bluuebunny/crossref_metadata_2025 huggingface dataset:
Cytokinin modulates the inhibitory effect of shade stress on photosynthesis, antioxidant capacity and hormone homeostasis to regulate the grain yield in wheat
Authors: Zhang Yongqiang , Li Juan , Xu Qijiang , Chen Chuanxin , Nie Shihui , Lei Junjie , Duan Liusheng
Published: November 2024

Agroforestry intercropping is an effective way to optimize land use and ensure food security. However, the physiological mechanism by which the shading of dominant plants inhibits the yield of non-dominant plants in this mode remains to be investigated. A two-year location experiment of walnut-winter wheat intercrop combined with exogenous 6-benzyladenine (6-BA, the first synthetic cytokinin) treatment was conducted to reveal the mechanism of 6-BA in inhibiting wheat growth and yield formation under shade stress by measuring the photosynthetic characteristics, antioxidant capacity, hormone homeostasis of wheat flag leaves and yield. The results showed that compared with far canopy area (FCA), antioxidant enzyme activity [e.g. superoxide dismutase (SOD), peroxidase (POD)], zeatin (ZT) and abscisic acid (ABA) content and photosynthesis of wheat flag leaves were significantly reduced in below canopy area (BCA) treatment during flowering and grain filling stages, thereby inhibiting wheat dry matter accumulation and yield formation. Exogenous 6-BA significantly increased hormone [i.e. indoleacetic acid (IAA), ZT and gibberellin (GA)] levels, antioxidant enzyme activities and photosynthesis in flag leaves, thereby increasing dry matter and yield, especially in the FCA condition. Furthermore, net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci), activities of ribulose bisphosphate carboxylase (RuBPCase) and phosphoenolpyruvate carboxylase (PEPCase), ABA and ZT concentrations of flag leaves at flowering and filling stages had a significant contribution to yield formation under 6-BA and shade treatments. Overall, cytokinin regulates the inhibitory effects of shade stress on wheat photosynthesis, antioxidant capacity and hormone homeostasis to reduce wheat yield loss.
Development of the dementia attitude scale focusing on well-being
Authors: Ono Ichie , Nakatani Hisae , Inoue Yuriko , Peng Xuxin , Hamada Hironobu
Published: November 2024

Introduction

This study aimed to develop the Dementia Attitude Scale Focusing on well-being (DASFWB) and to verify its reliability and validity. This scale measures the factors that individuals without dementia would consider important for their well-being if they were to develop dementia. It is expected to serve as a useful indicator for intervention strategies aimed at achieving an inclusive society.

Methods

The draft DASFWB scale was developed by extracting stories from older adults with mild Alzheimer's disease. The questionnaire was distributed to 1,614 adults aged 65 and older who were able to complete the self-administered questionnaire. Data from 815 individuals who completed the questionnaire without help (58.8% valid response rate) were analyzed. Exploratory and confirmatory factor analyses were performed to identify the factors underlying the scale. Reliability was tested using Cronbach's alpha. Validity was tested through sample, criterion-related, convergent, and discriminant validity.

Results

The development and validation of the DASFWB identified a three-factor, 12-item scale. Cronbach's alpha coefficients for the scale and its three factors were 0.857, 0.843, 0.723, and 0.644, respectively. The confirmatory factor analysis model indices were as follows: χ2 = 146.574, df = 51, p < 0.001, comparative goodness of fit index = 0.930, goodness of fit index = 0.945, Tucker-Lewis index = 0.909, and root mean square error of approximation = 0.068. The composite reliability value for convergent validity, which was >0.7, was higher than the average variance extracted value. The criterion-related validity showed a weak correlation (ρ = −0.245 to 0.341, p < 0.001).

Discussion

The DASFWB exhibits good reliability and validity, indicating its utility as a measuring instrument.
Validation of food frequency questionnaire for food intake of adults in Gida, West, Ethiopia
Authors: Fikadu Worku , Mulugeta Segni , Dejene Yeshimebet
Published: November 2024

Background

Diet is characterized by complex exposure to strongly intercorrelated components. Early efforts to understand diet-disease associations focused on the role of specific nutrients, but later, it became evident that dietary exposures may act synergistically in several instances. For research into how nutrition affects health and disease, scientifically sound descriptions of dietary intake at the population level are essential. Although food frequency questionnaires are important nutritional assessment methods, they should be validated and checked for reliability according to the eating habits of the specific population. Context-specific tools are needed to estimate food intake accurately, but a single study in Ethiopia has not established reliability. Hence, this study aimed to establish a valid and feasible dietary assessment method for 24-h dietary recall.

Objective

To adapt and validate FFQs for use as dietary assessment tools in epidemiological studies among adults in Gida woreda, West Ethiopia.

Method

A community-based cross-sectional study was conducted from February 15–30, 2023, among 150 participants. Data were collected through an interviewer-administered questionnaire, a focus group discussion, and a key informant interview. We compared the mean of three interactive 24-h dietary recalls to the FFQ to assess its relative validity. By comparing food category levels, mean differences, medians, and cross-classifications from the FFQ and a 24-h dietary recall, validity was assessed.

