bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of a Major QTL for Seed Protein Content in Cultivated Peanut (Arachis hypogaea L.) Using QTL-Seq
Authors: Chen Hao , Liu Nian , Huang Li , Huai Dongxin , Xu Rirong , Chen Xiangyu , Guo Shengyao , Chen Jianhong , Jiang Huifang
Published: August 2024

Peanut (Arachis hypogaea L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (qSPCB10.1) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the qSPCB10.1 locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying qSPCB10.1. Further locus interaction analysis revealed that qSPCB10.1 could not affect the seed oil accumulation unless qOCA08.1XH13 was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes.
How Do Plant Growth-Promoting Bacteria Use Plant Hormones to Regulate Stress Reactions?
Authors: Timofeeva Anna M. , Galyamova Maria R. , Sedykh Sergey E.
Published: August 2024

Phytohormones play a crucial role in regulating growth, productivity, and development while also aiding in the response to diverse environmental changes, encompassing both biotic and abiotic factors. Phytohormone levels in soil and plant tissues are influenced by specific soil bacteria, leading to direct effects on plant growth, development, and stress tolerance. Specific plant growth-promoting bacteria can either synthesize or degrade specific plant phytohormones. Moreover, a wide range of volatile organic compounds synthesized by plant growth-promoting bacteria have been found to influence the expression of phytohormones. Bacteria–plant interactions become more significant under conditions of abiotic stress such as saline soils, drought, and heavy metal pollution. Phytohormones function in a synergistic or antagonistic manner rather than in isolation. The study of plant growth-promoting bacteria involves a range of approaches, such as identifying singular substances or hormones, comparing mutant and non-mutant bacterial strains, screening for individual gene presence, and utilizing omics approaches for analysis. Each approach uncovers the concealed aspects concerning the effects of plant growth-promoting bacteria on plants. Publications that prioritize the comprehensive examination of the private aspects of PGPB and cultivated plant interactions are of utmost significance and crucial for advancing the practical application of microbial biofertilizers. This review explores the potential of PGPB–plant interactions in promoting sustainable agriculture. We summarize the interactions, focusing on the mechanisms through which plant growth-promoting bacteria have a beneficial effect on plant growth and development via phytohormones, with particular emphasis on detecting the synthesis of phytohormones by plant growth-promoting bacteria.
Trait-Mediated Variation in Seedling Performance in Costa Rican Successional Forests: Comparing Above-Ground, Below-Ground, and Allocation-Based Traits
Authors: Huanca-Nunez Nohemi , Chazdon Robin L. , Russo Sabrina E.
Published: August 2024

The interspecific relationship between functional traits and tree seedling performance can be inconsistent, potentially due to site-to-site or microsite variation in environmental conditions. Studies of seedling traits and performance often focus on above-ground traits, despite the importance of below-ground resource acquisition and biomass allocation to above versus below-ground functions. Here we investigate how varying environmental conditions across sites induce intraspecific variation in organ-level (above-ground, below-ground) and biomass allocation traits, affecting interspecific relationships between these traits and seedling performance. We analyzed trait expression for 12 organ-level and three allocation traits and their relationships with height growth (1716 seedlings) and mortality (15,862 seedlings) for 26 tree species across three sites along a forest successional gradient in Costa Rica. We found significant intraspecific differences across sites in all allocation traits, but only in three of seven above-ground and three of five below-ground organ-level traits. Allocation traits were better predictors of seedling performance than organ-level traits. Relationships between allocation traits and both growth and mortality varied among all sites, but for organ-level traits, only relationships with growth varied among sites. These results underscore that biomass allocation plays a key role in the earliest life stages of trees and that site-specific conditions can influence how functional traits mediate seedling establishment during succession.
Ohmic Heating in Food Processing: An Overview of Plant-Based Protein Modification
Authors: dos Santos Israel Felipe , Pimentel Tatiana Colombo , da Cruz Adriano Gomes , Stringheta Paulo César , Martins Evandro , Campelo Pedro Henrique
Published: August 2024

