bluuebunny/crossref_metadata_2025 huggingface dataset:
An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission
Authors: Karimullah Shaik , Khan Mudassir , Shaik Fahimuddin , Alabduallah Bayan , Almjally Abrar
Published: October 2024

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses the pressing need for early detection through a novel diagnostic approach that leverages innovative image processing techniques. The urgency of early lung cancer detection is emphasized by its alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, a comprehensive diagnosis requires a combination of imaging techniques. This research introduces an advanced diagnostic tool implemented through image processing methodologies. The methodology commences with histogram equalization, a crucial step in artifact removal from CT images sourced from a medical database. Accurate lung CT image segmentation, which is vital for cancer diagnosis, follows. The Otsu thresholding method and optimization, employing Colliding Bodies Optimization (CBO), enhance the precision of the segmentation process. A local binary pattern (LBP) is deployed for feature extraction, enabling the identification of nodule sizes and precise locations. The resulting image underwent classification using the densely connected CNN (DenseNet) deep learning algorithm, which effectively distinguished between benign and malignant tumors. The proposed CBO+DenseNet CNN exhibits remarkable performance improvements over traditional methods. Notable enhancements in accuracy (98.17%), specificity (97.32%), precision (97.46%), and recall (97.89%) are observed, as evidenced by the results from the fractional randomized voting model (FRVM). These findings highlight the potential of the proposed model as an advanced diagnostic tool. Its improved metrics promise heightened accuracy in tumor classification and localization. The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for lung cancer detection, setting it apart from traditional methods with superior performance metrics.
Molecular insights and inhibitory dynamics of flavonoids in targeting Pim-1 kinase for cancer therapy
Authors: Alhadrami Hani A. , Sayed Ahmed M. , Hassan Hossam M. , Alhadrami Albaraa H. , Rateb Mostafa E.
Published: October 2024

Pim-1 kinase, a serine/threonine kinase, is often overexpressed in various cancers, contributing to disease progression and poor prognosis. In this study, we explored the potential of flavonoids as inhibitors of Pim-1 kinase using a combination of molecular docking and steered molecular dynamics (SMD) simulations. Our docking studies revealed two main binding orientations for the flavonoid molecules. The SMD simulations showed that the binding mode with higher pulling forces was linked to stronger inhibitory activity, with a strong positive correlation (R2 ≈ 0.92) between pulling forces and IC50 values. Quercetin stood out as the most potent inhibitor, showing a pulling force of about 820 pN and an IC_(5) 0 of less than 6 µM. Further dynamic simulations indicated that quercetin’s hydroxyl groups at the C3, C-5 and C-7 positions formed stable hydrogen bonds with key residues GLU-121, Leu-44 and Val-126, respectively enhancing its binding stability and effectiveness. Our results emphasized the critical role of the hydroxyl group at the C-3 position, which plays a pivotal function in effectively anchoring these molecules in the active site of Pim-1 kinase. Principal component analysis (PCA) of Pim-1 kinase’s conformational changes revealed that potent inhibitors like quercetin, galangin, and kaempferol significantly restricted the enzyme’s flexibility, suggesting potential inhibitory effect. These findings provide insights into the structural interactions between flavonoids and Pim-1 kinase, offering a foundation for future experimental investigations. However, further studies, including in vitro and in vivo validation, are necessary to assess the pharmacological relevance and specificity of flavonoids in cancer therapy.
EcoFAB 3.0: a sterile system for studying sorghum that replicates previous field and greenhouse observations
Authors: Gupta Kshitiz , Tian Yang , Eudes Aymerick , Scheller Henrik V. , Singh Anup K. , Adams Paul D. , Andeer Peter F. , Northen Trent R.
Published: October 2024

Introduction

Studying plant-microbe interactions is one of the key elements in understanding the path to sustainable agricultural practices. These interactions play a crucial role in ensuring survival of healthy plants, soil and microbial communities. Many platforms have been developed over the years to isolate these highly complex interactions however, these are designed for small model plants. This creates a need for complementary devices for larger plants, such as sorghum.

Methods

This work introduces a novel platform, EcoFAB 3.0, which is designed to enable studying bioenergy plants such as sorghum for up to 4 weeks in a controlled sterile environment. Several other advantages of this platform such as dark root chambers and user-friendly assembly are also discussed in this work.