Results

The response rate of this study was 100% for both the FFQ and 24-item dietary recall. The mean (±SD) age of the participants was 37.6 9.7 years. Of the total participants, 40 (29%) were between 31 and 35 years old. Sixty-four (42%) of the study participants were protestant religious followers. The median ranges from zero for meat and poultry to 1,930 for cereal, as estimated by the 24-h dietary recall method. The median ranges from 14 mg/day for meat and poultry to 724 mg/day for cereal, as estimated by the FFQ.

Conclusion

This study revealed that this food frequency questionnaire had good validity for capturing the intake of food groups which indicated by their value: vegetables (0.8), legumes (0.9), roots/tubers (0.8), cereal (0.5), dairy products (0.75), and meat/poultry (0.64) at both the individual and group levels. Hence, it is recommended for health care providers to use FFQ as a tool for studying and managing dietary-related diseases.
Analysis of effects of meteorological variables on dengue incidence in Bangladesh using VAR and Granger causality approach
Authors: Hossain Md. Jamal , Sultana Nazia , Das Anwesha , Jui Fariea Nazim , Islam Md. Kamrul , Rahman Md. Mijanoor , Rahman Mohammad Mafizur
Published: November 2024

Background

Dengue fever is a serious public health issue in Bangladesh, where its incidence rises with the monsoon. Meteorological variables are believed to be responsible factors among others. Therefore, this study examines the effects of meteorological variables (temperature, rainfall, and humidity) on dengue incidence in Bangladesh. While previous studies have examined the relationship between dengue and meteorological variables using single model approaches, this study employs advanced econometric techniques to capture dynamic interactions. Furthermore, in the case of Bangladesh, this type of analysis is necessary due to the fact that dengue outbreak become one of the major issues. However, the analysis related to this issue is not available.

Methods

For estimation purposes, the Augmented Dickey-Fuller (ADF) test, Vector Autoregressive (VAR) model, Granger causality tests, Impulse Response Function (IRF), Variance Decomposition (VDC), and Vector Error Correction Model (VECM) are employed.

Results

Rainfall has a significant impact on dengue incidence compared to temperature and humidity. The Granger causality test demonstrates that rainfall and dengue incidence are causally related unidirectionally. Rainfall can potentially have a short-term and long-term effect on the incidence of dengue, as per the estimates of the VECM model.

Conclusions

These findings will assist policymakers in Bangladesh in developing a dengue fever early warning system depending on climate change. In order to efficiently avoid the spread of dengue in Bangladesh's dengue-endemic urban areas, this study suggests societal monitoring.
Socially interactive agents for robotic neurorehabilitation training: conceptualization and proof-of-concept study
Authors: Arora Rhythm , Prajod Pooja , Nicora Matteo Lavit , Panzeri Daniele , Tauro Giovanni , Vertechy Rocco , Malosio Matteo , André Elisabeth , Gebhard Patrick
Published: November 2024

Introduction

Individuals with diverse motor abilities often benefit from intensive and specialized rehabilitation therapies aimed at enhancing their functional recovery. Nevertheless, the challenge lies in the restricted availability of neurorehabilitation professionals, hindering the effective delivery of the necessary level of care. Robotic devices hold great potential in reducing the dependence on medical personnel during therapy but, at the same time, they generally lack the crucial human interaction and motivation that traditional in-person sessions provide.

Methods

To bridge this gap, we introduce an AI-based system aimed at delivering personalized, out-of-hospital assistance during neurorehabilitation training. This system includes a rehabilitation training device, affective signal classification models, training exercises, and a socially interactive agent as the user interface. With the assistance of a professional, the envisioned system is designed to be tailored to accommodate the unique rehabilitation requirements of an individual patient. Conceptually, after a preliminary setup and instruction phase, the patient is equipped to continue their rehabilitation regimen autonomously in the comfort of their home, facilitated by a socially interactive agent functioning as a virtual coaching assistant. Our approach involves the integration of an interactive socially-aware virtual agent into a neurorehabilitation robotic framework, with the primary objective of recreating the social aspects inherent to in-person rehabilitation sessions. We also conducted a feasibility study to test the framework with healthy patients.

Results and discussion

The results of our preliminary investigation indicate that participants demonstrated a propensity to adapt to the system. Notably, the presence of the interactive agent during the proposed exercises did not act as a source of distraction; instead, it positively impacted users' engagement.
Implementation of deep reinforcement learning models for emotion detection and personalization of learning in hybrid educational environments
Authors: Govea Jaime , Navarro Alexandra Maldonado , Sánchez-Viteri Santiago , Villegas-Ch William
Published: November 2024

