bluuebunny/crossref_metadata_2025 huggingface dataset:
Whole-Body Physiologically Based Pharmacokinetic Modeling of GalNAc-Conjugated siRNAs
Authors: Salim Emilie Langeskov , Kristensen Kim , Sjögren Erik
Published: January 2025

Background/Objectives: N-acetyl-galactosamine small interfering RNAs (GalNAc-siRNA) are an emerging class of drugs due to their durable knockdown of disease-related proteins. Direct conjugation of GalNAc onto the siRNA enables targeted uptake into hepatocytes via GalNAc recognition of the Asialoglycoprotein Receptor (ASGPR). With a transient plasma exposure combined with a prolonged liver half-life, GalNAc-siRNA exhibits distinct disposition characteristics. We aimed to develop a generic GalNAc-siRNAs whole-body physiologically based pharmacokinetic–pharmacodynamic (WB-PBPK-PD) model for describing the pharmacokinetic–pharmacodynamic (PK-PD) relationship and overall tissue distribution in the open-source platform Open Systems Pharmacology Suite. Methods: Model development was performed using published studies in mice leveraging the PK-Sim® standard implementation for large molecules with added implementations of ASGPR-mediated liver disposition and downstream target effects. Adequate model performance was achieved across study measurements and included studies adopting a combination of global and compound-specific parameters. Results: The analysis identified significant compound dependencies, e.g., endosomal stability, with direct consequences for the pharmacological effect. Additionally, knowledge gaps in mechanistic understanding related to extravasation and overall tissue distribution were identified during model development. The presented study provides a generic WB-PBPK-PD model for the investigation of GalNAc-siRNAs implemented in a standardized open-source platform.
Maximizing Optical Chirality Through Tailored Excitation Fields: A T-Matrix Approach
Authors: Qin Shengzheng , Cai Hanqing , Liu Jiachen , Hu Haifeng , Zhan Qiwen
Published: January 2025

Chiral materials are vital in various fields like physics, chemistry, and life sciences. Enhancing the interaction between light and chiral materials is crucial for advancements in these areas. We can manipulate the chirality-dependent interaction between light and materials by designing microstructural particles and tailoring incident fields. Compared to chiral photonic structures, the study of engineered optical fields is relatively underexplored, yet it has recently been recognized as a novel approach for manipulating light–matter interactions. In this study, we propose a method to design the incident field based on the T-matrix of dielectric chiral Mie spheres, aiming to maximize scattering circular dichroism. We anticipate that this approach will provide valuable insights, paving the way for predicting optimized optical chiral responses in various nanoparticles.
High-Stability PGC-EKF Demodulation Algorithm Integrated with a Phase Delay Compensation Module
Authors: Zhao Hengyang , Zhu Feng , Xu Xiaoxiao , Zhao Zongling , Deng Chuanlu
Published: January 2025

To effectively eliminate the nonlinear distortion caused by the modulation depth (C value) drift and carrier phase delay (θ) in the phase-generated carrier (PGC) demodulation scheme, the PGC-PDC-EKF joint algorithm is presented, which combines phase delay compensation (PDC) with an extended Kalman filter (EKF). The θ is accurately extracted and compensated by the PDC module. Furthermore, with the EKF algorithm, the harmonic distortion of the demodulated signal due to the fluctuation of C value is suppressed. The experimental results indicate that θ is compensated accurately with a resolution of 0.01745 rad. The signal-to-noise and distortion ratio (SINAD) of the improved scheme reaches 54.01 dB, which is 18.03 dB higher than the PGC-Arctan algorithm on average. The total harmonic distortion (THD) is as low as −62.28 dB, which is 26.04 dB lower than the PGC-Arctan algorithm. The linearity of the demodulation system exceeds 99.99%. The proposed method provides a significant reference for demodulation schemes of interferometric fiber optic sensing systems in practical applications.
Exploring Co-Occurrence Patterns to Understand Epiphyte–Liana Interactions
Authors: Ceballos Sergio J. , Aráoz Ezequiel , Rojas Tobías Nicolás
Published: January 2025

