bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Microplastics from Face Masks on Physicochemical and Biological Properties of Agricultural Soil: Development of Soil Quality Index “SQI”
Authors: Patiño-Galván Honorio , Bedolla-Rivera Héctor Iván , Negrete-Rodríguez María de la Luz Xochilt , Herrera-Pérez Alejandra , Álvarez-Bernal Dioselina , Lastiri-Hernández Marcos Alfonso , Bernardino-Nicanor Aurea , González-Cruz Leopoldo , Conde-Barajas Eloy
Published: February 2025

Microplastics (MPs) are of great interest for study because they accumulate in environmental systems, such as terrestrial ones, which include agricultural soils. Unfortunately, in recent years, due to the COVID-19 pandemic, many face masks have been discarded in the environment, causing an increase in this type of MP. This accumulation can influence the physicochemical and biological properties of soil derived from its microbial activity. In the present study, different concentrations of MPs from commercial polypropylene (PP) face masks were mixed with agricultural soil. Four different treatments with the following MP concentrations were studied: T1: 0%, T2: 0.5%, T3: 1%, and T4: 5% w w−1. C and N mineralization experiments were carried out over time at the microcosm level, where physicochemical, enzymatic and ecophysiological indicators were analyzed. Based on the analyzed indicators, a soil quality index called the Unified Weighted Additive Index (SQIu) was developed. The resulting SQIu showed Nmin as the indicator with the closest relationship to the quality of the soils with MPs. Once the SQIu was determined, the T4 treatment showed significant differences (p≤0.05) with respect to the control, presenting a higher quality value. The importance of conducting more research on the dynamics of C and N in different soils with different types, sizes, and concentrations of MPs can help to establish strategies to measure the effects of MPs on soils at the local, national, and international levels.
Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
Authors: Sekuła Przemysław , Shayesteh Narjes , He Qinglian , Zahedian Sara , Moscoso Rodrigo , Cholewa Michał
Published: February 2025

This paper presents the results of the Move Over law compliance study. This study was carried out for The Federal Highway Administration in cooperation with ten State Highway agencies that provided the data (video recordings). This paper describes an outline of the system that was invented, developed, and applied to determine Move Over law compliance, as well as the initial analysis of the impact of various factors on compliance. In order to carry out the analysis, we processed 68 videos that contained over 33,000 vehicles. The median compliance with the Move Over law was 42.5% and varied heavily depending on diverse factors. This study makes two key contributions: first, it introduces an automated deep learning-based system that detects and evaluates Move Over law compliance by leveraging object detection and tracking technologies. Second, it presents a large-scale, multi-state compliance assessment, providing new empirical insights into driver behavior across various incident conditions. These findings offer a data-driven foundation for refining Move Over laws, enhancing public awareness efforts, and improving enforcement strategies.
Fast and Accurate Prediction of Corrosion Rate of Natural Gas Pipeline Using a Hybrid Machine Learning Approach
Authors: Liu Hongbo , Cai Xinlei , Meng Xiangzhao
Published: February 2025

The precise prediction of natural gas pipeline corrosion rates holds great significance for pipeline maintenance and corrosion control. Existing prediction methods, especially traditional models, often fail to adequately consider noise interference and the strong nonlinear characteristics of corrosion data, resulting in insufficient prediction accuracy. To enhance predictive performance, a hybrid prediction model based on machine learning is been proposed. This model consists of three main components: data processing, model optimization, and prediction performance evaluation. In this model, data decomposition algorithms and principal component analysis are employed to eliminate redundant noise from the original data and capture their primary features. A stratified sampling method is utilized to divide the data into a training set and test set, avoiding biases caused by random sampling. A modified particle swarm optimization algorithm is applied to optimize the parameters of a back propagation neural network. The model’s predictive performance is assessed using various indicators, including R2, MAPE, RMSE, MAE, U1, U2, RE, forecasting effectiveness, comparing the results with existing literature, Grey Relational Analysis, and interpretability research. The proposed prediction model is compared with eight advanced prediction models using data from a natural gas pipeline in western China. This study reveals that the developed model outperforms the others, demonstrating excellent prediction accuracy and effectively guiding the formulation of corrosion control measures.
Exploring the Efficacy of Methylated Gamma-Cyclodextrin (M-γ-CD) in the Removal of Heavy Metals in Soil Systems
Authors: Maphuhla Nontobeko Gloria , Oyedeji Opeoluwa Oyehan
Published: February 2025

