bluuebunny/crossref_metadata_2025 huggingface dataset:
Multipotent Effect of Clozapine on Lipopolysaccharide-Induced Acetylcholinesterase, Cyclooxygenase-2,5-Lipoxygenase, and Caspase-3: In Vivo and Molecular Modeling Studies
Authors: Arfeen Minhajul , Dhaked Devendra Kumar , Mani Vasudevan
Published: January 2025

Dual inhibition of cyclooxygenase-2 (COX-2) and lipoxygenase (LOX) is a recognized strategy for enhanced anti-inflammatory effects in small molecules, offering potential therapeutic benefits for individuals at risk of dementia, particularly those with neurodegenerative diseases, common cancers, and diabetes type. Alzheimer’s disease (AD) is the most common cause of dementia, and the inhibition of acetylcholinesterase (AChE) is a key approach in treating AD. Meanwhile, Caspase-3 catalyzes early events in apoptosis, contributing to neurodegeneration and subsequently AD. Structure-based virtual screening of US-FDA-approved molecules from the ZINC15 database identified clozapine (CLOZ) as the dual inhibitor of COX-2 and AChE, with significant binding affinity. Further molecular docking of CLOZ in the active site of LOX and Caspase-3 also showed significant binding potential. Further, the results from molecular docking were validated using molecular dynamics simulation (MDS) studies, confirming the results from molecular docking. The results from MDS showed good binding potential and interactions with key residues. The CLOZ was further assessed using lipopolysaccharide (LPS)-challenged rats treated for thirty days at doses of 5 and 10 mg/kg, p.o. The results demonstrated modulation of COX-2, 5-LOX, AChE, Caspase-3, and MDA in LPS-induced brains. Additionally, the expression level of IL-10 was also measured. Our results showed a significant decrease in the levels of COX-2, 5-LOX, AChE, Caspase-3, and MDA. Our results also showed a significant decrement in the pro-inflammatory markers NF-κB, TNF-α, and IL-6 and an improvement in the levels of anti-inflammatory markers IL-10 and TGF-β1. Overall, the findings indicate that CLOZ has potential for neuroprotective effects against LPS-treated rats and can be explored.
Novel Intravenous Nanoemulsions Based on Cannabidiol-Enriched Hemp Oil—Development and Validation of an HPLC-DAD Method for Cannabidiol Determination
Authors: Sobczak Agnieszka , Zieliński Piotr , Jelińska Anna , Gostyńska-Stawna Aleksandra
Published: January 2025

Background: Intravenous nanoemulsions (NEs) are gaining attention as potential delivery systems for poorly water-soluble substances like cannabidiol (CBD). This study aimed to develop novel NEs based on CBD-enriched hemp oils and evaluate their physiochemical properties. Methods: The stability of hemp oils enriched with various concentrations of CBD (0.5%, 1.0%, and 1.5%) with and without the addition of α-tocopherol was determined, and the most stable oils were subsequently incorporated into NEs. In order to determine the CBD content in the obtained CBD-enriched oils and NEs, as well as to conduct stability tests, a new HPLC method was developed and validated. Results: The HPLC method demonstrated very good linearity, precision, accuracy, specificity, and robustness, enabling reliable assessment of the quality of newly developed formulations. The formulated NEs were characterized by droplet size of below 200 nm and polydispersity index PDI ≤ 0.14 satisfactory for intravenous application. Conclusion: This research presents a preliminary study on the development of CBD-enriched hemp oil-based NEs that showed promising potential for further investigation. A new HPLC-DAD method was appropriate to register changes in CBD concentration in various matrices, including CBD-hemp oil and intravenous NEs during their preparation and storage. Additionally, the effect of certain emulsifiers used in NE formulations on the course of the chromatographic analysis of CBD was examined, providing valuable insights concerning the application of the provided methodology in future formulation analysis.
Exploring Natural Deep Eutectic Solvents (NADES) for Enhanced Essential Oil Extraction: Current Insights and Applications
Authors: Acosta-Vega Luis , Cifuentes Alejandro , Ibáñez Elena , Galeano Garcia Paula
Published: January 2025

Essential oils (EOs) are highly valued in the cosmetic and food industries for their diverse properties. However, traditional extraction methods often result in low yields, inconsistent compositions, lengthy extraction times, and the use of potentially harmful solvents. Natural deep eutectic solvents (NADES) have emerged as promising alternatives, offering advantages such as higher efficiency, cost-effectiveness, biodegradability, and tunable properties. This review explores the application of NADES in enhancing EO extraction, focusing on current methodologies, key insights, and practical applications. It examines the factors that influence EO extraction with NADES, including the optimization of their physicochemical properties, extraction techniques, operational conditions, and the role of sample pretreatment in improving efficiency. Additionally, this review covers the chemical characterization and biological activities of EOs extracted using NADES. By providing a comprehensive overview, it highlights the potential of NADES to improve EO extraction and suggests directions for future research in this field.
The Anticancer Perspective of Tangeretin: A Small Review
Authors: Xu Yuan , Yan Xi , Zhuang Junpeng , Hao Haijun
Published: January 2025