The integration of artificial intelligence in education has shown great potential to improve student’s learning experience through emotion detection and the personalization of learning. Many educational settings lack adequate mechanisms to dynamically adapt to students’ emotions, which can negatively impact their academic performance and engagement. This study addresses this problem by implementing a deep reinforcement learning model to detect emotions in real-time and personalize teaching strategies in a hybrid educational environment. Using data from 500 students, captured through cameras, microphones, and biometric sensors and pre-processed with advanced techniques such as histogram equalization and noise reduction, the deep reinforcement learning model was trained and validated to improve the detection accuracy of emotions and the personalization of learning. The results showed a significant improvement in the accuracy of emotion detection, going from 72.4% before the implementation of the system to 89.3% after. Real-time adaptability also increased from 68.5 to 87.6%, while learning personalization rose from 70.2 to 90.1%. K-fold cross-validation with k = 10 confirmed the robustness and generalization of the model, with consistently high scores in all evaluated metrics. This study demonstrates that integrating reinforcement learning models for emotion detection and learning personalization can transform education, providing a more adaptive and student-centered learning experience. These findings identify the potential of these technologies to improve academic performance and student engagement, offering a solid foundation for future research and implementation.
Implications for conservation zoning in Teiči Strict Nature Reserve due to land use and cover change
Authors: Krumins Janis , Klavins Maris , Stankevica Karina
Published: November 2024

This study investigates land use and land cover changes in the Teiči Strict Nature Reserve, Latvia, from 1982 to 2023 and models potential future changes based on four legislative scenarios extending to 2064. The research aims to assess the effectiveness of existing conservation zoning in relation to historical and projected changes in land use and land cover, addressing the ongoing debate between fixed and adaptable conservation strategies. The study employs remote sensing imagery, geographical information system data, and land use and land cover modeling methods to analyze historical changes and predict future trends. The results suggest substantial land use and land cover changes over the past four decades, including intensified urbanization, agricultural expansion, and a decline in peat bogs and forested areas. Scenario projections indicate that continued land use and land cover changes could further challenge the efficiency of current conservation strategies, with varying outcomes depending on legislative measures and climate change impacts. The study concludes that adaptive management and variable conservation zoning are necessary to address these dynamic changes and preserve the reserve’s ecological integrity. The results emphasize the importance of integrating predictive modeling into conservation planning to improve flexibility and sustainability in protected areas.
Environmental and food security implications of livestock abortions and calf mortality: a case study in Kenya and Tanzania
Authors: Gurmu Endale B. , Bronsvoort Barend , Cook Elizabeth A. J. , Lankester Felix , Özkan Şeyda , Rosenstein Peri K. , Semango George , Wheelhouse Nick , Wilkes Andreas , Arndt Claudia
Published: November 2024

This study investigates the environmental and food security implications of livestock abortions and calf mortality in Tanzanian dairy systems and Kenyan beef systems by utilizing data from previously published studies. The environmental impact of livestock abortion is assessed in Tanzanian dairy systems, examining indigenous and exotic breeds of cattle and goats in Northern Tanzania. Calf mortality’s impact is evaluated in Kenyan beef systems, involving local cattle breeds in western Kenya. Greenhouse gas (GHG) emission intensity (EI) is estimated for both countries. The GHG emissions in Tanzania consider enteric fermentation, manure management, and feed production in different cattle and goat groups, as well as total milk production. In Kenya, enteric methane (CH4) EI related to calf mortality is assessed by estimating lifetime enteric CH4 emissions and total carcass production from dams and their offspring. The EI is compared between the observed scenario (16% calf mortality) and alternative scenarios (8, 4, and 0% calf mortality). A life cycle assessment using the Global Livestock Environmental Assessment Model-interactive (GLEAM-i) examines GHG sources and potential tradeoffs. Estimates are made for milk and carcass losses due to abortions and calf mortality, scaled to represent the entire country. Abortion increases milk EI by 4–18% in Tanzania, while Kenya’s EI ranges from 25.9 to 27.6 kg CO2 eq per kg carcass weight. Animal protein loss due to abortions is equivalent to the potential annual animal protein requirements of approximately 649 thousand people in Tanzania, while a 16% calf mortality rate in Kenya is equivalent to per capita consumption of 4.5 million people. The findings highlight the significant impact of abortions and calf mortality on GHG emissions and animal protein availability, emphasizing the potential for reduced emissions and improved food security through mitigation efforts. The contribution of emissions from enteric fermentation and manure management is significant across both countries, underscoring the importance of a systems perspective in evaluating the environmental impact of livestock production. This study provides insights into the environmental and food security implications of livestock abortions and calf mortality in Tanzania and Kenya, emphasizing the need for targeted interventions in sustainable livestock production.
Motion-Accurate Allocation of a Mechanical Transmission System Based on Meta-Action and Intuitionistic Trapezoidal Fuzzy Numbers
Authors: Mu Zongyi , Li Jian , Zhang Xiaogang , Zhang Genbao , Li Jinyuan , Wei Hao
Published: November 2024

The traditional mechanical transmission system motion accuracy allocation process has the following problems: the error modeling process can not reflect the error formation mechanism of the system, and the influence of maintenance costs and the motion accuracy robustness of the system are ignored in the process of establishing the optimal allocation model of motion accuracy. In this paper, firstly, meta-action theory is introduced and the meta-action unit is taken as the basic analysis unit, the error modeling of mechanical transmission systems is studied, and the formation mechanism of the motion error is correctly analyzed. Secondly, the comprehensive cost of a mechanical transmission system, considering the part manufacturing cost, assembly cost and maintenance cost per unit, is accurately evaluated using the multi-criteria decision-making (MCDM) method. Thirdly, based on the motion error model, a robust model for system motion accuracy is obtained by analyzing the sensitivity of each motion pair. Then, a multi-objective optimal allocation model of motion accuracy is established. The model is solved by an intelligent algorithm to obtain the Pareto non-dominated solution set, and the optimal solution is selected by the fuzzy set method. Finally, the method described in this paper is illustrated by an engineering example.
Optimization of Temperature Measurement Method for High-Pressure Gas Flow Standard Facility Based on Sonic Nozzle Array
Authors: Zhang Zhihao , Zhao Jiaxi , Liu Tingting , Zhang Rongping
Published: November 2024