Heavy-metal contamination of the environment is a serious worldwide issue, as it presents dangerous threats to both human health and aquatic ecosystems. This has led to a paradigm shift toward the development of simple, user-friendly, and economically viable remediation technologies that are essential for addressing heavy-metal soil pollution and for the global preservation of the environment. This review provides a comprehensive overview of environmental remediation strategies using cyclodextrin (CD) and its derivatives. Additionally, this study examines the effectiveness of methylated gamma-cyclodextrin (M-γ-CD) as a modified oligosaccharide for the elimination of toxic elements from impure soil matrices. M-γ-CD has emerged as a potent agent for treating soil impurities with noxious metals. As a modified form of cyclodextrin, M-γ-CD features hydrophobic cavities that are particularly adept at forming inclusion complexes with heavy-metal ions, thereby cumulating the aqueous solubility and efficiency of pollutants in environmental applications and improving soil bioremediation. This paper also reviews the unique structural configuration of M-γ-CD, which significantly enhances the solubility and mobility of cyclodextrins and facilitates the extraction of noxious metal particles such as Ni, Cu, and Pb from soil matrices. Furthermore, M-γ-CD is a promising soil remediation agent due to its capacity to boost contaminant solubility, improve environmental safety, offer cost-effectiveness, ensure adaptability, and minimize impact on soil parameters. Therefore, M-γ-CD is a desirable agent for the elimination of toxic metal impurities from soil.
Field Monitoring and Numerical Analysis of the Effect of Air Temperature and Water Load on the Static Behavior of a Tied-Arch Aqueduct
Authors: Lu Xiaobin , Li Yang , Li Xiulin , Li Meng
Published: February 2025

This study presents part of a pilot work for the structural health monitoring of a large tied-arch reinforced concrete aqueduct in eastern China. Based on field-monitored data for over a year, it mainly focuses on the effect of air temperature and water load variations on the static behavior of a typical span of the aqueduct through field monitoring and 3D FE model analysis. It is found that the longitudinal deformation of the composite tied-arch shows a good linear relationship with the air temperature during the non-operation period and also has a good bilinear correlation with the air temperature and water level during operation. However, isolation of the air temperature effect from the second bilinear correlation using the first linear relationship results in a poor correlation between the longitudinal deformation and water level due to the dominance of the temperature effect. Therefore, it is recommended to use the bilinear regression to predict the longitudinal deformation of the tied-arch during operation. The vertical deformation of the tied-arch is insignificantly affected by air temperature, whereas it shows a fair bilinear correlation with the air temperature and water level during operation, which can be used to provide a reasonable estimation of the vertical deformation of the tied-arch. The strain measurements of the tied-arch using vibrating-string gauges are more complicated due to the notable influence of the ambient temperature and solar radiation, but the relatively consistent bilinear regression of the strains versus the air temperature and water level can still give fair predictions for the strains of the bottom tension rods during operation. The 3D FE model can provide a fair estimation for the vertical deformation of the tied-arch under water load, but its predictions for longitudinal deformation and strains are less satisfactory when compared to monitored data excluding temperature effects.
Applicability of Virtual Commissioning Concepts in Industrial Robotics as a Solution for Compatibility Issues Between Virtual Simulation and Logic Control Software
Authors: Andrei Alexandru , Nicolescu Florin-Adrian , Pupăză Cristina , Coman Cezara-Georgia , Ghionea Ionuț Gabriel
Published: February 2025

This article presents the results of applied research focused on the development of a hybrid software solution, which is based on the ABB RobotStudio 2021 suite, because even in the latest version, RobotStudio 10 does not have a dedicated virtual commissioning module. The aim of the study is to explore the potential communication capabilities between ABB RobotStudio and Siemens Simatic Robot Integrator and to unify control systems for performing virtual commissioning tasks. The proposed solution offers valuable procedures for any end user responsible for implementing robotic cell virtual commissioning tasks and ensures the compatibility between hardware and software components from ABB and Siemens manufacturers integrated into any robotic cell type. Siemens, one of the leading PLC manufacturers, offers the complex PLM platform Process Simulate, which can be integrated with the Totally Integrated Automation (TIA) Portal and Mechatronics Concept Designer (MCD) for developing virtual commissioning (VC) applications. The TIA Portal is recognized by robotics application integrators as the most efficient work environment for VC. However, there is currently no direct compatibility between the ABB RobotStudio (RS) platform and Siemens TIA Portal. To address integration challenges, Siemens has introduced the Simatic Robot Integrator (SRI) platform, which ensures compatibility with certain proprietary software from various robot manufacturers, including KUKA, Motoman, and Staubli. While Siemens has collaborated with manufacturers like KUKA and Kawasaki, major industry manufacturers such as ABB and Fanuc have yet to establish a standardized interface within the Siemens SRI platform. A mathematical model was also developed, which serves as a key perspective and a valuable tool for the future integration of double-portal-type robots into the working environments of ABB and Siemens. This integration aims to facilitate virtual commissioning in scenarios where such robots need to be implemented. Currently, ABB’s library does not include double-portal-type robots, despite the fact that leading manufacturers of double-portal robot types utilize ABB’s IRC5 controllers.
Detection of Chatter in Machining Processes by the Multiscale Maximum Approximate Entropy and Continuous Wavelet Transform
Authors: Pérez-Canales Daniel , Jáuregui-Correa Juan Carlos , Álvarez-Ramírez José , Vela-Martínez Luciano
Published: February 2025

