bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of Drought Characteristics and Atmospheric Circulation Mechanisms via a “Cloud Model”, Inner Mongolia Autonomous Region, China
Authors: Wang Sinan , Miao Henglu , Wu Yingjie , Li Wei , Li Mingyang
Published: December 2024

Droughts are long-term natural disasters and encompass many unknown factors. Herein, yearly and seasonal standardized precipitation evapotranspiration index (SPEI) values were calculated by analyzing monthly temperature and precipitation data from 1971 to 2020. A cloud model was employed to obtain the spatiotemporal variations in the yearly distribution of drought weather. The cross-wavelet transform results revealed the relationship between the SPEI and atmospheric circulations. The results indicated that the average reduction rates of the SPEI-3 and SPEI-12 in Yinshanbeilu were 0.091 and 0.065 yr−1, respectively, and the annual drought occurrence frequency reached 30.37%. The annual station ratio and drought intensity showed increasing trends, whereas the degree of drought slightly decreased. The overall drought conditions indicated an increasing trend, the entropy (En) and hyper entropy (He) values demonstrated increasing trends, and the expectation (Ex) showed a downward trend. The fuzziness and randomness of the drought distribution were relatively low, and the certainty of drought was relatively easy to measure. The variation in the drought distribution was relatively low. There were resonance cycles between the SPEI and various teleconnection factors. The Pacific Decadal Oscillation (PDO) and the El Niño–Southern Oscillation (ENSO) exhibited greater resonance interactions with the SPEI than did other teleconnection factors. The cloud model exhibits satisfactory application prospects in Yinshanbeilu and provides a systematic basis for early warning, prevention, and reduction in drought disasters in this region.
Spent Coffee Ground and Its Derivatives as Soil Amendments—Impact on Soil Health and Plant Production
Authors: Hu Yi , Li Juan , Wu Yiping , Zhang Dongdong , Qi Zhiyong , Yang Rui
Published: December 2024

Spent coffee ground (SCG) is a main byproduct of the coffee industry and has been revalorized as a source of value-added products. The direct application of SCG as a soil amendment has drawn much attention in recent years as a more environmentally sustainable option. In this work, a comprehensive review of studies on the agricultural application of SCG is presented. The aims of this review were (1) to summarize the impacts of raw SCG on soil health and to identify the factors limiting its direct soil use, (2) to outline methods that are often used to upgrade SCG for soil application, and (3) to highlight the potential of SCG and its derivatives as a biocontrol agent and biofertilizer. The results indicate that raw SCG could improve certain soil health attributes, but plant growth is often limited owing to its phytotoxicity and induced N immobilization. To eliminate phytotoxicity, composting, vermicomposting, and thermochemical treatments have been employed but to a limited extent. SCG and its derivatives may be used as biocontrol agents for weeds, soil-borne pathogens, and pests. Novel bio-organic fertilizers based on SCG have also been developed. Future research should focus on the long-term effects of SCG and its derivatives as soil amendments under real scenarios and the underlying mechanisms, particularly the soil–plant–microorganism interactions.
Optimizing the Micropropagation of Red-Stalked Rhubarb Selections: A Strategy for Mass Production of High-Quality Planting Material
Authors: Wojtania Agnieszka , Waligórski Piotr
Published: December 2024

Culinary rhubarb is grown for its large, thick leaf petioles. Red-stalked cultivars and selection are more attractive for the fresh market and processing industry. In vitro cultures are important for rapidly multiplying value genotypes and producing plants free of viruses. This study aimed to develop an in vitro propagation method for six rhubarb selections from the Raspberry (R1, R2, R3), Leader (L1, L2), and Karpow Lipskiego (KL) groups. In addition, the planting material of six rhubarb selections was subjected to phytochemical analysis using the HPLC method to assess the content of bioactive compounds. The cultures were initiated from underground buds on the crowns. The initial growth was obtained for 45 to 75% of initial explants because of bacterial contamination and low bud activity of some genotypes. The type and concentration of cytokinin and its interaction with gibberellin acid (GA3) were shown to have a significant effect on the cyclic multiplication and commercially interesting rate of all rhubarb genotypes. A high in vitro rooting frequency, 93.7 to 95.8% for rhubarb Raspberry, 94.3 to 100% for rhubarb Leader, and 96.7% KP selections were obtained after two-cycle rooting on a medium containing IBA and NAA. The polyphenolic compounds, such as cyanidin-3-O-rutinoside, rhaponticin, resveratrol, caffeic acid, p-coumaric acid, cinnamic acid, syringic acid, and ferulic acid were detected in selected rhubarb genotypes. The highest content of anthocyanins (2.9 mg·1 g−1 DM) and rhaponticin (107.8 µg·1 g−1 DM) was found in Raspberry selections. On the other hand, Leader selections were characterized by the highest content of resveratrol (0.25 µg·1 g−1 DM) and phenolic acids (1.3 µg·1 g−1 DM). The less attractive for functional food production seems to be KL selection.
Barley Genetic Resources: Advancing Conservation and Applications for Breeding—Series II
Authors: Czembor Jerzy H. , Czembor Elzbieta
Published: December 2024

