bluuebunny/crossref_metadata_2025 huggingface dataset:
MPCTrans: Multi-Perspective Cue-Aware Joint Relationship Representation for 3D Hand Pose Estimation via Swin Transformer
Authors: Wan Xiangan , Ju Jianping , Tang Jianying , Lin Mingyu , Rao Ning , Chen Deng , Liu Tingting , Li Jing , Bian Fan , Xiong Nicholas
Published: November 2024

The objective of 3D hand pose estimation (HPE) based on depth images is to accurately locate and predict keypoints of the hand. However, this task remains challenging because of the variations in hand appearance from different viewpoints and severe occlusions. To effectively address these challenges, this study introduces a novel approach, called the multi-perspective cue-aware joint relationship representation for 3D HPE via the Swin Transformer (MPCTrans, for short). This approach is designed to learn multi-perspective cues and essential information from hand depth images. To achieve this goal, three novel modules are proposed to utilize features from multiple virtual views of the hand, namely, the adaptive virtual multi-viewpoint (AVM), hierarchy feature estimation (HFE), and virtual viewpoint evaluation (VVE) modules. The AVM module adaptively adjusts the angles of the virtual viewpoint and learns the ideal virtual viewpoint to generate informative multiple virtual views. The HFE module estimates hand keypoints through hierarchical feature extraction. The VVE module evaluates virtual viewpoints by using chained high-level functions from the HFE module. Transformer is used as a backbone to extract the long-range semantic joint relationships in hand depth images. Extensive experiments demonstrate that the MPCTrans model achieves state-of-the-art performance on four challenging benchmark datasets.
Super-Resolution Reconstruction of Remote Sensing Images Using Chaotic Mapping to Optimize Sparse Representation
Authors: Fang Hailin , Zheng Liangliang , Xu Wei
Published: November 2024

Current super-resolution algorithms exhibit limitations when processing noisy remote sensing images rich in surface information, as they tend to amplify noise during the recovery of high-frequency signals. To mitigate this issue, this paper presents a novel approach that incorporates the concept of compressed sensing and explores the super-resolution problem of remote sensing images for space cameras, particularly for high-speed imaging systems. The proposed algorithm employs K-singular value decomposition (K-SVD) to jointly train high- and low-resolution image blocks, updating them column by column to obtain overcomplete dictionary pairs. This approach compensates for the deficiency of fixed dictionaries in the original algorithm. In the process of dictionary updating, we innovatively integrate the circle chaotic mapping into the solution process of the dictionary sequence, replacing pseudorandom numbers. This integration facilitates balanced traversal and simplifies the search for global optimal solutions. For the optimization problem of sparse coefficients, we utilize the orthogonal matching pursuit method (OMP) instead of the L1 norm convex optimization method used in most reconstruction techniques, thereby complementing the K-SVD dictionary update algorithm. After upscaling and denoising the image using the dictionary pair mapping relationship, we further emphasize image edge details with local gradients as constraints. When compared with various representative super-resolution algorithms, our algorithm effectively filters out noise and stains in low-resolution images. It not only performs well visually but also stands out in objective evaluation indicators such as the peak signal-to-noise ratio and information entropy. The experimental results validate the effectiveness of the proposed method in super-resolution remote sensing images, yielding high-quality remote sensing image data.
A Federated Reinforcement Learning Framework via a Committee Mechanism for Resource Management in 5G Networks
Authors: Jeong Jaewon , Lee Joohyung
Published: November 2024

This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in Mobile Edge Computing (MEC) environments for 5G core network automation. It enables multiple MECs to collaboratively optimize resource allocation without centralized data sharing. In this framework, DRL agents in each MEC make local scaling decisions and exchange model parameters with other MECs, rather than sharing raw data. To enhance robustness against malicious server attacks, we employ a committee mechanism that monitors the DFL process and ensures reliable aggregation of local gradients. Extensive simulations were conducted to evaluate the proposed framework, demonstrating its ability to maintain cost-effective resource usage while significantly reducing blocking rates across diverse traffic conditions. Furthermore, the framework demonstrated strong resilience against adversarial MEC nodes, ensuring reliable operation and efficient resource management. These results validate the framework’s effectiveness in adaptive and efficient resource management, particularly in dynamic and varied network scenarios.
Sinusoidal Fitting Decomposition for Instantaneous Characteristic Representation of Multi-Componential Signal
Authors: Nie Donghu , Su Xin , Qiao Gang
Published: November 2024