Although epiphytes and lianas share the same habitat, most research has treated these two groups independently. This study aimed to evaluate the co-occurrence of vascular epiphytes and lianas in the subtropical montane forests of northwestern Argentina. We recorded epiphyte cover and liana basal area on trees ≥ 10-cm-dbh in 120 20 × 20 m plots in the Sierra de San Javier (Tucumán, Argentina). Of the 2111 trees sampled, 727 (34%) hosted lianas, and 1095 (52%) hosted epiphytes. Both plant groups were found together on 20% of the sampled trees. The species richness of lianas and epiphytes, along with liana basal area and epiphyte cover, increased with tree diameter and reached higher values in mature forests compared to successional forests. Both groups colonized the same canopy tree species with larger diameters, whereas smaller trees were typically colonized by either lianas or epiphytes, but not both. Epiphyte species were more likely to co-occur with liana species with specialized climbing mechanisms. Tree size and forest type (mature vs. successional) emerged as key factors influencing the co-occurrence of lianas and epiphytes in these forests. This study establishes a basis for future research into the interactions between lianas and epiphytes, seeking to determine whether they co-occur in the same habitats.
Exploring the Impact of Structural Modifications of Phenothiazine-Based Novel Compounds for Organic Solar Cells: DFT Investigations
Authors: Taouali Walid , Azazi Amel , Hassani Rym , EL-Araby Entesar H. , Alimi Kamel
Published: January 2025

This paper explores a novel group of D-π-A configurations that has been specifically created for organic solar cell applications. In these material compounds, the phenothiazine, the furan, and two derivatives of the thienyl-fused IC group act as the donor, the π-conjugated spacer, and the end-group acceptors, respectively. We assess the impact of substituents by introducing bromine atoms at two potential substitution sites on each end-group acceptor (EG1 and EG2). With the donor and π-bridge held constant, we have employed density functional theory and time-dependent DFT simulations to explore the photophysical and optoelectronic properties of tailored compounds (M1–M6). We have demonstrated how structural modifications influence the optoelectronic properties of materials for organic solar cells. Moreover, all proposed compounds exhibit a greater Voc exceeding 1.5 V, a suitable HOMO-LUMO energy gap (2.14–2.30 eV), and higher dipole moments (9.23–10.90 D). Various decisive key factors that are crucial for exploring the properties of tailored compounds—frontier molecular orbitals, transition density matrix, electrostatic potential, open-circuit voltage, maximum absorption, reduced density gradient, and charge transfer length (Dindex)—were also explored. Our analysis delivers profound insights into the design principles of optimizing the performance of organic solar cell applications based on halogenated material compounds.
Estimation and Uncertainty Quantification of Solvent Strength Parameters in Gradient Elution of Chromatography Using Sequential Monte Carlo Method
Authors: Yuan Ziting , Suzuki Kensuke , Yamamoto Yota , Yajima Tomoyuki , Kawajiri Yoshiaki
Published: January 2025

Gradient elution is a common method for the separation of complex components in chromatography. Strength-dependent models that describe the variation in chromatographic parameters, considering equilibrium and mass transfer under different solution strengths, are essential for the design and optimization of gradient elution. In this study, we proposed several hypothetical models based on experimental data obtained under varying mobile phase ratios in the water–methanol system. The uncertainties in the parameters were quantified using the sequential Monte Carlo (SMC) method with Bayesian inference. Finally, we performed model selection to determine the most appropriate model, utilizing multiple sources of information criteria.
Characteristics and Paleoenvironment of Stromatolites in the Southern North China Craton and Their Implications for Mesoproterozoic Gas Exploration
Authors: Yuan Ruize , Yu Qiang , Tian Tao , Yang Qike , Ren Zhanli , Li Rongxi , Wang Baojiang , Chang Wei , He Lijuan , Wang Tianzi
Published: January 2025