Cancer is an important disease that threatens human life and health. Many natural compounds from plants have been found to have a better inhibitory effect on cancer, and flavonoids are one of them. Tangeretin, a flavonoid, is widely present in a variety of citrus plants and has been shown to have a variety of biological activities that can inhibit tumor cells. Tangeretin can inhibit the growth, proliferation, and metastasis of cancer cells by acting on JAK/STAT (Janus Kinase/signal transducer and activator of transcription) and caspase-3 signal transduction and by regulating the cell cycle of tumor cells. Tangeretin can also work with other chemotherapy drugs, such as cisplatin, to reduce the drug resistance of cancer cells and improve the therapeutic effect of chemotherapy drugs. This review summarizes the effects of tangeretin on various cancers.
A Comprehensive Review of Niobium Nanoparticles: Synthesis, Characterization, Applications in Health Sciences, and Future Challenges
Authors: Khalid Muhammad Usman , Rudokaite Austeja , da Silva Alessandro Marcio Hakme , Kirsnyte-Snioke Monika , Stirke Arunas , Melo Wanessa C. M. A.
Published: January 2025

Niobium nanoparticles (NbNPs) have gained attention as promising materials in biomedical applications due to their exceptional biocompatibility, corrosion resistance, and versatility. These nanoparticles offer potential in drug delivery, imaging, and tissue engineering, where their nanoscale properties allow precise interactions with biological systems. Among niobium-based nanomaterials, niobium pentoxide (Nb2O5) is the most extensively studied due to its chemical stability, bioactivity, and optical properties. Nb2O5 nanoparticles have shown significant potential in catalysis, biosensing, and photodynamic therapy, as their stability and reactivity make them ideal for functionalization in advanced biomedical applications. Despite these advantages, challenges remain regarding the biodegradability and long-term retention of NbNPs in biological systems. Their accumulation in tissues can lead to risks such as chronic inflammation or toxicity, emphasizing the importance of designing nanoparticles with controlled clearance and biodegradability. Surface modifications, such as coatings with biocompatible polymers, have demonstrated the ability to mitigate these risks while enhancing therapeutic efficacy. This review provides a comprehensive overview of NbNPs, with a focus on Nb2O5, highlighting their unique properties, current biomedical applications, and limitations. By addressing the remaining challenges, this work aims to guide the development of safer and more effective niobium-based nanomaterials for future medical innovations.
Prescribed Hospital Diet Influence on Dietary Intake of Hospitalised Patients: A Cross-Sectional Study
Authors: Gameiro Joana , Oliveira Raquel , Baltazar Ana Lúcia , Rocha Clara , Pereira Marta , Martins Diana , Lima João P. M. , Mendes Fernando
Published: January 2025

Background: The dietary intake of hospitalised patients is often compromised during hospitalisation, which can be a causal factor for hospital malnutrition. This is considered a public health problem worldwide and is associated with an increased risk of other complications. Objectives: Our objective was to determine the dietary intake of hospitalised individuals and if the prescribed diet influences it. Methods: Food intake data were collected from 299 lunches of patients admitted to a hospital, using the visual estimation method with a five-point scale. Three existing diets were considered, and the energy and macronutrient values of the meal were calculated. The minimum energy and protein requirements were also calculated. Results: The components of the tray with the highest intake were soup and dessert; no significant differences were found between the percentage intake of each element and the prescribed diet. More than 50% of the individuals did not meet their minimum energy requirements, and only 36.5% had a protein intake that was within the recommendations. Conclusions: Dietary intake is much lower than prescribed, and nutritionists need to act to reduce the prevalence of hospital malnutrition.
Molecular Mechanisms and Therapeutic Potential of Mulberry Fruit Extract in High-Fat Diet-Induced Male Reproductive Dysfunction: A Comprehensive Review
Authors: Adthapanyawanich Kannika , Aitsarangkun Na Ayutthaya Kanyakorn , Kreungnium Siriporn , Mark Peter J. , Nakata Hiroki , Chen Wai , Chinda Kroekkiat , Amatyakul Patcharada , Tongpob Yutthapong
Published: January 2025