This review provides an analysis of ohmic heating in food processing and its effect on plant proteins. This study explores the effect of this technology on protein denaturation and aggregation, affecting both non-covalent and covalent bonds. These structural and chemical changes have significant implications for the techno-functional properties of proteins, contributing to their use in food processing. This article emphasizes the need to adjust processing conditions to maximize the benefits of ohmic heating, distinguishing it from other traditional thermal techniques due to its direct and controllable impact. By highlighting these contributions, this review serves as a resource for researchers and professionals interested in innovation and efficiency in food processing through the use of emerging technologies.
Container Scheduling Algorithms for Distributed Cloud Environments
Authors: Chen Honghua , Shen Cong , Qiu Xinyuan , Cheng Chuanqi
Published: August 2024

Due to the difficulty of existing container scheduling algorithms to adapt to large-scale complex scenarios and meet the diverse application and load requirements, this study delves into a groundbreaking hybrid scheduling approach that melds Deep Deterministic Policy Gradient (DDPG) with a Genetic Algorithm (GA). The proposed method initially employs a container grouping policy to reduce the overhead associated with frequent inter-container calls. Subsequently, to address the computational inefficiency of large-scale scheduling, a genetic algorithm is utilized for rapid global optimization. To overcome the problem of genetic algorithms being highly susceptible to falling into local optimality, the DDPG algorithm is applied for local optimization, with a cross-mutation operation introduced to escape local optima. The experimental outcomes demonstrate that the proposed algorithm enhances cluster load balancing by 81.13%, improves the fitness function by 3.26%, reduces completion time by 19.06%, and decreases container dependency overhead by 2.75%. Furthermore, under the experimental conditions, the system performs best when the group size is 10. This research offers a novel paradigm for the development of container scheduling algorithms in distributed intelligent data clouds, advancing the field of resource management in cloud computing environments.
A Novel Approach for State of Health Estimation of Lithium-Ion Batteries Based on Improved PSO Neural Network Model
Authors: Nasimov Rashid , Kumar Deepak , Rizwan M. , Panwar Amrish K. , Abdusalomov Akmalbek , Cho Young-Im
Published: August 2024

The operation and maintenance of futuristic electric vehicles need accurate estimation of the state of health (SOH) of lithium-ion batteries (LIBs). To address this issue, a robust neural network framework is proposed to estimate the SOH. This article developed a novel approach that combines improved particle swarm optimization (IPSO) with bidirectional long short-term memory (Bi-LSTM) to effectively address the issue of precisely estimating SOH. The proposed IPSO-Bi-LSTM model is more effective than the other models for SOH estimation. This is because Bi-LSTM can capture both past and future appropriate information, making it more suitable for modeling complicated temporal sequences. The IPSO main objective is to optimize the model hyperparameters. To increase the model’s accuracy, the IPSO improves the parameters. The PSO-Bi-LSTM model performed better than the other approaches, according to experimental findings based on the NASA-PCOE battery dataset, and all of the SOH estimated outcomes, such as root mean square errors, were less than 0.50%. This result suggests that the proposed PSO-Bi-LSTM model has the ability to robustly estimate the SOH with a high accuracy.
Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder
Authors: Liu Zhaoxiang , Liu Hui , Chen Fugang , Li Heng , Xue Xiaojun
Published: August 2024

The key to endpoint control in basic oxygen furnace (BOF) steelmaking lies in accurately predicting the endpoint carbon content and temperature. However, BOF steelmaking data are complex and change distribution due to variations in raw material batches, process adjustments, and equipment conditions, leading to concept drift and affecting model performance. In order to resolve these problems, this paper proposes a dynamic soft sensor model based on an adaptive feature matching variational autoencoder (VAE-AFM). Firstly, this paper innovatively proposes an adaptive feature matching (AFM) method. This method utilizes the maximum mean discrepancy to calculate the values of the marginal and conditional distributions. Based on the discrepancy between these two values, a dynamic adjustment algorithm is designed to adaptively assign different weights to the two distributions. This approach dynamically and quantitatively evaluates and adjusts the relative importance of different distributions in the domain adaptation process, thereby enhancing the effectiveness of cross-domain data alignment. Secondly, a variational autoencoder (VAE) is employed to process the data, as the VAE model can capture the complex data structures and latent features in the steelmaking process. Finally, the features extracted by the VAE are processed with the adaptive feature matching method, thereby constructing the VAE-AFM dynamic soft sensor model. Experimental studies on actual BOF steelmaking data validate the efficacy of the offered approach, offering a reliable solution to the challenges of high complexity and concept drift in BOF steelmaking data.
Analysis of Factors Influencing Tight Sandstone Gas Production and Identification of Favorable Gas Layers in the Shan 23 Sub-Member of the Daning-Jixian Block, Eastern Ordos Basin
Authors: Sun Junyi , Chen Ming , Wang Bo , Wang Gang , Tian Haonian , Hou Jie , Zhu Boning
Published: August 2024

