultaneously analyze temporal dependencies and inter-variable relationships have prompted researchers to develop specialized deep learning models to detect anomalous patterns. In this paper, we conducted a structured and comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection methods. Firstly, we proposed a taxonomy for the anomaly detection strategies from the perspectives of learning paradigms and deep learning models, and then provide a systematic review that emphasizes their advantages and drawbacks. We also organized the public datasets for time series anomaly detection along with their respective application domains. Finally, open issues for future research on MTSAD were identified.
Multi-Scale Contrastive Learning with Hierarchical Knowledge Synergy for Visible-Infrared Person Re-Identification
Authors: Qian Yongheng , Tang Su-Kit
Published: January 2025

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality retrieval task to match a person across different spectral camera views. Most existing works focus on learning shared feature representations from the final embedding space of advanced networks to alleviate modality differences between visible and infrared images. However, exclusively relying on high-level semantic information from the network’s final layers can restrict shared feature representations and overlook the benefits of low-level details. Different from these methods, we propose a multi-scale contrastive learning network (MCLNet) with hierarchical knowledge synergy for VI-ReID. MCLNet is a novel two-stream contrastive deep supervision framework designed to train low-level details and high-level semantic representations simultaneously. MCLNet utilizes supervised contrastive learning (SCL) at each intermediate layer to strengthen visual representations and enhance cross-modality feature learning. Furthermore, a hierarchical knowledge synergy (HKS) strategy for pairwise knowledge matching promotes explicit information interaction across multi-scale features and improves information consistency. Extensive experiments on three benchmarks demonstrate the effectiveness of MCLNet.
Design and Implementation of a Binary Phase-Shift Keying Frequency Diverse Array: Considerations and Challenges
Authors: Munson Nicholas R. , Correll Bill , Henry Justin K. A. , Narayanan Ram M. , Bufler Travis D.
Published: January 2025

The frequency diverse array (FDA) is an architecture capable of beamforming in both range and angle, improving upon the traditional phased array (PA) which can only achieve beamforming in angle. The FDA employing directional modulation (DM) for secure directional communications (SDC) can reduce bit error rates (BERs) in both range and angle, again improving upon the traditional PA which can only reduce BER in angle. In this paper, we document the challenges involved in the design and implementation of a two-element linear FDA employing fast-time binary phase-shift keying (BPSK) modulations. We also show that the experimentally collected field data match well with the results of simulations based on our analytical model.
Delay/Disruption Tolerant Networking Performance Characterization in Cislunar Relay Communication Architecture
Authors: Wang Ding , Wang Ethan , Wang Ruhai
Published: January 2025

Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path.
Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar
Authors: Gao Yukun , Cao Lin , Zhao Zongmin , Wang Dongfeng , Fu Chong , Guo Yanan
Published: January 2025

Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks. Firstly, an MRW image encoder is used to extract salient feature representations from all time-Doppler images through contrastive learning. This can extract the representative vector of each image and also obtain the pre-training parameters of the MRW image encoder. During the pre-training process, large-scale residual networks, medium-scale residual networks, and small-scale residual networks are used to extract global information, texture information, and semantic information, respectively. Moreover, the time–channel weighting mechanism can allocate weights to important time and channel dimensions to achieve more effective extraction of feature information. The model parameters obtained from pre-training are frozen, and the classifier is added to the backend. Finally, the classifier is fine-tuned using a small amount of labeled data. In addition, we constructed a new dataset with eight dangerous activities. The proposed MRW-CN model was trained on this dataset and achieved a classification accuracy of 96.9%. We demonstrated that our method achieves state-of-the-art performance. The ablation analysis also demonstrated the role of multi-scale convolutional kernels and time–channel weighting mechanisms in classification.
What Resilience Skills Do Emergency Workers Need During a Widespread Phase of a Socio-Health Emergency? A Focus on the Role of Hardiness and Resilience
Authors: Giostra Valeria , Maiorano Tiziana , Vagni Monia
Published: January 2025

