bluuebunny/crossref_metadata_2025 huggingface dataset:
Fluorescence Properties of Novel Multiresonant Indolocarbazole Derivatives for Deep-Blue OLEDs from Multiscale Computer Modelling
Authors: Dubinets Nikita O. , Sosorev Andrey Yu.
Published: January 2025

Multiresonant fluorophores are a novel class of organic luminophores with a narrow emission spectrum. They can yield organic light-emitting devices, e.g., OLEDs, with high colour purity. In this study, we applied DFT and multiscale modelling to predict the electronic and optical properties of several novel derivatives of indolocarbazole pSFIAc, which had recently shown a high potential in deep-blue OLEDs. We found that the addition of phenyls to a certain position of the pSFIAc core can considerably increase the fluorescent rate, leaving other properties (HOMO, LUMO, lowest excited singlet and lowest triplet states’ energies) virtually unaffected. This can improve the efficiency and stability of deep-blue organic light-emitting devices; the suggested phenyl-substituted indolocarbazoles have been shown to be compatible with two popular anthracene-based hosts. On the contrary, the addition of phenyls to another positions of the core is detrimental for optoelectronic properties. QM/MM and QM/EFP calculations yielded negligible inhomogeneous broadening of the emission spectrum of the studied luminophores when embedded as dopants in anthracene-based hosts, predicting high colour purity of the corresponding devices. On the basis of the obtained results, we selected one novel multiresonant indolocarbazole derivative that is most promising for organic light-emitting devices. We anticipate the revealed structure-property relationships will facilitate the rational design of efficient materials for organic (opto)electronics.
Pathophysiological Role of Neutrophil Extracellular Traps in Diet-Induced Obesity and Metabolic Syndrome in Animal Models
Authors: Feješ Andrej , Šebeková Katarína , Borbélyová Veronika
Published: January 2025

The global pandemic of obesity poses a serious health, social, and economic burden. Patients living with obesity are at an increased risk of developing noncommunicable diseases or to die prematurely. Obesity is a state of chronic low-grade inflammation. Neutrophils are first to be recruited to sites of inflammation, where they contribute to host defense via phagocytosis, degranulation, and extrusion of neutrophil extracellular traps (NETs). NETs are web-like DNA structures of nuclear or mitochondrial DNA associated with cytosolic antimicrobial proteins. The primary function of NETosis is preventing the dissemination of pathogens. However, neutrophils may occasionally misidentify host molecules as danger-associated molecular patterns, triggering NET formation. This can lead to further recruitment of neutrophils, resulting in propagation and a vicious cycle of persistent systemic inflammation. This scenario may occur when neutrophils infiltrate expanded obese adipose tissue. Thus, NETosis is implicated in the pathophysiology of autoimmune and metabolic disorders, including obesity. This review explores the role of NETosis in obesity and two obesity-associated conditions—hypertension and liver steatosis. With the rising prevalence of obesity driving research into its pathophysiology, particularly through diet-induced obesity models in rodents, we discuss insights gained from both human and animal studies. Additionally, we highlight the potential offered by rodent models and the opportunities presented by genetically modified mouse strains for advancing our understanding of obesity-related inflammation.
Integrating 16S rRNA Gene Sequencing and Metabolomics Analysis to Reveal the Mechanism of L-Proline in Preventing Autism-like Behavior in Mice
Authors: Fang Jingjing , Kang Seong-Gook , Huang Kunlun , Tong Tao
Published: January 2025