Results and discussion

EcoFAB 3.0 was found to replicate previous greenhouse and field observations when comparing an engineered sorghum line overproducing 4-hydroxybenzoic acid (4-HBA) and wildtype (variety BTx430). Consistent with greenhouse and field observations, it was found that the engineered line of sorghum grown in EcoFAB 3.0 had a higher 4-HBA content and a lower dry biomass.
Polysomnographic insights into the attention-deficit/hyperactivity disorder and obstructive sleep apnea connection in children
Authors: Lacki Olga , Slaven James , Rushton Jerry , Rao Harish , Thompson Rohan , Jalou Hasnaa , Bandyopadhyay Anuja
Published: October 2024

Introduction

There is a high prevalence of sleep disturbances and disorders such as obstructive sleep apnea (OSA) in children with attention-deficit hyperactivity disorder (ADHD), though this relationship remains poorly characterized by objective measures. Polysomnographic studies (PSGs) on sleep disruptions in ADHD have yielded inconsistent results. Few have studied polysomnograms in stimulant-medicated vs. non-medicated children with ADHD+/-OSA. This study aimed to elucidate pathognomonic polysomnographic sleep disturbances in children diagnosed with ADHD+/-OSA.

Methods

Medical charts and polysomnograms were retrospectively reviewed for children ages 4-18 who underwent overnight polysomnography at a tertiary care center from 2019 to 2022. ADHD diagnosis was determined by ICD code F90, and OSA was defined by apnea-hypopnea indices (AHI) ≥5 events/hour. Four groups were evaluated: children without OSA or ADHD, children with OSA alone, children with ADHD alone, and children with ADHD+OSA. Statistical analyses identified significant differences among variables of interest.

Results

4,013 children met the study criteria. 2,372 children were without OSA and without ADHD (59.1%), 1,197 with OSA alone (29.8%), 333 with ADHD alone (8.3%), and 111 with ADHD and OSA (2.8%). Insomnia (ICD code G47.00) was prevalent in children with ADHD alone. However, they exhibited significantly better sleep efficiency (SE), than children with OSA alone, and SE did not significantly differ from the other groups. No significant difference in periodic limb movements (PLMs) was found across all groups. The above results held true even after correcting for stimulant prescription.

Conclusion

The increased frequency of clinical insomnia diagnoses in children with ADHD is not associated with any traditional parameters on polysomnogram. Innovative subclinical polysomnographic biomarkers are needed to identify sleep characteristics unique to ADHD. In children with both ADHD and OSA, PSG results do not reveal any distinctive sleep disturbances which cannot be better explained by OSA alone. These findings underscore the importance of screening for OSA in patients with ADHD and clinically assessing for other sleep concerns.
A systematic review of the toxic potential of parabens in fish
Authors: Dasmahapatra Asok K. , Chatterjee Joydeep , Tchounwou Paul B.
Published: October 2024

Parabens are the most prevalent ingredients in cosmetics and personal care products (PCPs). They are colorless and tasteless and exhibit good stability when combined with other components. Because of these unique physicochemical properties, they are extensively used as antimicrobial and antifungal agents. Their release into the aquatic ecosystem poses potential threats to aquatic organisms, including fish. We conducted an electronic search in PubMed (http://www.ncbi.nlm.nih.gov/pubmed) using the search term parabens and fish and sorted 93 articles consisting of methyl paraben (MTP), ethyl paraben (ETP), propyl paraben (PPP), butyl paraben (BTP), and benzyl paraben (BNP) in several fish species. Furthermore, we confined our search to six fish species (common carp, Cyprinus carpio; fathead minnows, Pimephales promelas; Japanese medaka, Oryzias latipes; rainbow trout, Oncorhynchus mykiss; Nile tilapia, Oreochromis niloticus; and zebrafish, Danio rerio) and four common parabens (MTP, ETP, PPP, and BTP) and sorted 48 articles for review. Our search indicates that among all six fish, zebrafish was the most studied fish and the MTP was the most tested paraben in fish. Moreover, depending on the alkyl chain length and linearity, long-chained parabens were more toxic than the parabens with short chains. Parabens can be considered endocrine disruptors (EDs), targeting estrogen-androgen-thyroid-steroidogenesis (EATS) pathways, blocking the development and growth of gametes, and causing intergenerational toxicity to impact the viability of offspring/larvae. Paraben exposure can also induce behavioral changes and nervous system disorders in fish. Although the USEPA and EU limit the use of parabens in cosmetics and pharmaceuticals, their prolonged persistence in the environment may pose an additional health risk to humans.
Risk, Attributable Fraction and Attributable Number of Cause-Specific Heat-Related Emergency Hospital Admissions in Switzerland
Authors: Schulte Florian , Röösli Martin , Ragettli Martina S.
Published: October 2024

Objectives

We assessed the relationship between heat and emergency hospital admissions (EHAs) in Switzerland using clinically relevant metrics.