Emergency volunteers who intervene during disasters and emergency situations are subjected to risk to their psychological well-being. Experiencing highly stressful experiences can lead to developing secondary trauma and burnout. It is, therefore, important to know what protective factors can be strengthened in volunteers to protect their mental and social well-being. This study has the main objective of analyzing how the possible protective factors hardiness and resilience intervene in the relationship with emergency stress, secondary trauma, and burnout in a sample of 476 Italian Red Cross volunteers using an online transactional survey during the second phase of the COVID-19 pandemic. Pearson’s correlation, several hierarchical linear regression models, and moderation analysis were conducted. The results showed a strong positive association between emergency stress, secondary trauma, and burnout, and their negative correlation with resilience skills. Furthermore, emergency stress and the avoidance of secondary trauma led to an increase in emotional exhaustion and depersonalization of emergency volunteers. During the emergency phases, hardiness played a greater protective role in burnout than resilience skills. Hardiness was found to be a good moderator of both emergency stress and avoidance and intrusion of secondary trauma. Connor–Davidson resilience showed a significant moderating effect between the components of secondary trauma and burnout. These results suggest the importance of implementing training in hardiness and resilience for emergency volunteers to reduce the risk of stress, secondary trauma, and burnout during a health emergency and improve their social well-being.
The Building Energy Performance Gap in Multifamily Buildings: A Detailed Case Study Analysis of the Energy Demand and Collective Heating System
Authors: Van de Putte Stijn , Steeman Marijke , Janssens Arnold
Published: January 2025

The building energy performance gap, resulting from a discrepancy between the actual energy use and theoretical calculations, remains a persistent issue in building design. This study examines the energy performance of three multifamily buildings with a collective heating system powered by gas boilers and solar collectors: two that underwent deep renovation and one newly built. An extensive on-site monitoring system provides detailed data on both the heating demand and the final energy use. To ensure comparability, the total energy use of each unit is normalised using the energy signature method. The findings show the large spread of actual energy demands due to a wide variation in user profiles. The majority of dwellings have an actual energy use that is significantly higher than calculated, which is largely attributable to space heating. The gap is further exacerbated by substantial heat losses within the building’s heating system and by limited gains from the solar collectors, indicating discrepancies between design models and operational realities. To bridge this gap, there is a need for rigorous commissioning processes, at least during the initial operation phase start-up and ideally continuously. This can ensure more effective utilisation of renewable energy sources and reduce energy inefficiencies.
Experimental Investigation of Methyl Ester–Ethanol Blends as a Sustainable Biofuel Alternative for Heavy Duty Engines
Authors: Fratita Michael , Chivu Robert-Madalin , Rusu Eugen , Carp Gabriel Bogdan , Ion Ion , Brito Francisco P.
Published: January 2025

Agriculture may hold the key to a sustainable future. By efficiently capturing atmospheric CO2, we can simultaneously produce food, feed, biomass, and biofuels. For more eco-friendly soil processing practices, biofuels can replace diesel in agricultural machinery, significantly reducing the carbon footprint of crop production. Thus, biofuel production can be a sustainable solution for a future with a decreasing carbon footprint. This paper examines the possibility of replacing petroleum-based fuels with 100% biofuels to continue powering heavy-duty vehicles, where the use of electric vehicles is not the optimal solution. This study particularly focused on the operating scenario of heavy-duty engines under medium to high loads, typical of transport or soil processing in agriculture. Diesel was used as a benchmark, and each alternative, such as vegetable oil, methyl ester (B100), and methyl ester–ethanol blends (90B10E, 80B20E, and 70B30E), was tested individually. To find a sustainable fuel substitute, the goal was to identify a biofuel with a kinematic viscosity similar to that of diesel for a comparable spray process. Experimental results showed that an 80% methyl ester and 20% ethanol blend had a kinematic viscosity close to that of diesel. In addition to diesel, this blend resulted in a 48.6% reduction in exhaust gas opacity and a 6.54% lower specific fuel consumption (BSEC). The main aim of the tests was to find a 100% biofuel substitute without modifying the fuel injection systems of existing engines.
Research on Alkali-Activated Systems Based on Solid Waste-Derived Activators: A Review
Authors: Wan Xiaomei , Ren Lijie , Lv Tingjian , Wang Dengke , Wang Boshi
Published: January 2025