Background/Objectives: Autism spectrum disorder (ASD) is characterized by impaired social interaction and repetitive stereotyped behavior. Effective interventions for the core autistic symptoms are currently limited. Methods: This study employed a valproic acid (VPA)-induced mouse model of ASD to assess the preventative effects of L-proline supplementation on ASD-like behaviors. The method of 16S rRNA sequencing and untargeted metabolomics analyses were conducted to investigate the modulation of gut microbiota and gut metabolites by L-proline. Results: The results indicated that L-proline supplementation significantly prevented ASD-like behavioral disorders, including alleviating social communication deficits and reducing repetitive behavior in the ASD mice. The 16S rRNA sequencing analysis revealed that L-proline regulated the composition and structure of gut microbiota. L-Proline supplementation enhances the abundance of the Verrucomicrobia at the phylum level and the Akkermansia at the genus level, while concurrently reducing the abundance of the Patescibacteria at the phylum level, as well as the Ileibacterium, Candidatus_Saccharimonas, and Lachnospiraceae_UCG-006 at the genus level in the VPA-induced mouse model for ASD. Additionally, the untargeted metabolomics results indicated that L-proline also modified the gut metabolite profiles. Functional analysis of the gut microbiota and KEGG pathway enrichment analysis of differential metabolites between the L-proline-supplemented and VPA groups corroborated that L-proline decreased pathways related to nucleotide metabolism, taurine and hypotaurine metabolism, and pyruvate metabolism, while increasing pathways involved in alpha-linolenic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. The integrative metabolomic and microbiome analyses showed strong connections between the gut metabolites and gut microbiota affected by L-proline. These findings suggest that the modulatory effects of L-proline on gut microbiota and its metabolites may play a crucial role in preventing autism in mice. Conclusions: These findings suggest that dietary L-proline may represent a viable, effective option for preventing the physiological and behavioral deficits associated with ASD in mice.
Sex Disparity in the Nutrition-Related Determinants of Mild Cognitive Impairment: A Case–Control Study
Authors: He Mengjie , Su Danting , Zhang Ronghua , Xu Peiwei , Han Dan , Huang Lichun , Zou Yan
Published: January 2025

Background/Objectives: Sex differences in nutrition-related determinants of mild cognitive impairment (MCI) exist among the elderly. This study aimed to explore sex-specific influencing factors of MCI. Methods: A case–control study was conducted in 2020 involving 1086 elderly people aged 55 years and above from four sites in Zhejiang Province, China. Data on demographics, cognitive assessment, depression scale, daily food intake, and physical examinations were collected. The assessment of plant-based diet patterns depended on an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). Multivariate logistic regression models were employed to assess the determinants of MCI in females and males. Results: Among 571 females, 141 (24.7%) had MCI, and 126 (24.5%) had MCI among 514 male participants. In females, the multivariate analysis revealed that being unmarried/divorced/widowed (OR = 1.95, 95% CI: 1.10–3.45), having depression (OR = 6.06, 95% CI: 1.87–19.66), and having a uPDI score ≥ 55 (OR = 2.41, 95% CI: 1.50–3.89) were associated with a significantly elevated risk of MCI. Conversely, a cereal consumption of ≥300 g/d (OR = 0.32, 95% CI: 0.19–0.53) was linked to a significantly reduced risk. In males, vegetable consumption ≥ 150 g/d (OR = 0.39, 95% CI: 0.23–0.66), vegetable oil consumption ≥ 22 g/d (OR = 0.502, 95% CI: 0.307–0.820), and cereal consumption ≥ 300 g/d (OR = 0.44, 95% CI: 0.27–0.71) were associated with a lower MCI risk. Meanwhile, rural residence (OR = 1.90, 95% CI: 1.12–3.25) and advanced age, especially 75 years old and above (OR = 4.71, 95% CI: 2.44–9.12), were also risk factors in males. Notably, the Restricted Cubic Spline (RCS) model showed that females with a uPDI score < 55 had a lower prevalence of MCI, while those with a score ≥ 55 faced a higher risk. Conclusions: This study indicates potential sex disparities in the risk factors for MCI. Future research should prospectively establish causal relationships. Additionally, precise intervention strategies are urgently needed.
The Pyrus sinkiangensis Yu PsLEA4 Gene Enhances the Cold Resistance of Solanum lycopersicum
Authors: Yang Xueying , Zhao Wenjuan , Li Hui , Zhao Zhenxia , Zhu Jianbo , Li Jin
Published: January 2025

Plants have large amounts of the late embryogenesis abundant protein (LEA) family of proteins, which is involved in osmotic regulation. The Korla Pear (Pyrus sinkiangensis Yu) is an uncommon pear species that thrives in Xinjiang and can survive below-freezing conditions. We found that the PsLEA4 gene was more expressed after cold treatment by looking at the transcriptome data of the Korla Pear. In order to evaluate the biological function of the PsLEA4 protein under low-temperature stress and its potential for use in agricultural breeding, we cloned the PsLEA4 gene from the Korla Pear, made a plant overexpression vector, and transformed it into a tomato via Agrobacterium transformation. When exposed to low temperatures, we found that PsLEA4 overexpression can regulate proline metabolism and antioxidant enzyme activity in tomatoes compared to wild tomatoes. Because of this, transgenic tomatoes are more resilient to cold temperatures and produce more than their wild counterparts. Thus, expressing PsLEA4 has multiple advantages: (1) Improving frost resistance and reducing plant damage. (2) Increasing crop yield. Therefore, this study provides a theoretical basis for the role of the PsLEA4 protein in plants’ resilience to low temperatures, as well as for its potential application in crop breeding.
Comparing Hydrolysable and Condensed Tannins for Tannin Protein-Based Foams
Authors: Eckardt Jonas , Moro Lorenzo , Colusso Elena , Šket Primož , Giovando Samuele , Tondi Gianluca
Published: January 2025