The changing climate conditions and the growing world population require a constant increase in agricultural production [...]
Exogenous Application of Lanthanum Chloride to Rice at Booting Stage Can Increase Chlorophyll Content, Modulate Chlorophyll Fluorescence and Promote Grain Yield Under Deficit Irrigation
Authors: Liu Peng , Fang Xilin , Wang Yue , Yang Xin , Li Qiang
Published: December 2024

To sustain agricultural productivity and safeguard global food security, and confront the escalating challenges posed by climate change and water scarcity, it is essential to enhance the growth and productivity of rice under water stress. This study investigated the effects of lanthanum chloride on the chlorophyll fluorescence characteristics and grain yield of rice under different irrigation modes. The rice cultivar H You 518 was selected and sprayed 20, 100, or 200 mg·L−1 lanthanum chloride at the booting and heading stages under deficit irrigation (where no rewatering was applied after the initiation of stress, allowing the water layer to evaporate naturally under high temperatures) or conventional irrigation (with daily rewatering to maintain a consistent water level). The results showed that the application of low concentrations lanthanum chloride promoted the chlorophyll content, whereas high concentrations decreased the chlorophyll content, under deficit irrigation, the effect of lanthanum chloride on the green fluorescence parameters of rice leaves at the booting stage was greater than that at the heading stage, and the booting stage was more sensitive to water deficit. The application of 100 mg·L−1 lanthanum chloride reduced the initial fluorescence (F0) and the non-photochemical quenching coefficient (qN); promoted the activity of leaf photosynthetic system II (PSII); and maximized the photochemical quantum yield (Fv/Fm), photochemical quenching coefficient (qP), and PSII relative electron transfer efficiency (ETR). Under deficit irrigation, this treatment significantly enhanced grain yield by increasing the thousand-grain weight, spikelet filling rate, and number of grains per panicle. These results suggest that spraying 100 mg·L−1 lanthanum chloride at the booting stage under deficit irrigation can effectively increase the chlorophyll content, thereby increasing the light energy conversion efficiency of the PS II reaction center, ultimately resulting in increased spikelet filling rate and grain yields.
Simulation of Flax Threshing Process by Different Forms of Threshing Drums in Combined Harvesting
Authors: Shi Ruijie , Chang Leilei , Zhao Wuyun , Dai Fei , Liang Zhenwei
Published: December 2024

Flax, an important oil and fiber crop, is widely cultivated in temperate and sub-frigid regions worldwide. China is one of the major producers of flax, with Gansu Province predominantly practicing cultivation in hilly areas. However, common issues such as feeding difficulties, stem entanglement, and low threshing efficiency significantly restrict the improvement of planting efficiency. This study addresses the key technical challenges in flax combine harvesting in hilly regions by developing a discrete element model of the flax plant and utilizing DEM-FEA co-simulation technology. The performance of two threshing drum models (T1 and T2) was analyzed, focusing on motion trajectory, stress distribution, and threshing effects. The simulation results show that the T2 model, with its combination of rib and rod tooth design, significantly improves threshing and separation efficiency. The loss rate was reduced from 5.6% in the T1 model to 1.78% in the T2 model, while the maximum stress and deformation were significantly lower, indicating higher structural stability and durability. Field validation results revealed that the T1 model had a total loss rate of 3.32%, an impurity rate of 3.57%, and an efficiency of 0.09 hm2/h. In contrast, the T2 model achieved a total loss rate of 2.29%, an impurity rate of 3.39%, and an efficiency of 0.22 hm2/h, representing a 144.4% improvement in working efficiency. These findings indicate that the T2 model has a higher potential for flax harvesting in hilly and mountainous regions, especially in improving threshing efficiency and operational stability, providing an important theoretical basis for optimizing threshing equipment design.
Multifractal Analysis of Temporal Variation in Soil Pore Distribution
Authors: Jia Yanhui , Feng Yayang , Zhang Xianchao , Sun Xiulu
Published: December 2024