Methods

Applying distributed lag non-linear models, we investigated temperature-admission associations between May and September 1998–2019 for various disease groups, by age class and gender. We estimated the relative risk (RR) for moderate (29°C) and extreme (34°C) daily maximum temperatures relative to disease-specific optimum temperature, and calculated attributable fractions (AFs) for hot days and the following week. We also calculated the total number of heat-related EHAs.

Results

We attributed 31,387 (95% confidence interval: 21,567–40,408) EHAs to above-optimal temperatures, 1.1% (0.7%–1.4%) of the total. Extreme temperatures increased the EHA risk for mental, infectious and neurological diseases. We observed particularly high AFs due to extreme heat for dehydration (85.9%, 95% CI: 82.4%–88.8%) and acute kidney injury (AKI, 56.1%, 95% CI: 45.3%–64.7%). While EHA risk generally increased with age, we also found high RRs for infectious diseases in children (0–15 years) and AKI in young adults (15–64 years).

Conclusion

Hot weather increases the EHA risk in Switzerland. Therefore a comprehensive clinical and public health response is needed.
Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
Authors: Dormido-Canto Sebastián , Rohland Joaquín , López Matías , Garcia Gonzalo , Fabregas Ernesto , Farias Gonzalo
Published: October 2024

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption.
Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention
Authors: Luo Yuanqing , Yang Yuhang , Kang Shuang , Tian Xueyong , Liu Shiyue , Sun Feng
Published: October 2024

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.
Dynamics Modeling and Motion Evaluation of a Near-Ground Tethered Balloon Cable System under Severe Wind Environments
Authors: Lai Zhenhua , Tang Mao , Hu Xiaojin , Shu Xin , Huang Weicheng , Pan Yongjun
Published: October 2024

Weather survival poses a significant challenge for the utilization of tethered balloons. The dynamic modeling of tethered balloon systems presents challenges due to the flexible nature of the cables and the intricate nature of gust forces. The present study introduces a new approach for modeling near-ground tethered balloon systems, which enables the analysis of their dynamic responses and performance evaluation under complex boundary conditions. First, finite cylindrical rigid bodies that are joined together by bushing forces to describe the dynamics of the tethered cable. The properties of the flexible cables under severe bending and translation can be illustrated by the dynamics model. Second, a three-dimensional dynamics model based on the multibody dynamics theory is created to deal with the interaction of the tethered balloon system and flexible cables. The dynamic responses of the tethered balloon system under challenging operating conditions are investigated, focusing on the number of cable segments and the place and direction of gust wind impacts. This model allows for precise assessment and optimization of the system’s overall performance to improve weather resistance. The results show that compared to computational fluid dynamics (CFDs) methods, the multibody system dynamics-based balloon model improved the solution time by 80%, with a pitch angle deviation of only 0.0016°. Moreover, the bushing model effectively reduced cable force and enabled accurate reflection of the system’s motion characteristics.
Nonlinear Flux Linkage Observer with Model Reference Adaptive System for Improved Permanent Magnet Synchronous Motor Control
Authors: Zhu Shaopeng , Hu Kaida , Lin Jian , Liu Yongqing , Chen Huipeng , Wang Weiyang , Gao Jian
Published: October 2024

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change.
Piezoelectric Shunt Damping for a Planar Motor Application under Cryogenic Conditions
Authors: Ambaum Niek , Mokrani Bilal , Vermeulen Hans
Published: October 2024