High-fat diet (HFD)-induced obesity represents a significant challenge to male reproductive health, affecting approximately 13% of the global adult population. This comprehensive review synthesizes current evidence regarding mulberry (Morus alba L.) fruit extract’s therapeutic potential for HFD-induced male reproductive dysfunction. Through comprehensive analysis of the peer-reviewed literature from multiple databases (PubMed, Web of Science, Scopus, and Google Scholar; 2005–2024), we evaluated mulberry extract’s effects on testicular morphology, spermatogenesis, sperm parameters, and the underlying molecular mechanisms. Mechanistic studies reveal that standardized mulberry extract mediates protective effects through multiple pathways: enhanced antioxidant enzyme activities (SOD: +45%, Catalase: +38%, GPx: +35%), reduced inflammatory markers (TNF-α: −64%, IL-6: −58%), and modulated NF-κB signaling (−42.3%). These effects are facilitated by mulberry’s rich phytochemical profile, particularly anthocyanins (2.92–5.35 mg/g dry weight) and polyphenols (4.23–6.38 mg/g). The extract demonstrates particular efficacy in preserving seminiferous tubule integrity and maintaining blood-testis barrier function, with treated groups maintaining up to 85% of normal tubular architecture compared to HFD controls. Key molecular mechanisms include AMPK/SIRT1 pathway activation (2.3-fold increase), enhanced mitochondrial function (67% increase in mtDNA copy number), and epigenetic regulation of metabolic pathways. Temporal analysis indicates optimal therapeutic effects after 28 days of treatment, with initial improvements observable within 14 days. While current evidence is promising, limitations include predominant reliance on rodent models and lack of standardized extraction protocols. Future research priorities include well-designed human clinical trials, standardization of preparation methods, and investigation of potential synergistic effects with other therapeutic agents. This comprehensive review indicates that mulberry extract is a promising therapeutic candidate for obesity-related male infertility, warranting further clinical investigation.
The Transmission Muography Technique for Locating Potential Radon Gas Conduits at the Temperino Mine (Tuscany, Italy)
Authors: Borselli Diletta , Beni Tommaso , Bonechi Lorenzo , Brocchini Debora , Casagli Nicola , Ciaranfi Roberto , Ciulli Vitaliano , D’Alessandro Raffaello , Dini Andrea , Frosin Catalin , Gigli Giovanni , Gonzi Sandro , Guideri Silvia , Lombardi Luca , Nocentini Massimiliano , Paccagnella Andrea , Vezzoni Simone
Published: January 2025

Transmission muography is an imaging technique that allows us to obtain two-dimensional and three-dimensional average-target density images by measuring the transmission of atmospheric muons. Through this technique, it is possible to observe density anomalies inside a target volume and locate them three-dimensionally. In this work, the potential of the technique will be illustrated through the description of the results of two measurements carried out in the tourist path of the Temperino mine (Livorno, Italy) in an area where a higher concentration of Radon gas is measured. This section of the gallery, located at a depth of about 50 m and dating back to the Etruscan period, might contain ancient cavities not yet discovered that could represent preferential conduits into which Radon gas is released into the tourist route. The muographic results are illustrated, focusing on the search for low-density anomalies attributable to cavities. The measurements are part of the MIMA-SITES project aimed at ensuring the safety of specific zones within the Temperino mine.
Phosphorus Fertilization and Chemical Root Pruning: Effects on Root Traits During the Nursery Stage in Two Mediterranean Species from Central Chile
Authors: Calderón-Ureña Fiorella , Álvarez-Maldini Carolina , Acevedo Manuel , Sánchez-Olate Manuel E. , Dumroese R. Kasten , Sierra-Olea Antay , Ovalle Juan F. , Esquivel-Segura Edwin
Published: January 2025

The role of a plant root system in resource acquisition is relevant to confront drought events caused by climate change. Accordingly, nursery practices like phosphorous (P) fertilization and root pruning have been shown to modify root architecture; however, their combined benefits require further investigation in Mediterranean species. We evaluated the effect of applied P concentrations (0, 15, 60, and 120 mg L−1 P) with or without chemical (copper) root pruning (WCu, WoCu, respectively) in Aristotelia chilensis and Quillaja saponaria on morpho-physiological and root architecture traits. Higher P concentration increased nutrient content in both species concurrent with higher growth. In A. chilensis, higher P concentrations only increased the length and volume of medium roots. In Q. saponaria, P additions increased root length and diameter and the length and volume of fine and medium roots. The root-to-shoot ratio declined with WCu in A. chilensis (23.1%) and Q. saponaria (15.7%). Unlike our hypothesis, fine root architecture remained unaffected with root pruning in A. chilensis, while fine root length and volume decreased with increasing P concentrations in Q. saponaria. Thus, P fertilization enhances root development more consistently than root pruning, highlighting the need for further testing under water deficit conditions to optimize nursery practices.
The Optimization of the Debittering Process and the Exploration of Bitter Metabolites of Paeonia ostii ‘Fengdan’ Seeds
Authors: Li Shuting , Xu Yanfeng , Liu Xinyue , Su Qizhen , Zhang Junyu , Zhang Xinran , Guo Xinmiao , Zhang Yanlong , Zhang Qingyu
Published: January 2025