Soil structure, a critical indicator of soil quality, significantly influences agricultural productivity by impacting on the soil’s capacity to retain and deliver water, nutrients, and salts. Quantitative study of soil structure has always been a challenge because it involves complex spatial-temporal variability. This study employs multifractal analysis to assess the temporal variation in soil pore distribution, a pivotal factor in soil structure. Field observation data were collected in a sandy loam area of the People’s Victory Canal Irrigation scheme in Henan Province, China. A 200 m × 200 m test plot with five sampling points was used to collect soil samples at three depth layers (10–30 cm, 30–50 cm, and 50–70 cm) for soil water retention curve and particle size composition analysis, with a total of seven sampling events throughout the growing season. The results revealed that while soil particle-size distribution (Particle-SD) showed minor temporal changes, soil pore-size distribution (Pore-SD) experienced significant temporal fluctuations over a cropping season, both following a generalized power law, indicative of multifractal traits. Multifractal parameters of Pore-SD were significantly correlated with soil bulk density, with the strongest correlation in the topsoil layer (10–30 cm). The dynamic changes in soil pore structure suggest potential variations during saturation–unsaturation cycles, which could be crucial for soil water movement simulations using the Richards equation. The study concludes that incorporating time-varying parameters in simulating soil water transport can enhance the accuracy of predictions.
Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.)
Authors: Jiang Xiangtai , Gao Lutao , Xu Xingang , Wu Wenbiao , Yang Guijun , Meng Yang , Feng Haikuan , Li Yafeng , Xue Hanyu , Chen Tianen
Published: December 2024

One of the most important nutrients needed for fruit tree growth is nitrogen. For orchards to get targeted, well-informed nitrogen fertilizer, accurate, large-scale, real-time monitoring, and assessment of nitrogen nutrition is essential. This study examines the Leaf Nitrogen Content (LNC) of the custard apple tree, a noteworthy fruit tree that is extensively grown in China’s Yunnan Province. This study uses an ensemble learning technique based on multiple machine learning algorithms to effectively and precisely monitor the leaf nitrogen content in the tree canopy using multispectral canopy footage of custard apple trees taken via Unmanned Aerial Vehicle (UAV) across different growth phases. First, canopy shadows and background noise from the soil are removed from the UAV imagery by using spectral shadow indices across growth phases. The noise-filtered imagery is then used to extract a number of vegetation indices (VIs) and textural features (TFs). Correlation analysis is then used to determine which features are most pertinent for LNC estimation. A two-layer ensemble model is built to quantitatively estimate leaf nitrogen using the stacking ensemble learning (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Boosting Decision Trees (GBDT), Linear Regression (LR), and Extremely Randomized Trees (ERT) are among the basis estimators that are integrated in the first layer. By detecting and eliminating redundancy among base estimators, the Least Absolute Shrinkage and Selection Operator regression (Lasso)model used in the second layer improves nitrogen estimation. According to the analysis results, Lasso successfully finds redundant base estimators in the suggested ensemble learning approach, which yields the maximum estimation accuracy for the nitrogen content of custard apple trees’ leaves. With a root mean square error (RMSE) of 0.059 and a mean absolute error (MAE) of 0.193, the coefficient of determination (R2) came to 0. 661. The significant potential of UAV-based ensemble learning techniques for tracking nitrogen nutrition in custard apple leaves is highlighted by this work. Additionally, the approaches investigated might offer insightful information and a point of reference for UAV remote sensing applications in nitrogen nutrition monitoring for other crops.
The Influence of the Farming System and Forecrop on the Yield and Chemical and Health-Promoting Composition of Spring Wheat Grain
Authors: Harasim Elżbieta , Kwiatkowski Cezary A. , Buczek Jan
Published: December 2024

Spring wheat was grown on a loess-derived Luvisol under the conditions of two farming systems (conventional and organic) and five forecrops (sugar beet, spring barley, red clover, winter wheat, and oat) over the period 2021–2023. In the conventional system, mineral NPK fertilization and pesticides (herbicides, fungicide, insecticide, and retardant) were applied at the recommended rates for wheat. Mechanical weed control was also used (double harrowing). In the organic system, the organic fertilizer Humac Agro was applied and the fields under the wheat were harrowed twice. No plant protection products were used under organic farming conditions. The organic system was proven to have an effect on reducing spring wheat yield, on average by 23%, compared to the conventional system (the grain yield was, respectively, 4.59 t ha−1 compared to 5.96 t ha−1). In spite of the lower yield potential, the organic cultivation of spring wheat significantly improved the quality and health-promoting parameters of this cereal grain. Except for the total nitrogen and potassium content, the organic system contributed to a significant increase in the grain content of total dietary fiber (by 0.89 p.p.), o-dihydroxyphenols (by about 19%), and polyphenols (by about 12%), and increased the content of the following elements: Se, Mg, Ca, Cu, Mn, Fe, and Zn. Among the forecrops, red clover and sugar beet had the most beneficial effect on grain quality (including the amino acid composition and EAAI index), followed by oat (especially under organic farming conditions). The other wheat forecrops (spring barley and winter wheat) clearly deteriorated the yield and quality of spring wheat grain. To sum up the obtained research results, appropriate management of organic spring wheat cultivation (forecrop sugar beet or red clover, Humac Agro fertilizer) contributes to high grain nutritional quality relative to the conventional system and also reduces the yield gap relative to conventional farming.
Optimization of Irrigation and Fertilization in Maize–Soybean System Based on Coupled Water–Carbon–Nitrogen Interactions
Authors: Yang Aizheng , Luo Shuyuan , Xu Yaowen , Zhang Pingan , Sun Zhenyi , Hu Kun , Li Mo
Published: December 2024