Stromatolites, distinctive fossil records within Precambrian strata, are essential for investigating the depositional environments of early Earth and the geological settings conducive to hydrocarbon formation. The Luonan area is located in Shaanxi Province, China, where a large number of stromatolites have been discovered within the Mesoproterozoic Erathem, providing new perspectives on paleoenvironment and reservoir spaces. This study analyzes the morphology of stromatolites, associated microorganisms, mineralogy, and cathodoluminescence from the carbonate rocks of the Jixian System. Carbon and oxygen isotope analyses help reconstruct paleosalinity and climate, enhancing understanding of their petroleum geological significance. Combining carbon and oxygen isotope analysis with the fine observation and description of stromatolite can better reconstruct the paleoenvironmental features of the Mesoproterozoic Era. The results indicated a narrow range of carbon isotope values (δ13C: −5.81‰ to −2.43‰; mean: −4.03‰) and oxygen isotope values (δ18O: −9.06‰ to −5.64‰). The Longjiayuan Formation is characterized by high CaO and MgO content, with low SiO2 and minimal terrigenous input, in contrast with the Fengjiawan Formation, which exhibits elevated SiO2 and greater terrigenous material. The Luonan stromatolites display prominent rhythmic laminations, primarily composed of dolomite, indicating a potential for hydrocarbon source rocks. Stromatolite morphologies, including layered, columnar, and wavy forms, reflect varied depositional microfacies. The alternating bright and dark laminae, rich in CaO and CO2 but differing in Ca2+ and Mg2+ concentrations, signify seasonal growth cycles. These Mesoproterozoic stromatolites developed in a warm, humid, and stable climatic regime, within a marine anoxic-to-suboxic setting, typically in intertidal or supratidal zones with low hydrodynamic energy. In the southern margin of the North China Craton, stromatolites from the Mesoproterozoic Era are extensively developed and exhibit distinct characteristics. Due to the biogenic alteration of stromatolites, the porosity of the rock increased. These stromatolites have altered the physical properties of the host rocks to some extent, suggesting the possibility of becoming effective hydrocarbon reservoirs. This has significant implications for deep oil and gas exploration, providing valuable guidance for future prospecting efforts.
Phytoremediaton Strategies for Co-Contaminated Soils: Overcoming Challenges, Enhancing Efficiency, and Exploring Future Advancements and Innovations
Authors: Lee Yun-Yeong , Cho Kyung-Suk , Yun Jeonghee
Published: January 2025

Soils co-contaminated with petroleum hydrocarbons (PHs) and heavy metals pose significant challenges, such as reduced bioavailability of pollutants, toxic effects on soil microorganisms, and unpredictable chemical interactions. These complex interactions hinder effective remediation. Phytoremediation, which utilizes plant and microbial processes, offers a sustainable and eco-friendly approach. However, its effectiveness is often constrained by the intricate interplay among PHs, heavy metals, and soil components, which complicates pollutant degradation and microbial activity. This review explores the interactions between enhancement strategies, including soil amendments, plant growth-promoting bacteria (PGPB), and genetic engineering, which can synergistically enhance pollutant degradation and remediation efficiency. Key challenges include competition for soil adsorption sites among contaminants, microbial community disruptions, and environmental variability. Moreover, the limitations of these strategies, including their reliance on specific plant species, sensitivity to environmental variability, and the necessity for long-term monitoring, are discussed. The proposed solutions focus on integrating emerging technologies and interdisciplinary approaches to overcome these challenges and improve pollutant removal efficiency. Future advancements in interdisciplinary approaches, integrating biological techniques with technological innovations, are highlighted as key to addressing the complexities of co-contaminated environments and improving pollutant removal efficiency.
Output-Normalized Score (OnS) for Ranking Researchers Based on Number of Publications, Citations, Coauthors, and Author Position
Authors: Onjia Antonije
Published: January 2025

This article discusses current methods for ranking researchers and proposes a new metric, the output-normalized score (OnS), which considers the number of publications, citations, coauthors, and the author’s position within each publication. The proposed OnS offers a balanced approach to evaluating a researcher’s scientific contributions while addressing the limitations of widely used metrics such as the h-index and its modifications. It favors publications with fewer coauthors while giving significant weight to both the author’s position in the publication and the total number of citations.
Public Religions and Value Creation
Authors: Nardella Carlo
Published: January 2025

Religions are gaining increasing visibility in the public space [...]
Mapping Habitat Structures of Endangered Open Grassland Species (E. aurinia) Using a Biotope Classification Based on Very High-Resolution Imagery
Authors: Dietenberger Steffen , Mueller Marlin M. , Henkel Andreas , Dubois Clémence , Thiel Christian , Hese Sören
Published: January 2025

Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (Euphydryas aurinia), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of E. aurinia and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of E. aurinia. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species.
Quantifying Time-Lag and Time-Accumulation Effects of Climate Change and Human Activities on Vegetation Dynamics in the Yarlung Zangbo River Basin of the Tibetan Plateau
Authors: Li Ning , Wang Di
Published: January 2025