To improve the accuracy of the wind tunnel test, relying on the high-pressure gas source of the China Aerodynamic Research and Development Center, a secondary flow standard facility based on a sonic nozzle array was developed, with a pressure range of (1~6) MPa and a flow range of (0.12~5.55) kg/s. Currently, most facilities use the average temperature measured by the temperature array to represent the upstream temperature of the sonic nozzle array. However, the small flow calibration test results showed that the maximum temperature difference upstream of the standard sonic nozzle array was 1.97 K, and the temperature field upstream of the sonic nozzle array showed non-uniformity, so the above method cannot accurately obtain the upstream temperature. To solve this problem, each nozzle used in the standard sonic nozzle array was accurately measured by temperature sensors. The uncertainty of the facility and the discharge coefficient of the calibrated nozzle between the two methods were compared. The results showed that compared with the discharge coefficient obtained using the temperature sensor array of 0.9902, the accurate measurement of 0.9904 was closer to the National Institute of Metrology, China (NIM) traceable result of 0.9907, and the relative uncertainty of the facility was reduced from 0.124% (k = 2) to 0.120% (k = 2).
Consumers’ Attitudes Towards Whole Dried Figs Attributes: A Preliminary Study in Italy
Authors: Monaco Federica , De Marinis Pietro , Sali Guido
Published: November 2024

Dried fig production and trade are prominent in Mediterranean and Middle east countries, with Turkey as the major player. Concerns about the quality of Turkish supply in the EU market give chances to the Italian production to uphold quality standards and cope with the increasing demand. National consumption of dried figs is showcased in market reports and nutritional surveys, with insufficient insights on consumers’ preferences. This research combines qualitative and quantitative approaches to enrich the current knowledge and uncover consumers’ attitudes towards whole dried figs. A face-to-face survey was conducted to assess the relevance of selected intrinsic, extrinsic and credence attributes upon purchasing. A hierarchical cluster analysis detected four typologies of consumers and validate previous findings on taste being the prime purchasing driver. Price affects more the behaviour of low-income large families; the groups are featured by contrasting opinions on environmental and societal impacts of dried fig production, and its rootedness to the local context. This allowed to draft some considerations on possible marketing strategies tailored to different consumers.
Influence of Artificial Lighting on the Germination of Quina Seeds (Cinchona spp.) in Controlled Conditions Within a Geodesic Dome Powered by Photovoltaic Energy
Authors: Gosgot Angeles Wildor , Florida Garcia Julio , Yalta Chappa Merbelita , Santillan Gomez Homar , Oliva Cruz Manuel , Gamarra-Torres Oscar Andrés , Barrena Gurbillón Miguel Angel
Published: November 2024

This study evaluated the germination of Cinchona spp. seeds under controlled environmental conditions within a geodesic dome equipped with photovoltaic energy. The main objective was to assess how stable temperature and humidity, along with potassium nitrate (KNO3) and specific LED light treatments, affect the germination rate and plant growth. The results indicate that Cinchona spp. seeds germinate effectively inside the dome, even under temperature and humidity conditions that differ from their natural habitat. Among the evaluated conditions, the treatment with 1000 ppm of KNO3 and white LED light (LM 1000 ppm) showed the highest germination rate, achieving 72.5% with an average of 1.5 seeds germinated per day. Agronomic evaluations showed that this treatment also led to superior growth metrics, including an average plant height of 2.1 cm, an average leaf count of 3.6, and a dry weight of 0.0013 g. This research highlights the potential of controlled environments, such as geodesic domes, to optimize germination and early growth in endangered plant species. The combination of environmental control with KNO3 treatments offers a valuable approach to enhancing the propagation of Cinchona spp., providing practical implications for conservation and reforestation efforts. This work provides a foundation for further studies on optimizing germination and growth conditions for other native and endangered species in controlled environments.
Agromorphological Evaluation of Elite Lines of Native Tomato (Solanum lycopersicum L.) from Central and Southern Mexico
Authors: Valencia-Juárez María Concepción , González-Pérez Enrique , Villalobos-Reyes Salvador , Núñez-Colín Carlos Alberto , Canul-Ku Jaime , Anaya-López José Luis , Chiquito-Almanza Elizabeth , Yáñez-López Ricardo
Published: November 2024