Effective water and nitrogen management plays a pivotal role in enhancing crop yields while simultaneously reducing greenhouse gas emissions. This study differs from previous research by investigating the effects of water–nitrogen co-regulation involving organic carbon on the yield increase and emission mitigation in a soybean–maize system. A dryland experiment was conducted, employing 20 distinct combinations of water and nitrogen treatments that were meticulously designed for the maize–soybean system. The DSSAT crop model was employed to quantitatively elucidate the intricate interactions between water and nitrogen. A multi-objective optimization model, integrating experimental data and mechanistic insights, was constructed and refined using the NSGA-III genetic algorithm to identify the optimal water and nitrogen application ratios. An analysis of maize and soybean data from Acheng in Heilongjiang, China, indicates that optimized irrigation and nitrogen application regimes—152.2 mm and 247.1 kg·ha−1 for maize and 91.7 mm and 106.2 kg·ha−1 for soybean—substantially enhanced the net economic returns within the dryland ecosystem. There is a significant positive correlation between the yield (Y), soil nitrogen content, and soil organic carbon (SOC). Nitrate nitrogen has a significant positive correlation with CO2 gas emissions. Organic carbon changes the soil’s carbon to nitrogen ratio by participating in the water and nitrogen cycles, thereby affecting nitrogen and phosphorus loss and carbon emissions. This study presents a sustainable method for regulating water and nitrogen in the maize–soybean system.
Grape Target Detection Method in Orchard Environment Based on Improved YOLOv7
Authors: Sun Fuchun , Lv Qiurong , Bian Yuechao , He Renwei , Lv Dong , Gao Leina , Wu Haorong , Li Xiaoxiao
Published: December 2024

In response to the poor detection performance of grapes in orchards caused by issues such as leaf occlusion and fruit overlap, this study proposes an improved grape detection method named YOLOv7-MCSF based on the You Only Look Once v7 (YOLOv7) framework. Firstly, the original backbone network is replaced with MobileOne to achieve a lightweight improvement of the model, thereby reducing the number of parameters. In addition, a Channel Attention (CA) module was added to the neck network to reduce interference from the orchard background and to accelerate the inference speed. Secondly, the SPPFCSPC pyramid pooling is embedded to enhance the speed of image feature fusion while maintaining a consistent receptive field. Finally, the Focal-EIoU loss function is employed to optimize the regression prediction boxes, accelerating their convergence and improving regression accuracy. The experimental results indicate that, compared to the original YOLOv7 model, the YOLOv7-MCSF model achieves a 26.9% reduction in weight, an increase in frame rate of 21.57 f/s, and improvements in precision, recall, and mAP of 2.4%, 1.8%, and 3.5%, respectively. The improved model can efficiently and in real-time identify grape clusters, providing technical support for the deployment of mobile devices and embedded grape detection systems in orchard environments.
Salinity Tolerance of Artificially Restored Vegetation Under Different Irrigation Strategies in Arid, Abandoned Mining Areas
Authors: Aili Aishajiang , Zhang Yuguang , Lin Tao , Xu Hailiang , Waheed Abdul , Zhao Wanyu , Wang Jia , Wang Chuhan
Published: December 2024

Ecological restoration of abandoned mining areas in arid regions presents significant challenges, especially in terms of soil salinization, vegetation loss, and limited water resources. In the Hami arid area of Xinjiang, vegetation restoration is crucial for stabilizing ecosystems and combating land degradation. This study investigated the effects of two irrigation methods—drip and border irrigation—on the growth and survival of four plant species: Tamarix chinensis, Calligonum mongolicum, Haloxylon ammodendron, and Phragmites australis, each exposed to salinity levels of 8 g/L, 12 g/L, and 16 g/L. Our results showed that drip irrigation significantly improved the growth and survival outcomes for most species, particularly T. chinensis and H. ammodendron, with average heights, crown sizes, and base diameters substantially higher under drip irrigation compared to border irrigation (p < 0.05). C. mongolicum, however, displayed optimal vertical growth under border irrigation, although drip irrigation promoted a denser, more compact crown structure. Salinity tolerance varied by species, with 8 g/L salinity being optimal for all, while higher salinity levels (12 g/L and 16 g/L) reduced growth across species, underscoring the importance of salinity management in restoration efforts. P. australis, assessed only under border irrigation due to its high water requirements, showed stable growth but reduced tolerance at higher salinities. These findings highlight that drip irrigation, particularly when combined with moderate salinity (8 g/L), is a more effective strategy for enhancing vegetation growth and survival in arid, saline environments. Our study provides practical recommendations for irrigation and salinity management in ecological restoration, offering insights for improving vegetation resilience in arid mining landscapes.
Enhancing Immunoglobulin G Goat Colostrum Determination Using Color-Based Techniques and Data Science
Authors: Betancor-Sánchez Manuel , González-Cabrera Marta , Morales-delaNuez Antonio , Hernández-Castellano Lorenzo E. , Argüello Anastasio , Castro Noemí
Published: December 2024

