bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Fiber Cross-Sectional and Surface Properties on the Degradation of Biobased Polymers
Authors: Schick Simon , Weinberger Andreas , Groten Robert , Seide Gunnar H.
Published: November 2024

Biobased polymers such as polylactic acid (PLA) and polybutylene succinate (PBS) break down naturally under certain environmental conditions. The efficiency of degradation can be linked directly to fiber surface properties, which influence polymer accessibility. Here, the degradation of PLA and PBS fibers with six different cross-sections was investigated. The fibers were aged by hydrolysis and UV exposure in an accelerated weathering test, followed by an ISO 20200 laboratory-scale disintegration test with non-aged fibers as controls. The polymers were analyzed by differential scanning calorimetry, Fourier transform infrared spectroscopy, and gel permeation chromatography, comparing the polymer granulate, virgin fibers, and UV-exposed fibers. It was found that the molecular mass and crystallinity of PBS changed more than PLA during spinning. Several PLA samples were completely degraded, whereas all the PBS samples remained intact. Furthermore, surface openings appeared on the PLA fibers during weathering, suggesting greater sensitivity to UV exposure and hydrolysis than PBS. A clear correlation between the fiber surface area and the degradation rate was observed for all samples, but the correlation was positive for PLA and negative for PBS. The slower degradation of PBS fibers with a larger surface area may reflect the ability of PBS to preserve itself by further crystallization during degradation processes at temperatures higher than the glass transition point. The data clearly show that the analysis of single degradation mechanisms is insufficient to predict the behavior of material under real-world conditions, where different degradation mechanisms may work in parallel or consecutively, and may show interdependencies.
Energy Consumption Prediction of Injection Molding Process Based on Rolling Learning Informer Model
Authors: Huang Jianfeng , Li Yi , Li Xinyuan , Ding Yucheng , Hong Fenglian , Peng Shitong
Published: November 2024

Accurate energy consumption prediction in the injection molding process is crucial for optimizing energy efficiency in polymer processing. Traditional parameter optimization methods face challenges in achieving optimal energy prediction due to complex energy transmission. In this study, a data-driven approach based on the Rolling Learning Informer model is proposed to enhance the accuracy and adaptability of energy consumption forecasting. The Informer model addresses the limitations of long-sequence prediction with sparse attention mechanisms, self-attention distillation, and generative decoder techniques. Rolling learning prediction is incorporated to enable continuous updating of the model to reflect new data trends. Experimental results demonstrate that the RL-Informer model achieves a normalized root mean square error of 0.1301, a root mean square error of 0.0758, a mean absolute error of 0.0562, and a coefficient of determination of 0.9831 in energy consumption forecasting, outperforming other counterpart models like Gated Recurrent Unit, Temporal Convolutional Networks, Long Short-Term Memory, and two variants of the pure Informer models without Rolling Learning. It is of great potential for practical engineering applications.
A Comprehensive Review on the Incremental Sheet Forming of Polycarbonate
Authors: Formisano Antonio , Durante Massimo
Published: November 2024

Incremental sheet forming has emerged as an excellent alternative to other material forming procedures, incrementally deforming flat metal sheets into complex three-dimensional profiles. The main characteristics of this process are its versatility and cost-effectiveness; additionally, it allows for greater formability compared to conventional sheet forming processes. Recently, its application has been extended to polymers and composites. The following review aims to present the current state of the art in the incremental sheet forming of polycarbonate, an outstanding engineering plastic, beginning with initial studies on the feasibility of this process for polymers. Attention is given to the advantages, drawbacks, and main applications of incrementally formed polycarbonate sheets, as well as the influence of process parameters and toolpath strategies on features such as formability, forming forces, deformation and failure mechanisms, geometric accuracy, surface quality, etc. Additionally, new hybrid forming methods for process optimisation are presented. Finally, a discussion is provided on the technical challenges and future research directions for incremental sheet forming of polycarbonate and, more generally, thermoplastics. Thus, this review aims to offer an extensive overview of the incremental forming of polycarbonate sheets, useful to both academic and industrial researchers working on this topic.
Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning
Authors: Wang Jingrui , Li Yi , Zhang Zhongwei , Wu Zhaoyun , Wu Lihui , Jia Shun , Peng Tao
Published: November 2024