Tree peony seeds, traditionally used for edible oil production, are rich in α-linolenic acid (ALA). However, little attention is paid to their development as a healthcare food due to their bitter and astringent taste. The aim of this study was to optimize the debittering process of peony seeds on the basis of maintaining nutritional value and to identify the compounds that cause the taste of bitterness. We first optimized the debittering process by orthogonal experiments which reduced the polyphenol content by 90.25%, and we measured the main nutritional value of fatty acid composition, indicating that the high content of ALA is not affected by debittering. Then, we identified and determined the types and content of polyphenols, the metabolites causing bitter taste, in the samples based on LC-ESI-QQQ-MS. Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) were used to compare and analyze the seeds at different stages of debittering. Thirty-eight key metabolites were identified, of which paeoniflorin, taxifolin, alibiflorin, protocatechuic acid, benzoyl paeoniflorin, quercetin-3-galactoside, and oxpaeoniflorin were significantly compared, and most of them were positively correlated with bitter taste. These results are conducive to the exploration and study of the bitter taste and nutritional value of tree peony seeds in the future.
Microbial Biodegradation of Synthetic Polyethylene and Polyurethane Polymers by Pedospheric Microbes: Towards Sustainable Environmental Management
Authors: Najam Maryam , Javaid Sana , Iram Shazia , Pasertsakoun Kingkham , Oláh Marianna , Székács András , Aleksza László
Published: January 2025

This study attempted to isolate and identify pedospheric microbes originating in dumpsites and utilized them for the degradation of selected synthetic polymers for the first time in a cost-effective, ecologically favorable and sustainable manner. Specifically, low-density polyethylene (LDPE) and polyurethane (PUR) were converted by the isolated fungi, i.e., Aspergillus flavus, A terreus, A. clavatus, A. nigers and bacterial coccus and filamentous microbes and assessed in a biotransformative assay under simulated conditions. Commendable biodegradative potentials were exhibited by the isolated microbes against polymers that were analyzed over a span of 30 days. Among the selected fungal microbes, the highest activity was achieved by A. niger, expressing 55% and 40% conversion of LDPE and PUR, respectively. In the case of bacterial strains, 50% and 40% conversion of LDPE and PUR degradation was achieved by coccus. Fourier transform infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA) were utilized to analyze the degradative patterns in terms of vibrational and thermal characteristics, and stereomicroscopic analysis was performed for the visual assessment of morphological variations. Profound structural transformations were detected in FT-IR spectra and TGA thermograms for the selected microbes. Stereomicroscopic analysis was also indicative of the remarkable transformation of the surface morphology of these polymers after degradation by microbes in comparison to the reference samples not treated with any pedospheric microbes. The results are supportive of the utilization of the selected pedospheric microbes as environmental remediators for the cleanup of persistent polymeric toxins. This current work can be further extended for the successful optimization of further augmented percentages by using other pedospheric microbes for the successful adoption of these biotechnological tools at a practical level.
Novel Ultrafiltration Polyethersulfone Membranes Blended with Carrageenan
Authors: Al Marri Saeed H. , Manawi Yehia , Simson Simjo , Lawler Jenny , Kochkodan Viktor
Published: January 2025

The development of ultrafiltration (UF) polymeric membranes with high flux and enhanced antifouling properties bridges a critical gap in the polymeric membrane fabrication research field. In the present work, the preparation of novel PES membranes incorporated with carrageenan (CAR), which is a natural polymer derived from edible red seaweed, is reported for the first time. The PES/CAR membranes were prepared by using the nonsolvent-induced phase separation (NIPS) method at 0.1–4.0 wt.% CAR loadings in the casting solutions. The use of dimethylsulfoxide (DMSO), which is a bio-based and low-toxic solvent, is reported. Scanning electron microscopy, atomic force microscopy, water contact angle, porosity, and zeta potential measurements were used to evaluate the surface morphology, structure, pore size, hydrophilicity, and surface charge of the prepared membranes. The filtration performance of PES/CAR membranes was tested with bovine serum albumin (BSA) solutions. It was shown that CAR incorporation in the casting solutions notably increased hydrophilicity, porosity, pore size, surface charge, and fouling resistance of the prepared membranes compared with plain PES membranes due to the hydrophilic nature and pore-forming properties of CAR. The PES/CAR membranes showed a significant reduction in irreversible and total fouling during filtration of BSA solutions by 38% and 32%, respectively, an enhancement in the flux recovery ratio by 20–40%, and an improvement in mechanical properties by 1.5-fold when compared with plain PES membranes. The findings of the present study indicate that CAR can be used as a promising additive for the development of PES UF membranes with enhanced properties and performance for water treatment applications.
Identification of Lighting Strike Damage and Prediction of Residual Strength of Carbon Fiber-Reinforced Polymer Laminates Using a Machine Learning Approach
Authors: Dong Rui-Zi , Fan Yin , Bian Jiapeng , Chen Zhili
Published: January 2025

Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters. Original lightning strike images, preprocessed with the Sobel operator for edge enhancement, are fed into a UNet neural network using four channels to detect damaged areas. These identified areas, along with lightning parameters and layup details, are inputs for a neural network predicting the damage depth in CFRP laminates. Due to its close relation to residual strength, damage depth is then used to estimate the residual strength of lightning-damaged CFRP laminates. The effectiveness of the current method is confirmed, with the mean Intersection over Union (mIoU) achieving over 93% for damage identification, the Mean Absolute Error (MAE) reducing to 5.4% for damage depth prediction, and the Mean Relative Error (MRE) reducing to 7.6% for residual strength prediction, respectively.
Application of Plant Extracts Rich in Anthocyanins in the Development of Intelligent Biodegradable Packaging: An Overview
Authors: Klaric Stephany Vasconcellos , Galvão Maciel Amanda , Arend Giordana Demaman , Tres Marcus Vinícius , de Lima Marieli , Soares Lenilton Santos
Published: January 2025

Consumers are increasingly opting for food with high quality, in addition to practicality, as there are changes in time, habits and preferences, demanding that the food and packaging industries adapt to a new lifestyle. Intelligent packaging provides consumers with real-time information about the quality and safety of packaged products. A critical analysis of the processes used to develop these packages was carried out. In this context, this review aims to analyze the concept of intelligent packaging, emphasizing the incorporation of extracts rich in anthocyanins, verifying its relationship with the development of new technologies and discussing current aspects of the scientific production process of the packaging. It was also highlighted that anthocyanin compounds are susceptible to pH variations. As an indicator of pH variation, a plant extract was necessary to incorporate into a solid matrix to immobilize the dye. The pH indicator film represents a simple and visual method to detect changes in food products. In this sense, technological processes and resources have been gaining prominence with the premise of offering quality, convenience and safety for consumers and companies.
A Prediction Model for Methane Concentration in the Buertai Coal Mine Based on Improved Black Kite Algorithm–Informer–Bidirectional Long Short-Term Memory
Authors: Qu Hu , Shao Xuming , Gao Huanqi , Chen Qiaojun , Guang Jiahe , Liu Chun
Published: January 2025

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R2) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R2 values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications.
Effective One-Component Organocatalysts for Eco-Friendly Production of Cyclic Carbonates
Authors: Francés-Poveda Enrique , Navarro Marta , Beroíza-Duhart Monserrat , Mahecha Genesys L. , Urzúa Julio I. , Valenzuela María Luisa , de la Cruz-Martínez Felipe , Douglas-Gallardo Oscar A. , Werlinger Francisca , Lara-Sánchez Agustín , Martínez Javier
Published: January 2025

One-component or bifunctional organocatalysts are some of the most capable compounds to perform the synthesis of cyclic carbonates from epoxides and carbon dioxide (CO2) since the presence of a co-catalyst is not required. In this study, we designed, synthesized, and evaluated five halogenated compounds as bifunctional organocatalysts for this catalytic transformation. Among them, 1,3-dimethylimidazolium iodide (1) exhibited the highest catalytic efficiency, enabling the synthesis of a broad range of monosubstituted cyclic carbonates with diverse functional groups under mild conditions (80 °C, 20 bar CO2) within 1 h, using only 1 mol% catalyst loading. Remarkably, this organocatalyst also facilitated the synthesis of five internal cyclic carbonates and a carvone-derived exo-cyclic carbonate, which was obtained for the first time without the use of a metal catalyst, under more demanding conditions. A mechanistic proposal was developed through a combination of 1H-NMR studies and density functional theory (DFT) simulations. Styrene oxide and cyclohexene oxide were used as model substrates to investigate the reaction pathway, which was computed using an optimized climbing-image nudged elastic band (CI-NEB) method. The results revealed the critical role of 1,3-dimethylimidazolium iodide in key reaction steps, particularly in facilitating the epoxy ring opening process. These findings highlight the potential use of bifunctional compounds as efficient and versatile catalysts for CO2 valorization.
Automatic Grape Cluster Detection Combining YOLO Model and Remote Sensing Imagery
Authors: Codes-Alcaraz Ana María , Furnitto Nicola , Sottosanti Giuseppe , Failla Sabina , Puerto Herminia , Rocamora-Osorio Carmen , Freire-García Pedro , Ramírez-Cuesta Juan Miguel
Published: January 2025