Circulating immunoglobulin G (IgG) concentrations in newborn goat kids are not sufficient to protect the animal against external agents. Therefore, consumption of colostrum, rich in immune components, shortly after birth is crucial. Traditional laboratory methods used to measure IgG concentrations, such as ELISA or RID, are reliable but costly and impractical for many farmers. This study proposes a more accessible alternative for farmers to predict IgG concentration in goat colostrum by integrating color-based techniques with machine learning models, specifically decision trees and neural networks, through the development of two regression models based on colostrum color data from Majorera dairy goats. A total of 813 colostrum samples were collected in a previous study (June 1997–April 2003) that utilized multiple regression analysis as a reference to verify that applying data science techniques improves accuracy and reliability. The decision tree model outperformed the neural network, achieving higher accuracy and lower error rates. Both models provided predictions that closely matched IgG concentrations obtained by ELISA. Therefore, this methodology offers a practical and affordable solution for the on-farm assessment of colostrum quality (i.e., IgG concentration). This approach could significantly improve farm management practices, ensuring better health outcomes in newborn animals by facilitating timely and accurate colostrum quality evaluation.
Effects of Medium- and Short-Chain Fatty Acids on Growth Performance, Nutrient Digestibility, Gut Microbiota and Immune Function in Weaned Piglets
Authors: Dong Shuang , Zhang Nan , Wang Jihua , Cao Yu , Johnston Lee J. , Ma Yongxi
Published: December 2024

The aim of this study was to investigate the combination effects of α-glycerol monolaurate (GML) and glyceryl tributyrate (TB) on growth performance, nutrient digestibility, gut microbiota, and immune function in weaned piglets. A total of 120 weaned piglets with an average body weight (BW) of 6.88 kg were randomly allocated to one of the three dietary treatments: (1) CON: a basal diet; (2) 0.1%: a basal diet with 0.1% MSCFA (GML/TB = 1:1); (3) 0.2%: a basal diet with 0.2% MSCFA (GML/TB = 1:1). The experiment lasted 28 days. There were no differences on average daily growth (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR). Supplementation with 0.1% MSCFA increased apparent total tract digestibility (ATTD) of crude protein (CP) and gross energy (GE, p < 0.05) on d 14 and increased GE (p < 0.05) on d 28 compared with the CON group. The ATTD of dry matter (DM), organic matter (OM) and crude protein (CP) of piglets supplemented with 0.1% MSCFA was higher (p < 0.05). Compared with the CON group, supplementation with 0.1% MSCFA increased immunoglobulin M (IgM) concentration, decreased interleukin-6 (IL-6) content (p < 0.05) on d 14 and decreased malonaldehyde (MDA), interleukin-1beta (IL-1β), IL-6 concentrations (p < 0.05) on d 28. Supplementation with 0.1% MSCFA increased total antioxidant capacity (T-AOC) concentration (p < 0.05), decreased GSH-Px, MDA content (p < 0.05) in jejunum compared with the CON group. Moreover, supplementation with MSCFA increased the activity of duodenal lipase (p < 0.05) and the abundance of firmicutes and decreased the abundance of proteobacteria compared with the CON group. Overall, supplementation with MSCFA can improve nutrient digestibility, enhance immunity and antioxidant capacity, and improve the intestinal health of piglets. The combined use of MSCFA is a nutrition regulation strategy worthy of further exploration in modern animal husbandry.
Stability Control of Multilayer Roof Strata in the Large Mining Height Gob-Side Entry: A Case Study
Authors: Chen Yong , Wang Yingpeng , Qin Zhengyuan , Yang Feng , Agarwal Vivek
Published: December 2024