Tannin-based foams have gained attention as a potential bio-based alternative to conventional synthetic foams. Traditionally, namely condensed tannins (CT) have been used, leaving the potential of hydrolysable tannins (HT) largely unexplored. This study compared the performance of chestnut (HT) and quebracho (CT) in tannin–protein-based foams at different tannin ratios. Using soy protein isolate (SPI) and hexamine under acidic conditions, a series of tannin foams were produced through a mechanical foaming method and analyzed for cell structure, compression strength, thermal conductivity, and chemical stability. Results show that chestnut tannin is viable in hexamine SPI formulations but is harder to process due to lower reactivity, further resulting in higher material densities compared to quebracho. Foams with higher quebracho content featured smaller, more interconnected cells, while increasing chestnut content led to larger, less interconnected cells. Compression strength decreased with higher chestnut content, while fire resistance and thermal conductivity were influenced by material density rather than tannin type. The 13C-NMR analysis revealed covalent bonding of hexamine with both tannins, but potential covalent bonds with SPI were undetectable. Overall, chestnut tannin can substitute quebracho tannin in hexamine-SPI foams, though with compromises in terms of specific material properties and processability.
Ternary Epoxy Nanocomposites with Synergistic Effects: Preparation, Properties Evaluation and Structure Analysis
Authors: Suroń Patryk , Białkowska Anita , Bakar Mohamed , Hanulikova Barbora , Masař Milan , Kroisová Dora
Published: January 2025

The objective of the present work was to prepare hybrid epoxy composites with improved mechanical and thermal properties. The simultaneous use of two different modifiers in an epoxy resin was motivated by the expected occurrence of synergistic effects on the performance properties of the matrix. Such a hybrid composite can be used in more severe conditions and/or in broader application areas. Hybrid epoxy composites were prepared with polyurethane (PUR), Nanomer nanoclay and carbon nanotubes (CNT), followed by the evaluation of their mechanical and thermal properties. Synergistic improvements in mechanical properties of hybrid composites were observed for 0.5 wt% Nanomer and 1 wt% carbon nanotubes (CNT), 7.5 wt% PUR and 1 wt% CNT, and 5 wt% PUR and 1 wt% CNT, confirming the occurrence of synergistic effects as to the impact strength (IS) of the matrices, compared to binary systems. The toughening induced by CNT/Nanomer modifiers can be attributed to the specific interfacial interactions between the two nanoparticles, while in the case of CNT/PUR, it can be explained by the combined effects of flexible polymer chains and the specific arrangement of nanoparticles in epoxy systems. Spectroscopy analysis confirmed the occurrence of interaction between OH groups in the epoxy matrix with CNT and reactive groups of PUR. The fracture surface showed plastic deformations, with good dispersion of CNT, explaining the improved mechanical properties of the matrix composites.
Establishment of Optimal Drug Delivery System and Evaluation of Utilization of Hydrogel Contact Lens According to the Addition Method of Tretinoin and Bovine Serum Albumin
Authors: Park Hye-In , Sung A-Young
Published: January 2025

This study aims to build an optimal drug delivery system by manufacturing and evaluating a hydrogel contact lens using Tretinoin (ATRA) and protein nanoparticles to improve the drug delivery system as an ophthalmic medical contact lens. To evaluate the optical and physical properties of the manufactured lens, the spectral transmittance, refractive index, water content, contact angle, AFM, tensile strength, drug delivery, and antibacterial properties were analyzed. The contact lens was manufactured to contain ATRA and bovine serum albumin (BSA) in different ways, and the results confirmed that A, B, and C each had different physical properties. In particular, for Sample A, using the soak and release method and using ATRA solution in the contact lens with BSA added, the wettability was 55.94°, the tensile strength was 0.1491 kgf/mm2, and drug delivery released 130.35 μm over 336 h, which was found to be superior to samples B and C. Therefore, the three hydrogel contact lenses compared in this study according to the addition method of ATRA and BSA can be used in various ways to build an optimal drug delivery system that is very useful as an ophthalmic medical lens.
Low-Field NMR Investigation of Imbibition in Coalbed Methane Reservoirs: Implications of Gas–Water Coexistence
Authors: Li Dong , Yang Yi , Yue Zekun , Xu Fei , Liu Yanzhi , Jiang Yuntian , Zheng Sijian
Published: January 2025