Chatter is a complex dynamic instability in machining processes and presents nonlinear and nonstationary behavior. Detection of this phenomenon before a catastrophic failure occurs has great importance in the industry today. This behavior demands online monitoring signal-processing techniques suitable for facing these kinds of dynamics such as approximate entropy (AE) and wavelet transform. Moreover, AE is useful for dealing with noisy signals and requires a relatively small amount of observations. In this study, we propose an improved AE methodology, the multiscale maximum approximate entropy (MMAE), to detect chatter in milling processes. The maximum AE is achieved by the calculation of the parameter r proposed by Sheng and Chon. In the past, the calculation of this parameter was a drawback of the AE technique. The results show the effectiveness of this proposed technique in detecting clearly different gradual and drastic changes in chatter conditions. Moreover, a more known technique is presented: the time–frequency maps provided by continuous wavelet transform (CWT). The results also show the efficacy of this technique in detecting different levels of chatter. The results are corroborated by the machining piece observation of the chatter phenomenon. MMAE is also compared with sample entropy (SE) and the Hurst exponent obtained by the R/S analysis. At the end, a comparison analysis of the mentioned techniques is carried out, showing that they all have advantages and disadvantages. However, the disadvantages of MMAE and CWT can be solved, as mentioned in the comparison section. Thus, the conclusion is that MMAE and CWT techniques are optimal for the online monitoring of chatter in machining processes.
Assessing the Causal Relationship Between Plasma Proteins and Pulmonary Fibrosis: A Systematic Analysis Based on Mendelian Randomization
Authors: Han Moxuan , Cui Yan , Fang Zhengyuan , Li He , Wang Yueqi , Sima Mingwei , Bi Yan , Yue Donghui
Published: February 2025

Pulmonary fibrosis (PF) is a chronic interstitial lung disease characterized by the destruction of alveolar structures, the abnormal accumulation of extracellular matrix (ECM), and ultimately respiratory failure. Although previous studies have shown that plasma proteins play an important role in the onset and progression of PF, there is currently a lack of systematic studies on causal relationships. To address the identified gap, the study employs the MR method to identify potential drug targets associated with PF. Plasma protein data (pQTL, exposure) were sourced from Ferkingstad et al. (n = 35,559), and PF-related summary statistics were obtained from the GWAS database (n = 469,126). The study integrates enrichment analysis, protein–protein interaction (PPI) networks, drug prediction, molecular docking, and single-cell sequencing to further evaluate the biological functions and pharmacological potential of the identified targets. In the MR analysis, 64 genetic loci were significantly associated with the occurrence of PF. Further reverse Mendelian analysis revealed a positive causal relationship between PF and genes such as NPTX1, IL31, and CTSE, suggesting that these proteins may play a promotive role in the onset and progression of pulmonary fibrosis. The PPI network analysis identified core genes such as CDH1, CRP, VTN, COL1A1, and MAPK8, which are involved in the key pathological processes of PF, including cell signaling, ECM remodeling, and immune responses. The drug prediction analysis identified potential drugs such as sorafenib, vitamin C, and vitamin E, and the molecular docking analysis showed good binding between the drugs and the proteins. The single-cell sequencing results showed that core genes were highly expressed in fibroblasts and alveolar type II cells, confirming their potential role in the pathogenesis of PF. This study successfully identifies 64 potential drug targets for PF, with 10 core targets considered particularly promising for clinical trials. These findings offer valuable insights into the molecular mechanisms underlying PF and open new avenues for the development of targeted therapies. This research may accelerate the development of effective PF treatments and reduce drug development costs by providing more precise and personalized approaches to managing the disease.
A Novel Quantification Method for Gene-Edited Animal Detection Based on ddPCR
Authors: Wang Kaili , Ji Yi , Peng Cheng , Wang Xiaofu , Yang Lei , Lan Hangzhen , Xu Junfeng , Chen Xiaoyun
Published: February 2025

As gene-editing technologies continue to evolve, gene-edited products are making significant strides. These products have already been commercialized in the United States and Japan, prompting global attention to their safety and regulatory oversight. However, the detection of gene editing still relies on qPCR, and there is a lack of quantitative detection methods to quantify gene-editing components in products. To ensure consumer safety and transparency, we developed a novel droplet digital PCR (ddPCR)-based detection method for gene-edited products. Primers and probes were designed targeting the editing sites of MSTN-edited cattle, and the method was evaluated for specificity, sensitivity, real sample testing, and detection thresholds. Our results demonstrate that this ddPCR method is highly specific, with a detection limit of 5 copies/µL, and it successfully detected MSTN edits in all 11 tested samples. Tests using both actual gene-edited cattle samples and plasmid DNA at concentrations of 5%, 1%, and 0.01% yielded consistent results, indicating the method’s suitability for real-world applications. This ddPCR assay provides a sensitive and specific tool for detecting MSTN gene-edited cattle and determining the presence of gene-edited products, offering crucial support for regulatory monitoring of gene-edited animal-derived foods.
Sequences in the Cytoplasmic Tail Contribute to the Intracellular Trafficking and the Cell Surface Localization of SARS-CoV-2 Spike Protein
Authors: Burkova Evgeniya E. , Bakhno Irina A.
Published: February 2025