The Daning-Jixian Block harbors abundant tight sandstone gas resources. However, significant variations in gas production exist among the different wells within the block. A comprehensive study was conducted on key factors such as sedimentary strata and petrophysical characteristics to elucidate their impact on gas reservoir productivity. Linear regression equations were employed to classify the favorable reservoirs within the study area. The analysis revealed that within the first 6 months of production from the Shan 23 gas layer, daily gas production ranged from 2576.19 to 156,078.17 m3/d, averaging 24,037.9 m3/d. Over the first year, average daily production varied from 2185.05 to 136,806.99 m3/d, averaging 23,469.23 m3/d, indicating relatively stable production from the Shan 23 layer alone. In the dominant central area of the underwater distributary channel delta front in Shan23, the sand body exhibits a superimposed cutting type, resulting in high production rates. Conversely, the sand bodies on the periphery gradually transition to superimposed and isolated types, leading to decreased production. Through a correlation analysis of gas layer thickness, porosity, permeability, and initial gas well production, it was determined that gas production from the wells within the same layer is primarily influenced by gas layer thickness, porosity, and permeability. Gas saturation demonstrates a minimal impact on production according to single-factor analysis. The evaluated factors such as the gas productivity coefficient, energy storage coefficient, and enrichment coefficient exhibited similar distribution patterns across the study area. The high-value areas for the gas productivity coefficient, energy storage coefficient, and enrichment coefficient are concentrated in distributary channel zones and delta lobes. In contrast, regions with underdeveloped skeletal sand bodies generally display lower values for these parameters. The linear relationships between these parameters and the average gas production were calculated to further classify the favorable reservoirs in the study area. This study aimed to establish a scientific basis for the efficient development of the tight sandstone gas reservoirs within the Daning-Jixian Block.
Reverse Quantum Annealing Assisted by Forward Annealing
Authors: Jattana Manpreet Singh
Published: August 2024

Quantum annealers conventionally use forward annealing to generate heuristic solutions. Reverse annealing can potentially generate better solutions but necessitates an appropriate initial state. Ways to find such states are generally unknown or highly problem dependent, offer limited success, and severely restrict the scope of reverse annealing. We use a general method that improves the overall solution quality and quantity by feeding reverse annealing with low-quality solutions obtained from forward annealing. An experimental demonstration of solving the graph coloring problem using the D-Wave quantum annealers shows that our method is able to convert invalid solutions obtained from forward annealing to at least one valid solution obtained after assisted reverse annealing for 57% of 459 random Erdos–Rényi graphs. Our method significantly outperforms random initial states, obtains more unique solutions on average, and widens the applicability of reverse annealing. Although the average number of valid solutions obtained drops exponentially with the problem size, a scaling analysis for the graph coloring problem shows that our method effectively extends the computational reach of conventional forward annealing using reverse annealing.
Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach
Authors: Tian Tian , Yu Le , Tu Ying , Chen Bin , Gong Peng
Published: August 2024

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.
Local Field-Scale Winter Wheat Yield Prediction Using VENµS Satellite Imagery and Machine Learning Techniques
Authors: Chiu Marco Spencer , Wang Jinfei
Published: August 2024