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.
Production Law of Shielding Oil in Fractured Carbonate Reservoirs: A Case Study of Northwest Oilfield, China
Authors: Li Jinghui , Qu Bochao , Zhen Enlong , Ma Shufen , Liu Yifei
Published: November 2024

Multi-scale fractures in Northwest Oilfield are extremely developed. Fractures in both oil flow channels and water channels lead to the phenomenon of water channeling in the later stages of reservoir development, which may be affected by the bottom water cone. Gel plugging agents are widely used in the treatment of this phenomenon because of their low cost and high-water plugging efficiency. However, because there is often residual oil in small-scale fractures, the pressure law of different grades of differential fractures on gel is not clear, thus leading to low gel application efficiency and low oil recovery. There is still a lack of effective means through which to understand the regularity of shielding the remaining oil with gel strength and fractures of different grades. In this study, we conducted a novel analysis of the law of shielding oil production in fractured reservoirs. The gelling and rheological properties of anti-temperature gels (ATGs) with different strengths were studied. The plugging rate of oil phase was 96.39%, and that of water phase was 25.37%. ATG showed good oil–water selectivity. The influences of different grades of differential fractures on the production law of shielding the remaining oil were also studied, as well as the corresponding influence law chart. When the gel strength was in the range of 2.0–28.1 Pa and the fracture grade difference was 5–10, the recovery ratio was increased by 10.6–24%. The enhanced oil recovery can be quantitatively predicted by the scale of reservoir fractures and the strength of gel used. It has a certain guiding role for the field application of gel to efficiently shield residual oil in differential fractures.
Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
Authors: Yu Shui , Peng Jing , Ma Ming , Gong Hang , Li Zongnan , Ni Shaojie
Published: November 2024

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×10−15 and 2.30×10−15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.
Detecting Water Stress in Winter Wheat Based on Multifeature Fusion from UAV Remote Sensing and Stacking Ensemble Learning Method
Authors: Zhao He , Wang Jingjing , Guo Jiali , Hui Xin , Wang Yunling , Cai Dongyu , Yan Haijun
Published: November 2024

The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (gs) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, gs was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that gs at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the gs value and SWSI value was high (R2 > 0.79). Therefore, the gs value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R2 increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R2: 0.69–0.87, RMSE: 0.04–0.08 mol m−2 s−1; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m−2 s−1) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation.
Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data
Authors: Yin Hanmin , Ma Xiaofei , Liao Xiaohan , Ye Huping , Yu Wentao , Li Yue , Wei Junbo , Yuan Jincheng , Liu Qiang
Published: November 2024

With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change.
A Geophysical Investigation in Which 3D Electrical Resistivity Tomography and Ground-Penetrating Radar Are Used to Determine Singularities in the Foundations of the Protected Historic Tower of Murcia Cathedral (Spain)
Authors: García-Nieto María C. , Martínez-Segura Marcos A. , Navarro Manuel , Valverde-Palacios Ignacio , Martínez-Pagán Pedro
Published: November 2024

This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral “Santa Iglesia Catedral de Santa María” in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations.
Regulation and Liquid Sensing of Electromagnetically Induced Transparency-like Phenomena Implemented in a SNAP Microresonator
Authors: Liu Chenxiang , Chai Minggang , Zheng Chenglong , Xie Chengfeng , Sun Chuanming , Shi Jiulin , He Xingdao , Wang Mengyu
Published: November 2024