Spike protein is a surface glycoprotein of the SARS-CoV-2 coronavirus, providing interaction of the coronavirus with angiotensin-converting enzyme 2 (ACE2) on the host cell. The cytoplasmic tail of the S protein plays an important role in an intracellular transport and translocation of the glycoprotein to the plasma membrane. The cytoplasmic domain of the S protein contains binding sites for COPI, COPII, and SNX27, which are required for the intracellular trafficking of this glycoprotein. In addition, the cytoplasmic domain of the S protein contains S-palmitoylation sites. S-palmitoylation increases the hydrophobicity of the S protein by regulating its transport to the plasma membrane. The cytoplasmic tail of the S protein has a signaling sequence that provides interaction with the ERM family proteins, which may mediate communication between the cell membrane and the actin cytoskeleton. This review examines the role of the cytoplasmic tail of the SARS-CoV-2 S protein in its intracellular transport and translocation to the plasma membrane. Understanding these processes is necessary not only for the development of vaccines based on mRNA or adenovirus vectors encoding the full-length spike (S) protein, but also for the therapy of the new coronavirus infection (COVID-19).
HDGF Knockout Suppresses Colorectal Cancer Progression and Drug Resistance by Modulating the DNA Damage Response
Authors: Su Riya , Wang Qin , Hu Qun , Wendurige  , Li Kexin , Wang Changshan , Tao Liang
Published: February 2025

Colorectal cancer (CRC) is a highly heterogeneous gastrointestinal malignancy. Despite significant advances in molecular targeted therapies for CRC in recent years, the increase in the overall survival rates for CRC patients remains limited. Therefore, there is an urgent need to explore novel drug targets. Herein, we show that heparin binding growth factor (HDGF) is highly expressed in CRC, and that its overexpression is associated with a poor disease-free interval. Additionally, we reveal that HDGF knockout reduces proliferation, migration, and invasion, while enhancing apoptosis in CRC cells, thereby validating HDGF as a potential therapeutic target for CRC. Mechanistically, we found that HDGF modulates DNA damage response and, by recruiting C-terminal binding protein-interacting protein (CtIP), it facilitates homologous recombination repair to influence CRC drug sensitivity. Furthermore, we propose that HDGF may serve as a recognition protein for H3K36me3, participating in the repair of damaged transcriptionally active genes, thus maintaining genomic stability in CRC.
Transcriptome Analysis Reveals the Role of OsCBM1 in Rice Defense Against Xanthomonas oryzae pv.oryzae
Authors: Bie Shuaijun , Xiao Youlun , Zhang Li , Liu Yong , He Xiaomin , Peng Jing , Xie Hongjun , Gao Yang , Li Xiaojuan , Tan Xinqiu , Huang Renyan , Zhang Deyong
Published: February 2025

Carbohydrate-binding malectin/malectin-like domain-containing proteins (CBMs) represent a newly discovered subclass of lectins that participate in various biological processes across the bacterial, animal, and plant kingdoms. The OsCBM1 gene in rice enhances reactive oxygen species (ROS) burst, contributing to drought-stress tolerance. Nonetheless, the functions of OsCBM1 in response to biotic stress remain poorly understood. In this research, we discovered that OsCBM1 was activated by Xoo infection, and overexpression of OsCBM1 increased rice resistance to bacterial blight, while suppression of its expression shows the opposite trend. OsCBM1 may influence resistance to bacterial blight by regulating ROS burst and the SA signaling pathway through RNA-seq analysis. Overexpression of OsCBM1 increased SA content and enhanced activities of SOD, POD, and CAT enzymes, whereas knockdown of OsCBM1 exhibited the opposite trend. The expression of genes associated with the SA and enzyme activity pathways was validated through quantitative real-time polymerase chain reaction (qRT-PCR). These results further clarify the function of OsCBM1 in biotic stress resistance, providing references for disease-resistant rice breeding.
High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis
Authors: Zhao Zihe , Meng Hongbei , Li Shangru , Wang Shengbo , Wang Jiaqi , Gao Shuo
Published: February 2025

An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent further deterioration, such as amblyopia and even permanent vision loss. To satisfy this need, this work showcases a novel strabismus screening method based on a wearable eye-tracking device combined with an artificial intelligence (AI) algorithm. To identify the minor and occasional inconsistencies in strabismus patients during the binocular coordination process, which are usually seen in early-stage patients and rarely recognized in current studies, the system captures temporally and spatially continuous high-definition infrared images of the eye during wide-angle continuous motion, and is effective in inducing intermittent strabismus. Based on the collected eye motion information, 16 features of the oculomotor process with strong physiological interpretations, which help biomedical staff understand and evaluate results generated later, are calculated through the introduction of pupil-canthus vectors. These features can be normalized, and reflect individual differences. After these features are processed by the random forest (RF) algorithm, this method experimentally yields 97.1% accuracy in strabismus detection in 70 people under diverse indoor testing conditions, validating the high accuracy and robustness of the method, and implying that the method has strong potential to support widespread and highly accurate strabismus screening.
Parallel Detection of the Unamplified Carbapenem Resistance Genes blaNDM-1 and blaOXA-1 Using a Plasmonic Nano-Biosensor with a Field-Portable DNA Extraction Method
Authors: Kao Kaily , Alocilja Evangelyn C.
Published: February 2025