The development of solid waste-derived activators for the preparation of alkali-activated systems is of great significance for further enhancing the environmental friendliness and sustainability of alkali-activated materials. In this review, the potential feasibility of applying solid waste-derived activators to prepare alkali-activated systems is explored. Through a discussion of alkali-activation mechanisms, the effect of activators on high-calcium and low-calcium systems is highlighted first. The properties and limitations of commonly used industrial-grade alkali activators are reviewed next. Finally, recent reports on waste-derived activators in alkaline activation systems are summarized, revealing the advantages and development trends of solid wastes with CaO, K2O/Na2O, and SiO2 as the main components of effective substitutes for alkaline industrial exciters.
Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
Authors: Khan Burhan Ul Islam , Goh Khang Wen , Khan Abdul Raouf , Zuhairi Megat F. , Chaimanee Mesith
Published: January 2025

A typical Wireless Sensor Network (WSN) defines the usage of static sensors; however, the growing focus on smart cities has led to a rise in the adoption of mobile sensors to meet the varied demands of Internet of Things (IoT) applications. This results in significantly increasing dependencies towards secure storage and effective resource management. One way to address this issue is to harness the immutability property of the Ethereum blockchain. However, the existing challenges in IoT communication using blockchain are noted to eventually lead to symmetry issues in the network dynamics of Ethereum. The key issues related to this symmetry are scalability, resource disparities, and centralization risk, which offer sub-optimal opportunities for nodes to gain benefits, influence, or participate in the processes in the blockchain network. Therefore, this paper presents a novel blockchain-based computation model for optimizing resource utilization and offering secure data exchange during active communication among mobile sensors. An empirical method of trust computation was carried out to identify the degree of legitimacy of mobile sensor participation in the network. Finally, a novel cost model has been presented for cost estimation and to enhance the users’ quality of experience. With the aid of a simulation study, the benchmarked outcome of the study exhibited that the proposed scheme achieved a 40% reduced validation time, 28% reduced latency, 23% improved throughput, 38% minimized overhead, 27% reduced cost, and 38% reduced processing time, in contrast to the existing blockchain-based solutions reported in the literature. This outcome prominently exhibits fairer symmetry in the network dynamics of Ethereum presented in the proposed system.
ZnO Nanoparticles by Hydrothermal Method: Synthesis and Characterization
Authors: Anaya-Zavaleta Juan Carlos , Ledezma-Pérez Antonio Serguei , Gallardo-Vega Carlos , Rodríguez-Hernández Joelis , Alvarado-Canché Carmen Natividad , García-Casillas Perla Elvia , de León Arxel , Herrera-May Agustín Leobardo
Published: January 2025

The synthesis of reliable, cost-effective, and eco-friendly ZnO piezoelectric nanoparticles (NPs) can contribute to nanotechnology applications in electronics, sensors, and energy harvesting. Herein, ZnO NPs were synthesized using a hydrothermal method under varied reaction times and adding ammonium hydroxide, which provided an advantage of a low-cost, scalable, low-temperature, and environmentally friendly process. Characterization through UV–Vis spectroscopy revealed absorption peaks between 374 and 397 nm, showing a blue shift compared to bulk ZnO (400 nm) attributable to nanoscale dimensions. Transmission Electron Microscopy (TEM) analysis indicated particle dimensions with length and width ranges from 150 to 341 nm and from 83 to 120 nm, respectively. X-ray diffraction (XRD) confirmed high-crystalline quality, with crystallite sizes calculated using the Scherrer equation. In addition, the effective mass model provided an estimated band gap that matched with the reported data. Also, the lattice parameters, interplanar distances, and Zn-O bond lengths were consistent with Joint Committee on Powder Diffraction Standards (JCPDS). Finally, a ZnO NP film was deposited on a steel substrate, which generated a displacement of 150 nm under a square wave voltage of 10 V. The piezoelectric behavior of the synthesized ZnO NPs can be useful for fabrication of piezoelectric nanogenerators. The proposed synthesis can allow ZnO NPs with potential application in electronic devices, energy harvesters, and transducers.
Influence Mechanism of Vermicompost with Different Maturity on Atrazine Catabolism and Bacterial Community
Authors: Zhang Luwen , Xu Lixin , Zhang Zunhao , Li Jiaolin , Ren Limeng , Liu Zhichen , Zhang Yan , Chen Yuxiang
Published: January 2025