Optical microresonators supporting whispering-gallery modes (WGMs) have become a versatile platform for achieving electromagnetically induced transparency-like (EIT-like) phenomena. We theoretically and experimentally demonstrated the tunable coupled-mode induced transparency based on the surface nanoscale axial photonics (SNAP) microresonator. Single-EIT-like and double-EIT-like (DEIT-like) effects with one or more transparent windows are achieved due to dense mode families and tunable resonant frequencies. The experimental results can be well-fitted by the coupled mode theory. An automatically adjustable EIT-like effect is discovered by immersing the sensing region of the SNAP microresonator into an aqueous environment. The sharp lineshape and high slope of the transparent window allow us to achieve a liquid refractive index sensitivity of 2058.8 pm/RIU. Furthermore, we investigated a displacement sensing phenomenon by monitoring changes in the slope of the transparent window. We believe that the above results pave the way for multi-channel all-optical switching devices, multi-channel optical communications, and biochemical sensing processing.
Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning
Authors: Yan Yan , Lei Jingjing , Huang Yuqing
Published: November 2024

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R2 = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R2 = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment.
A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
Authors: Tao Yixin , Tian Bingwei , Adhikari Basanta Raj , Zuo Qi , Luo Xiaolong , Di Baofeng
Published: November 2024

Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness.
QuEChERS-Based Method for the Determination of Fipronil in Protein Baits and Vespa velutina Larvae by HPLC-DAD and GC-MS
Authors: de la Hera Omaira , Izaguirre Aritza , Rivas Arrate , Alonso Rosa María
Published: November 2024

Protein baits containing fipronil as a biocide have shown their effectivity as a control method for Vespa velutina nigrithorax (Lepeletier, 1836) in apiaries. This biocide is not selective for Vespa velutina, so it is important to use the minimum dose to inactivate a nest. Therefore, the aim of this work was the development of analytical methods for the determination of fipronil in protein baits for quality control purposes and in larvae of Vespa velutina to determine the biocide content after protein bait ingestion and to acquire knowledge on fipronil metabolism in larvae. For this purpose, a Quechers-based HPLC-PDA method was developed and validated for the determination of fipronil in both matrixes. Furthermore, a GC-MS method was developed for the analysis of fipronil and its metabolites in dead Vespa velutina larvae fed with a mash containing 0.01% fipronil. Quechers-based HPLC-DAD allowed for the determination of the fipronil content in baits. Fipronil and the metabolites fipronil sulfone and fipronil sulfide were identified by GC-MS in extracts of larvae fed with a protein mash containing 0.01% fipronil. The transformation of fipronil into fipronil sulfone inside the larvae and the high toxicity of this metabolite open the possibility to produce protein baits with lower biocide concentrations.
Information, Entanglement, and Emergent Social Norms: Searching for ‘Normal’
Authors: Cardinal James , Loughmiller-Cardinal Jennifer
Published: November 2024

Social norms are often regarded as informal rules or strategies. Previously, we have proposed that norms are better understood as information. Social norms represent a behavioral adaptation that identifies and curates the information required to create and maintain a predictable environment. Here, we further demonstrate that social norms act as the leading edge of individual and collective search and optimization processes. These processes provide efficient and effective evolutionary mechanisms for constant adjustment and adaptation to an environment. We show that social norms constitute the forefront of our ability to ingest and process information, and are responsible for the conditions under which social and collective cognition are possible. This new model of social information processing provides not only insight into how humans adapt and evolve to diverse environments, but also provides concrete definitions of human sociality and its distinctions from that of other social animals. Our social constructs and behaviors are not separate from the physical world we inhabit, but are instead the primary means by which we exist within it.
A Class of φ-Contractions in Orthogonal Metric Spaces with an Application
Authors: Filali Doaa , Akram Mohammad , Dilshad Mohammad
Published: November 2024

This article deals with certain outcomes on fixed points of an orthogonal nonlinear contraction map in the framework of O-complete metric spaces. The findings investigated herein enhance and sharpen a few outcomes on fixed points. In order to demonstrate our outcomes, we provide a number of illustrative examples. Finally, via our findings, we discuss the existence and uniqueness of solutions to a periodic boundary value problem.
The Acute Toxicity and Cardiotoxic Effects of Protocatechuic Aldehyde on Juvenile Zebrafish
Authors: Yin Jiufeng , Wang Hui , Zhao Feng , Liang Dan , Yang Wenqing , Zhang Dan
Published: November 2024