For several decades, Moore’s law has driven the semiconductor industry, with computational power and production costs as the main drivers. Such drivers have enabled several technological innovations in the mechatronics and dynamics architecture of photolithography machines, used for semiconductor circuits manufacturing. Among current investigations, the use of superconductive magnets would enable higher accelerating stages and, thus, higher throughput and lower manufacturing costs. However, this involves a complex magnet structure that needs to operate at cryogenic temperatures and mechanical resonances at relatively low frequencies as a result of the thermal architecture of the system. The damping options are also limited due to the very low temperature. This paper explores the use of shunted piezoelectric transducers for damping the internal modes of the magnet mass. A classical resistive and inductive RL shunt is considered. The study was conducted both numerically and experimentally on a demonstrator of a superconductive magnet plate concept, where piezoelectric transducers are incorporated to support the superconducting coils. The study demonstrates the effectiveness of piezoelectric shunts as a damping solution at very low temperatures, with limited impact of the temperature variation on the performance.
Analysis of Robot–Environment Interaction Modes in Anguilliform Locomotion of a New Soft Eel Robot
Authors: Sayahkarajy Mostafa , Witte Hartmut
Published: October 2024

Bio-inspired robots with elongated anatomy, like eels, are studied to discover anguilliform swimming principles and improve the robots’ locomotion accordingly. Soft continuum robots replicate animal–environment physics better than noncompliant, rigid, multi-body eel robots. In this study, a slender soft robot was designed and tested in an actual swimming experiment in a still-water tank. The robot employs soft pneumatic muscles laterally connected to a flexible backbone and activated with a rhythmic input. The position of seven markers mounted on the robot’s backbone was recorded using QualiSys® Tracking Manager (QTM) 1.6.0.1. The system was modeled as a fully coupled fluid–solid interaction (FSI) system using COMSOL Multiphysics® 6.1. Further data postprocessing and analysis were conducted, proposing a new mode decomposition algorithm using simulation data. Experiments show the success of swimming with a velocity of 28 mm/s and at a frequency of 0.9 Hz. The mode analysis allowed the modeling and explanation of the fluctuation. Results disclose the presence of traveling waves related to anguilliform waves obtained by the superposition of two main modes. The similarities of the results with natural anguilliform locomotion are discussed. It is concluded that soft robot undulation is ruled by dynamic modes induced by robot–environment interaction.
Aerodynamic Analysis of Blade Stall Flutter Prediction for Transonic Compressor Using Energy Method
Authors: Arshad Ali , Murali Akshay
Published: October 2024

In this study, stall flutter onset prediction in a transonic compressor is carried out using the (uncoupled) energy method with Fourier transform. As the study is conducted computationally using computational fluid dynamics (CFD)-based simulations, the energy method was employed due to its higher computational efficiency by implementing the one-way FSI (Fluid Structure Interaction) model. The energy method is relatively uncommon for determining the aerodynamic damping and flutter prediction, specifically in blade stall conditions for the 3D blade passages. The NASA Rotor 67 was chosen for the validation of the study due to the availability of a wide range of experimental data. A flutter prediction analysis was performed computationally using CFD for the two-blade passages of the rotor in the peak efficiency and stall regions. Prior to this, the modal analysis on the prestressed blade was conducted, considering the centrifugal effects. The modal analysis provided accurate blade frequency and amplitude, which were the inputs of the flutter analysis. The first three modes of blade resonance were studied with a range of nodal diameters within near-peak efficiency and stall regions. The energy method implemented in this study for the flutter analysis was successfully able to predict the aerodynamic damping coefficients of the first three modes for a range of nodal diameters from the periodic-unsteady solution of the defined blade oscillation within the regions of interest (peak efficiency and stall point). The results of the study confirm the rotor blade’s stability within the near-peak region and, most importantly, the prediction of the flutter onset in the stall region. The study concluded that the computationally inexpensive and time-efficient energy method is capable of predicting the stall flutter onset. In the future, further validations of the energy method and investigations related to flow mechanism of stall flutter onset are planned.
Thermal Infrared Orthophoto Geometry Correction Using RGB Orthophoto for Unmanned Aerial Vehicle
Authors: Lee Kirim , Lee Wonhee
Published: October 2024

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data.
Applications of Organic Acids in Poultry Production: An Updated and Comprehensive Review
Authors: Abd El-Ghany Wafaa A.
Published: October 2024