Atrazine causes serious contamination of agricultural soils and groundwater. This study investigated the influence mechanism of sterilized soil (CKs), unsterilized soil (CKn), sterilized soil amended with 45 (SsV1), 60 (SsV2), 75 (SsV3) days of vermicompost (the maturity days of vermicompost), and unsterilized soil amended with 45 (SnV1), 60 (SnV2), 75 (SnV3) days of vermicompost on atrazine catabolism. The atrazine degradation experiment lasted for 40 days. The results showed that the atrazine degradation rates for CKs, CKn, SsV1, SsV2, SsV3, SnV1, SnV2, and SnV3 were 24%, 56.9%, 62.8%, 66.1%, 65.9%, 87.5%, 92.9%, and 92.3%, respectively. Indigenous microorganisms capable of degrading atrazine were present in unsterilized soil, and the addition of vermicompost enhanced atrazine degradation. The humic acid content of SnV2 was the highest, at 4.11 g/kg, which was 71.97% higher than that of CKn. The addition of the vermicompost enhanced the production of hydroxyatrazine, deethylatrazine, and deisopropylatrazine. Vermicompost increased the abundance of atrazine-degrading bacteria (Mycobacterium, Devosia, etc.), and introduced new atrazine-degrading bacteria (Mesorhizobium, Demequina). The above results showed that the best degradation of atrazine was achieved with 60 days of vermicompost addition. This study provides a new, efficient, economical, and environmentally friendly strategy for the remediation of atrazine-contaminated soil.
Exposure to Subclinical Doses of Fumonisins, Deoxynivalenol, and Zearalenone Affects Immune Response, Amino Acid Digestibility, and Intestinal Morphology in Broiler Chickens
Authors: Shanmugasundaram Revathi , Kappari Laharika , Pilewar Mohammad , Jones Matthew K. , Olukosi Oluyinka A. , Pokoo-Aikins Anthony , Applegate Todd J. , Glenn Anthony E.
Published: January 2025

Fusarium mycotoxins often co-occur in broiler feed, and their presence negatively impacts health even at subclinical concentrations, so there is a need to identify the concentrations of these toxins that do not adversely affect chickens health and performance. The study was conducted to evaluate the least toxic effects of combined mycotoxins fumonisins (FUM), deoxynivalenol (DON), and zearalenone (ZEA) on the production performance, immune response, intestinal morphology, and nutrient digestibility of broiler chickens. A total of 960 one-day-old broilers were distributed into eight dietary treatments: T1 (Control); T2: 33.0 FUM + 3.0 DON + 0.8 ZEA; T3: 14.0 FUM + 3.5 DON + 0.7 ZEA; T4: 26.0 FUM + 1.0 DON + 0.2 ZEA; T5: 7.7 FUM + 0.4 DON + 0.1 ZEA; T6: 3.6 FUM + 2.5 DON + 0.9 ZEA; T7: 0.8 FUM + 1.0 DON + 0.3 ZEA; T8: 1.0 FUM + 0.5 DON + 0.1 ZEA, all in mg/kg diet. The results showed that exposure to higher mycotoxin concentrations, T2 and T3, had significantly reduced body weight gain (BWG) by 17% on d35 (p < 0.05). The T2, T3, and T4 groups had a significant decrease in villi length in the jejunum and ileum (p < 0.05) and disruption of tight junction proteins, occludin, and claudin-4 (p < 0.05). Higher mycotoxin groups T2 to T6 had a reduction in the digestibility of amino acids methionine (p < 0.05), aspartate (p < 0.05), and serine (p < 0.05); a reduction in CD4+, CD8+ T-cell populations (p < 0.05) and an increase in T regulatory cell percentages in the spleen (p < 0.05); a decrease in splenic macrophage nitric oxide production and total IgA production (p < 0.05); and upregulated cytochrome P450-1A1 and 1A4 gene expression (p < 0.05). Birds fed the lower mycotoxin concentration groups, T7 and T8, did not have a significant effect on performance, intestinal health, and immune responses, suggesting that these concentrations pose the least negative effects in broiler chickens. These findings are essential for developing acceptable thresholds for combined mycotoxin exposure and efficient feed management strategies to improve broiler performance.
Comparative Adsorption of Porcine Reproductive and Respiratory Syndrome Virus Strains to Minnesota Soils
Authors: Alvarez-Norambuena Joaquin , Quinonez-Munoz Angie , Corzo Cesar A. , Goyal Sagar M.
Published: January 2025