Protocatechuic aldehyde (PCA) is a natural phenolic acid compound with pharmacological effects such as anti-oxidative stress, antibacterial, anti-apoptotic, anti-inflammatory, anti-platelet aggregation, and anti-tumor. Despite the favorable therapeutic effects of PCA, it is imperative to recognize that adverse drug reactions can arise even with satisfactory quality assurance measures and during standard clinical application and dosing. Additionally, the acute toxicity and cardiotoxic sequelae of PCA are frequently under reported in the available documentation. To investigate the acute toxicity and cardiotoxic effects of PCA, the present study comprehensively assessed the acute toxicity and cardiotoxic effects of PCA by administering different concentrations of PCA and by monitoring the phenotypic changes in zebrafish, using AB wild-type Tg(cmlc2:EGFP) zebrafish as the experimental model organism. Meanwhile, the target genes of PCA that may cause cardiotoxicity were predicted and validated using a network pharmacology approach. Our findings indicated that PCA exhibited severe acute toxicity and cardiotoxic effects in zebrafish at 70 μg/mL and 80 μg/mL. Furthermore, PIK3CA, PARP1, and GSK3β may be involved in the mechanism of action of the cardiotoxicity-inducing effects of this compound. The present investigation has afforded a deeper insight into the acute toxicity and cardiotoxic impacts of PCA on zebrafish and has established a significant theoretical foundation for the evaluation of toxicity in pharmaceuticals incorporating PCA.
The High-Efficiency Degradation of Multiple Mycotoxins by Lac-W Laccase in the Presence of Mediators
Authors: Jia Mengshuang , Yu Xiaohu , Xu Kun , Gu Xiaodan , Harmer Nicholas J. , Zhao Youbao , Xiang Yuqiang , Sheng Xia , Li Chenglong , Du Xiang-Dang , Pan Jiajia , Hao Wenbo
Published: November 2024

Mycotoxin cocontamination is a severe threat to health and economic security worldwide. The mycotoxins aflatoxin B1 (AFB1), zearalenone (ZEN), deoxynivalenol, T-2 toxin, fumonisin B1, and ochratoxin A are of particular concern due to their substantial toxicity. Lac-W is a laccase with the unique property of degrading these six mycotoxins in the absence of redox mediators. Nevertheless, their degradation rates are low. This work aims to improve the ability of Lac-W to degrade these six mycotoxins and to elucidate its detoxification mechanism. Including redox mediators increased the Lac-W degradation efficiency drastically, and completely degraded AFB1 and ZEN within one hour. Additionally, Lac-W-AS has good temperature, pH, and ions adaptability in ZEN degradation. Lac-W-AS reduced the ZEN toxicity because ZEN degradation products significantly restored the bioluminescence intensity of Vibrio fischeri. A Lac-W-AS-mediated oxidation product of ZEN was structurally characterized as 15-OH-ZEN by UHPLC-MS/MS. Linear sweep voltammetry showed that AS affected the potential of Lac-W and accelerated the oxidation of ZEN. Finally, the combination of mediators (acetosyringone and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonate)) improved the degradation rate of mycotoxins. This work highlights that the combination of Lac-W with mediators serves as a good candidate for degrading multi-mycotoxin contaminants in food and feedstuff.
Mycotoxin Biodegradation by Bacillus Bacteria—A Review
Authors: Nguyen Thanh , Chen Xiaojing , Ma Linlin , Feng Yunjiang
Published: November 2024

Mycotoxins are toxic secondary metabolites produced by various types of fungi that are known to contaminate various food products; their presence in the food chain poses significant risks to human and animal health and leads to enormous economic losses in the food and feed industry worldwide. Ensuring food safety and quality by detoxifying mycotoxin is therefore of paramount importance. Several procedures to control fungal toxins have been extensively investigated, such as preventive measures, physical and chemical methods, and biological strategies. In recent years, microbial degradation of mycotoxins has attracted much attention due to its reliability, efficiency, and cost-effectiveness. Notably, bacterial species from the Bacillus genus have emerged as promising candidates for mycotoxin decontamination owing to their diverse metabolic capabilities and resilience in harsh environmental conditions. This review manuscript aims to provide a summary of recent studies on the biodegradation of fungal toxins by Bacillus bacteria, thereby illustrating their potential applications in the development of mycotoxin-degrading products.
Analogous Hawking Radiation in Dispersive Media
Authors: Belgiorno Francesco , Cacciatori Sergio L. , Trevisan Simone
Published: November 2024