Vegetation, as a fundamental component of terrestrial ecosystems, plays a pivotal role in the flux of water, heat, and nutrients between the lithosphere, biosphere, and atmosphere. Assessing the impacts of climate change and human activities on vegetation dynamics is essential for maintaining the health and stability of fragile ecosystems, such as the Yarlung Zangbo River (YZR) basin of the Tibetan Plateau, the highest-elevation river basin in the world. Vegetation responses to climate change are inherently asymmetric, characterized by distinct temporal effects. However, these temporal effects remain poorly understood, particularly in high-altitude ecosystems. Here, we examine the spatiotemporal changes in leaf area index (LAI) and four climatic factors—air temperature, precipitation, potential evapotranspiration, and solar radiation—in the YZR basin over the period 2000–2019. We further explore the time-lag and time-accumulation impacts of these climatic factors on LAI dynamics and apply an enhanced residual trend analysis to disentangle the relative contributions of climate change and human activities. Results indicated that (1) a modest increase in annual LAI at a rate of 0.02 m2 m−2 dec−1 was detected across the YZR basin. Spatially, LAI increased in 66% of vegetated areas, with significant increases (p < 0.05) in 10% of the basin. (2) Temperature, precipitation, and potential evapotranspiration exhibited minimal time-lag (<0.5 months) but pronounced notable time-accumulation effects on LAI variations, with accumulation periods ranging from 1 to 2 months. In contrast, solar radiation demonstrated significant time-lag impacts, with an average lag period of 2.4 months, while its accumulation effects were relatively weaker. (3) Climate change and human activities contributed 0.023 ± 0.092 and –0.005 ± 0.109 m2 m−2 dec−1 to LAI changes, respectively, accounting for 60% and 40% on the observed variability. Spatially, climate change accounted for 85% of the changes in LAI in the upper YZR basin, while vegetation dynamics in the lower basin was primarily driven by human activities, contributing 63%. In the middle basin, vegetation dynamics were influenced by the combined effects of climate change and human activities. Our findings deepen insights into the drivers of vegetation dynamics and provide critical guidance for formulating adaptive management strategies in alpine ecosystems.
Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models
Authors: Low Pei Jing , Ng Bo Yan , Mahzan Nur Insyirah , Tian Jing , Leung Cheung-Chi
Published: January 2025

Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches. The first approach employs a combination of handcrafted feature extraction techniques and a sequential classification model to analyze motion and object-related features. The second approach leverages a multiple-frame convolutional neural network (CNN) to exploit temporal and spatial patterns in the video data. The third approach explores a 3D CNN-based deep learning model, which is capable of processing video data as volumetric inputs. To assess the performance of these methods, we conduct a comprehensive comparative study, demonstrating the strengths and limitations of each approach within this specialized domain.
A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors
Authors: Palazzo Lucia , Suglia Vladimiro , Grieco Sabrina , Buongiorno Domenico , Brunetti Antonio , Carnimeo Leonarda , Amitrano Federica , Coccia Armando , Pagano Gaetano , D’Addio Giovanni , Bevilacqua Vitoantonio
Published: January 2025

Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time.
Electrochemical Sensor for Hydrogen Leakage Detection at Room Temperature
Authors: Rîmbu Gimi Aurelian , Pîslaru-Dănescu Lucian , Zărnescu George-Claudiu , Ștefănescu Carmen Alina , Iordoc Mihai , Teișanu Aristofan Alexandru , Telipan Gabriela
Published: January 2025

The use of hydrogen as fuel presents many safety challenges due to its flammability and explosive nature, combined with its lack of color, taste, and odor. The purpose of this paper is to present an electrochemical sensor that can achieve rapid and accurate detection of hydrogen leakage. This paper presents both the component elements of the sensor, like sensing material, sensing element, and signal conditioning, as well as the electronic protection and signaling module of the critical concentrations of H2. The sensing material consists of a catalyst type Vulcan XC72 40% Pt, from FuelCellStore, (Bryan, TX, USA). The sensing element is based on a membrane electrode assembly (MEA) system that includes a cathode electrode, an ion-conducting membrane type Nafion 117, from FuelCellStore, (Bryan, TX, USA). and an anode electrode mounted in a coin cell type CR2016, from Xiamen Tob New Energy Technology Co., Ltd, (Xiamen City, Fujian Province, China). The electronic block for electrical signal conditioning, which is delivered by the sensing element, uses an INA111, from Burr-Brown by Texas Instruments Corporation, (Dallas, TX, USA). instrumentation operational amplifier. The main characteristics of the electrochemical sensor for hydrogen leakage detection are operation at room temperature so it does not require a heater, maximum amperometric response time of 1 s, fast recovery time of maximum 1 s, and extended range of hydrogen concentrations detection in a range of up to 20%.
Validation of Aerobic Capacity (VO2max) and Pulse Oximetry in Wearable Technology
Authors: Carrier Bryson , Marten Chaves Sofia , Navalta James W.
Published: January 2025