Investigating the imbibition characteristics of coals can yield profound insights for advancing coalbed methane extraction and utilization strategies. However, there has been little exploration of the micro-pore imbibition phenomenon during the two-phase flow of gas and water, as research has focused more on the process of static imbibition. In this study, we used an independently developed low-field nuclear magnetic resonance (NMR) displacement experimental device to conduct a systematic study on the dynamic imbibition phenomenon of low-permeability coals under conditions in which gas and water coexist. The experimental results show that the imbibition process under conditions of gas–water coexistence was significantly influenced by the physical properties of the coal samples, such as the wetting contact angle, porosity, and permeability. A smaller wetting contact angle and lower porosity and permeability values were indicative of a stronger imbibition effect. Meanwhile, changes in effective stress and pore pressure had a significant effect on the imbibition process. Changes in effective stress were observed to elastically compress (or expand) the coal pores, leading to intensified (or weakened) imbibition. Greater pore pressure led to a more violent imbibition reaction. These findings provide a new theoretical basis for understanding and predicting imbibition phenomena in the two-phase flow of gas and water in coalbed methane engineering, offering the potential to illuminate the intricate self-absorption phenomena occurring during CO2 geological sequestration processes.
Assessing Methods to Measure Stem Diameter at Breast Height with High Pulse Density Helicopter Laser Scanning
Authors: Sumnall Matthew J. , Raigosa-Garcia Ivan , Carter David R. , Albaugh Timothy J. , Campoe Otávio C. , Rubilar Rafael A. , Alexander Bart , Cohrs Christopher W. , Cook Rachel L.
Published: January 2025

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management.
A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite
Authors: Gao Ge , Jiao Ziti , Li Zhilong , Wang Chenxia , Guo Jing , Zhang Xiaoning , Ding Anxin , Tan Zheyou , Chen Sizhe , Yang Fangwen , Dong Xin
Published: January 2025

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CILOS is smaller than the CILFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI.
Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection
Authors: Le Ke , Yuan Yanhong
Published: January 2025

The automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet the precise scheduling requirements of modern production. The complex environmental conditions in textile workshops, combined with the cylindrical shape and repetitive textural features of yarn bobbins, limit the application of traditional visual solutions. Therefore, we propose a visual measurement method based on the geometric characteristics of binocular imaging: First, all contours in the image are extracted, and the distance sequence between the contours and the centroid is extracted. This sequence is then matched with a predefined template to identify the contour information of the yarn bobbin. Additionally, four equations for the tangent line from the camera optical center to the edge points of the yarn bobbin contour are established, and the angle bisectors of each pair of tangents are found. By solving the system of equations for these two angle bisectors, their intersection point is determined, giving the radius of the yarn bobbin. This method overcomes the limitations of monocular vision systems, which lack depth information and suffer from size measurement errors due to the insufficient repeat positioning accuracy when patrolling back and forth. Next, to address the self-occlusion issues and matching difficulties during binocular system measurements caused by the yarn bobbin surface’s repetitive texture, an imaging model is established based on the yarn bobbin’s cylindrical characteristics. This avoids pixel-by-pixel matching in binocular vision and enables the accurate measurement of the remaining yarn margin. The experimental data show that the measurement method exhibits high precision within the recommended working distance range, with an average error of only 0.68 mm.
Calibration of Low-Cost Capacitive Soil Moisture Sensors for Irrigation Management Applications
Authors: Abdelmoneim Ahmed A. , Al Kalaany Christa M. , Khadra Roula , Derardja Bilal , Dragonetti Giovanna
Published: January 2025