In the framework of the analogous Hawking effect, we significantly improve our previous analysis of the master equation that encompasses very relevant physical systems, like Bose–Einstein condensates (BECs), dielectric media, and water. In particular, we are able to provide two significant improvements to the analysis. As our main result, we provide a complete set of connection formulas for both the subluminal and superluminal cases without resorting to suitable boundary conditions, first introduced by Corley, but simply on the grounds of a rigorous mathematical setting. Moreover, we provide an extension to the four-dimensional case, showing explicitly that, apart from obvious changes, adding transverse dimensions does not substantially modify the Hawking temperature in the dispersive case. Furthermore, an important class of exact solutions of the so-called reduced equation that governs the behavior of non-dispersive modes is also provided.
The Research Landscape of AI in Urban Planning: A Topic Analysis of the Literature with ChatGPT
Authors: Sanchez Thomas W. , Fu Xinyu , Yigitcanlar Tan , Ye Xinyue
Published: November 2024

This study investigated the current state of artificial intelligence (AI) in urban planning by analyzing 744 research publications. Utilizing topic modeling analysis with latent Dirichlet allocation (LDA) and ChatGPT, we interpreted and categorized weighted keywords from this analysis, and then generated topic names based on these insights. The analysis identified 16 key themes within the corpus, encompassing a range of topics including urban and transport planning, urban and architectural design methods, as well as algorithms and predictive modeling techniques. The most prevalent topic identified was “Urban Design and Architectural Methods”, emphasizing the integration of AI in urban design strategies. Other significant themes included “Smart Urban Development and Social Governance” and “Algorithms and Predictive Modeling in Transportation”. The findings demonstrate the diverse applications of AI in urban planning, such as enhancing public services, optimizing transportation systems, and managing urban development. This study underscores the increasing application of AI in addressing urban challenges and provides a comprehensive overview of the current state of research, offering useful insights for future studies and potential implementations in urban planning. The study findings offer researchers and practitioners invaluable insights, uncovering both opportunities and gaps in the literature that can guide and shape future research and practical initiatives.
New Genotype G3 P[8] of Rotavirus Identified in a Mexican Gastroenteric Rabbit
Authors: Reynoso-Utrera Emmanuel , Bautista-Gómez Linda Guiliana , Fonseca-Coronado Salvador , Pérez-de la Rosa Juan Diego , Rodríguez-Villavicencio Valeria Jazmín , Romero-Núñez Camilo , Flores-Ortega Ariadna , Hernández-García Pedro Abel , Martínez-Castañeda José Simón
Published: November 2024

Rotavirus species A (RVA) is a major cause of acute viral gastroenteritis in young humans and diverse animal species. The study of the genetic characteristics of RVAs that infect rabbits (Oryctolagus cuniculus) (lapine strain [LRV]) has been limited, and, to date, the most common and epidemiologically important combinations of G/P genotypes in rabbits have been reported to be G3 P[14] and G3 P[22]. In this study, a rotavirus species A detected from an outbreak of enteritis in a Mexican commercial rabbitry was genotypically characterized. Based on sequence and phylogenetic analysis of the VP7 and VP4 genes, the strain identified in this study (C-3/15) demonstrated a G3 P[8] genotype of rotavirus, which had not previously been reported in rabbits. Moreover, both genes were closely related to human, not lapine, rotaviruses. The G3 genotype has been reported in a wide variety of hosts, including humans and rabbits, whereas the P[8] genotype has only been reported in humans. Because this combination of genotypes has never been identified in rabbits, it is proposed that the finding presented here is possibly the result of an interspecies transmission event. This is the first work to study the molecular characteristics of rotaviruses in rabbits in Mexico, as well as the identification of human G3 and P[8] genotypes in a rabbit with enteric disease.
Immune Response Modulation by HPV16 Oncoproteins in Lung Cancer: Insights from Clinical and In Vitro Investigations
Authors: São Marcos Bianca de França , Santos Daffany Luana dos , Sousa Georon Ferreira de , Cruz Leonardo Carvalho de Oliveira , Barros Bárbara Rafaela da Silva , de Sena Matheus Gardini Amâncio Marques , Santos Vanessa Emanuelle Pereira , Oliveira Talita Helena de Araújo , Lagos de Melo Cristiane Moutinho , de Freitas Antonio Carlos
Published: November 2024