Introduction: As wearable technology becomes increasingly popular and sophisticated, independent validation is needed to determine its accuracy and potential applications. Therefore, the purpose of this study was to evaluate the accuracy (validity) of VO2max estimates and blood oxygen saturation measured via pulse oximetry using the Garmin fēnix 6 with a general population participant pool. Methods: We recruited apparently healthy individuals (both active and sedentary) for VO2max (n = 19) and pulse oximetry testing (n = 22). VO2max was assessed through a graded exercise test and an outdoor run, comparing results from the Garmin fēnix 6 to a criterion measurement obtained from a metabolic system. Pulse oximetry involved comparing fēnix 6 readings under normoxic and hypoxic conditions against a medical-grade pulse oximeter. Data analysis included descriptive statistics, error analysis, correlation analysis, equivalence testing, and bias assessment, with the validation criteria set at a concordance correlation coefficient (CCC) > 0.7 and a mean absolute percentage error (MAPE) < 10%. Results: The Garmin fēnix 6 provided accurate VO2max estimates, closely aligning with the 15 s and 30 s averaged laboratory data (MAPE for 30 s avg = 7.05%; Lin’s concordance correlation coefficient for 30 s avg = 0.73). However, it failed to accurately measure blood oxygen saturation (BOS) under any condition or combined analysis (MAPE for combined conditions BOS = 4.29%; Lin’s concordance correlation coefficient for combined conditions BOS = 0.10). Conclusion: While the Garmin fēnix 6 shows promise for estimating the VO2max, reflecting its utility for both individuals and researchers, it falls short in accurately measuring BOS, limiting its application for monitoring acclimatization and managing pulmonary diseases. This research underscores the importance of validating wearable technology to leverage its full potential in enhancing personal health and advancing public health research.
An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks
Authors: Alsehaimi Basma , Alzamzami Ohoud , Alowidi Nahed , Ali Manar
Published: January 2025

Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed. These approaches often rely on a single model to capture temporal dependencies, which neglects the varying influences of different time periods on traffic flow. Additionally, these models frequently utilize either static or dynamic graphs to represent spatial dependencies, which limits their ability to address complex and overlapping spatial relationships. Moreover, some approaches struggle to fully capture spatio-temporal variations, leading to the exclusion of critical information and ultimately resulting in suboptimal prediction performance. Thus, this paper introduces the Adaptive Spatio-Temporal Attention-Based Multi-Model (ASTAM), an architecture designed to capture spatio-temporal dependencies within traffic data. The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. It utilizes static and dynamic parallel multi-graphs to facilitate the modeling of complex spatial dependencies. Furthermore, this model incorporates a spatio-temporal self-attention mechanism to adaptively capture the dynamic and long-term spatio-temporal variations in traffic flow. Experiments conducted on four real-world datasets reveal that the proposed architecture outperformed 13 baseline approaches, achieving average reductions of 5.0% in MAE, 13.28% in RMSE, and 6.46% in MAPE across four datasets.
A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques
Authors: Han Zhenqi , Liu Lizhuang
Published: January 2025

Accurate 6D object pose estimation is critical for autonomous docking. To address the inefficiencies and inaccuracies associated with maximal cliques-based pose estimation methods, we propose a fast 6D pose estimation algorithm that integrates feature space and space compatibility constraints. The algorithm reduces the graph size by employing Laplacian filtering to resample high-frequency signal nodes. Then, the truncated Chamfer distance derived from fusion features and spatial compatibility constraints is used to evaluate the accuracy of candidate pose alignment between source and reference point clouds, and the optimal pose transformation matrix is selected for 6D pose estimation. Finally, a point-to-plane ICP algorithm is applied to refine the 6D pose estimation for autonomous docking. Experimental results demonstrate that the proposed algorithm achieves recall rates of 94.5%, 62.2%, and 99.1% on the 3DMatch, 3DLoMatch, and KITTI datasets, respectively. On the autonomous docking dataset, the algorithm yields rotation and localization errors of 0.96° and 5.82 cm, respectively, outperforming existing methods and validating the effectiveness of our approach.
An Innovative Linear Wireless Sensor Network Reliability Evaluation Algorithm
Authors: Ma Tao , Guo Huidong , Li Xin
Published: January 2025