Reliable and accurate crop yield prediction at the field scale is critical for meeting the global demand for reliable food sources. In this study, we tested the viability of VENμS satellite data as an alternative to other popular and publicly available multispectral satellite data to predict winter wheat yield and produce a yield prediction map for a field located in southwestern Ontario, Canada, in 2020. Random forest (RF) and support vector regression (SVR) were the two machine learning techniques employed. Our results indicate that machine learning models paired with vegetation indices (VIs) derived from VENμS imagery can accurately predict winter wheat yield 1~2 months prior to harvest, with the most accurate predictions achieved during the early fruit development stage. While both machine learning approaches were viable, SVR produced the most accurate prediction with an R2 of 0.86 and an RMSE of 0.3925 t/ha using data collected from tillering to the early fruit development stage. NDRE-1, NDRE-2, and REP from various growth stages were ranked among the top seven variables in terms of importance for the prediction. These findings provide valuable insights into using high-resolution satellites as tools for non-destructive yield potential analysis.
A Dynamic Topology Optimization Method for Tactical Edge Networks Based on Virtual Backbone Networks
Authors: Kong Zhixiang , Jin Zilong , Pan Chengsheng
Published: August 2024

To address the issues of low survivability and communication efficiency in wireless sensor networks caused by frequent node movement or damage in highly dynamic and high-mobility battlefield environments, we propose a dynamic topology optimization method based on a virtual backbone network. This method involves two phases: topology reconstruction and topology maintenance, determined by a network coverage threshold. When the coverage falls below the threshold, a virtual backbone network is established using a connected dominating set (CDS) and non-backbone node optimization strategies to reconstruct the network topology, quickly restore network connectivity, effectively improve network coverage, and optimize the network structure. When the coverage is above the threshold, a multi-CDS scheduling algorithm and slight position adjustments of non-backbone nodes are employed to maintain the network topology, further enhancing network coverage with minimal node movement. Simulations demonstrate that this method can improve coverage and optimize network structure under different scales of network failures. Under three large-scale failure operational scenarios where the network coverage threshold was set to 80%, the coverage was enhanced by 26.12%, 15.88%, and 13.36%, and in small-scale failures, the coverage was enhanced by 7.55%, 4.90% and 7.84%.
Miniaturized Antenna Design for Wireless and Powerless Surface Acoustic Wave Temperature Sensors
Authors: Sreang Naranut , Chung Jae-Young
Published: August 2024

This paper presents the introduction, design, and experimental validation of two small helical antennae. These antennae are a component of the surface acoustic wave (SAW) sensor interrogation system, which has been miniaturized to operate at 915 MHz and aims to improve the performance of wireless passive SAW temperature-sensing applications. The proposed antenna designs are the normal-mode cylindrical helical antenna (CHA) and the hemispherical helical antenna (HSHA); both designed structures are developed for the ISM band, which ranges from 902 MHz to 928 MHz. The antennae exhibit resonance at 915 MHz with an operational bandwidth of 30 MHz for the CHA and 22 MHz for the HSHA. A notch occurs in the operating band, caused by the characteristics of the SAW sensor. The presence of this notch is crucial for the temperature measurement by aiding in calculating the frequency shifting of that notch. The decrement in the resonance frequency of the SAW sensor is about 66.67 kHz for every 10 °C, which is obtained by conducting the temperature measurement of the system model across temperature environments ranging from 30 °C to 90 °C to validate the variation in system performance.
FedAvg-P: Performance-Based Hierarchical Federated Learning-Based Anomaly Detection System Aggregation Strategy for Advanced Metering Infrastructure
Authors: Alshede Hend , Jambi Kamal , Nassef Laila , Alowidi Nahed , Fadel Etimad
Published: August 2024