Antimicrobial resistance (AMR) is a rapidly growing global concern resulting from the overuse of antibiotics in agricultural and clinical settings. The challenge is exacerbated by the lack of rapid surveillance for resistant bacteria in clinical, environmental, and food supply settings. The increasing resistance to carbapenems, an important sub-class of beta-lactam antibiotics, is a major concern in the healthcare community. Carbapenem resistance (CR) has been found in the environment and food supply chain, where it has the potential to spread to pathogens, animals, and humans through direct or indirect contact. Rapid detection for preventative and control measures should be developed. This study utilized a gold nanoparticle-based plasmonic biosensor for the parallel detection of the CR genes blaNDM-1 and blaOXA-1. To explore the field portability, DNA was extracted using two methods: a commercial extraction kit and a boiling method. The results were compared between the two methods using a spectrophotometer and a cellphone application for RGB values to quantify the visual results. The results showed that the boiling method of extraction was more effective than extraction with a commercial kit for this analysis. The parallel detection of unamplified genes extracted via the boiling method is novel. When combined with other portable testing equipment, the approach has the potential to be an inexpensive, rapid, and simple on-site CR gene detection protocol.
Neural Correlates of Growth Mindset: A Scoping Review of Brain-Based Evidence
Authors: Zeng Hang
Published: February 2025

Growth mindset, which asserts that intelligence and abilities can be cultivated through effort and learning, has garnered substantial attention in psychological and educational research. While the psychological and behavioral impacts of growth mindset are well-established, the underlying neural mechanisms remain relatively underexplored. Furthermore, there is a lack of comprehensive reviews synthesizing the neural evidence on growth mindset, hindering a fuller understanding of this concept. This scoping review aims to synthesize existing empirical studies on the neural mechanisms of growth mindset, focusing on research objectives, methods, and participant characteristics. A total of 15 studies were reviewed, revealing six primary research objectives: (1) neural mechanisms of error and feedback processing, (2) domain-specific mindsets, (3) neural changes resulting from mindset interventions, (4) mindsets and grit, (5) the neuroanatomy of mindsets, and (6) neural mechanisms of stereotype violation, with error and feedback processing being the most frequently investigated. Ten of the 15 studies employed EEG, while other techniques included structural MRI, task-based fMRI, and resting-state fMRI, with the majority of research focusing on adult populations. Although the existing literature offers valuable insights, further research is needed to explore additional aspects of mindsets, particularly in children, and to refine the methodologies used to investigate the neural mechanisms underlying growth mindset.
Highly Effective Asymmetric Henry Reaction Catalyzed by Chiral Complex of Cu (II)-Aziridine-Functionalized Organophosphorus Compounds
Authors: Rachwalski Michał , Wojtaszek Julia , Szymańska Julia , Pieczonka Adam M.
Published: February 2025

A synthesis of organophosphorus compounds containing an aziridine ring, previously described by our group, has been performed and the catalytic activity of the aforementioned chiral heterorganic compounds has been investigated in the asymmetric nitroaldol (Henry) reaction between aromatic/aliphatic aldehydes and nitromethane in the presence of catalytic amounts of copper (II) acetate. In several cases, the chiral β-nitroalcohols have been obtained with high chemical yields and exhibited very high enantiomeric excess values (over 95%). Notably, the use of two enantiomerically pure catalysts, differing in the absolute configuration of the aziridine unit, resulted in the formation of two enantiomeric products of the Henry reaction.
Decontamination-Sludge-Induced Corrosion of Steel in Simulated Pore Solutions and Alkali-Activated Slag Pastes
Authors: Criado María , Torres Elena , Hinojosa-Platero Jaime , Pachón-Montaño Alicia
Published: February 2025