In recent years, wireless sensor networks (WSNs) have become a crucial technology for infrastructure monitoring. To ensure the reliability of monitoring services, evaluating the network’s reliability is particularly important. Sensor nodes are distributed linearly when monitoring linear structures, such as railway bridges, forming what is known as a Linear Wireless Sensor Network (LWSN). Although existing evaluation methods, such as enumeration and Binary Decision Diagram (BDD)-based methods, can be used to assess the reliability of various types of networks, their efficiency is relatively low. Therefore, we classified network states based on the number of failed nodes located at the network’s ends and analyzed the arrangement characteristics of nodes under different network states. This paper proposed a new reliability assessment method for LWSNs. This method is based on the combinatorial patterns of nodes and uses the concept of integer partitions to calculate the total number of states at different performance levels, applying probability formulas to assess network reliability. Compared to Multi-Valued Decision Diagram (MDD)-based evaluation algorithms, this method is suitable for large-scale LWSNs and offers lower time complexity.
Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques
Authors: Nodi Sadia Sabrin , Paul Manoranjan , Robinson Nathan , Wang Liang , Rehman Sabih ur , Kabir Muhammad Ashad
Published: January 2025

Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user’s perception. As smartphones are widely used and come with high-quality cameras, a popular one was used for capturing images for this study. This study aims to predict Munsell soil colour (MSC) from the Munsell soil colour book (MSCB) by using deep learning techniques on mobile-captured images. MSCB contains 14 pages and 443 colour chips. So, the number of classes for chip-by-chip prediction is very high, and the captured images are inadequate to train and validate using deep learning methods; thus, a patch-based mechanism was proposed to enrich the dataset. So, the course of action is to find the prediction accuracy of MSC for both page level and chip level by evaluating multiple deep learning methods combined with a patch-based mechanism. The analysis also provides knowledge about the best deep learning technique for MSC prediction. Without patching, the accuracy for chip-level prediction is below 40%, the page-level prediction is below 65%, and the accuracy with patching is around 95% for both, which is significant. Lastly, this study provides insights into the application of the proposed techniques and analysis within real-world soil and provides results with higher accuracy with a limited number of soil samples, indicating the proposed method’s potential scalability and effectiveness with larger datasets.
Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices
Authors: Morisio Maurizio , Noris Emanuela , Pagliarani Chiara , Pavone Stefano , Moine Amedeo , Doumet José , Ardito Luca
Published: January 2025

The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones. A dataset of 4112 images, each having 2Mpixel resolution per tree and covering RGB, Red Edge, and near-infrared frequencies, was obtained from 185 hazelnut trees located in two different orchards of the Piedmont region (northern Italy). To increase accuracy, and especially to reduce false negatives, the image of each tree was divided into nine quadrants. For each quadrant, nine different vegetation indices (VIs) were computed, and in parallel, each tree quadrant was tagged as “healthy/unhealthy” by visual inspection. Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. Out of the nine VIs considered, only five (GNDVI, GCI, NDREI, NRI, and GI) were good predictors, while NDVI SAVI, RECI, and TCARI were not. Using them, a model accuracy of about 65%, with 13% false negatives was reached in a way that was rather independent of the algorithms, demonstrating that some VIs allow inferring the physio-pathological condition of these trees. These achievements support the use of drone-captured images for performing a rapid, non-destructive physiological characterization of hazelnut trees. This approach offers a sustainable strategy for supporting farmers in their decision-making process during agricultural practices.
Assessing the Key Construction Safety Challenges in Sri Lanka: A Survey-Based Study
Authors: Weerakoon Asela , Thorpe David , Heravi Amirhossein , Chakraborty Subrata
Published: January 2025

The construction industry is a key driver of economic development in a developing country like Sri Lanka. However, many construction projects still face serious safety challenge areas such as poor attitude, bad conduct, negligence, out-of-date legislation, a lack of qualified safety officers and financial constraints. This study aims to determine which safety challenges are the key ones in the Sri Lankan construction industry and how these challenges are classified and related to each other. The significance of this research is that it identifies the most important safety challenge areas and addresses them, which helps to overcome other safety challenge areas that rely on them. To achieve this aim, survey questionnaires were developed, and a survey was conducted among key project stakeholders, including safety officers, engineers, managers, quantity surveyors and human resources personnel, to collect information on significant safety factors that can help in improving safety. Forty-three safety challenge elements were identified and assessed through factor analysis. The ten most significant safety challenge elements were extracted and categorised into two main categories: (i) economy-related and (ii) human-resource-related challenges. The results of this survey provide a very good foundation for project managers or safety managers to identify certain criteria when selecting construction staff and making project plans.
Probing the Impact of Seed Coat Removal on the Germination Performance of Bitter Gourd (Momordica charantia) Using an Image Analyzer
Authors: Byregowda Roopashree , Nagarajappa Nethra , Madhusudan Kadurappa , Nagarathna Thimmappa Krishnappa , Kesaratagi Sharankumar Ashok
Published: January 2025