The calibration of capacitive soil moisture sensors is an essential step towards their integration into smart solutions. This study investigates the calibration of a widely used low-cost capacitive soil moisture sensor (SKU:SEN0193, DFRobot, Shanghai, China) in a loamy silt soil typically found in the Puglia region of Italy. The calibration function was derived from a random sample of 12 sensors, with three soil sample replicas per sensor, each of which had one of five gravimetric soil moisture contents, from relatively dry (5%) to full saturation (40%). The study reports the resulting calibration function along with the accuracy achieved with the generalized calibration function. The sensors proved to be accurate, with an R2 value ranging between 0.85 and 0.87 and a root mean square value (RMSE) ranging between 4.5 and 4.9%. The variation between the sensors was also investigated. The results showed that with higher soil moisture contents (above 30%), the sensor-to-sensor variability becomes significant, with a coefficient of variation (CV) ranging between 10 and 16%; meanwhile, in lower soil moisture contents, the CV ranged between 6.5 and 10.3%, implying that it is more consistent in lower moisture content within this soil condition. The resulting calibration function enhances the integration of such low-cost sensors into smart farming solutions. With proper calibration, these affordable capacitive sensors can achieve a high degree of accuracy, making them a viable option for widespread use in cost-effective precision agricultural applications.
Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors’ Locations
Authors: Anaya-Campos Leonardo Eliu , Sánchez-Fernández Luis Pastor , Quiñones-Urióstegui Ivett
Published: January 2025

Portable monitoring devices based on Inertial Measurement Units (IMUs) have the potential to serve as quantitative assessments of human movement. This article proposes a new method to identify the optimal placements of the IMUs and quantify the smoothness of the gait. First, it identifies gait events: foot-strike (FS) and foot-off (FO). Second, it segments the signals of linear acceleration and angular velocities obtained from the IMUs at four locations into steps and strides. Finally, it applies three smoothness metrics (SPARC, PM, and LDLJ) to determine the most reliable metric and the best location for the sensor, using data from 20 healthy subjects who walked an average of 25 steps on a flat surface for this study (117 measurements were processed). All events were identified with less than a 2% difference from those obtained with the photogrammetry system. The smoothness metric with the least variance in all measurements was SPARC. For the smoothness metrics with the least variance, we found significant differences between applying the metrics with the complete signal (C) and the signal segmented by strides (S). This method is practical, time-effective, and low-cost in terms of computation. Furthermore, it is shown that analyzing gait signals segmented by strides provides more information about gait progression.
Effect of Embroidery Style on the Bandwidth of Textronic RFID UHF Transponder Antenna
Authors: Nizioł Magdalena , Jankowski-Mihułowicz Piotr , Węglarski Mariusz
Published: January 2025

The production of consumer electronics using electrically conductive materials is a dynamically developing sector of the economy. E-textiles (electronic textiles) are also used in radio frequency identification technology, mainly in the production of tag antennas. For economic reasons, it is important that the finished product is universal, although frequencies in radio systems have different values in different regions of the world. Therefore, the antenna bandwidth must be sufficiently wide so that the read range of the tag is maximally large for all frequencies of the specified band. The bandwidth of an antenna depends on its type and geometric dimensions, but this parameter can also be influenced by the way a given type of antenna is made. The authors prepared samples of embroidered RFID tag antennas for the UHF band using various types of embroidery. Then, its impedance and the read range of the tag were examined in order to determine the exact influence of the type of embroidery on the parameter of interest (antenna bandwidth). The results obtained during the research indicate the influence of different embroidery styles is present; however, that influence is not significant.
MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network
Authors: Guo Wenjie , Du Wenbiao , Yang Xiuqi , Xue Jingfeng , Wang Yong , Han Weijie , Hu Jingjing
Published: January 2025

While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs. Firstly, to ensure semantic richness, a Bidirectional Encoder Representations from Transformers-based (BERT) attribute-enhanced function embedding method is proposed for the extraction of node attributes in the function call graph. Subsequently, this work designs a hierarchical graph neural network that integrates attention mechanisms and pooling operations, complemented by function node sampling and structural learning strategies. This framework delivers a comprehensive profile of malicious code across semantic, syntactic, and structural dimensions. Extensive experiments conducted on the Kaggle and VirusShare datasets have demonstrated that the proposed framework outperforms other graph neural network (GNN)-based malware detection methods.
Graphene/TiO2 Heterostructure Integrated with a Micro-Lightplate for Low-Power NO2 Gas Detection
Authors: Vafaei Paniz , Kodu Margus , Alles Harry , Kiisk Valter , Casals Olga , Prades Joan Daniel , Jaaniso Raivo
Published: January 2025

Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing material composed of single-layer graphene and monolayer-thick TiO2. Applying ultraviolet (380 nm) light with quantum energy above the TiO2 bandgap effectively enhanced the sensor responses. Low (<1 μW optical) power operation of the device was demonstrated by measuring NO2 gas at low concentrations, which is typical in air quality monitoring, with an estimated limit of detection < 0.1 ppb. The gas response amplitudes remained nearly constant over the studied light intensity range (1–150 mW/cm2) owing to the balance between the photoinduced adsorption and desorption processes of the gas molecules. The rates of both processes followed an approximately square-root dependence on light intensity, plausibly because the electron–hole recombination of photoinduced charge carriers is the primary rate-limiting factor. These results pave the way for integrating 2D materials with micro-LED arrays as a feasible path to advanced electronic noses.
Fine-Grained Building Classification in Rural Areas Based on GF-7 Data
Authors: Liu Mingbo , Wang Ping , Han Peng , Liu Longfei , Li Baotian
Published: January 2025

Building type information is widely used in various fields, such as disaster management, urbanization studies, and population modelling. Few studies have been conducted on fine-grained building classification in rural areas using China’s Gaofen-7 (GF-7) high-resolution stereo mapping satellite data. In this study, we employed a two-stage method combining supervised classification and unsupervised clustering to classify buildings in the rural area of Pingquan, northern China, based on building footprints, building heights, and multispectral information extracted from GF-7 data. In the supervised classification stage, we compared different classification models, including Extreme Gradient Boosting (XGBoost) and Random Forest classifiers. The best-performing XGBoost model achieved an overall roof type classification accuracy of 88.89%. Additionally, we proposed a template-based building height correction method for pitched roof buildings, which combined geometric features of the building footprint, street view photos, and height information extracted from the GF-7 stereo image. This method reduced the RMSE of the pitched roof building heights from 2.28 m to 1.20 m. In the cluster analysis stage, buildings with different roof types were further classified in the color and shape feature spaces and combined with the building height information to produce fine-grained building type codes. The results of the roof type classification and fine-grained building classification reveal the physical and geometric characteristics of buildings and the spatial distribution of different building types in the study area. The building classification method proposed in this study has broad application prospects for disaster management in rural areas.
An Enhanced Approach Using AGS Network for Skin Cancer Classification
Authors: Lee Hwanyoung , Cho Seeun , Song Jiyoon , Kim Hoyoung , Shin Youjin
Published: January 2025

Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models have shown promise in assisting with skin cancer classification in various studies, obtaining the large-scale medical image datasets required for AI model training is not straightforward. To address this limitation, this study proposes the AGS network, designed to overcome the challenges of small datasets and enhance the performance of skin cancer classifiers. The AGS network integrates three key modules: Augmentation (A), GAN (G), and Segmentation (S). It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. Five model configurations were also tested to assess the contribution of each module. The results showed that all eight classifiers demonstrated consistent performance improvements with the AGS network. In particular, EfficientNet V2 + AGS achieved the most significant performance gains over the baseline model, with an increase of +0.1808 in Accuracy and +0.1674 in F1-Score. Among all configurations, ResNet50+AGS achieved the best overall performance, with an Accuracy of 95.87% and an F1-Score of 95.73%. While most previous studies focused on single augmentation methods, this study demonstrates the effectiveness of combining multiple augmentation techniques within an integrated framework. The AGS network demonstrates how integrating diverse methods can improve the performance of skin cancer classification models.
Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving
Authors: Cao Yu , Zhang Bo , Hou Xiaohui , Gan Minggang , Wu Wei
Published: January 2025

Existing autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting in decisions misaligned with drivers’ intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers’ electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers’ spatial cognition across two dimensions: relative distance and relative orientation. It consists of two components: EEG signal preprocessing and spatial cognition decoding, enabling the autonomous driving system to make more contextually aligned decisions regarding the targets drivers focus on. To enhance the detection accuracy of drivers’ spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. The DTFNet outperforms existing methods, achieving 65.67% and 50.65% accuracy in three-class tasks and 84.46% and 70.50% in binary tasks. Furthermore, we investigated the temporal dynamics of drivers’ spatial cognition and observed that drivers’ perception of relative distance occurs slightly later than their perception of relative orientation, providing valuable insights into the temporal aspects of cognitive processing.
Democratizing Artificial Intelligence for Social Good: A Bibliometric–Systematic Review Through a Social Science Lens
Authors: Chan Chitat , Nurrosyidah Afifah
Published: January 2025