Feed additive antibiotics have been used for many decades as growth promotors or antibacterial substances worldwide. However, the adverse impacts of using antibiotics in animal or poultry feeds are not widely recognized. Therefore, the search for alternatives, such as probiotics, prebiotics, phytobiotics, post-biotics, bacteriophages, enzymes, essential oils, or organic acids (OAs), has become urgent. OAs are produced by beneficial intestinal bacteria through the fermentation of carbohydrates. OAs and their salts are still used as feed preservatives. They have long been added to feed in order to minimize contamination and the growth of harmful bacteria and fungi, reduce deterioration, and prolong the shelf life of feed commodities. Moreover, they have been mostly added to poultry feed as a blend to obtain maximal beneficial effects. The supplementation of poultry with OAs could improve the growth performance parameters and carcass traits, promote the utilization of nutrients, boost the immune response, and inhibit the growth of pathogenic bacteria. Therefore, this review article provides valuable insights into the potential benefits of using OAs in reducing microbial load, enhancing performance parameters in broilers and layers, improving gut health, and boosting the immune response.
Lasioptera rubi, a Pest of Rubus idaeus: Galls Morphology, Anatomy and Histochemistry
Authors: Popescu Irinel Eugen , Gostin Irina Neta
Published: October 2024

Rubus species are affected by the gall inducer Lasioptera rubi (Diptera, Cecidomyiidae), which produces galls predominantly on the stem but sometimes also on the petiole. The presence of these neo-structures at the stem level can affect their survival and implicit production in raspberry crops. The purpose of the present study is to achieve the first detailed description of the histological and morphological peculiarities of these galls, which present great variability from these points of view. Three morphotypes were described in the galls produced in the stem, depending on the position of the larval chambers and the degree of damage to the vascular system. The histochemical investigations highlighted the presence of reducing sugars and proteins as the main nutrients in the gall tissues, as well as the presence of polyphenols and terpenoids as secondary products of metabolism. Indoleacetic acid was identified near the larval chambers and in the neighboring tissues, its presence having an important role in the changes in the morphogenesis of the secondary xylem in the stem and in inducing the appearance of meristematic nodules in the gall structure.
Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features
Authors: Qiu Yixin , Tan Yong , Zhou Yingying , Li Zhipeng , Miao Zhuang , Li Changming , Mei Xitian , Liu Chunyu , Teng Xing
Published: October 2024

The origin of agricultural products significantly influences their quality and safety. Fluorescence spectroscopy was used to analyse Japonica rice 830, grown in different areas of Jilin Province, by examining rice seed, brown rice, and rice flour from 12 origins. Fluorescence spectra were pre-processed through normalisation and smoothing to remove noise. These processed spectra were input into decision trees, support vector machines (SVMs), K-nearest neighbour (KNN), and neural network models for classification. The analysis revealed that the combined four models achieved an average classification accuracy of 98.05% with a computation time of 180 s, while the reduced-scale models improved accuracy to 98.36% and reduced computation time to 11.25 s. Additionally, prediction models using standard rice starch content values across different states achieved R² values over 0.8. This method provides a rapid, precise approach for assessing rice quality and origin, demonstrating significant potential for application in rice analysis.
Factors Influencing Informal Credit Access and Utilization among Smallholder Farmers: Insights from Mountainous Regions of Pakistan
Authors: Ullah Ayat , Verner Vladimir , Madaki Mustapha Yakubu , Adams Faizal , Bavorova Miroslava
Published: October 2024

Access to credit plays an important role in the adoption of modern agricultural practices, such as better seeds, pesticides, and fertilizers, as well as in the sustainable utilization of land by smallholder farmers. This study explores the dynamics of smallholders’ access to and utilization of informal credit to manage local farming systems. The data were collected from rural households in the Dir Kohistan mountainous region of Pakistan in 2021. A bivariate probit model was employed to analyze the data from 300 randomly selected farming households. Our findings indicate that a significant proportion of respondents (70%) reported having access to informal credit, with 65% actively utilizing credit to bolster the economic viability of their farms. This study reveals various strategies that farmers employ in response to credit constraints. The majority of farmers (69%) prioritize meeting their own farm/household needs. Social networks, particularly friends, emerge as key sources of informal credit (58.1%). The results of the bivariate probit regression analysis identify positive relationships with fellow farmers and neighbors/relatives, along with a lower perceived risk, as the most significant factors influencing access to and utilization of informal credit. Therefore, our study recommends the promotion of relationships and linkages among farmers through facilitated networking events, introducing risk mitigation measures and promoting financial literacy programs to empower smallholder capacities on the credit market. Policymakers are urged to recognize the role of social relationships and consider policies that promote community credit practices for the overall financial well-being of farmers, especially in more isolated mountain areas.
The Relationship between the Density of Winter Canola Stand and Weed Vegetation
Authors: Vykydalová Lucie , Kubík Tomáš Jiří , Martínez Barroso Petra , Děkanovský Igor , Winkler Jan
Published: October 2024