Tomato (Solanum lycopersicum L.) is one of the most important cultivated vegetables in the world. However, in some countries such as Mexico the lack of cultivars adapted to different environmental production conditions is a limitation. Moreover, recent studies have indicated that breeding aimed at increasing yield has led to a loss of genetic diversity. Therefore, it is necessary to explore and characterize new sources of germplasms. This study aimed to characterize new sources of germplasm and identify the most transcendental traits for distinguishing tomato types and lines that are useful for the genetic improvement of the species. Sixty characters were evaluated in 16 advanced lines of native tomatoes from Central and Southern Mexico during the fall–winter cycles 2023–2024 at the Bajío Experimental Station, Celaya, Guanajuato, Mexico, based on the guidelines of the International Union for the Protection of New Varieties of Plants (UPOV) and the International Plant Genetic Resources Institute (IPGRI). The data were analyzed using descriptive statistics, analysis of variance and post hoc tests, canonical discriminant analysis, and the Eigenanalysis selection index method (ESIM). Morphological variation showed that five qualitative traits were determinant factors in distinguishing tomato types and lines, whereas agronomic discriminant traits were the equatorial and polar diameters of the fruit and its ratio, number of locules, pedicel length, stem length, and internode distance. In addition, significant positive correlations were found between leaf length and width, equatorial diameter of the fruit, and polar diameter of the fruit. Lines JCM-17, JMC-10, and JCM-01 were the most selectable lines according to the ESIM values. The morphological variation found and the characteristics with higher selection values identified may be valuable for optimizing the tomato genetic improvement process in general.
Leveraging Zero-Shot Detection Mechanisms to Accelerate Image Annotation for Machine Learning in Wild Blueberry (Vaccinium angustifolium Ait.)
Authors: Mullins Connor C. , Esau Travis J. , Zaman Qamar U. , Toombs Chloe L. , Hennessy Patrick J.
Published: November 2024

This study conducted an analysis of zero-shot detection capabilities using two frameworks, YOLO-World and Grounding DINO, on a selection of images in the wild blueberry (Vaccinium angustifolium Ait.) cropping system. The datasets included ripe wild blueberries, hair fescue (Festuca filiformis Pourr.), blueberry buds, and red leaf disease (Exobasidium vaccinii). Key performance metrics such as Intersection over Union (IoU), precision, recall, and F1 score were utilized for model comparison. Grounding DINO consistently achieved superior performance across all metrics and datasets, achieving significantly higher mean IoUs on berries, red leaf, hair fescue, and buds (0.642, 0.921, 0.735, and 0.629, respectively) compared to YOLO-World (0.516, 0.567, 0.232, and 0.408, respectively). Evidenced by their high recall rates relative to precision, the models displayed a preference for identifying true positives at the cost of increasing false positives. Grounding DINO’s higher precision (overall mean of 0.672), despite the tendency to over-detect, indicated a better balance in minimizing false positives than YOLO-World (overall mean of 0.501). These findings contrast with the foundational study of YOLO-World where it demonstrated superior performance on standard datasets, highlighting the importance of dataset characteristics and optimization processes in model performance. The practical implications of this study include providing a solution for accelerated object detection image annotation in the wild blueberry cropping system. This work, representing a significant advancement in facilitating accurate and efficient annotation of wild blueberry datasets, guides future research in the application of zero-shot detection models to agricultural datasets.
Identification of Shade Avoidance Response MicroRNAs and Their Targets in Solanum tuberosum L. via High-Throughput Sequencing
Authors: Liu Mei , Yang Jiangwei , Zhang Ning , Qiao Run , Li Xinxia , Zhu Fengjiao , Si Huaijun
Published: November 2024

MicroRNAs (miRNAs) are non-coding, single-stranded RNA molecules that regulate gene expression post-transcriptionally. Potato, an essential crop for food and fodder, experiences reduced quality and yield under shading. Although miRNAs have known roles in various plants, their regulatory mechanisms in potato shade avoidance remain unexplored. To investigate this, we constructed nine small RNA libraries from potato samples at 0, 5, and 10 days post-shade treatment. High-throughput sequencing identified 525 miRNAs (307 known and 218 novel) from 99 families, and 166 differentially expressed miRNAs (DEMs) were detected. qRT-PCR verified 10 DEMs, confirming sequencing reliability. Using TargetFinder, we predicted 4320 target genes of DEMs, which were enriched in plant–pathogen interaction and hormone signal transduction pathways, among others. These findings indicate that miRNAs may play key regulatory roles in potato shade avoidance by targeting specific genes, providing valuable insights for future functional studies and potential yield enhancement.
Assessing Surface Water Quality Using Risk Indicators, Geographic Information System Modeling Techniques, and Multi-Statistical Methods in Arid Regions to Maintain the Sustainability of Water Resources
Authors: Hendawy Ehab , Belal Abdel-Aziz A. , Rebouh Nazih Y. , Shokr Mohamed S. , Mohamed Elsayed Said , Sheta Abd El Aziz S. , Abou-Hadid Ayman F.
Published: November 2024