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial damage, such as a deficit in national security, a disturbance of public order, or significant economic harm. As a result, it is critical to guarantee a steady and dependable supply of information and electricity. Furthermore, storing massive quantities of data in one central entity leads to compromised data privacy. As such, it is imperative to engineer decentralized, federated learning (FL) solutions. In this context, the performance of participating clients has a significant impact on global performance. Moreover, FL models have the potential for a Single Point of Failure (SPoF). These limitations contribute to system failure and performance degradation. This work aims to develop a performance-based hierarchical federated learning (HFL) anomaly detection system for an AMI through (1) developing a deep learning model that detects attacks against this critical infrastructure; (2) developing a novel aggregation strategy, FedAvg-P, to enhance global performance; and (3) proposing a peer-to-peer architecture guarding against a SPoF. The proposed system was employed in experiments on the CIC-IDS2017 dataset. The experimental results demonstrate that the proposed system can be used to develop a reliable anomaly detection system for AMI networks.
Clique-like Point Cloud Registration: A Flexible Sampling Registration Method Based on Clique-like for Low-Overlapping Point Cloud
Authors: Huang Xinrui , Gao Xiaorong , Li Jinlong , Luo Lin
Published: August 2024

Three-dimensional point cloud registration is a critical task in 3D perception for sensors that aims to determine the optimal alignment between two point clouds by finding the best transformation. Existing methods like RANSAC and its variants often face challenges, such as sensitivity to low overlap rates, high computational costs, and susceptibility to outliers, leading to inaccurate results, especially in complex or noisy environments. In this paper, we introduce a novel 3D registration method, CL-PCR, inspired by the concept of maximal cliques and built upon the SC2-PCR framework. Our approach allows for the flexible use of smaller sampling subsets to extract more local consensus information, thereby generating accurate pose hypotheses even in scenarios with low overlap between point clouds. This method enhances robustness against low overlap and reduces the influence of outliers, addressing the limitations of traditional techniques. First, we construct a graph matrix to represent the compatibility relationships among the initial correspondences. Next, we build clique-likes subsets of various sizes within the graph matrix, each representing a consensus set. Then, we compute the transformation hypotheses for the subsets using the SVD algorithm and select the best hypothesis for registration based on evaluation metrics. Extensive experiments demonstrate the effectiveness of CL-PCR. In comparison experiments on the 3DMatch/3DLoMatch datasets using both FPFH and FCGF descriptors, our Fast-CL-PCRv1 outperforms state-of-the-art algorithms, achieving superior registration performance. Additionally, we validate the practicality and robustness of our method with real-world data.
Content-Aware Few-Shot Meta-Learning for Cold-Start Recommendation on Portable Sensing Devices
Authors: Lv Xiaomin , Fang Kai , Liu Tongcun
Published: August 2024

The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations. Our model incorporates a double-tower network (DT-Net) that learns user and item representations through a meta-encoder and a mutual attention encoder, effectively mitigating the impact of noisy data on auxiliary information. By framing the cold-start problem as few-shot meta-learning, we employ a model-agnostic meta-optimization strategy to train the model across a variety of tasks during the meta-learning phase. Extensive experiments conducted on three real-world datasets—ShortVideos, MovieLens, and Book-Crossing—demonstrate the superiority of our model in cold-start recommendation scenarios. Compared to MetaCs-DNN, the second-best approach, CFSM, achieves improvements of 1.55%, 1.34%, and 2.42% under the AUC metric on the three datasets, respectively.
Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study
Authors: Ai Haiming , Huang Yong , Tai Dar-In , Tsui Po-Hsiang , Zhou Zhuhuang
Published: August 2024

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation.
Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation
Authors: Wacker Kahlin , Kim Changhyeon , van Iersel Marc W. , Sidore Benjamin , Pham Tony , Haidekker Mark , Seymour Lynne , Ferrarezi Rhuanito Soranz
Published: August 2024

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it’s essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, “greenness”) images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.
Editorial: Worldwide Accidents: Trends, Investigation and Prevention
Authors: Rashid Muhammad , Athar Muhammad
Published: August 2024

The industrial revolution aimed to ease the experience of life for human beings by reducing the manual efforts that life required and fulfilling the needs of a society with an increasing population [...]
Anti-Inflammatory Benefits of Vitamin D and Its Analogues against Glomerulosclerosis and Kidney Diseases
Authors: Adamantidi Theodora , Maris George , Altantsidou Petroula , Tsoupras Alexandros
Published: August 2024