The research on how to effectively extract the instantaneous characteristic components of non-stationary signals continues to be both a research hotspot and a very challenging topic. In this paper, a new method of multi-component decomposition is proposed to decompose a signal into finite mono-component signals and extract their Instantaneous Amplitude (IA), Instantaneous Phase (IP), and Instantaneous Frequency (IF), which is called Sinusoidal Fitting Decomposition (SFD). The proposed method can ensure that the IA extracted from the given signal must be positive, the IP is monotonically increasing, and the signal synthesized by both IA and IP must be mono-componential and smooth. It transforms the decomposition process into a synthesis iterative process and does not rely on any dictionary or basis function space or carry out the sifting operation. In addition, the proposed method can describe the instantaneous-frequency-amplitude characteristics of the signal very well on the time-frequency plane. The results of numerical simulation and the qualitative analysis of the amount of calculation show that the proposed method is effective.
Steering-Angle Prediction and Controller Design Based on Improved YOLOv5 for Steering-by-Wire System
Authors: Ye Cunliang , Wang Yunlong , Wang Yongfu , Liu Yan
Published: November 2024

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances.
FBG and BOTDA Based Monitoring of Mine Pressure Under Remaining Coal Pillars Using Physical Modeling
Authors: Zhang Dingding , Li Zhi , Duan Yanyan , Yang Long , Liu Hongrui
Published: November 2024

Strong mine pressure often emerges when the working face of the lower coal seam in a closely spaced coal seam system passes through the remaining coal pillar in the overlying goaf. This study investigates the law of overburden movement and the manifestation of mine pressure during mining under the remaining coal pillar. A physical model measuring 2.5 × 0.2 × 1.503 m is constructed. Fiber Bragg grating sensing technology (FBG) and Brillouin optical time domain analysis technology (BOTDA) are employed in the physical model experiment to monitor the internal strain of the overlying rock as the working face advances. This study determines the laws of overlying rock fracture and working face pressure while mining coal seams beneath the remaining coal pillar. It analyzes the relationship between the pressure at the working face and the strain characteristics of the horizontally distributed optical fiber. A fiber grating characterization method is established for the stress evolution law of overlying rock while passing the remaining coal pillar. The experimental results indicated that the fracture angle of overlying rock gradually decreases during the mining stage through and after the coal pillar. In the mining stage through the coal pillar, the cycle pressure step distance of the working face is reduced by 33.3% compared to the stage after mining through the coal pillar. Initially, the strain pattern of the horizontal optical fiber is unimodal when pressure is first applied to the working face, and it transitions from unimodal to bimodal during periodic pressure. The peak value of fiber Bragg grating compressive strain and the range of influence of advanced support pressure are 3.6 times and 4.8 times, respectively, before passing through the remaining coal pillar. Finally, the accuracy of the FBG characterization method is verified by comparing it to the monitoring curve of the coal seam floor pressure sensor. The research results contribute to applying fiber optic sensing technology in mining physical model experiments.
Comparison of Classical and Inverse Calibration Equations in Chemical Analysis
Authors: Chen Hsuan-Yu , Chen Chiachung
Published: November 2024

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique’s response and the target analyte’s standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument’s response is dependent. The inverse equation is the opposite: the instrument’s response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument’s response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced.
Research on the Quantitative Inversion of Soil Iron Oxide Content Using Hyperspectral Remote Sensing and Machine Learning Algorithms in the Lufeng Annular Structural Area of Yunnan, China
Authors: Qi Yingtao , Gan Shu , Yuan Xiping , Hu Lin , Hu Jiankai , Zhao Hailong , Lu Chengzhuo
Published: November 2024