Assessing the water quality of surface water bodies is one of the primary duties of environmental authorities in charge of water management. Irrigation water quality (IWQ) of the irrigation canals in the middle Nile delta, Egypt, was assessed by GIS-based research of water suitability indicators (risks connected with salinity, permeability, ion toxicity, and other factors on delicate crops), utilizing a thorough examination of 27 samples gathered from the irrigation canals surrounding the Kitchener drain Egypt, based on thirteen chemical characteristics in 2023. The maps in this work were created with ArcGIS version 10.7. A procedure known as Inverse Distance Weight (IDW) was used to show the variations in the concentrations of the different heavy metals and to offer a geographic representation of the water quality. We utilized principal component analysis (PCA) to pinpoint potential sources of heavy metals. To assess soil contamination levels in the study area, various methods were used such as contamination factors (CFs), heavy metal pollution index (HPI), ecological risks index (ERI), pollution load index (PLI), and the modified degree of contamination (mCd) for seven targeted metals: As, Cd, Co, Cu, Ni, Pb, and Zn. The findings showed that every sample had a medium irrigation appropriateness rating as the IWQI values range from 25.43 to 34.50. According to the different contamination indices, the study area is suffering high contamination as the mean values of HPI, ERI, PLI, and MCd are 3570.26 ± 621.40, 804.62 ± 164.88, 6.62 ± 6.06, and 5.10 ± 0.89, respectively. PCA results revealed significant metal contamination in multiple enterprises showing that they are present simultaneously and may have a common source. This source could be an industrial discharge, agricultural runoff or other process that affects the metals’ concentrations in surface water. These results give decision-makers important information for managing surface water resources and encouraging sustainable water management in the research region. By educating the local community about artificial groundwater recharge, rainwater collection, and surface water canal management, government authorities can gradually lessen the potential effects of poor water quality in these areas. It is also recommended to develop a risk management module that can assess water threats for agricultural and public health applications. The ultimate goal is to incorporate this descriptive and sensitive research into a risk management system that can generate quick reports for policymakers and decision-makers.
Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control
Authors: Zhao Chang-Tao , Wang Rui-Feng , Tu Yu-Hao , Pang Xiao-Xu , Su Wen-Hao
Published: November 2024

Weed management plays a crucial role in the growth and yield of lettuce, with timely and effective weed control significantly enhancing production. However, the increasing labor costs and the detrimental environmental impact of chemical herbicides have posed serious challenges to the development of lettuce farming. Mechanical weeding has emerged as an effective solution to address these issues. In precision agriculture, the prerequisite for autonomous weeding is the accurate identification, classification, and localization of lettuce and weeds. This study used an intelligent mechanical intra-row lettuce-weeding system based on a vision system, integrating the newly proposed LettWd-YOLOv8l model for lettuce–weed recognition and lettuce localization. The proposed LettWd-YOLOv8l model was compared with other YOLOv8 series and YOLOv10 series models in terms of performance, and the experimental results demonstrated its superior performance in precision, recall, F1-score, mAP50, and mAP95, achieving 99.732%, 99.907%, 99.500%, 99.500%, and 98.995%, respectively. Additionally, the mechanical component of the autonomous intra-row lettuce-weeding system, consisting of an oscillating pneumatic mechanism, effectively performs intra-row weeding. The system successfully completed lettuce localization tasks with an accuracy of 89.273% at a speed of 3.28 km/h and achieved a weeding rate of 83.729% for intra-row weed removal. This integration of LettWd-YOLOv8l and a robust mechanical system ensures efficient and precise weed control in lettuce cultivation.
Arthropod Pests, Nematodes, and Microbial Pathogens of Okra (Abelmoschus esculentus) and Their Management—A Review
Authors: Ounis Samara , Turóczi György , Kiss József
Published: November 2024

Okra (Abelmoschus esculentus) is an important agricultural crop of the Malvaceae family, cultivated across tropical, subtropical, and warm temperate regions. However, okra production faces numerous challenges from diverse pest species, including insects, nematodes, arachnids, and mites, that significantly reduce its yield. Major economic pests include the cotton aphid, cotton spotted bollworm, Egyptian bollworm, cotton mealybug, whitefly, cotton leafhopper, cotton bollworm, two-spotted spider mite, root-knot nematode, reniform nematode, cotton leaf roller, and flea beetle. Additionally, less prevalent pests such as the blister beetle, okra stem fly, red cotton bug, cotton seed bug, cotton looper, onion thrips, green plant bug, and lesion nematode are also described. This review also addresses fungal and oomycete diseases that present high risks to okra production, including damping-off, powdery mildew, Cercospora leaf spot, gray mold, Alternaria leaf spot and pod rot, Phyllosticta leaf spot, Fusarium wilt, Verticillium wilt, collar rot, stem canker, anthracnose, and fruit rot. In addition to these fungal diseases, okra is also severely affected by several viral diseases, with the most important being okra yellow vein mosaic disease, okra enation leaf curl disease, and okra mosaic disease, which can cause significant yield losses. Moreover, okra may also suffer from bacterial diseases, with bacterial leaf spot and blight, caused primarily by Pseudomonas syringae, being the most significant. This manuscript synthesizes the current knowledge on these pests. It outlines various management techniques and strategies to expand the knowledge base of farmers and researchers, highlighting the key role of integrated pest management (IPM).
Innovative Surface Plasmon Resonance Aptasensor for Detecting Cocaine in Human Urine
Authors: Keklik Şah Gül , Isbir Turan Aybüke A.
Published: November 2024