Canola (Brassica napus L.) is an important oilseed crop that provides essential vegetable oil but faces significant competition from weeds that are influenced by various agronomic practices and environmental conditions. This study examines the complex interactions between canola stand density and weed intensity over three growing seasons, identifying a total of 27 weed species. It is important to establish a connection between the density of winter canola stands, the intensity of weeding and the response of individual weed species in real conditions. The case study was executed on plots located in the Přerov district (Olomouc region, Czech Republic). The assessment was carried out during two periods—autumn in October and spring in April. Canola plants (plant density) were counted in each evaluated area, weed species were identified, and the number of plants for each weed species was determined. Half of the plots were covered with foil before herbicide application to prevent these areas from being treated with herbicides. We used redundancy analysis (RDA) to evaluate the relationships between canola density and weed dynamics, both with and without herbicide treatment. The results show the ability of canola to compete with weeds; however, that is factored by the density of the canola stand. In dense stands (over 60 plants/m²), canola is able to suppress Galium aparine L., Geranium pusillum L., Lamium purpureum L., Papaver rhoeas L. and Chamomilla suaveolens (Pursh) Rydb. Nevertheless, there are weed species that grow well even in dense canola stands (Echinochloa crus-galli (L.) P. Beauv., Phragmites australis (Cav.) Steud., Tripleurospermum inodorum (L.) Sch. Bip. and Triticum aestivum L.). These findings highlight the potential for using canola stand density as a strategic component of integrated weed management to reduce herbicide reliance and address the growing challenge of herbicide-resistant weed populations. This research contributes significantly to our understanding of the dynamics of weed competition in canola systems and informs sustainable agricultural practices for improved crop yield and environmental stewardship.
Digital Economy and Industrial Structure Transformation: Mechanisms for High-Quality Development in China’s Agriculture and Rural Areas
Authors: Liu Jingruo , Feng Xiuju , Liu Jianxu , Yamaka Woraphon
Published: October 2024

The digital economy’s transformative impact on agriculture presents both opportunities and challenges for China’s pursuit of high-quality agricultural and rural development. This study investigates the complex interplay between digital economy, industrial structure transformation, and agricultural advancement using panel data from 31 Chinese provinces spanning 2012–2021. We employed mediation analysis and threshold effect models to uncover several key findings: (1) The digital economy influences high-quality agricultural and rural development through the dual-mediating mechanisms of industrial structure intensification and upgrading in China. (2) These mediating effects exhibit heterogeneous patterns: while industrial intensification positively channels the digital economy’s impact, industrial upgrading shows an initial negative indirect effect, suggesting potential short-term disruptions. (3) The relationship between digital economy and agricultural development is nonlinear, characterized by significant threshold effects. The digital economy’s positive impact becomes more pronounced as industrial structure surpasses certain sophistication and advancement thresholds. Our findings reveal the nuanced dynamics of digital-driven agricultural transformation, highlighting the need for targeted policies that leverage industrial-structure changes while mitigating potential adverse effects. This research contributes to a more comprehensive understanding of how digitalization can be harnessed to promote sustainable and high-quality agricultural and rural development in China, with implications for other developing economies navigating similar transitions.
Intelligent Rapid Asexual Propagation Technology—A Novel Aeroponics Propagation Approach
Authors: Tang Lingdi , Syed Ain-ul-Abad , Otho Ali Raza , Junejo Abdul Rahim , Tunio Mazhar Hussain , Hao Li , Asghar Ali Mian Noor Hussain , Brohi Sheeraz Aleem , Otho Sohail Ahmed , Channa Jamshed Ali
Published: October 2024

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.
Inductive Effect of Exogenous Abscisic Acid on the Weed-Suppressive Activity of Allelopathic and Non-Allelopathic Rice Accessions at the Root Level
Authors: Li Jiayu , Wang Ting , Fan Yuhui , Chen Shuyu , Ye Xinyi , Wang Yanping , Cheng Chen
Published: October 2024