In most countries, low- and intermediate-level wastes (LILWs) are cemented in carbon steel drums for later disposal. The durability of waste packages is determined by the chemical environment generated by both cement-based engineered barrier systems and the aggressive species present in the waste. Decontamination sludges are challenging wastes that are currently not accepted for final disposal due to their acidic nature and high concentrations of organic species and complexants. Thus, it was proposed to use electrochemical measurements to study the corrosion of steel sheets, simulating drums embedded in new alkali-activated slag (AAS) formulations with surrogate decontamination liquids, and determine their viability for use as confining matrices in order to increase the service life of the drums. The carbon steel coupon embedded in the Portland cement reference (R-L) paste showed the best corrosion resistance, followed by that of steel embedded in sodium silicate-activated slag (BFS-S-L) paste. This behaviour may be related to an improvement in the protective nature of the surface film. However, in sodium carbonate-activated slag (BFS-C-L) paste, the effect of the sludge in the matrix seemed to be more intense, leading to a pH decrease in the paste porewater, an effect that could hinder the formation of a passive layer on the surface of the carbon steel. Under such conditions, the initiation of the corrosion process seems to be favoured, resulting in the formation of a non-protective scale consisting mainly of hematite.
P-Doped Metal–Organic Framework (MOF)-Derived Co3O4 Nanowire Arrays Supported on Nickle Foam: An Efficient Urea Electro-Oxidation Catalyst
Authors: Liu Yong , Ma Junqing , Pei Yifei , Han Xinyue , Ren Xinyuan , Liang Yanfang , Li Can , Liang Tingting , Wang Fang , Liu Xianming
Published: February 2025

The urea electro-oxidation reaction (UOR) is emerging as a new energy conversion technology and a promising method for alleviating water eutrophication problems. However, a rationally designed structure of the electrode materials is urgently required to achieve high UOR performance. Herein, P-doped MOF-derived Co3O4 nanowire arrays grown on nickel foam (P-Co3O4/NF) are successfully synthesized via the growth of Co-MOF and subsequent calcination followed by phosphorization treatment. Owing to the optimized electronic structure, the as-prepared P-Co3O4/NF composite exhibits much higher UOR electrocatalytic performance than the undoped Co3O4/NF sample. Beyond this, the meticulous structure of the one-dimensional nanowire arrays and the three-dimensional skeleton structure of nickel foam contribute to the enhanced electrocatalytic activity and stability toward UOR. As a result, the P-Co3O4/NF composite displays a low overpotential of 1.419 V vs. RHE at 50 mA cm−2, a small Tafel slope of 82 mV dec−1, as well as favorable long-term stability over 65,000 s in 1.0 M KOH with 1.0 M urea. This work opens a new avenue in designing non-precious electrocatalysts for high-performance urea electro-oxidation reactions.
Brain Morphometry and Cognitive Features in the Prediction of Irritable Bowel Syndrome
Authors: Lundervold Arvid , Bjørsvik  Ben René , Billing  Julie , Berentsen  Birgitte , Lied  Gülen Arslan , Steinsvik  Elisabeth K. , Hausken  Trygve , Pfabigan  Daniela M. , Lundervold  Astri J.
Published: February 2025

Background/Objectives: Irritable bowel syndrome (IBS) is a gut–brain disorder characterized by abdominal pain, altered bowel habits, and psychological distress. While brain–gut interactions are recognized in IBS pathophysiology, the relationship between brain morphometry, cognitive function, and clinical features remains poorly understood. The study aims to conduct the following: (i) to replicate previous univariate morphometric findings in IBS patients and conduct software comparisons; (ii) to investigate whether multivariate analysis of brain morphometric measures and cognitive performance can distinguish IBS patients from healthy controls (HCs), and evaluate the importance of structural and cognitive features in this discrimination. Methods: We studied 49 IBS patients and 29 HCs using structural brain magnetic resonance images (MRIs) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Brain morphometry was analyzed using FreeSurfer v6.0.1 and v7.4.1, with IBS severity assessed via the IBS-Severity Scoring System. We employed univariate, multivariate, and machine learning approaches with cross-validation. Results: The FreeSurfer version comparison revealed substantial variations in morphometric measurements, while morphometric measures alone showed limited discrimination between groups; combining morphometric and cognitive measures achieved 93% sensitivity in identifying IBS patients (22% specificity). The feature importance analysis highlighted the role of subcortical structures (the hippocampus, caudate, and putamen) and cognitive domains (recall and verbal skills) in group discrimination. Conclusions: Our comprehensive open-source framework suggests that combining brain morphometry and cognitive measures improves IBS-HC discrimination compared to morphometric measures alone. The importance of subcortical structures and specific cognitive domains supports complex brain–gut interaction in IBS, emphasizing the need for multimodal approaches and rigorous methodological considerations.
How Standard of Truth Methodology Impacts Diagnostic PSMA-Targeting Radiopharmaceutical Evaluation: Learnings from the Phase 3 SPOTLIGHT Study
Authors: Lowentritt Benjamin H. , Chau Albert , Davis Phillip
Published: February 2025