This study describes the development of an optical-based surface plasmon resonance (SPR) aptasensor for the detection of cocaine. The aptasensor was prepared by first attaching gold nanoparticles to a clean SPR chip surface, followed by the addition of an aptamer to create a modified surface. This surface was characterized using contact angle and atomic force microscopy, revealing surface roughness values of 0.28 nm and 28.12 nm for the blank and modified surfaces, respectively. The detection of cocaine was carried out in the concentration range of 1 ng/mL to 1000 ng/mL, with a detection time of approximately 8 min and a cocaine limit of detection (LOD) of 0.43 ng/mL. Repeatability studies were conducted, and the stability of the signal response was examined at a concentration of 200 ng/mL. Adsorption isotherm models, including Scatchard, Langmuir, and Freundlich models, were calculated to assess the surface homogeneity of the SPR aptasensor chip, with the results indicating compatibility with the Langmuir isotherm model.
Melatonin Sources in Sheep Rumen and Its Role in Reproductive Physiology
Authors: Niu Tian , Ding Ziqiang , Zeng Jianlin , Yan Zhenxing , Duan Hongwei , Lv Jianshu , Zhang Yong , Zhang Lihong , Hu Junjie
Published: November 2024

In mammals, the melatonin (Mel) concentration in the gastrointestinal tract is 400 times greater than in the pineal gland. However, the origin of Mel in the gastrointestinal tract and its role in reproductive regulation remains unclear. Therefore, we analyzed three potential Mel sources (feed, microorganisms, and the rumen wall) for their contribution to high Mel levels in the rumen and their biological effects. The feed contained high Mel concentrations, and Mel in rumen fluid and blood peaked two hours after feeding. Rumen microbial analysis showed a strong positive correlation between Mel and specific microbes, including Megasphaera, Butyrivibrio, Acetobacter, and Olsenella. In vitro experiments indicated that rumen microorganisms synthesized Mel from tryptophan. The rumen wall also contains key enzymes, AANAT and HIOMT, which catalyze Mel synthesis and membrane receptors MT1 and MT2 that mediate the function of Mel, suggesting that the rumen wall synthesizes Mel. Mel peaked in both rumen fluid and blood two hours after feeding. Feeding also altered blood levels of Mel, Gonadotropin-releasing hormone (GnRH), Luteinizing hormone (LH), Follicle-stimulating hormone (FSH), progesterone (P4), and Estradiol (E2), with a correlation between Mel and fluctuations in GnRH, LH, P4, and E2 levels. Our findings suggest that feed is the primary source of high Mel levels in the rumen and impacts reproductive hormone fluctuations. This study elucidates the origin of high rumen Mel concentrations and reveals that food intake affects the natural secretion of various hormones, offering a new perspective on food sources for regulating reproductive physiology.
Correction of Multispectral Singlet Oxygen Luminescent Dosimetry (MSOLD) for Tissue Optical Properties in Photofrin-Mediated Photodynamic Therapy
Authors: Yang Weibing , Johnson Madelyn , Lu Baozhu , Sourvanos Dennis , Sun Hongjing , Dimofte Andreea , Vikas Vikas , Busch Theresa M. , Hadfield Robert H. , Wilson Brian C. , Zhu Timothy C.
Published: November 2024

The direct detection of singlet-state oxygen (1O2) constitutes the holy grail dosimetric method for type-II photodynamic therapy (PDT), a goal that can be quantified using multispectral singlet oxygen near-infrared luminescence dosimetry (MSOLD). The optical properties of tissues, specifically their scattering and absorption coefficients, play a crucial role in determining how the treatment and luminescence light are attenuated. Variations in these properties can significantly impact the spatial distribution of the treatment light and hence the generation of singlet oxygen and the detection of singlet oxygen luminescence signals. In this study, we investigated the impact of varying optical properties on the detection of 1O2 luminescence signals during Photofrin-mediated PDT in tissue-mimicking phantoms. For comparison, we also conducted Monte Carlo (MC) simulations under the same conditions. The experimental and simulations are substantially equivalent. This study advances the understanding of MSOLD during PDT.
The Effect of Moringa oleifera Leaf Extract on C2C12 Myoblast Proliferation and Redox Status Under Oxidative Insult
Authors: Ceci Roberta , Maldini Mariateresa , La Rosa Piergiorgio , Sireno Laura , Antinozzi Cristina , Olson Mark E. , Dimauro Ivan , Duranti Guglielmo
Published: November 2024

Skeletal muscle tissue can regenerate after damage through the action of satellite cells, which proliferate as myoblasts when activated. Oxidative stress, marked by high rates of reactive oxygen species (e.g., hydrogen peroxide, H2O2), impairs this process by increasing myoblast cell death. Moringa oleifera leaf extract (MOLE), known for its antioxidant properties, was tested for its protective effects on C2C12 myoblasts under oxidative stress. We assessed MOLE’s impact on total antioxidant capacity (TAC), glutathione homeostasis (GSH/GSSG), cell viability, and wound recovery. The metabolomic analysis of MOLE using an LC-MSMS ZenoTOF 7600 mass spectrometry system identified key compounds, including peculiar glucosinolates (42.1%) and flavonoids (18.8%), as well as phenolic acids (4.5%) and other significant metabolites (34.6%; among them, amino acids, vitamins, and fatty acids). H2O2 disrupted myoblast redox balance and caused cell death, but MOLE treatment restored the GSH/GSSG ratio, improved TAC, and increased cell viability. Additionally, MOLE promoted faster wound closure in myoblasts exposed to H2O2. These findings suggest that MOLE can protect C2C12 myoblasts by restoring redox balance and enhancing recovery under oxidative stress.
Editorial: Deep Learning and Edge Computing for Internet of Things
Authors: Wan Shaohua , Wu Yirui
Published: November 2024