This study used hyperspectral remote sensing to rapidly, economically, and non-destructively determine the soil iron oxide content of the Dinosaur Valley annular tectonic region of Lufeng, Yunnan Province. The laboratory determined the iron oxide content and original spectral reflectance (OR) in 138 surface soil samples. We first subjected the OR data to Savizky–Golay smoothing, followed by four spectral transformations—continuum removal reflectance, reciprocal logarithm reflectance, standard normal variate reflectance, and first-order differential reflectance—which improved the signal-to-noise ratio of the spectral curves and highlighted the spectral features. Then, we combined the correlation coefficient method (CC), competitive adaptive reweighting algorithm, and Boruta algorithm to screen out the characteristic wavelength. From this, we constructed the linear partial least squares regression model, nonlinear random forest, and XGBoost machine learning algorithms. The results show that the CC-Boruta method can effectively remove any noise and irrelevant information to improve the model’s accuracy and stability. The XGBoost nonlinear machine learning algorithm model better captures the complex nonlinear relationship between the spectra and iron oxide content, thus improving its accuracy. This provides a relevant reference for the rapid and accurate inversion of iron oxide content in soil using hyperspectral data.
DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection
Authors: Feng Junbiao , Yu Haikun , Lu Xiaoping , Lv Xiaoran , Zhou Junli
Published: November 2024

Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland’s multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes.
Thermal Bed Design for Temperature-Controlled DNA Amplification Using Optoelectronic Sensors
Authors: Garcia-Torales Guillermo , Torres-Ortega Hector Hugo , Estrada-Marmolejo Ruben , Beltran-Gonzalez Anuar B. , Strojnik Marija
Published: November 2024

Loop-Mediated Isothermal Loop-Mediated Isothermal Amplification (LAMP) is a widely used technique for nucleic acid amplification due to its high specificity, sensitivity, and rapid results. Advances in microfluidic lab-on-chip (LOC) technology have enabled the integration of LAMP into miniaturized devices, known as μ-LAMP, which require precise thermal control for optimal DNA amplification. This paper introduces a novel thermal bed design using PCB copper traces and FR−4 dielectric materials, providing a reliable, modular, and repairable heating platform. The system achieves accurate and stable temperature control, which is critical for μ-LAMP applications, with temperature deviations within ±1.0 °C. The thermal bed’s performance is validated through finite element method (FEM) simulations, showing uniform temperature distribution and a rapid thermal response of 2.5 s to reach the target temperature. These results highlight the system’s potential for applications such as disease diagnostics, biological safety, and forensic analysis, where precision and reliability are paramount.
Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound
Authors: Ibrahim Nehad M. , Alanize Hadeel , Alqahtani Lara , Alqahtani Lama J. , Alabssi Raghad , Alsindi Wadha , Alabssi Haila , AlMuhanna Afnan , Althani Hanadi
Published: November 2024

Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagnosed through cranial ultrasound imaging. This study presents an advanced deep learning approach for automated GMH grading using the YOLOv8 model. By analyzing a dataset of 586 infants, we classified ultrasound images into five distinct categories: Normal, Grade 1, Grade 2, Grade 3, and Grade 4. Utilizing transfer learning and data augmentation techniques, the YOLOv8 model achieved exceptional performance, with a mean average precision (mAP50) of 0.979 and a mAP50-95 of 0.724. These results indicate that the YOLOv8 model can significantly enhance the accuracy and efficiency of GMH diagnosis, providing a valuable tool to support radiologists in clinical settings.
Sensing the Changes in Stratum Corneum Using Fourier Transform Infrared Microspectroscopy and Hyperspectral Data Processing
Authors: Banas Krzysztof , Banas Agnieszka M. , Pastorin Giorgia , Hong Ngai Mun , Gupta Shikhar , Dziedzic-Kocurek Katarzyna , Breese Mark B. H.
Published: November 2024