Precision agriculture has recently experienced significant advancements through the use of technologies such as unmanned aerial vehicles (UAVs) and satellite imagery, enabling more efficient and precise agricultural management. Yield estimation from these technologies is essential for optimizing resource allocation, improving harvest logistics, and supporting decision-making for sustainable vineyard management. This study aimed to evaluate grape cluster numbers estimated by using YOLOv7x in combination with images obtained by UAVs from a vineyard. Additionally, the capability of several vegetation indices calculated from Sentinel-2 and PlanetScope satellites to estimate grape clusters was evaluated. The results showed that the application of the YOLOv7x model to RGB images acquired from UAVs was able to accurately predict grape cluster numbers (R2 value and RMSE value of 0.64 and 0.78 clusters vine−1). On the contrary, vegetation indexes derived from Sentinel-2 and PlanetScope satellites were found not able to predict grape cluster numbers (R2 lower than 0.23), probably due to the fact that these indexes are more related to vegetation vigor, which is not always related to yield parameters (e.g., cluster number). This study suggests that the combination of high-resolution UAV images, multispectral satellite images, and advanced detection models like YOLOv7x can significantly improve the accuracy of vineyard management, resulting in more efficient and sustainable agriculture.
STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets
Authors: Zhang Liuwei , Zhou Zhitao , Xi Yuyang , Tan Fanjiao , Hou Qingyu
Published: January 2025

Infrared dim and small target detection (IRDSTD) aims to obtain target position information from the background, clutter, and noise. However, for infrared dim and small targets with low signal-to-clutter ratios (SCRs), the detection difficulty lies in the fact that their poor local spatial saliency will lead to missed detections and false alarms. In this work, a spatiotemporally integrated detection network (STIDNet) is proposed for IRDSTD. In the network, a spatial saliency feature generation module (SSFGM) employs a U-shaped network to extract deep features from the spatial dimension of the input image in a frame-by-frame manner and splices them based on the temporal dimension to obtain an airtime feature tensor. IRDSTs with direction-of-motion consistency and strong interframe correlation are reinforced, and randomly generated spurious waves, noise, and other false alarms are inhibited via a fixed-weight multiscale motion feature-based 3D convolution kernel (FWMFCK-3D). A mapping from the features to the target probability likelihood map is constructed in a spatiotemporal feature fusion module (STFFM) by performing 3D convolutional fusion on the spatially localized saliency and time-domain motion features. Finally, several ablation and comparison experiments indicate the excellent performance of the proposed network. For infrared dim and small targets with SCRs < 3, the average AUC value still reached 0.99786.
Nighttime Cough Characteristics in Chronic Obstructive Pulmonary Disease Patients
Authors: den Brinker Albertus C. , Ouweltjes Okke , Rietman Ronald , Thackray-Nocera Susannah , Crooks Michael G. , Morice Alyn H.
Published: January 2025

Coughing is a symptom of many respiratory diseases. An increased amount of coughs may signal an (upcoming) health issue, while a decreasing amount of coughs may indicate an improved health status. The presence of a cough can be identified by a cough classifier. The cough density fluctuates considerably over the course of a day with a pattern that is highly subject-dependent. This paper provides a case study of cough patterns from Chronic Obstructive Pulmonary Disease (COPD) patients as determined by a stationary semi-automated cough monitor. It clearly demonstrates the variability of cough density over the observation time, its patient specificity and dependence on health status. Furthermore, an earlier established empirical finding of a linear relation between mean and standard deviation of a session’s cough count is validated. An alert mechanism incorporating these findings is described.
Co-Optimization Operation of Distribution Network-Containing Shared Energy Storage Multi-Microgrids Based on Multi-Body Game
Authors: Wu Hao , Cao Ge , Jia Rong , Liang Yan
Published: January 2025