Roof separation, destabilization and collapse of multilayer roof structures are difficult to control in large mining height gob-side entries due to severe mining pressure. In the 22301 working face in Tunlan Mine, a mining height of 4.75 m posed great challenges to gob-side entry retaining techniques. Through mechanical analysis, the strata movement of a multilayer roof structure was investigated, and numerical analysis was conducted to identify key aspects of the supporting scheme, e.g., ensuring the stability of immediate roof above the filling area, transferring the resistance from the cemented backfill to the roof structure and maintaining self-supporting capacity. A new support strategy was proposed and applied in industrial settings, with entry stability evaluated by monitoring the deformation characteristics, roof separation values and overall support performance. The results showed that the gob-side entry retaining technique was successful in Tunlan Mine, providing valuable insights for similar techniques in large mining height gob-side entries.
Resilience of Machine Learning Models in Anxiety Detection: Assessing the Impact of Gaussian Noise on Wearable Sensors
Authors: Alkurdi Abdulrahman , Clore Jean , Sowers Richard , Hsiao-Wecksler Elizabeth T. , Hernandez Manuel E.
Published: December 2024

The resilience of machine learning models for anxiety detection through wearable technology was explored. The effectiveness of feature-based and end-to-end machine learning models for anxiety detection was evaluated under varying conditions of Gaussian noise. By adding synthetic Gaussian noise to a well-known open access affective states dataset collected with commercially available wearable devices (WESAD), a performance baseline was established using the original dataset. This was followed by an examination of the impact of noise on model accuracy to better understand model performance (F1-score and Accuracy) changes as a function of noise. The results of the analysis revealed that with the increase in noise, the performance of feature-based models dropped from a high of 90% F1-score and 92% accuracy to 65% and 70%, respectively; while end-to-end models showed a decrease from an 85% F1-score and 87% accuracy to below 60% and 65%, respectively. This indicated a proportional decline in performance across both feature-based and end-to-end models as noise levels increased, challenging initial assumptions about model resilience. This analysis highlights the need for more robust algorithms capable of maintaining accuracy in noisy, real-world environments and emphasizes the importance of considering environmental factors in the development of wearable anxiety detection systems.
Integrating Multi-Model Simulations to Address Partial Observability in Population Dynamics: A Python-Based Ecological Tool
Authors: Yu Yide , Li Huijie , Liu Yue , Ma Yan
Published: December 2024

Species richness is a crucial factor in maintaining ecological balance and promoting ecosystem services. However, simulating population dynamics is a complex task that requires a comprehensive understanding of ecological systems. The current tools for wildlife research face three major challenges: insufficient multi-view assessment, a high learning curve, and a lack of seamless secondary development with Python. To address these issues, we developed a novel software tool named WAPET (Wildlife Analysis and Population Ecology Tool) (Python 3.10.12). WAPET integrates Monte Carlo simulation with ecological models, including Logistic Growth, Random Walk, and Cellular Automata, to provide a multi-perspective assessment of ecological systems. Our tool employs a fully parameterized input paradigm, allowing users without coding to easily explore simulations. Additionally, WAPET’s development is entirely Python-based, utilizing PySide6 and Mesa libraries and enabling seamless development in Python environments. Our contributions include the following: (I) integrating multiple ecological models for a comprehensive understanding of ecological processes, (II) developing a no-code mode of human–computer interaction for biodiversity stakeholders and researchers, and (III) implementing a Python-based framework for easy extension and customization. WAPET bridges the gap between comprehensive modeling capabilities and user-friendly interfaces, positioning itself as a versatile tool for both experienced researchers and non-computational stakeholders in biodiversity decision-making processes.
Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling
Authors: Park Yeonjin , Hong Jungpyo
Published: December 2024

This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations.
Influence of Site Effects on Scaling Relation Between Rotational and Translational Signals Produced by Anthropogenic Seismicity
Authors: Nawrocki Dariusz , Mendecki Maciej J. , Mutke Grzegorz , Teper Lesław
Published: December 2024

The measurements of rotational and translational seismic signals were carried out at the Imielin and Planetarium stations located in the central part of the Upper Silesian Coal Basin, Southern Poland. Local seismicity, produced by the surrounding hard coal mines, allowed the collection of 130 seismic events. This study aimed to analyze the influence of site effects on rotational ground motion using the horizontal-to-vertical spectral ratio method. We performed the analysis using two approaches: obtaining the spectral ratio of the rotational motion and investigating the impact of the site effect on the scaling relation. The spectral ratio comparison between rotations and translations shows that the value of the rotational amplification coefficient is almost three times lower than that for the translations, and the resonance frequency of rotational motion is higher than that of translations. The comparisons of the scaling relation models, obtained for raw data and corrected by the amplification factor data, revealed that removing the site effect from the signals increases the data to model approximation, reducing the value of the SH-wave phase velocity almost three times. The studies suggest that the local site conditions independently affect the rotational and translational motions.
IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring
Authors: Peddareddigari Siva , Vijayan Sri Vigna Hema , Annamalai Manickavasagan
Published: December 2024