The germination speed and completeness of bitter gourd seeds were enhanced using two seed coat treatments: scarification and removal of the seed coat. In comparison to the scarified seed and control, removing the seed coat improved the germination speed and completeness, making it the most successful method. When there are not enough seeds or when a research project needs a full, homogenous group of seedlings, this method would be the most helpful. The seed image analyzer, which can capture the small variations in seed parameters between groups, showed a triphasic pattern of water uptake during the imbibition process of germination of bitter gourd seeds, which was in accordance with the conventional methods of analyzing it. This was shown as a correlation between the mean germination rate and the perimeter of seeds.
Researching as a Commoner: Affect and the Lives of Underprivileged Mobile Youth in Greece
Authors: Pantazidis Stelios , Pechtelidis Yannis
Published: January 2025

This paper examines the role of commons-based activist ethnography in reshaping the dynamics between researchers and underprivileged mobile youth (UMY), focusing on the opportunities and ethical challenges of this approach. Conducted in youth centers in Thessaloniki, Greece, the study explores how trust, affect, and collaborative knowledge creation supported agency among UMY while confronting systemic barriers and power asymmetries. Using qualitative ethnographic methods, the research investigates the role of convivial spaces and the ambiance of the commons in creating meaningful connections and emotional resilience, enabling the participants to articulate aspirations and reclaim agency. The dual role of the researcher as an advocate and observer raised questions about representation and power dynamics. This paper contributes to discussions on activist ethnography by offering insights into its possibilities and limitations in advancing transformative research with marginalized populations.
A Common Language of Software Evolution in Repositories (CLOSER)
Authors: Garrity Jordan , Cutting David
Published: January 2025

Version Control Systems (VCSs) are used by development teams to manage the collaborative evolution of source code, and there are several widely used industry standard VCSs. In addition to the code files themselves, metadata about the changes made are also recorded by the VCS, and this is often used with analytical tools to provide insight into the software development, a process known as Mining Software Repositories (MSRs). MSR tools are numerous but most often limited to one VCS format and, therefore, restricted in their scope of application in addition to the initial effort required to implement parsers for verbose textual VCS output. To address this limitation, a domain-specific language (DSL), the Common Language of Software Evolution in Repositories (CLOSER), was defined that abstracted away from specific implementations while isomorphically mapping to the data model of all major VCS formats. Using CLOSER directly as a data model or as an intermediate stage in a conversion analysis approach could make use of all major repositories rather than be limited to a single format. The initial barrier to adoption for MSR approaches was also lowered as CLOSER output is a concise, easily machine-readable format. CLOSER was implemented in tooling and tested against a number of common expected use cases, including a direct use in MSR analysis, proving the fidelity of the model and implementation. CLOSER was also successfully used to convert raw output logs from one VCS format to another, offering the possibility that legacy analysis tools could be used on other technologies without any changes being required. In addition to the advantages of a generic model opening all major VCS formats for analysis parsing, the CLOSER format was found to require less code and complete parsing faster than traditional VCS logging outputs.
Swell Magnitude of Unsaturated Clay as Affected by Different Wetting Conditions
Authors: Nachum Shay
Published: January 2025

The wetting of compacted clays and their subsequent swelling often result in damage to structures and infrastructures. Estimations of the swell that is expected to develop during wetting are usually based on standard laboratory tests. The standard procedure requires inundating the test specimens; this procedure represents an extreme wetting condition and provides an upper limit to the swell. However, wetting may result from less extreme conditions, for example by the absorption of water due to suction forces, which may result in a smaller swell. This paper describes a laboratory investigation of the swell difference in high-plasticity clay that may result from different wetting conditions. Swell tests were carried out on specimens prepared at different initial conditions and wetted under different wetting conditions of inundation or absorption. The results indicate that as the initial void ratio decreases and the degree of saturation increases, it is more likely that different wetting conditions will result in different swell magnitudes, where inundation may create a larger swell than absorption. The soil at a low initial void ratio and high degree of saturation seems to be characterized by mono-modal pore size distributions in the micropore range. This unique pore size distribution may be the explanation of the different swell magnitudes.
An LMDI-Based Analysis of Carbon Emission Changes in China’s Waterway Transportation Sector
Authors: Zheng Shanshan , Chen Cheng , Xie Sikai
Published: January 2025