This study provides a comprehensive analysis of the opportunities for democratizing artificial intelligence (AI) for social good using a bibliometric–systematic literature review method. It combines the quantitative analysis of bibliometric methods with the qualitative synthesis of systematic reviews. This approach helps identify patterns, trends, and gaps in the literature, advancing theoretical insights and mapping future research directions. Design/methodology/approach: Scopus, PubMed, and Web of Science, as prominent scientific databases, were utilized to examine publications between 2014 and 2024. The article selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The bibliometric analysis was conducted using CiteSpace software. Findings: The bibliometric analysis identified the most influential articles, journals, countries, authors, and key themes. The systematic thematic analysis identified established modes of using AI for social good. Moreover, future research directions are suggested and discussed in this article. Practical implications: The findings give future research directions and guidance to academics, practitioners, and policymakers for real-world applications.
A Narrative Review of the Velocity and Acceleration Profile in Football: The Influence of Playing Position
Authors: Cotteret Charles , González-de-la-Flor Ángel , Prieto Bermejo Jaime , Almazán Polo Jaime , Jiménez Saiz Sergio L.
Published: January 2025

To enhance athletic performance and reduce the risk of injury, load quantification has allowed for a better understanding of the individual characteristics of the physical demands on soccer players during training or competition. In this regard, it appears crucial to summarize scientific evidence to provide useful information and future directions related to the speed and acceleration profiles of male soccer players. This review aims to evaluate the findings reflected in the available literature on both profiles in football, synthesizing and discussing data from scientific articles, while providing insights into quantification methods, employed thresholds, tracking systems, terminology, playing position, and microcycle day. Therefore, it is hoped that this narrative review can support objective decision-making in practice for coaches, sports scientists, and medical teams regarding individualized load management and the appropriate selection of metrics, to explore current trends in soccer player profiles.
Hydro-Climatic Trends in Central Italy: A Case Study from the Aterno-Pescara River Watershed
Authors: Tariq Mohsin , Aruffo Eleonora , Chiacchiaretta Piero , Di Carlo Piero
Published: January 2025

Climate change is reshaping water systems and trends in hydro-climatic variables, such as temperature, precipitation, and river runoff, providing critical insights into the hydrological shifts influenced by climate change. However, the impact of climate variability on these variables varies by geographic location, making it necessary to study hydro-climatic variations in the Mediterranean’s changing climate to determine its impacts. This study analyzed the hydro-climatic trends in the Aterno-Pescara River watershed in central Italy from 1936 to 2013. We employed linear regression, Mann–Kendall, Sen’s slope, and Spearman correlation tests to estimate annual and seasonal trends. The results showed a significant warming trend on annual (0.037 °C/year) and seasonal time scales. Precipitation trends exhibited significant reductions annually, specifically during the autumn season, with a decrease of −0.68 mm/year; while showing a decline, other seasons were statistically insignificant. River runoff revealed drying trends annually and seasonally, decreasing by −0.29 m3 s−1/year over the study period. Furthermore, linear regression and Spearman correlation coefficients suggested a significant relationship between hydro-climatic variables with varying strengths (at 95% and 99% confidence levels) annually and seasonally. This decrease in precipitation and river runoff trends with the continuous rate points towards potential meteorological and hydrological droughts occurring in the future in this watershed. This study’s findings provide scientific grounds that could help develop sustainable strategies in the watershed.
Enhancing Economic and Environmental Sustainability in Lignocellulosic Bioethanol Production: Key Factors, Innovative Technologies, Policy Frameworks, and Social Considerations
Authors: Afedzi Alfred Elikem Kwami , Afrakomah Getrude Shallom , Gyan Kwame , Khan Jamil , Seidu Ramatu , Baidoo Theophilus , Sultan Imrana Niaz , Tareen Afrasiab Khan , Parakulsuksatid Pramuk
Published: January 2025

Lignocellulosic bioethanol is a promising renewable energy source that can reduce greenhouse gas emissions and improve energy security. However, its commercialization faces significant economic and environmental challenges, including high feedstock costs, complex pretreatment processes, expensive enzyme formulations, and substantial energy and water requirements. This review examines the key factors affecting its viability, including feedstock costs, enzyme efficiency, co-product generation, greenhouse gas emissions, water use, energy efficiency, and land use impacts. Recent advancements in pretreatment technologies, enzyme recycling, genetically engineered microbial strains, and fermentation strategies are discussed for their potential to improve process efficiency and reduce production costs. This review also explores co-product valorization, including lignin and biogas utilization, which can enhance the economic sustainability of bioethanol production by generating additional revenue streams, offsetting operational costs, and improving overall process efficiency. Identifying research gaps, it highlights the need for cost-effective feedstock supply chains, advanced enzyme technologies, and optimized fermentation methods. Additionally, the role of life cycle assessments and government policies, including subsidies, is considered in shaping production costs and the environmental impact. By integrating economic and environmental perspectives, this review provides insights into advancing the sustainable production of lignocellulosic bioethanol, emphasizing the importance of continued innovation to overcome existing challenges.
Cashew Gum (Anacardium occidentale) Hydrogel for Sustainable Irrigation of Cactus Pear: Effects on Growth, Chemical Composition, and Mineral Content
Authors: Barros Dhiéssica , Edvan Ricardo , Pessoa João Paulo , Nascimento Romilda , Camboim Luan Felipe , Silva Shirlenne , Pereira Filho José Morais , Sousa Heldeney , Silva-Filho Edson Cavalcanti , Fonseca Mozart , Bezerra Leilson
Published: January 2025