Technological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, the risk of contamination has increased severalfold, often resulting in significant damage in terms of food wastage, economic loss to the producers, danger to public health, or all of these. In general, governments across the world have recognized the importance of having food safety processes in place to impose food recalls as required. However, the primary challenges to the existing practices are delays in identifying unsafe food, siloed data handling, delayed decision making, and tracing the source of contamination. Leveraging the Internet of Things (IoT), 5G, blockchains, cloud computing, and big data, a novel framework has been proposed to address the current challenges. The framework enables real-time data gathering and in situ application of machine learning-powered algorithms to predict contamination and facilitate instant decision making. Since the data are processed in real time, the proposed approach enables contamination to be identified early and informed decisions to be made confidently, thereby helping to reduce damage significantly. The proposed approach also throws up new challenges in terms of the implementation of changes to data collection across all phases of food production, onboarding various stockholders, and adaptation to a new process.
IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
Authors: Liu Hongbin , Zhao Yongze , Dong Peng , Guo Xiuyi , Wang Yilin
Published: December 2024

Multi-object tracking aims to track multiple objects across consecutive frames in a video, assigning a unique classifier to each object. However, issues such as occlusions, directional changes, or shape alterations can cause appearance variations, leading to detection and matching problems that in turn result in frequent ID switches. To solve these issues, this paper proposes a two-stage multi-object tracking framework based on a spatial and temporal fusion algorithm. First, the video frames are processed by a detector to identify objects and form rectangular detection areas. Meanwhile, an estimator predicts the target rectangular areas in the next frame. Then, we extract the optical flow of the target pixels within the detection and prediction areas, and then a temporal information model is established by calculating the average of the target pixels’ optical flow. Afterward, we present a spatial information model using the R-IoU (Reverse of Intersection over Union) between the detection and prediction areas. This spatial and temporal information is combined with weighted matrix fusion, which achieves the feature matching and association task. Finally, we implement a two-stage association multi-object tracking model using the mentioned fusion algorithm. Experiments on the MOTChallenge dataset using the official detector show that our two-stage multi-object tracking method based on the spatial and temporal fusion algorithm is robust in handling occlusions and ID switch issues. As of the submission of this paper, the proposed method has achieved the top ranking in the MOT17 benchmark when evaluated with the official detector.
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
Authors: Tran Anh T. , Desser Dmitriy , Zeevi Tal , Abou Karam Gaby , Zietz Julia , Dell’Orco Andrea , Chen Min-Chiun , Malhotra Ajay , Qureshi Adnan I. , Murthy Santosh B. , Majidi Shahram , Falcone Guido J. , Sheth Kevin N. , Nawabi Jawed , Payabvash Seyedmehdi
Published: December 2024

Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team’s preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (n = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47–88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate.
The Effect of Meat and Bone Meal on Yield and Herbage Quality in Silage Maize
Authors: Nogalska Anna , Borsuk-Stanulewicz Marta , Nogalski Zenon
Published: December 2024

The purpose of this field study was to assess the impact of meat and bone meal (MBM) on maize. The experimental procedures were as follows: (1) without fertilization; (2) inorganic nitrogen, potassium, and phosphorus; (3) 1.0 Mg·ha−1 MBM; (4) 1.5 Mg·ha−1 MBM; and (5) 2.0 Mg·ha−1 MBM. Each year, MBM was applied before sowing. Nitrogen and potassium were applied at constant rates, and phosphorus was applied at increasing rates of 0.0, 45, 68, and 90 kg·ha−1. The herbage yields were significantly higher in fertilized treatments. Fertilization contributed to a significant increase in the crude protein content of maize herbage. The yield and other herbage parameters were affected by weather conditions and the amount of time that passed since the MBM application. The study demonstrated that the lowest dose of MBM (1.0 Mg·ha−1), which supplied 45 kg phosphorus·ha−1, fully met the phosphorus requirements of maize.
Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
Authors: Al-Sakini Sahar R. , Bilal Ghassan A. , Sadiq Ahmed T. , Al-Maliki Wisam Abed Kattea
Published: December 2024

Power transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, including the Duval triangle method (DTM), the Doernenburg ratio method (DRM), and the Rogers ratio method (RRM), which are suitable for on-line diagnosis of transformers. The imbalanced, insufficient, and overlapping state of gas-decomposed DGA datasets, however, remains a limitation to the deployment of a powerful and accurate diagnosis approach. This study presents a new approach for transformer fault diagnosis based on DGA, one which aims to improve the performance evaluation criteria to predict current faults and to lower the associated costs. We used six optimized machine learning methods (MLMs) for dataset transformation to organize the dataset. The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). The MLMs were implemented by using 628 dataset samples, which were obtained from laboratories, other studies, and electricity stations in Iraq. Accordingly, 502 dataset samples constituted the training set while the remaining 126 dataset samples served as the testing set. The results were examined according to six important measurements (accuracy ratio, precision, recall, specificity, F1 score, and Matthews correlation coefficient (MCC)). The best results were found for case A with RF (95.2%). In cases B and C, the best results were found for RF and DT (100% and 99.2%, respectively). With respect to the advanced machine learning method, the transformer fault diagnosis based on the MLMs was exceedingly more accurate in its predictions than the conventional and artificial intelligence-based methods.
Enhancing Particulate Matter Estimation in Livestock-Farming Areas with a Spatiotemporal Deep Learning Model
Authors: Kim Dohyeong , Kim Heeseok , Hwang Minseon , Lee Yongchan , Min Choongki , Yoon Sungwon , Seo Sungchul
Published: December 2024