The waterway transportation industry, recognized for its high capacity, cost-effectiveness, and energy efficiency, plays a vital role in global freight transport and trade. In China, it serves as a key pillar supporting the national economy and foreign trade. However, its heavy dependence on fossil fuels has intensified carbon emission challenges, creating significant barriers to achieving sustainable development goals. This study employs Input-Output Analysis and the Logarithmic Mean Divisia Index model to examine the changes in carbon emissions and their driving factors in China’s waterway transportation industry from 2002 to 2020, while also exploring potential pathways for emission reduction. The findings reveal the following: (1) From 2002 to 2020, despite a substantial rise in total carbon emissions, the industry has been progressively transitioning towards a low-carbon trajectory through the adoption of clean energy technologies and optimization of its energy structure. (2) Economic scale effects have been the primary drivers of carbon emission growth, with population-scale effects playing a lesser role. Since 2011, the implementation of green technologies and low-carbon management strategies has effectively stabilized emission growth rates. (3) Improvements in energy carbon intensity and transportation energy intensity have significantly reduced carbon emissions. Moreover, the promotion of clean energy technologies and energy-saving measures has substantially lowered the industry’s carbon emission intensity.
A Review of the Biomass Valorization Hierarchy
Authors: Cansado Isabel Pestana da Paixão , Mourão Paulo Alexandre Mira , Castanheiro José Eduardo , Geraldo Pedro Francisco , Suhas  , Suero Silvia Román , Cano Beatriz Ledesma
Published: January 2025

The sustainability of the planet is based on reducing the use of fossil fuels and greenhouse gas emissions. The recovery of biomass waste puts economically valuable materials into circulation, which can successfully replace fossil fuels and which would otherwise be sent to landfills. Based on the review of several published works, we observe that the referenced processes to value biomass or biomass waste are not necessarily the most profitable and environmentally friendly. The most used methods to valorize biomass and biomass waste are mainly based on researchers knowledge and experience, neglecting some methods that are more appropriate or developing technologies. The valorization of biomass and biomass wastes should promote the production of products with the highest added value, and it must also be environmentally friendly and cost-effective. This manuscript proposes a hierarchy for the use of various valorization processes of biomass waste, from various agricultural activities, urban solids waste, food processing industries, and even wood industries. The proposed hierarchy is based on a number of recommendations aimed at increasing the use and valorization of biomass, in order to reach the objective of carbon neutrality and to comply with the principles of the circular economy.
Should Charging Stations Provide Service for Plug-In Hybrid Electric Vehicles During Holidays?
Authors: Zhang Tianhua , Li Xin , Zhang Yiwen , Shu Chenhui
Published: January 2025

The development of the new energy vehicle (NEV) market in China has promoted the sustainability of the automotive industry, but has also brought pressures to NEV charging infrastructure. This paper aims to determine the strategic role of charging stations, particularly on whether they should provide service for plug-in hybrid electric vehicles (PHEVs) in the highway service area during peak holidays. Firstly, the charging service resource allocation for a charging station that provides services for both electronic vehicles (EVs) and PHEVs is studied. Secondly, different queueing disciplines are compared. At last, a comparison between scenarios where charging services are limited to EVs and those where services extend to both EVs and PHEVs is conducted. A queueing system considering customer balking and reneging is developed. The impacts of parameters, such as the NEV arrival rate and patience degree of different NEV drivers, on the optimal allocation plan, profit, and comparison results are discussed. The main conclusions are as follows: (1) If the EV arrival rate is greater than the charging service rate, the charging station should not provide charging services for PHEVs. Providing service only for EVs derives more revenues and profits and results in a shorter waiting queue. Conversely, if the total arrival rate of NEVs (including EVs and PHEVs) is lower than the charging service rate, then the charging station should also serve PHEVs. (2) If providing service for PHEVs, a mixed queueing discipline should be applied when the total arrival rate approximates the service rate. When the total NEV arrival rate is significantly lower than the charging service rate, the separate queueing discipline should be adopted. (3) When applying a separate queueing discipline, if a certain type of NEV has a higher arrival rate and the drivers exhibit greater patience, then more charging resources should be allocated to this type of NEV. If the charging service is less busy, the more patient the drivers are, the less service resources should be allocated to them, whereas, during peak times, the more patient the drivers are, the more service resources should be allocated to them.