Apart from the significant progress the scientific community has made during the last few decades, inflammation-mediated kidney-related diseases like chronic and diabetic kidney diseases (CKD and DKD) and glomerulosclerosis still continue to raise mortality rates. Recently, conventional therapeutic interventions have been put aside, since natural vitamin D-derived treatment has gained attention and offered several promising outcomes. Within this article, the utilization of vitamin D and its analogues as potential treatment toward kidney-related diseases, due to their anti-inflammatory, antioxidant and anti-fibrotic activity, is outlined. Vitamin D analogues including calcitriol, paricalcitol and 22-oxacalcitriol have been previously explored for such applications, but their hidden potential has yet to be further elucidated. Several clinical trials have demonstrated that vitamin D analogues’ supplementation is correlated with inflammatory signaling and oxidative stress regulation, immunity/metabolism augmentation and subsequently, kidney diseases and healthcare-related infections’ prevention, and the results of these trials are thoroughly evaluated. The highlighted research outcomes urge further study on a plethora of vitamin D analogues with a view to fully clarify their potential as substantial anti-inflammatory constituents of renal diseases-related treatment and their health-promoting properties in many kidney-associated healthcare complications and infections.
Critical Evaluation of the Cgrain Value™ as a Tool for Rapid Morphometric Phenotyping of Husked Oat (Avena sativa L.) Grains
Authors: Evershed David , Durkan Eamon J. , Hasler Rachel , Corke Fiona , Doonan John H. , Howarth Catherine J.
Published: August 2024

Mechanised non-contact, non-destructive imaging methodologies have revolutionised plant phenotyping, increasing throughput well beyond what was possible using traditional manual methods. Quantifying the variation in post-harvest material such as seeds and fruits, usually the economically important part of the crop, can be critical for commercial quality assessment as well as breeding and research. Therefore, reliable methods that gather metrics of interest, quickly and efficiently, are of widespread interest across sectors. This study focuses on evaluating the phenotyping capabilities of the Cgrain Value™, a novel grain imaging machine designed for quality and purity assessment and used primarily in commercial cereal production and processing. The performance of the Cgrain Value™ in its generation of high-throughput quantitative phenotypic data is compared with a well-established machine, MARVIN, assessing repeatability and reproducibility across a range of metrics. The findings highlight the potential of the Cgrain Value™, and some shortcomings, to provide detailed three-dimensional size, shape, and colour information rapidly, offering insights into oat grain morphology that could enhance genome-wide association studies and inform the breeding efforts in oat improvement programmes.
Applying Fuzzy Decision-Making Trial and Evaluation Laboratory and Analytic Network Process Approaches to Explore Green Production in the Semiconductor Industry
Authors: Tsai Bi-Huei
Published: August 2024

As environmental awareness grows, society emphasizes green business. Thus, semiconductor companies encompass energy-saving processes and innovative product development. This study employs the fuzzy decision-making trial and evaluation laboratory (DEMATEL) analysis method to assess the impact of four key dimensions, externalities, market orientation, green technology, and corporate social responsibility, on semiconductor companies’ green production decisions. This study uses the DEMATEL-based analytic network process (DANP) approach to rank the criteria order in green production decision-making. The results from the DEMATEL causality diagram highlight externalities as the most critical dimension influencing other dimensions of green production decisions. This study suggests that regulatory pollution punishment and subsidies emerge as the primary drivers for green production decisions. Companies adopt environmentally friendly production practices to prevent regulatory pollution penalties or reduce carbon trading costs. Additionally, the DANP results reveal that corporate image criteria in the corporate social responsibility dimension hold the utmost priority in semiconductor firms’ green production decision-making. This implies that considerations for improving a company’s image should take precedence as semiconductor companies seek shareholder support and governmental subsidies to ensure sustainable operations. Externalities arise as the secondary priority dimension in green production decision-making, aligning with the impact of externalities identified in DEMATEL findings.
Impact of Bioplastic Design on Biodigestion Treatment
Authors: Oliver Inés , Martínez-Pérez Noelia , Fullana Andrés , Conesa Juan
Published: August 2024