Under the carbon peaking and carbon neutrality target background, efficient collaborative scheduling between distribution networks and multi-microgrids is of great significance for enhancing renewable energy accommodation and ensuring stable system operation. Therefore, this paper proposes a collaborative optimization method for the operation of distribution networks and multi-microgrids with shared energy storage based on a multi-body game. The method is modeled and solved in two stages. In the first stage, a multi-objective optimization configuration model for shared energy storage among multi-microgrids is established, with optimization objectives balancing the randomness of renewable energy fluctuations and the economics of each microgrid undertaking shared energy storage. The charging and discharging interactive power of energy storage and each microgrid at various time periods are obtained and passed to the second stage. In the second stage, with the distribution network as the leader and shared energy storage and multi-microgrids as followers, a game optimization model with one leader and 2 followers is established. The model is solved based on an outer-layer genetic algorithm nested with an inner-layer solver to determine the electricity purchase and sale prices among the distribution network, multi-microgrids, and shared energy storage at various time periods, thereby minimizing operational costs. Finally, based on the power interaction of microgrids to measure their contributions, an improved Shapley value cost allocation method is proposed, effectively achieving a balanced distribution of benefits among the distribution network, shared energy storage, and multi-microgrids, thereby improving overall operational revenue. Meanwhile, a new method for calculating the shared energy storage capacity and the upper limit of charging and discharging power based on a game framework was proposed, which can save 37.23% of the power upper limit and 44.89% of the capacity upper limit, effectively saving the power upper limit and capacity upper limit.
Multi-Person Localization Based on a Thermopile Array Sensor with Machine Learning and a Generative Data Model
Authors: Klir Stefan , Lerch Julian , Benkner Simon , Khanh Tran Quoc
Published: January 2025

Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR). Over the course of this paper, both algorithms are adapted to localize people in 32 × 32-pixel thermal array images. The drawbacks in precision due to the sparse amount of labeled data were counteracted with a novel generative image generator (IIG). This generator creates synthetic thermal frames from the sparse amount of available labeled data. Multiple robustness tests were performed during the evaluation process to determine the overall usability of the aforementioned algorithms as well as the advantage of the image generator. Both algorithms provide a high mean average precision (mAP) exceeding 98%. They also prove to be robust against disturbances of warm air streams, sun radiation, the replacement of the sensor with an equal type sensor, new persons, cold objects, movements along the image frame border and people standing still. However, the precision decreases for persons wearing thick layers of clothes, such as winter clothing, or in scenarios where the number of present persons exceeds the number of persons the algorithm was trained on. In summary, both algorithms are suitable for detection and localization purposes, although YOLOv5m has the advantage in real-time image processing capabilities, accompanied by a smaller model size and slightly higher precision.
Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning
Authors: Liu Ning , Shen Yu , Zhang Shuangcheng , Zhu Xuejian
Published: January 2025

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliability of the new model and provides a reference method for meteorological monitoring and weather forecasting.
Integrated Spectral Sensitivity as Physics-Based Figure of Merit for Spectral Transducers in Optical Sensing
Authors: McCluskey Felix L. , van Klinken Anne , Fiore Andrea
Published: January 2025

The design of optical sensors aims at providing, among other things, the highest precision in the determination of the target measurand. Many sensor systems rely on a spectral transducer to map changes in the measurand into spectral shifts of a resonance peak in the reflection or transmission spectrum, which is measured by a readout device (e.g., a spectrometer). For these spectral transducers, figures of merit have been defined which are based on specific assumptions on the readout and the data analysis. In reality, however, different transducers achieve optimal performance with different types of readout. Additionally, some transducers present a more complex spectral response for which existing figures of merit do not apply. In this paper, we investigate an approach to quantifying the potential performance of a given transducer for a more general class of readout methods. Starting from the Cramér–Rao lower bound, we define a new figure of merit, the integrated spectral sensitivity, which is directly related to the physical limit of precision and applicable to a wide variety of sensing systems. We apply this analysis to two different examples of transducers. The results bring useful insights into the design of optical sensors.
A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier
Authors: Tayyab Muhammad , Alateyah Sulaiman Abdullah , Alnusayri Mohammed , Alatiyyah Mohammed , AlHammadi Dina Abdulaziz , Jalal Ahmad , Liu Hui
Published: January 2025

This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features. Feature fusion was employed to enhance the discriminative power of the extracted data and the physical parameters calculated by different feature extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. Compared to state-of-the-art methods, our approach achieved better performance in event recognition.
Effects of Physical Activity Level, Strength, Balance, and Body Composition on Perceived Health in Healthy Adults
Authors: Delfa-de-la-Morena José Manuel , Paes Pedro Pinheiro , Camarotti Júnior Frederico , de Oliveira Débora Priscila Lima , Cordeiro Feitosa Rubem , de Oliveira Byanka Santos Cavalcante , Mijarra-Murillo Juan-José , Martínez Moya Aranzazu , García-González Miriam , De Asís-Fernández Francisco
Published: January 2025