The evolution of 5G and Internet of Things (IoT) technologies is leading to ubiquitous connections among humans and their environment, such as autopilot transportation, mobile e-commerce, unmanned vehicles, and healthcare applications, bringing revolutionary changes to our daily lives [...]
Maize Replacement for Controlling Invasive Eupatorium adenophorum
Authors: Hao Baoqiang , Li Qingjie , Ouyang Canbin
Published: November 2024

Replacement control technology is a sustainable strategy for the control of invasive weeds. Two consecutive years of field experiments were conducted in Xichang city to assess the ecological and economical possibility of replacement control of Eupatorium adenophorum (Spreng.) King & H.Rob. with maize. Four treatment groups were planted with maize at different densities after cutting E. adenophorum. Two reference groups were set by not treating and only cutting the aerial parts of E. adenophorum. All maize replacements in the “after tillage” treatments provided control effects of up to 100% and significantly reduced water and fertilizer use by E. adenophorum. Maize replacement provided a substantial economic benefit of up to 20,273.50 and 23,633.22 yuan/hm2 in two consecutive years and increased incomes. Evaluated in terms of niche theory, the large leaves as well as high plant cover assisted in effectively occupying the available niche and reduced light interception, seed germination and growth of E. adenophorum. This study provided a scientific basis for the sustainable and eco-friendly control of weeds by ecological methods.
A Multi-Objective Optimization Framework for Peer-to-Peer Energy Trading in South Korea’s Tiered Pricing System
Authors: Kharatovi Laura , Gantassi Rahma , Masood Zaki , Choi Yonghoon
Published: November 2024

This study proposes a multi-objective optimization framework for peer-to-peer (P2P) energy trading in South Korea’s tiered electricity pricing system. The framework employs the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to optimize three conflicting objectives: minimizing consumer costs, maximizing prosumer benefits, and enhancing energy utilization. Using real microgrid data from a South Korean community, the framework’s performance is validated through simulations. The results highlight that MOEA/D achieved an optimal cost of KRW 32,205.0, a benefit of KRW 32,205.0, and an energy utilization rate of 57.46%, outperforming the widely used NSGA-II algorithm. Pareto front analysis demonstrates MOEA/D’s ability to generate diverse and balanced solutions, making it well suited for regulated energy markets. These findings underline the framework’s potential to improve energy efficiency, lower costs, and foster sustainable energy trading practices. This research offers valuable insights for advancing decentralized energy systems in South Korea and similar environments.
Extending a Macro-Element Approach for the Modeling of 3D Masonry Structures Under Transient Dynamic Loading
Authors: Decret Damien , Malecot Yann , Sieffert Yannick , Vieux-Champagne Florent , Daudeville Laurent
Published: November 2024

Masonry structures, particularly those used in developing countries and in historic buildings, typically consist of unreinforced masonry (URM) walls connected by timber or reinforced concrete elements. This study proposes enhancements to the existing two-dimensional (2D) deformable frame model (DFM) to enhance its ability in simulating masonry walls with a specific focus on accurately predicting the transient dynamic response of three-dimensional (3D) masonry structures while maintaining a minimal number of degrees of freedom (DOF). For the modeling of URM walls, the DFM framework employs elastic beams and diagonal struts with nonlinear constitutive behavior. Structural elements, such as reinforced concrete or timber reinforcements, are represented using conventional beam finite elements. This paper first reviewed the current DFM configuration, which primarily addresses the in-plane (IP) behavior of URM structures. It then introduced modifications tailored for 3D structural analysis. The reliability of the enhanced model was validated through two approaches. First, a modal analysis compared the results from the updated DFM with those from a reference 3D model based on cubic finite elements. Second, a shaking table experiment conducted on a half-scale masonry house was simulated. The findings demonstrate that, despite its limited number of DOF, the updated DFM effectively captures the main natural vibration modes. Furthermore, it shows the model’s ability to predict the nonlinear transient dynamic response of 3D masonry structures with accuracy and limited computational time.
Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
Authors: Lazcano Ana , Hidalgo Pablo , Sandubete Julio E.
Published: November 2024

Deep learning techniques have significantly advanced time series prediction by effectively modeling temporal dependencies, particularly for datasets with numerous observations. Although larger datasets are generally associated with improved accuracy, the results of this study demonstrate that this assumption does not always hold. By progressively increasing the amount of training data in a controlled experimental setup, the best predictive metrics were achieved in intermediate iterations, with variations of up to 66% in RMSE and 44% in MAPE across different models and datasets. The findings challenge the notion that more data necessarily leads to better generalization, showing that additional observations can sometimes result in diminishing returns or even degradation of predictive metrics. These results emphasize the importance of strategically balancing dataset size and model optimization to achieve robust and efficient performance. Such insights offer valuable guidance for time series forecasting, especially in contexts where computational efficiency and predictive accuracy must be optimized.