In this study, the impact of bioplastic design on anaerobic digestion for biogas production was investigated. This research aims to facilitate the integration of bioplastics into a circular economy, which is why our study proposes considering not only aspects related to their degradation in the formulation but also ensuring efficient behavior in anaerobic digestion plants. Thermoplastic starch (TPS) samples, derived from different starch sources and formulated with varying concentrations of calcium carbonate and thicknesses, were subjected to anaerobic digestion tests. Three key parameters were explored: the influence of filler concentration, the effect of sample thickness, and the role of starch origin. Biogas production and kinetics were assessed using biochemical methane potential (BMP) tests. The results reveal that calcium carbonate concentration negatively influenced the methane production rate, reaching 30 NmL/gVS/day for the filler-free sample, highlighting the importance of understanding filler effects on anaerobic digestion. Additionally, thicker samples exhibited slower biogas production, with a rate of 25 NmL/gVS/day compared to 30 NmL/gVS/day for the thinnest sample, emphasizing the relevance of sample thickness in influencing digestion kinetics. The starch origin did not yield significant differences in biogas production, providing valuable insights into the feasibility of using diverse starch sources in bioplastic formulations. This study enhances our understanding of bioplastic behavior during anaerobic digestion, offering essential insights for optimizing waste management strategies and advancing circular economy practices.
Foreign or Domestic Public Debt for Cameroon’s Development? An Externality Approach
Authors: Nguepi Nelson Derrick , Ngouhouo Ibrahim , Bilan Irina
Published: August 2024

Public debt plays a major role in financing projects that support economic growth and sustainable development. As governments may choose between domestic and external borrowing, a comprehensive assessment of their effects would support this choice. Our study provides an integrative view of economic and social outcomes and compares, through externalities, the impacts of external and domestic public debt as methods of financing development, with a focus on the Cameroonian economy. Utilizing a dynamic computable general equilibrium (CGE) model and a microsimulation analysis, we find that domestic debt has more advantages for Cameroon compared to external debt, as it increases the large-scale economic impact by improving household welfare, boosting GDP growth, and progressively reducing poverty and inequality. It is therefore recommended that the Cameroonian government focus on increasing the use of domestic debt as a method of financing development by implementing policies that support domestic saving and promote the development of domestic debt markets.
Geographical Variation in Pasturelands and Their Impact on the Physicochemical Characterization and Fatty Acid Composition of Cheese in Caraș-Severin County, Romania
Authors: Ibric Alexandra-Ioana , Cocan Ileana , Alexa Ersilia , Jianu Călin , Negrea Monica , Dragoescu Alina Andreea , Jurcuț Raul-Cristian , Iancu Tiberiu
Published: August 2024

In Caraș-Severin County, Romania, the integration of sustainable agricultural practices with organic dairy production represents a unique opportunity to promote agro-tourism farms and guesthouses. This study examines the synergy between the chemical composition of pastures from three farms in different alleys and the impact on the chemical composition of dairy products produced on those farms. Thus, the comparative analysis of two types of traditional cow’s cheeses (soft and mature) from three different areas of Caraș-Severin County was pursued, as well as of the feed given to the animals from which the raw material for obtaining the cheeses originated. Initially, the physicochemical profile of the pastures (fresh grass and grass hay) was analyzed in terms of proximate composition (moisture, lipids, proteins, ash and carbohydrates), and the content of bioactive compounds (total polyphenols, antioxidant activity using the DPPH method). The proximate composition of the cheese samples, including moisture, lipids, proteins, ash and carbohydrates was analysed, and the content of fatty acids. A correlation was also made between the content of bioactive compounds in feed samples and fatty acid profile of the cheese samples. Our findings demonstrate that the geographical area and the chemical composition of the pasture have a considerable influence on the physicochemical characterization and fatty acid composition of the cheese. The cheese from the mountain area had a higher fat, protein and polyunsaturated fatty acid content compared to the samples from the hill and plain areas.
Reevaluating Economic Drivers of Household Food Waste: Insights, Tools, and Implications Based on European GDP Correlations
Authors: Gencia Adrian Daniel , Balan Ioana Mihaela
Published: August 2024