Rice allelopathy is a natural method of weed control that is regarded as an eco-friendly practice in agroecology. The root growth of allelopathic rice at the seedling stage plays an important role in its weed control. Our study characterizes a plant hormone that promotes root growth, abscisic acid (ABA), to explore its role in the induction of rice allelopathy. Increasing the root morphology traits (root length, root tip number, and root biomass) in rice using different concentrations of exogenous ABA resulted in increased inhibitory ratios against barnyard grass (Echinochloa crus-galli), both in a hydroponic experiment and pot test. In particular, the relative proportion of induced allelopathy to total allelopathy in non-allelopathic rice Lemont (Le) was higher than that in allelopathic rice PI31277 (PI). The total content of phenolic acid, which is an important allelochemical in rice, as previously reported, was significantly elevated in the root exudates of both PI and LE. The gene expression levels of OsPAL, OsC4H, and OsCOL related to phenolic acid synthesis were also up-regulated, with a higher regulatory fold in PI. ABA also increased the expression of OsKSL4 and CYP75B4 involved in the biosynthesis of momilactone B and tricin. Moreover, low concentrations of exogenous ABA mainly positively regulate the expression of OsIAA11, an AUX/IAA transcription factor gene, in the root of PI and Le. These findings suggest that the application of ABA could significantly enhance the weed-suppressive activity of both rice cultivars through regulating root growth and the synthesis of allelochemicals secreted by rice roots, providing an option for the improvement of rice allelopathy through chemical induction.
Genome-Wide Analysis of Potato CCT Family Genes and Its Response to Auxin Substances
Authors: Huang Xiongjie , Yang Jingtian , Bai Yiting , Liu Lei , Liu Fang , Cui Qi , Liu Yuan , Chen Youjun , Zhang Wenlu , Li Juan , Zhang Shengyan , Chen Chen
Published: October 2024

The control of flowering time plays an important role in the growth and development of potato tubers. The CCT (CO, COL and TOC1) gene family is involved in the flowering process of plants. In this study, a total of 32 StCCT family genes were identified and further classified into five subfamilies, including COL (17 members), PRR (4 members), ZIM (3 members), ASML2 (6 members) and TCR1 (2 members), based on their phylogenetic relationship. An analysis of the gene structure, motif compositions and conserved domain provided support for this classification. The StCCT genes were unevenly distributed on 12 chromosomes of the potato plant. In total, six gene duplication events were observed, which played a crucial role in the expansion of the StCCT family genes in the potato. The expression profiles exhibited diverse expression patterns of the StCCT genes in six tissues (leaf, shoot, root, tuber, stolon, and flower), StCCT32 is only expressed in flowers, while StCCT19 and StCCT8 are highly expressed in flowers and tubers, respectively. The StCCT genes exhibit different expression patterns in response to IAA and TIBA treatments at different concentrations across three tissues (leaf, stem, and tuber). After IAA and TIBA treatments, it was found that the expression level of StCCT7 was low in leaves and stems but significantly increased in tubers. Collectively, this study provided valuable information for the further study of potato formation and development and provided candidate genes for molecular breeding in the potato.
The Combination of Biochar and Phosphorus-Containing Materials Can Effectively Enhance the Remediation Capacity of Amaranth on Cadmium-Contaminated Soil and Improve the Structure of Microbial Communities
Authors: Jiang Zhiyang , Hua Hongmei , Yin Zheng , Wu Tingsen , Zhou Yuzhi , Chen Daokun , Li Xinbin , Zhao Mingze , Wang Wenshuo
Published: October 2024

Cadmium (Cd) pollution in soil has become a huge problem for agricultural production in China and even the world. Passivation and phytoremediation are two important remediation technologies for Cd pollution. In this study, the cadmium-contaminated and phosphorus-poor farmland soil around a mining area in Huainan was taken as the research object, and the remediation effect of biochar and phosphorus-containing materials on soil cadmium pollution was discussed. The results showed that the combined application of biochar and phosphorus-containing materials significantly reduced the pH of non-rhizosphere soil and rhizosphere soil, and increased the content of soil dissolved organic carbon (DOC). The combined application of biochar and phosphorus-containing materials significantly reduced soil pore water Cd and soil available Cd. In addition, both a single application of biochar and synergistic application of biochar and phosphorus-containing materials significantly increased the biomass of aboveground and underground parts of amaranth and soil urease and catalase activities. Phosphorus application reduced the bioavailability of Cd in soil. With the increase in phosphorus application, the content of available Cd in soil decreased significantly, and there was a certain negative correlation between Cd content and phosphorus content in plants. The abundance of beneficial microorganisms such as Ochrobactrum, Anaerolinea, Achromobacter, and Cellvibrio in soil was significantly increased after the synergistic application of biochar and phosphorus-containing materials.
Effects of Fallow Season Water and Straw Management on Methane Emissions and Associated Microorganisms
Authors: Wang Wei , Chen Qiping , Huang Hexian , Xie Yonghong
Published: October 2024