Livestock farms are recognized sources of ammonia emissions, impacting nearby regions’ fine dust particle concentrations, though the full extent of this impact remains uncertain. Air dispersion models, commonly employed to estimate particulate matter (PM) levels, are heavily reliant on data quality, resulting in varying levels of accuracy. This study compares the performance of both air dispersion models and spatiotemporal deep learning models in estimating PM concentrations in Republic of Korea’s livestock-farming areas. Hourly PM concentration data, alongside temperature, humidity, and air pressure, were collected from seven monitoring stations across the study area. Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). Results highlight the potential of the deep learning model to enhance PM prediction, indicating its promise as an effective alternative or supplement to conventional air dispersion models, particularly in data-scarce areas such as those surrounding livestock farms. Gaining a comprehensive understanding and evaluating the advantages and disadvantages of each approach would offer valuable scientific insights for monitoring atmospheric pollution levels within a specific area.
Experimental Study on Concentration and Size Distribution Characteristics of Particulate Matter in Cold and Hot Rolling
Authors: Wu Yanpeng , Jin Meitong , Li Xiaoyu , Tian Junkai , Yang Bin , Liu Junliang , Zhou Shujun , Wang Faming
Published: December 2024

Workers’ health is jeopardized in steel rolling workshops due to the production of particulate matter. These particles’ physicochemical properties have a direct impact on their effects on human health, emphasizing the importance of understanding these properties for exposure control. This study investigated particulate matter emissions in hot and cold steel rolling workshops, focusing on mass concentrations, particle size distributions, and elemental compositions. The findings revealed that particles predominantly range from 0.3 to 1.0 μm in size, with irregular block-like and fibrous morphologies. Elemental analysis showed distinct compositions: the main components in the hot rolling workshop were oxygen, calcium, silicon, carbon, and iron while those in the cold rolling workshop were oxygen, silicon, aluminum, carbon, and iron. The particulate matter concentrations were higher in the hot rolling workshop than in the cold rolling workshop. The Rosin–Rammler particle size distribution function was applied to characterize particle size distribution at emission sources. This paper highlights the dynamic variations and spatial distribution patterns of particulate matter during rolling processes, providing key data for understanding particulate matter behavior in industrial environments and informing targeted pollution control strategies.
The Impact of the Urban Heat Island Effect on Ground-Level Ozone Pollution in the Sichuan Basin, China
Authors: Song Xingtao , Shi Haoyuan , Jin Langchang , Pang Sijing , Zeng Shenglan
Published: December 2024

With urbanization, ozone (O3) pollution and the urban heat island (UHI) effect have become increasingly prominent. UHI can affect O3 production and its dilution and dispersion, but the underlying mechanisms remain unclear. This study investigates the spatial and temporal distribution of O3 pollution and the UHI effect, as well as the influence of UHI on O3 pollution in the Sichuan Basin. Atmospheric pollution data for O3 and NO2 from 2020 were obtained from local environmental monitoring stations, while temperature and single-layer wind field data were sourced from ERA5-Land, a high-resolution atmospheric reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The results indicate the following: (1) O3 concentrations in the Sichuan Basin exhibit distinct seasonal variations, with the highest levels in spring, followed by summer and autumn, and the lowest in winter. In terms of spatial variation, the overall distribution is highest in western Sichuan, second highest along the Sichuan River, and lowest in central Sichuan. (2) There are significant regional differences in UHII across Sichuan, with medium heat islands (78.63%) dominating western Sichuan, weak heat islands (82.74%) along the Sichuan River, and no heat island (34.79%) or weak heat islands (63.56%) in central Sichuan. Spatially, UHII is mainly distributed in a circular pattern. (3) Typical cities in the Sichuan Basin (Chengdu, Chongqing, Nanchong) show a positive correlation between UHII and O3 concentration (0.071–0.499), though with an observed temporal lag. This study demonstrates that UHI can influence O3 concentrations in two ways: first, by altering local heat balance, thereby promoting O3 production, and second, by generating local winds that contribute to the diffusion or accumulation of O3, forming distinct O3 concentration zones.