The stratum corneum (SC) forms the outermost layer of the skin, playing a critical role in preventing water loss and protecting against external biological and chemical threats. Approximately 90% of the SC consists of large, flat corneocytes, yet its barrier function primarily relies on the intercellular lipid matrix that surrounds these cells. Traditional methods for characterizing these lipids, such as Fourier transform infrared spectroscopy (FTIR), typically involve macroscopic analysis using attenuated total reflection (ATR) techniques. In this study, we introduce a novel approach for investigating SC samples at a microscopic level to gain detailed chemical insights and assess sample heterogeneity. Special emphasis is placed on advanced hyperspectral data pre-processing to ensure the accuracy and reliability of the results. We also evaluate methods for filtering out spectral data that significantly deviate from the mean and analyze the extracted mean spectra, the intensities of specific infrared peaks, and their ratios. The novelty of this work lies in its microscopic approach to analyzing the SC lipid matrix, diverging from the traditional macroscopic FTIR–ATR methods. By focusing on hyperspectral imaging and developing robust pre-processing techniques, this study provides more localized, high-resolution chemical insights. This microscopic perspective opens up the possibility of detecting subtle heterogeneities within the skin’s lipid matrix, offering deeper, previously unattainable understanding of the SC’s barrier function. Additionally, the exploration of spectral filtering methods enhances the precision of the analysis, paving the way for more refined and reliable investigations of skin structure and behavior in future research.
Inverse-Designed Ultra-Compact Passive Phase Shifters for High-Performance Beam Steering
Authors: Fu Tianyang , Chu Mengfan , Jin Ke , Sha Honghan , Yan Xin , Yuan Xueguang , Zhang Yang’an , Zhang Jinnan , Zhang Xia
Published: November 2024

Ultra-compact passive phase shifters are inversely designed by the multi-objective particle swarm optimization algorithm. The wavelength-dependent phase difference between two output beams originates from the different distances of the input light passing through the 4 μm × 3.2 μm rectangular waveguide with random-distributed air-hole arrays. As the wavelength changes from 1535 to 1565 nm, a phase difference tuning range of 6.26 rad and 6.95 rad is obtained for TE and TM modes, respectively. Compared with the array waveguide grating counterpart, the phase shifters exhibit higher transmission with a much smaller footprint. By combining the inverse-designed phase shifter and random-grating emitter together, integrated beam-steering structures are built, which show a large scanning range of ±25.47° and ±27.85° in the lateral direction for TE and TM mode, respectively. This work may pave the way for the development of ultra-compact high-performance optical phased array LiDARs.
Effects of Kitchen Cooking Height on Upper Limb Muscle Activation, Posture, and Perceived Discomfort of Chinese Older and Young Women
Authors: Li Ye , Chang Le , Zhang Fan
Published: November 2024

Inappropriate kitchen cooking height may lead to uncomfortable and muscle fatigue. This study aims to compare the effects of kitchen cooking height on upper limb muscle activation, posture, and perceived discomfort among different age groups. Fifteen older women and fifteen young Chinese women each completed three consecutive 20 s simulated cooking tasks at five different heights. Surface electromyography, motion capture, and Borg CR10 scale were used to measure muscle loading. Results showed that the main power muscles of the cooking task were the anterior deltoid, brachioradialis, and biceps brachii. The higher muscle contribution rate of biceps brachii and triceps brachii was found in the younger group compared to the older group (p < 0.05). Muscle activation of the anterior deltoid (different in 1.28–2.87%), pectoralis major (different in 1.43–1.69%), and erector spinae (different in 0.6–1.21%), as well as right shoulder abduction (different in 5.91°–7.96°), were significantly higher in older group than in young group (p < 0.05). Muscle activation of the anterior deltoid and right shoulder abduction decreased significantly with decreasing height (p < 0.05). A height of 200–250 mm below the elbow was considered a more comfortable cooking height for both age groups. This provides data to support the design of cabinet sizes.
Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors
Authors: Lee Jae-Lim , Kim Dong-Sun
Published: November 2024

Nonlinearity in sensor measurements reduces the sensor’s accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.
Construction Site Noise Exposure Assessment Using Binaural Measurements and Analysis
Authors: Samardzic Nikolina , Best Virginia , Hammond Christian , Karatas Aslihan , Esmaeili Behzad , Sarsam Haitham
Published: November 2024