The effects of fallow season water and straw management on methane (CH4) emissions during the fallow season and the subsequent rice-growing season are rarely reported, and the underlying microbial mechanisms remain unclear. A field experiment was conducted with four treatments: (1) fields flooded in both the fallow and rice seasons (FF), (2) fields drained in the fallow season and flooded in the rice season (DF), (3) FF with straw retention (FFS), and (4) DF with straw retention (DFS). The CH4 emissions in fields under different water and straw treatments were monitored using the static closed chamber method. Methanogenic and methanotrophic communities in these fields were examined using terminal restriction fragment length polymorphism (T-RFLP) analysis based on the mcrA gene and pmoA gene encoding methyl coenzyme M reductase and particulate methane monooxygenase, respectively. The results showed that CH4 emissions were significantly affected by water management, straw retention, season, and their interactions. Over 80% of CH4 emissions occurred during the rice season. Field drainage during the fallow season reduced CH4 emissions by 47.0% and 53.8% with and without straw during the rice season, respectively. Water management altered the abundance and composition of methanogens and methanotrophs, whereas the effects of straw retention were less pronounced. The quantitative polymerase chain reaction (qPCR) assay revealed that field drainage in the fallow season decreased the mcrA gene abundance by 30.0% and 23.2% with and without straw in rice season, respectively, and increased the pmoA gene abundance by 108.9% and 213.7% with and without straw in rice season, respectively. CH4 flux was significantly positively associated with mcrA gene copy number and the ratio of mcrA to pmoA gene copy number, whereas it was significantly negatively correlated with the pmoA gene copy number. Results indicated that fallow drainage greatly decreased CH4 emission not only during the fallow season but also during the subsequent rice season by altering the community composition of methanogens and methanotrophs. These findings provide scientific insight into the role of water and straw management in controlling CH4 emissions through microbial community dynamics.
Potato E3 Ubiquitin Ligase StXERICO1 Positively Regulates Drought Resistance by Enhancing ABA Accumulation in Potato and Tobacco and Interacts with the miRNA Novel-miR1730-3p and Proteins StUBC and StTLP
Authors: Yi Jing , Wang Lai , Chen Yongkun , Li Canhui , Gong Ming
Published: October 2024

Potato (Solanum tuberosum L.) is sensitive to drought, which severely impacts tuber yield and quality. In this study, we characterized a XERICO gene, encoding a RING-H2 type E3 ubiquitin ligase, StXERICO1, from a diploid potato, investigated its role in enhancing drought resistance and ABA accumulation, and identified its interaction with the miRNA novel-miR1730-3p, as well as its protein interactions with StUBC and StTLP. StXERICO1, with a complete Open Reading Frame (ORF) of 459 bp encoding 152 amino acids, was highly responsive to drought, ABA treatment, and abiotic stresses in potato plants. Overexpression of the StXERICO1 significantly enhanced drought resistance and ABA accumulation in transgenic potato and tobacco plants and exhibited greater sensitivity to ABA treatment, which was associated with the upregulation of expression of ABA biosynthetic genes NCED and CYP707A. Furthermore, our results revealed that StXERICO1 and its encoding protein interacted with miRNAs and other proteins. 5′ RLM-RACE (cDNA terminal rapid amplification) experiment showed that the miRNA novel-miR1730-3p targets 5′ UTR region of the StXERICO1 gene. Dual luciferase assay and virus-based miRNA silencing experiment showed that the novel-miR1730-3p negatively regulates StXERICO1 expression. Moreover, yeast two-hybrid assay indicated that StXERICO1 interacts with StUBC (an E2 ubiquitin ligase) and StTLP (a Tubby-like protein), suggesting that StXERICO1 might function on ABA homeostasis at the post-translational level. These findings elucidate the molecular mechanisms by which StXERICO1, a RING-H2 type E3 ubiquitin ligase, enhances drought resistance through increased ABA accumulation, how its expression is regulated by miRNA, and how it exerts its function through interactions with other proteins. The results also provide a potential candidate gene for subsequent precision molecular breeding aimed at improving crop drought resistance.