Porcine reproductive and respiratory syndrome (PRRS) is an endemic disease affecting the swine industry. The disease is caused by the PRRS virus (PRRSV). Despite extensive biosecurity and control measures, the persistence and seasonality of the virus have raised questions about the virus’s environmental dynamics during the fall season when the yearly epidemic onset begins and when crop harvesting and manure incorporation into the field occur. Therefore, this study aimed to assess the potential for PRRSV to percolate through different soil types, simulating conditions that could lead to groundwater contamination which could represent a risk of herd introduction. An experimental soil column model was used to mimic field conditions. Three PRRSV-2 strains were tested across thirteen Minnesota soils with different physical and chemical characteristics. The findings revealed that PRRSV can percolate through all soil types and that the amount of virus percolated decreases with increased amounts of soil. These results suggest that PRRSV can percolate through different soil types. Further investigations should be undertaken to determine the associated implications for swine health and biosecurity measures.
A Nanoparticle Comprising the Receptor-Binding Domains of Norovirus and Plasmodium as a Combination Vaccine Candidate
Authors: Xia Ming , Huang Pengwei , Vago Frank S. , Jiang Wen , Jiang Xi , Tan Ming
Published: January 2025

Background: Noroviruses, which cause epidemic acute gastroenteritis, and Plasmodium parasites, which lead to malaria, are two infectious pathogens that pose threats to public health. The protruding (P) domain of norovirus VP1 and the αTSR domain of the circumsporozoite protein (CSP) of Plasmodium sporozoite are the glycan receptor-binding domains of the two pathogens for host cell attachment, making them excellent targets for vaccine development. Modified norovirus P domains self-assemble into a 24-meric octahedral P nanoparticle (P24 NP). Methods: We generated a unique P24-αTSR NP by inserting the αTSR domain into a surface loop of the P domain. The P-αTSR fusion proteins were produced in the Escherichia coli expression system and the fusion protein self-assembled into the P24-αTSR NP. Results: The formation of the P24-αTSR NP was demonstrated through gel filtration, electron microscopy, and dynamic light scattering. A 3D structural model of the P24-αTSR NP was constructed, using the known cryo-EM structure of the previously developed P24 NP and P24-VP8* NP as templates. Each P24-αTSR NP consists of a P24 NP core, with 24 surface-exposed αTSR domains that have retained their general conformations and binding function to heparan sulfate proteoglycans. The P24-αTSR NP is immunogenic, eliciting strong antibody responses in mice toward both the norovirus P domain and the αTSR domain of Plasmodium CSP. Notably, sera from mice immunized with the P24-αTSR NP bound strongly to Plasmodium sporozoites and blocked norovirus VLP attachment to their glycan receptors. Conclusion: These data suggest that the P24-αTSR NP may serve as a combination vaccine against both norovirus and Plasmodium parasites.
Optimization of Water Quantity Allocation in Multi-Source Urban Water Supply Systems Using Graph Theory
Authors: Zhang Jinxin , Zhang Xinhai , Li Hanze , Fan Yazhou , Meng Zhenzhu , Liu Dan , Pan Suli
Published: January 2025

The optimization of urban multi-source water supply systems is essential for addressing the growing challenges of water allocation, cost management, and system resilience in modern cities. This study introduces a graph-theory-based optimization model to analyze the structural and operational dynamics of urban water supply systems, incorporating constraints such as water quality, pressure, and system connectivity. Using Lishui City as a case study, the model evaluates three water allocation plans to meet the projected 2030 water demand. Advanced algorithms, including Floyd’s shortest path algorithm and the GA-COA-SA hybrid optimization algorithm, were employed to address constraints such as pipeline pressure, water quality attenuation, and nonlinear flow dynamics. Results indicate a 1.4% improvement in cost-effectiveness compared to the current allocation strategy, highlighting the model’s capability to enhance efficiency. Among the evaluated options, Plan 2 emerges as the most cost-effective solution, achieving a supply capacity of 4.5920 × 105 m3/d with the lowest annual cost of 5.7015 × 107 yuan, highlighting the model’s capability to improve both efficiency and resilience. This study prioritizes cost-efficiency tailored to regional challenges, distinguishing itself from prior research that emphasized redundancy and water quality analysis. The findings demonstrate the potential of graph-theoretic approaches combined with advanced optimization techniques to enhance decision-making for sustainable urban water management.
Assessment of Erosive Rainfall and Its Spatial and Temporal Distribution Characteristics: Case Study of Henan Province, Central China
Authors: Gu Zhijia , Li Yuemei , Huang Shuping , Yao Chong , Ji Keke , Feng Detai , Yi Qiang , Li Panying
Published: January 2025