Objectives: To explore the impact of different standard of truth (SoT) methodologies on efficacy endpoints traditionally used in clinical trials of diagnostic radiopharmaceuticals, using data from the SPOTLIGHT study (NCT04186845) in patients with recurrent prostate cancer. Methods: Data from patients with baseline prostate-specific antigen (PSA) ≤ 5 ng/mL, who underwent 18F-flotufolastat imaging and had data for SoT determination, were reviewed. Majority-read patient level endpoints (verified detection rate [VDR] and patient-level positive predictive value [PPV]), and region-level PPV (in the prostate/prostate bed, pelvic lymph nodes, and extrapelvic sites) according to on-study reads by three blinded readers, were stratified by the SoT methodology (histopathology; post-PET confirmatory imaging; baseline/historic conventional imaging) used by the independent Truth Panel to verify 18F-flotufolastat-avid lesions. Differences between SoT groups for each endpoint were compared using a chi-square test (statistically significant if p < 0.0167). Results: Our analysis included 297 patients (median baseline PSA = 0.8 ng/mL): 56% (n = 166) had post-PET confirmatory imaging, 26% (n = 78) had baseline/historic conventional imaging, and 18% (n = 53) had histopathological confirmation of ≥1 PET-positive lesion. For all endpoints assessed, the highest majority-read values were achieved with histopathology SoT. For histopathology versus baseline/historic conventional imaging, VDR (77%) was 3.6-fold higher (p < 0.0001), patient-level PPV (79%) was 2.2-fold higher (p < 0.0001), and region-level PPV (50%) was 3.7-fold higher in the prostate/prostate bed (p = 0.009); smaller increases were seen in majority-read PPV in the pelvic lymph nodes (77%; 1.5-fold) and other sites (75%; 1.3-fold), but these were not of statistical significance. Conclusions: These data illustrate how SoT methods can substantially impact efficacy endpoints traditionally used in clinical trials of diagnostic radiopharmaceuticals. Notably lower endpoint values are achieved with imaging SoT than with histopathology.
Safety Systems for Emergency Landing of Civilian Unmanned Aerial Vehicles (UAVs)—A Comprehensive Review
Authors: Farajijalal Mohsen , Eslamiat Hossein , Avineni Vikrant , Hettel Eric , Lindsay Clark
Published: February 2025

The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for the emergency landing of civilian UAVs. It covers a wide range of recovery methods, categorizing them based on different recovery approaches and UAV types, including multirotor and fixed-wing. The study highlights the diversity of recovery strategies, ranging from parachute and airbag systems to software-based methods and hybrid solutions. It emphasizes the importance of considering UAV-specific characteristics and operational environments when selecting appropriate safety systems. Furthermore, by comparing various emergency landing systems, this study reveals that integrating multiple approaches based on the UAV type and mission requirements can achieve broader cover of emergency situations compared to using a single system for a specific scenario. Examples of UAVs that utilize emergency landing systems are also provided. For each recovery system, three key parameters of operating altitude, flight speed and added weight are presented. Researchers and UAV developers can utilize this information to identify a suitable emergency landing method tailored to their mission requirements and available UAVs. Based on the key trends and challenges found in the literature, this review concludes by proposing specific, actionable recommendations. These recommendations are directed towards researchers, UAV developers, and regulatory bodies, and focus on enhancing the safety of civilian UAV operations through the improvement of emergency landing systems.
GRU-Based Deep Learning Framework for Real-Time, Accurate, and Scalable UAV Trajectory Prediction
Authors: Yoon Seungwon , Jang Dahyun , Yoon Hyewon , Park Taewon , Lee Kyuchul
Published: February 2025

Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations.
Three-Phase Confusion Learning
Authors: Caleca Filippo , Tibaldi Simone , Ercolessi Elisa
Published: February 2025

The use of Neural Networks in quantum many-body theory has undergone a formidable rise in recent years. Among the many possible applications, their pattern recognition power can be utilized when dealing with the study of equilibrium phase diagrams. Learning by Confusion has emerged as an interesting and unbiased scheme within this context. This technique involves systematically reassigning labels to the data in various ways, followed by training and testing the Neural Network. While random labeling results in low accuracy, the method reveals a peak in accuracy when the data are correctly and meaningfully partitioned, even if the correct labeling is initially unknown. Here, we propose a generalization of this confusion scheme for systems with more than two phases, for which it was originally proposed. Our construction relies on the use of a slightly different Neural Network: from a binary classifier, we move to a ternary one, which is more suitable to detect systems exhibiting three phases. After introducing this construction, we test it on free and interacting Kitaev chains and on the one-dimensional Extended Hubbard model, consistently achieving results that are compatible with previous works. Our work opens the way to wider use of Learning by Confusion, demonstrating once more the usefulness of Machine Learning to address quantum many-body problems.
Maximum Entropy-Minimum Residual Model: An Optimum Solution to Comprehensive Evaluation and Multiple Attribute Decision Making
Authors: Tang Qi-Yi , Lin Yu-Xuan
Published: February 2025