Background: Nowadays, not only is a high, long life expectancy desired, but also longevity with quality. Quality of life in adulthood is a multidimensional construct related to the perception of one’s own health, psychological and socio-emotional factors, functionality for daily activities, and body composition. Objective: This study evaluates the effects of physical activity level (PAL), strength, balance, and body composition on perceived health in healthy adults. Methods: An observational, cross-sectional study with consecutive, non-probabilistic inclusion of cases was conducted. Body fat percentage (BFP) was measured by DXA. Physical activity level was assessed using accelerometry. The strength index (S_Index) was estimated using dynamometry. Postural control was assessed through posturography. The composite equilibrium score from the Sensory Organization Test (SOT_CES) was conducted to measure postural stability under various sensory conditions using dynamic posturography. Perceived health was calculated using the SF36 questionnaire, which detects health states, both positive and negative. A linear regression model was generated between each domain of SF36 with SOT_CES, BFP, PAL, and S_Index. Results: A total of 64 males with a mean age of 55 ± 5 years and a mean body mass index of 27 ± 4 kg/m2 were recruited. Results showed a negative correlation between physical function (ß = −0.7; t = −3.163; p = 0.003; R2 = 23.7%) and general health (ß = −0.227; t = −3.425; p = 0.001; R2 = 17.4%) with BFP. Also, it showed a negative correlation between physical function (ß = 0.047; t = −2.643; p = 0.011; R2 = 17.5%) and general health (ß = 0.016; t = −3.044; p = 0.004; R2 = 14.6%) with S_Index. On the other hand, no relation was observed between SF36 and SOT_CES. Finally, only the emotional role showed a positive correlation (ß = −0.02; t = −2.629; p = 0.011; R2 = 23.1%) with PAL. Conclusion: A lower BFP and higher S_Index are associated with increased physical function and general health. Also, the higher the PAL, the greater the emotional health. On the other hand, no relation was observed between SF36 and the balance detected from SOT_CES.
Correlation Between Conductivity and Oxygen Evolution Reaction Activity in Perovskite Oxides CaMnO3-δ, Ca0.5Sr0.5MnO3-δ and SrMnO3-δ
Authors: Martinson Amara , Guinn Mandy , Mortensen Peter , Hona Ram Krishna
Published: January 2025

The perovskite oxides CaMnO3-δ, Ca0.5Sr0.5MnO3-δ, and SrMnO3-δ were synthesized in air using a solid-state method, and their structural, electrical, and electrocatalytic properties were studied in relation to their oxygen evolution reaction (OER) performance. Iodometric titration showed δ values of 0.05, 0.05, and 0.0, respectively, indicating that Mn is predominantly in the 4+ oxidation state across all materials, consistent with prior reports. Detailed characterization was performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), iodometric titration, and variable-temperature conductivity measurements. Four-point probe DC measurements revealed that CaMnO3-δ (δ = 0.05) has a semiconductive behavior over a temperature range from 25 °C to 300 °C, with its highest conductivity attributed to polaron activity. Cyclic voltammetry (CV) in 0.1 M KOH was employed to assess OER catalytic performance, which correlated with room-temperature conductivity. CaMnO3-δ exhibited superior catalytic activity, followed by Ca0.5Sr0.5MnO3-δ and SrMnO3-δ, demonstrating that increased conductivity enhances OER performance. The conductivity trend, CaMnO3-δ > Ca0.5Sr0.5MnO3-δ > SrMnO3-δ, aligns with OER activity, underscoring a direct link between electronic transport properties and catalytic efficiency within this series.
Optimized Deep Learning Model for Predicting Liver Metastasis in Colorectal Cancer Patients
Authors: Wang Molan , Chen Jiaqing , Liu Yuqi
Published: January 2025

Colorectal cancer is a leading type of cancer worldwide and a major contributor to cancer fatalities, and liver metastasis is the most likely distant metastasis in colorectal cancer patients. Classifying and predicting whether liver metastasis occurs in colorectal cancer patients can help doctors timely determine the progress of the disease and form a more reasonable treatment plan, which results in a better prognosis for patients. In this paper, using the Surveillance, Epidemiology, and End Results database, selecting both symmetric and asymmetric features, we extracted the disease-related data of 40,870 patients who were pathologically diagnosed with colorectal cancer from 2010 to 2015 and classified and modeled whether the patients developed liver metastasis to show the symmetry of this study. A total of six deep learning models were utilized, and hyperparameter optimization was performed on the models using the Crested Porcupine Optimizer. The best-performing model was selected and model interpretation was performed to explore the features that affect whether patients develop liver metastasis. Among the six deep learning models selected, the FT-Transformer model, which was hyperparameter optimized by the Crested Porcupine Optimizer, performed the best, with an accuracy of 0.945, with a 95% confidence interval (CI) of [0.942, 0.952], and an AUC of 0.949, with a 95% CI of [0.942, 0.957]. This study can help doctors make medical decisions, detect patients with liver metastases of colorectal cancer earlier, monitor the indicators that have a significant impact on the occurrence of liver metastasis in patients, and use timely surgical treatment, radiotherapy, chemotherapy, and other corresponding therapeutic interventions to improve the survival rate of patients.