Lung cancer has the highest mortality rates worldwide, and Human Papillomavirus (HPV) has been associated with its carcinogenesis. In this study, HPV16 genes’ expressions were investigated in patient samples, along with the immunological response promoted by lymphocytes and monocytes in A549 cells transfected with HPV oncogenes and co-cultured with PBMC. An increase in the expression of E5 was observed in the patients’ samples. In the in vitro analysis, a decrease in the number of monocytes and cytotoxic cells was observed when co-stimulated by E6 and E7, and it promoted an increase in the Th2 profile. In contrast, the high proliferation of cytotoxic cells in A549 cells transfected with E5, associated with the high expression of costimulatory molecules in monocytes, suggests a low capacity of E5 to inhibit the presentation of antigens by antigen-presenting cells (APC) and a possible use of E5 in future therapeutic strategies against lung cancers associated with HPV.
Shallow Groundwater Quality Assessment and Pollution Source Apportionment: Case Study in Wujiang District, Suzhou City
Authors: Hou Lili , Qi Qiuju , Zhou Quanping , Lv Jinsong , Zong Leli , Chen Zi , Jiang Yuehua , Yang Hai , Jia Zhengyang , Mei Shijia , Jin Yang , Zhang Hong , Li Jie , Xu Fangfei
Published: November 2024

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I (28%), NH4-N (18%), and Mn (14%); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry (10.63%) > urban domestic sewage discharge (8.69%) > agricultural non-point source pollution (6.26%).
Groundwater Model for Karst and Pelitic Aquifer Systems from a Semi-Arid Region Under Climate Change Scenarios: A Case Study in the Vieira River Watershed, Brazil
Authors: Pedrosa Bhering Apolo , Antunes Isabel Margarida Horta Ribeiro , Nascimento Catão Gustavo , Gomes Marques Eduardo Antonio , de Paula Rodrigo Sergio , Brito Andrade Isabella , Rebelo Diório Giovana
Published: November 2024

Water scarcity is a global issue, especially in semi-arid and arid regions where precipitation is irregularly distributed over time and space. Predicting groundwater flow in heterogeneous karst terrains, which are essential water sources, presents a significant challenge. This article integrates geology, hydrology, and water monitoring to develop a pioneering conceptual and numerical model of groundwater flow in the Montes Claros Region (Vieira River Watershed, Brazil). This model was evaluated under various climate change scenarios, considering changes in rainfall, groundwater consumption, and population growth over the current century. The results indicate that a decline in water table levels is inevitable, primarily driven by population growth and high pumping rates rather than rainfall fluctuations. This underscores the urgent need for improved monitoring, model upgrading, and more importantly, targeted water resource management for Montes Claros.
Landslide Displacement Prediction Stacking Deep Learning Algorithms: A Case Study of Shengjibao Landslide in the Three Gorges Reservoir Area of China
Authors: Jiang Hongwei , Wang Yunmin , Guo Zizheng , Zhou Hao , Wu Jiayi , Li Xiaoshuang
Published: November 2024

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area.
Nutrient Water Pollution from Unsustainable Patterns of Agricultural Systems, Effects and Measures of Integrated Farming
Authors: Madjar Roxana Maria , Vasile Scăețeanu Gina , Sandu Mirela Alina
Published: November 2024