This article examines the relationship between household food waste and Gross Domestic Product (GDP) in various European regions, aiming to determine how economic prosperity influences the levels of household food waste. Using comparative analysis of secondary and tertiary data, a synthetic indicator (IpFW) was developed to assess the interaction between GDP per capita and household food waste per capita. Linear correlation analysis was also applied for better interpretation of the data. Despite expectations, higher GDP is not consistently correlated with lower household food waste, challenging economic prosperity and environmental stewardship assumptions. This research highlights the complexity of the interaction between economic factors and household food waste management, revealing a lack of significant correlation even at the regional level. The findings indicate a need to re-evaluate current policies and highlight that improving food supply chains and influencing consumer behavior can promote more sustainable consumption patterns, which is in line with the Sustainable Development Goals.
Research on Urban Street Spatial Quality Based on Street View Image Segmentation
Authors: Gao Liying , Xiang Xingchao , Chen Wenjian , Nong Riqin , Zhang Qilin , Chen Xuan , Chen Yixing
Published: August 2024

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage.
Experimental and Numerical Studies on the Fire Performance of Thin Sustainable Wood-Based Laminated Veneers
Authors: Chanda Avishek , Das Oisik , Bhattacharyya Debes
Published: August 2024

Wood and wood-based products are abundantly used, especially in structural applications, due to the impetus for sustainable development. The present work helps highlight the fire performance of plywood, one of the most used wood-based laminated structural components, under three different heat fluxes of 35 kW/m2, 50 kW/m2, and 65 kW/m2. The effects on the various fire reaction properties, namely, time to ignition, heat release rate, peak heat release rate, time to peak heat release rate, time to flameout, total burn time, and mass loss, were observed and reported. The times to ignition (42.2% and 35.4%), peak heat release rate (27.7% and 18.9%), flameout (22.2% and 28.6%), burn time (10.6% and 16.1%), and residual mass (25% and 53.3%) were reduced with the increase in heat flux from 35 kW/m2 to 65 kW/m2, respectively, whereas the peak heat release (21.7% and 2.4%) and ignition temperature (6.5% and 6.6%) were observed to increase. The vertical burning test (UL-94) illustrated the plywood samples to have a V-1 rating, with self-extinguishing capabilities. A numerical predictive model has also been developed based on the Fire Dynamics Simulator to predict the time to ignition, time to flameout, and heat release rate trend along with the peak heat release rate—it is shown to have good agreement with the experimental results, with an average correlation coefficient of 0.87.
Quantifying the Impact of Coal Transition on GDP Growth through System Dynamics: The Case of the Region of Western Macedonia, Greece
Authors: Tranoulidis Apostolos , Sotiropoulou Rafaella-Eleni P. , Bithas Kostas , Tagaris Efthimios
Published: August 2024

The transition from coal to more sustainable energy sources represents a critical shift for economies reliant on coal production. To investigate the intricate processes involved in such a transition, the use of powerful analytical tools is essential. This study assesses the impact of the delignification process on GDP growth over a 20-year horizon (2015–2035) in the Region of Western Macedonia, Greece, using the Vensim PLE Plus 9.0.1 software, a robust tool for system dynamics modeling. By developing a dynamic model that captures the key variables and feedback loops associated with coal transition, this research examines economic, social, and investment variables, emphasizing their causal relationships. The study integrates societal, economic, and educational impacts on production transition, addressing issues such as unemployment, financial support, and investments in human resources and R&D. Additionally, it considers the influence of climate change on GDP. The model highlights population dynamics, economic development, and education as critical factors. Scenarios explore the impact of increased funding on education, research, and financial aid efficiency, providing insights into enhancing GDP in decarbonizing regions. The study reveals that increased investment in education and human capital leads to slight improvements in local GDP, though the effects are not immediate. Enhanced efficiency in government and European spending significantly boosts local GDP by creating strong value chains and local economies of scale. It is found that the increase in financial support to the regions in transition is of the utmost importance and has a multiplicative nature, something that should encourage the European Union to increase its financial support tools. The model’s simulations align closely with historical GDP data, validating its accuracy. The contributions of the present work offer valuable insights to policymakers and stakeholders engaged in the transition processes.