Hydrogels are substances designed to retain significant quantities of water and slowly release it to support plant growth. This study produced and evaluated the use of a test hydrogel (TH), a natural polymer derived from cashew gum (Anacardium occidentale), on the growth, chemical composition, and mineral content of cactus pear genotypes. The TH was compared to a negative control (no hydrogel; NH), and to a positive control or commercial hydrogel (CH). The CH was a synthetic polyacrylamide-based hydrogel. The study was carried out in a completely randomized design with a 3 × 3 factorial arrangement (n = 36), involving three hydrogel types × three cactus pear genotypes [Elephant Ear (Opuntia stricta); Giant (Opuntia ficus-indica); and Sweet (Nopalea cochenillifera)]. There was a significant effect of interaction (p ≤ 0.05) between genotypes and hydrogel types on cladode area, plant height, dry biomass of cladodes, and macro- and micromineral content. The use of TH promoted greater growth and improved chemical and mineral composition (p ≤ 0.05) to cactus genotypes. The Sweet genotype hydrated with TH presented the highest crude protein accumulation (p ≤ 0.05). Cashew gum-based hydrogel improved both growth and chemical composition of the cactus genotypes, and it is an eco-friendly alternative to synthetic polymers.
Realizing Open Space Conservation: A Cross-State Survey of Perceptions and Preferences Within Residential Developments
Authors: Swaner Sumner , leBrasseur Richard
Published: January 2025

The conversion of open space to residential development increasingly continues across the United States, impacting both humans and nature. Residential development requires public input to generate meaningful places and understand contextually relevant priorities. Most municipal policies do not guarantee the provision of open spaces when residential development occurs, missing opportunities for benefits to those communities and reducing both environmental and spatial justice. This study operated a seven-state verbal questionnaire to collect and analyze a small-sample population perceptions concerning open space conservation and green space preferences towards future residential development priorities. Statistical analytical results indicated patterns, trends, and relationships within data. Although 46% of United States residents living in rural, suburban, and urban community types believe the amount of open space required in new developments should be determined on a case-by-case basis, just under half believe that requiring at least 50% open space in new developments is appropriate. More than half of Americans in the states targeted, particularly Colorado and liberal-leaning respondents, believe a lack of coherent planning will prevent open space conservation and that open space planning and conservation should be a priority for city governments. Beyond the United States, this study provides research and insight into conservation strategies that foster healthier landscapes and living environments globally.
Financial Sustainability of Energy Business Development: The Unregulated Activity Phenomenon
Authors: Gitelman Lazar D. , Kozhevnikov Mikhail V. , Ditenberg Maksim K.
Published: January 2025

The article presents study results showing the increasing role of unregulated activity as a boost for innovative processes in energy companies and their investment appeal. A summary of academic literature, reports by leading consulting companies, and international energy agencies make it possible to outline the landscape of the most economically viable areas of business activity in the energy industry, the most promising of which are energy efficiency, design and deployment of EV charging networks, smart grids, and telecom services. Analysis of financial performance statements of over 30 energy companies from different countries demonstrates the contribution of unregulated activities to their financial stability, which shows in growing profits, capitalization, and stock prices. It is revealed that despite the active promotion of unregulated activities by the state that primarily seeks to achieve the goals of the low-carbon transition, there is a stronger government presence in the capital structure of energy companies, which in the future will slow down investment activity in the industry. In this regard, the discussion considers the barriers to organizing unregulated business in the electricity sector and methods to eliminate them. In particular, a set of necessary conditions is defined, under which entrepreneurship in the industry contributes to improving the efficiency of the main business processes—production and transportation of energy.