To assess a subject with multiple factors or attributes, a comprehensive evaluation index, or say a composite indicator, is often constructed to make a holistic judgement. The key problem is to assign weights to the factors. There are various weighting methods in the literature, but a gold standard is lacking. Some weighting methods may lead to a trivial weight assignment that is one factor having a weight equal to 1 and the others all zero, while some methods generate a solution contradicting intuitive judgement, or even infeasible to calculate. This paper proposes a new model to generate weights based on the maximum entropy-minimum residual (MEMR) principle, directly estimating the relationship between factor weights and the composite indicator. The MEMR composite indicator extracts the common feature of multiple factors while preserving their diversity. This paper compares the MEMR model with other commonly used weighting methods in various case studies. The MEMR model has more robust, consistent, and interpretable results than others and is suitable for all comprehensive evaluation cases involving quantitative factors. The optimization technique of the proposed MEMR model and the related statistical tests are included as a package in the DPS (data processing system) software V21.05 for the convenience of application in all fields.
The Impact of Sociomaterials on Architectural Learning Processes in Virtual and Physical Design Studios
Authors: Pelman Barak , Zoran Amit Raphael
Published: February 2025

Since architectural education has been integrated into academic campuses, the design studio has become its most prominent pedagogical approach. However, in the last three decades, advances in computer-aided design (CAD) and online communication led to the development of virtual design studio (VDS) formats, which gained prominence during the COVID-19 pandemic. VDS and physical design studio (PDS) are characterized by different sociomaterial environments, each offering unique learning opportunities. This study examines how these environments influence learning processes, analyzing two desk critique sessions—one conducted in a VDS and the other in a PDS. Our data, comprising video recordings and on-site observations, were analyzed and interpreted through a sociomaterial lens. The findings indicate that PDS facilitates more spontaneous interactions, allowing for the communication of complex ideas and better addressing misunderstandings compared to VDS, which is constrained by the limitations of digital communication platforms. This research provides both theoretical and pedagogical contributions. Theoretically, it demonstrates how architectural concepts emerge through sociomaterial interactions, framing architectural learning as material practice. In addition, it illustrates the role of sociomaterials in communicating complex ideas and shaping collaborative learning processes. Pedagogically, the findings emphasize the importance of creating rich sociomaterial environments that effectively support intended learning processes.
Proximal Policy Optimization-Based Hierarchical Decision-Making Mechanism for Resource Allocation Optimization in UAV Networks
Authors: Sun Kun , Yang Jianyong , Li Jinglei , Yang Bo , Ding Shuman
Published: February 2025

To address the resource allocation problem in dynamic environments where multiple unmanned aerial vehicle base stations (UAV-BSs) provide efficient downlink services to ground users, this paper proposes a novel hierarchical decision-making mechanism based on the Proximal Policy Optimization (PPO) algorithm. The proposed method optimizes time-frequency resource allocation in the downlink, aiming to maximize the total user throughput over multiple time slots. By constructing channel and interference models, the complex multi-channel resource allocation problem is decomposed into a series of single-channel decision subproblems, significantly reducing the action space complexity. Specifically, the original exponential complexity O(NM) (where N is the number of users and M is the number of channels) is reduced to a linear complexity O(N), effectively alleviating the curse of dimensionality. Simulation results demonstrate that the proposed hierarchical architecture, integrated with the PPO algorithm, achieves superior performance in terms of total throughput, convergence speed, and stability compared to existing methods. This study provides new insights and technical support for efficient resource management in UAV-BS systems operating in complex and dynamic environments.
Explainable Intelligent Inspection of Solar Photovoltaic Systems with Deep Transfer Learning: Considering Warmer Weather Effects Using Aerial Radiometric Infrared Thermography
Authors: Qureshi Usamah Rashid , Rashid Aiman , Altini Nicola , Bevilacqua Vitoantonio , La Scala Massimo
Published: February 2025

Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can compromise the performance and resilience of SPV panels through thermal deterioration and degradation, which may lead to lessened operational life and potential failure. These heatwave-related consequences highlight the need for timely inspection and precise anomaly diagnosis of SPV panels to ensure optimal energy production. This case study focuses on intelligent remote inspection by employing aerial radiometric infrared thermography within a predictive maintenance framework to enhance diagnostic monitoring and early scrutiny capabilities for SPV power plant sites. The proposed methodology leverages pre-trained deep learning (DL) algorithms, enabling a deep transfer learning approach, to test the effectiveness of multiclass classification (or diagnosis) of various thermal anomalies of the SPV panel. This case study adopted a highly imbalanced 6-class thermographic radiometric dataset (floating-point temperature numerical values in degrees Celsius) for training and validating the pre-trained DL predictive classification models and comparing them with a customized convolutional neural network (CNN) ensembled model. The performance metrics demonstrate that among selected pre-trained DL models, the MobileNetV2 exhibits the highest F1 score (0.998) and accuracy (0.998), followed by InceptionV3 and VGG16, which recorded an F1 score of 0.997 and an accuracy of 0.998 in performing the smart inspection of 6-class thermal anomalies, whereas the customized CNN ensembled model achieved both a perfect F1 score (1.000) and accuracy (1.000). Furthermore, to create trust in the intelligent inspection system, we investigated the pre-trained DL predictive classification models using perceptive explainability to display the most discriminative data features, and mathematical-structure-based interpretability to portray multiclass feature clustering.