Erosive rainfall is essential for initiating surface runoff and soil erosion to occur. The analysis on its temporal and spatial distribution characteristics is crucial for calculating rainfall erosivity, predicting soil erosion, and implementing soil and water conservation. This study utilized daily rainfall observation data from 90 meteorological stations in Henan from 1981 to 2020, and conducted geostatistical analysis, M-K mutation test analysis, and wavelet analysis on erosive rainfall to reveal the spatiotemporal distribution characteristics over the past 40 years. Building on this foundation, the correlation between erosive rainfall, rainfall, and rainfall erosivity were further explored. The findings indicated that the average annual rainfall in Henan Province varied between 217.66 mm and 812.78 mm, with an average yearly erosive rainfall of 549.24 mm and a standard deviation of 108.32 mm. Erosive rainfall constitutes for 77% of the average annual rainfall on average, and the analysis found that erosive rainfall is highly correlated with rainfall volume. The erosive rainfall increased from northwest to southeast, and had the same spatial distribution characteristics as the total rainfall. The number of days with erosive rainfall was 20.5 days and the annual average sub-erosive rainfall was 26.86 mm. The average annual rainfall erosivity in Henan Province ranged from 1341.81 to 6706.64 MJ·mm·ha−1·h−1, averaging at 3264.63 MJ·mm·ha−1·h−1. Both the erosive rainfall and the rainfall erosivity are influenced by the monsoon, showing a unimodal trend, with majority of the annual total attributed to rainfall erosivity from June to September, amounting to 80%. The results can provide a basis for forecasting of heavy rainfall events, soil conservation and planning, ecological treatment, and restoration.
Competitive Adsorption Studies of Cd(II) and As(III) by Poly (Butylene Succinate) Microplastics: Based on Experimental and Theoretical Calculation
Authors: Jiang Hui , Ding Zhaoyao , Lei Xiaoling , Li Xia , Que Sisi , Zhou Jinshan , Tang Jiafeng , Huang Yuanyuan , Sun Da
Published: January 2025

Microplastics (MPs) can serve as vectors for heavy metals in aquatic environments; however, the adsorption behavior of MPs on multiple heavy metal systems is still unclear. This study investigated the adsorption characteristics of biodegradable poly (butylene succinate) (PBS) for cadmium (Cd(II)) and arsenic (As(III)) in both single and binary systems. Adsorption isotherms were studied using the Linear, Langmuir, and Freundlich models, and further analysis of MPs adsorption characteristics was conducted using site energy distribution theory and density functional theory. The results indicate that the maximum adsorption capacities of PBS for Cd(II) and As(III) are 2.997 mg/g and 2.606 mg/g, respectively, with the Freundlich model providing the best fit, suggesting multilayer adsorption on heterogeneous sites. As(III) has a higher adsorption affinity for PBS than Cd(II), with a binding energy of −11.219 kcal/mol. Additionally, the adsorption mechanisms of Cd(II) and As(III) on PBS include electrostatic interactions and surface complexation, with the primary adsorption sites at the C=O of the carboxyl group and the hydroxyl group. The comprehension of interfacial interactions between biodegradable plastics and heavy metals is facilitated by a combination of theoretical calculations and experimental investigations.
A Modified Algorithm to Generate Flow Nets from the Nodal Potential and Stream Values of Eight-Node Quadrilateral Elements
Authors: Liu Fangxue , Wang Yue , Lin Hai
Published: January 2025