Nowadays, agricultural practices require special attention due to their important contribution to water pollution, the more so as they are associated with environmental and health impairments. Despite legislation addressing nutrient pollution, there are still high levels of nutrients in water bodies, as evidenced by the results identified in the literature. Among nutrients of environmental concern identified in water and associated with agricultural practices are nitrogen and phosphorus. When applied in excess under fertilizer form, these nutrients accumulate in water bodies with consequences such as eutrophication or human health impairments if water is used for drinking. The purpose of this review paper is to present the current state of nutrient water pollution generated by unsustainable agricultural practices. In addition, are presented the main legislative regulations addressing water quality imposed by the European Union, are described aspects related to nitrogen and phosphorus pollution from agriculture, and levels of nutrients in water bodies from different European countries. Also, effects of agricultural pollution on the environment and humans are discussed, and possible strategies that could be employed to decrease or prevent water pollution are reviewed.
Impact of Climate and Socio-Economic on Irrigation Water Management and Agricultural Water Productivity
Authors: Li Xiaojie , Zhang Chenglong
Published: November 2024

Water security and food security are fundamental pillars of sustainable social and economic development [...]
Integration of Coagulation–flocculation(with Natural Coagulant) to Constructed Wetlands for Color Removal from Tequila Vinasses
Authors: Zurita Florentina , Tejeda Allan , Ramirez-Ramirez Anderson , Montoya Arturo
Published: November 2024

The aim of this study was to evaluate a natural coagulant, Moringa oleifera seeds (MOC), to reduce the color concentration in treated tequila vinasses (TVs). TV-A was the effluent of horizontal subsurface flow wetlands (HSSFW); TV-B was the effluent of vertical up-flow wetlands (VUFW); and TV-C was the effluent of vertical down-flow constructed wetlands (VDFW). Raw TVs were also evaluated with MOC. Jar tests were performed to find the optimal dose and pH value for apparent color (AC) removal. With the optimal dose and pH for each type of TV, tests were performed in triplicate to evaluate the removal of apparent color (AC), true color (TC), turbidity, total suspended solids (TSS), chemical oxygen demand (COD), and electrical conductivity (EC). For TV-A and TV-B, the optimal values were 1 g/L of MOC and pH 8, and the removals were 52%, 43%, 50% and 72% of AC, turbidity, TC, and TSS, respectively. For TV-C, the optimal values were 2.5 g/L and pH 5, with removals of 66%, 73%, and 98% for AC, TC, and TSS, respectively. For TV-D, the MOC had no coagulant effect in any of the experimental conditions evaluated, probably due to the high concentration of turbidity and TSS in the raw vinasses, which prevented the interaction between MOC and melanoidins. Deeper studies are required to understand and evaluate those factors that influence MOC efficiency so that the coagulation–flocculation process can be optimized.
Knowledge-oriented management systems for complex objects
Authors: Palagin Olehander , Symonov Denys
Published: November 2024

The article examines the issue of improving the efficiency of complex systems management through the use of knowledge-based methods and technologies. The article emphasizes the need to revise traditional approaches to scientific research and knowledge management by introducing a transdisciplinary paradigm and artificial intelligence to achieve sustainable development and knowledge evolution. The authors try to solve the problem of management in the face of uncertainty and incomplete information, which is typical for such systems. The methods of integrating knowledge from different fields to create an integrated management system are considered. The basis of management is the holarchic structure, which organizes the system in the form of a hierarchy of holons. Each holon has a specific role and knowledge, which ensures the flexibility and adaptability of the system. The study also uses self-organization and learning methods to ensure continuous improvement of the systemʼs functioning. The article discusses the requirements for knowledge-based management systems, such as flexibility, adaptability, data and knowledge integration, learning and self-organization, transparency of decisions, and resilience to errors and failures. The findings of the study emphasize the importance of implementing knowledge-based approaches in the management system of complex facilities. This allows to increase the level of adaptability and stability of management, which is critical in todayʼs environment of dynamic development of technology and knowledge. The proposed methods can be applied in various fields, which makes them universal and useful for a wide range of scientific and practical tasks, providing a high level of adaptability, efficiency and stability of complex objects management, taking into account modern requirements and challenges.