Eight-node quadrilateral isoparametric elements of the serendipity type have frequently been used in finite-element analyses of two-dimensional seepage problems. The shape functions for these elements are quadratic. Hence, nonlinear variation in the potential and stream function values across each element could be approximated to a high degree of accuracy. This also necessitates a commensurate high-order interpolation function to locate, in a straightforward way, equipotential lines and streamlines. In this paper, a quadratic interpolation algorithm for locating deformation contours is modified to suit flow net generation. The modification lies in the procedure for identifying the pairs of the points of intersection to be joined when there are four, six, or eight points of intersection of the contour segments of the same level and the edges of an element. The original algorithm finds the pairs of intersection points in a local coordinate system by testing all possible cases that may be encountered. The modified algorithm considers that in most, if not all, scenarios, equipotential lines and streamlines extend monotonically from one impervious boundary of the flow domain to another and from an inflow boundary to an outflow boundary, respectively. The intersection points are rapidly paired by converting their local coordinates to global coordinates and sorting the order of the intersection points according to their global coordinates. The modified algorithm eliminates the need for an exhaustive search and complex matching process, enhancing computational efficiency. The modified algorithm is verified against an exact analytical solution to the flow net for a levee under-seepage flow. Excellent agreement is obtained. Two additional illustrative examples are analyzed. One is unconfined seepage through a rectangular dam, and the other is confined seepage beneath unsymmetrical cofferdams. The equipotential lines and streamlines obtained from the modified algorithm are shown to be smoother and more accurate than those obtained using popular commercial software (GeoStudio 24.2.0), especially when a coarse finite-element mesh is adopted.
Challenges and Innovations in Urban Drainage Systems: Sustainable Drainage Systems Focus
Authors: Monachese Anna Pia , Gómez-Villarino María Teresa , López-Santiago Jesús , Sanz Ernesto , Almeida-Ñauñay Andrés F. , Zubelzu Sergio
Published: January 2025

Sustainable Urban Drainage Systems (SUDS) represent a paradigm shift in stormwater management, offering holistic solutions to urban water challenges. This review examines SUDS principles, design strategies, effectiveness and barriers to implementation. SUDS prioritize infiltration and mimic natural hydrological processes to reduce flood risk, improve water quality and support ecosystems in urban environments. Effective SUDS design integrates different components such as permeable pavements, green roofs, and rain gardens, tailored to the local context. Evidence suggests that well-designed SUDS can mitigate peak flows, reduce runoff volumes, and purify water. However, barriers to widespread adoption include lack of awareness, upfront costs, and regulatory complexity. Overcoming these will require collaborative stakeholder action to prioritize education, policy support, and funding opportunities. Future research should focus on optimizing SUDS design, assessing long-term performance, and quantifying socio-economic benefits. By integrating SUDS into urban landscapes, cities can strengthen hydrological resilience, promote sustainability, and enrich urban life.
Bioaccumulation Study of Cadmium and Lead in Cyprinus carpio from the Colorado River, Using Automated Electrochemical Detection
Authors: Vallese Federico Danilo , Stupniki Sofia , Trillini Mariano , Belén Federico , Di Nezio María Susana , Juan Alfredo , Pistonesi Marcelo Fabian
Published: January 2025

The monitoring of heavy metals in aquatic ecosystems is of critical importance due to the toxic effects that these elements can have on wildlife and the potential risks that they pose to human health. Rivers situated in close proximity to agricultural regions are particularly susceptible to contamination from a combination of natural and anthropogenic sources. The study of bioaccumulation is of great importance for the early detection of environmental stressors. The combination of electrochemical techniques, such as square-wave anodic stripping voltammetry (SWASV), with automated flow-batch systems represents an efficient and cost-effective approach for the detection of trace metals in environmental samples. This study examines the bioaccumulation of cadmium and lead in Cyprinus carpio, a bioindicator of contamination in the Colorado River, Argentina. The fish were exposed to sublethal metal concentrations for 24, 48, and 96 h. Metal quantification was conducted using a novel automatic flow-batch system with SWASV and a bismuth film electrode. To the best of our knowledge, this constitutes the first application of this methodology on aquatic bioindicators for the assessment of metal accumulation in a natural environment. The technique demonstrated enhanced sensitivity and selectivity for the detection of trace metals. The bioaccumulation results demonstrated an increase in cadmium and lead concentrations in fish liver tissue after 96 h, reaching 10.5 µg g−1 and 11.9 µg g−1, respectively. Validation with inductively coupled plasma–atomic emission spectrometry (ICP-AES) demonstrated a satisfactory correlation, confirming the reliability of the method. This novel electrochemical approach offers enhanced accuracy and efficiency, making it a promising tool for environmental monitoring. The results indicate that Colorado River water is within safe levels for aquatic life regarding these metals. However, continuous monitoring is recommended to detect changes in contamination levels and protect ecosystem health, especially during water crises and under climate change.