Acoustic measures of construction site noise are important for hearing loss prevention and safety. This work examines noise exposure assessment using binaural measurements to pave the way for more accurate noise characterization and hearing loss prevention in loud workplaces. Recordings were made over three days on a construction site, and noise exposure was estimated using state-of-the-art methods (a shoulder-worn dosimeter) and binaural measurements (microphones at each ear). For the binaural assessment, noise exposure was quantified at each ear to identify the higher- and lower-exposure ears, and the assessment incorporated kurtosis, a statistical measure that quantifies impulsiveness of the noise. The impulsiveness of the noise of four construction tools was also assessed. For this set of measurements, traditional hearing loss risk assessment consistently underestimated the noise exposure relative to binaural assessment. Moreover, the binaural measurement method exposed multiple cases of asymmetric noise exposure that are not detectable using the traditional method. Overall, there are clear benefits to assessing risk using binaural measurements and more detailed analysis methods.
Evaluating Various Lactose Types as Solid Carriers for Improving Curcumin Solubility in Solid Self-Nanoemulsifying Drug Delivery Systems (S-SNEDDSs) for Oral Administration
Authors: Teerapipattanapong Panida , Jaikon Pimrada , Ningsanonda Nichapa , Yonemochi Etsuo , Furuishi Takayuki , Hirun Namon , Kraisit Pakorn
Published: November 2024

Curcumin, a bioactive compound derived from turmeric, possesses numerous pharmaceutical properties; however, its poor aqueous solubility and permeability result in low bioavailability. This study aims to develop a solid self-nanoemulsifying drug delivery system (S-SNEDDS) using different lactose types as solid carriers for the oral administration of curcumin to enhance its solubility. The system comprised curcumin, an oil phase, and a surfactant. Jasmine oil, as the oil phase, and Cremophor® RH40, as the surfactant, were selected due to their superior ability to solubilize curcumin. A microemulsion was then prepared using a ternary phase diagram. The liquid SNEDDSs were converted into S-SNEDDSs by employing three solid carriers: Tablettose® 80, FlowLac® 100, and GranuLac® 200. Dissolution studies conducted in simulated gastric fluid demonstrated a significant improvement in curcumin solubility in the S-SNEDDS formulations compared to curcumin powder. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) analyses confirmed the appearance of curcumin in the S-SNEDDS, while Fourier-transform infrared (FTIR) spectroscopy indicated compatibility between the excipients and curcumin. Additionally, an accelerated stability study conducted over four weeks at 40 °C and 75% relative humidity showed no significant changes in the physical appearance of the S-SNEDDS formulations. These findings suggest that the S-SNEDDS formulation effectively enhances curcumin’s solubility, potentially improving its bioavailability for oral administration.
Anti-Obesity Potential of Secamone afzelii K. Leaf and Vine Ethanol Extract: Insights into Lipase Inhibition and Secondary Metabolites
Authors: Iheagwam Franklyn Nonso , Itina Gloria Aniedi , Faluyi Oyinkansola Ayobolanle , Bella-Omunagbe Mercy , Ejoh Samuel Akpoyowvare
Published: November 2024

Pancreatic lipase is an enzyme crucial for breaking down fats through hydrolysis, and inhibiting it is important for managing obesity. This study evaluated the lipase inhibitory profile of the leaf (SALE) and vine (SAVE) of Secamone afzelii, explored the modes of inhibition, identified the primary compounds responsible for this effect, and examined their molecular interactions with lipase using in vitro and in silico techniques. SALE (IC50: 0.41 ± 0.02 mg/mL) exhibited higher lipase inhibitory activity compared to SAVE (IC50: 0.95 ± 0.05 mg/mL), although it was significantly lower than orlistat (IC50: 0.07 ± 0.00 mg/mL) across all concentrations. S. afzelii extracts inhibited lipase activity through an uncompetitive mode of inhibition. Gas chromatography-mas spectroscopy identified 54 and 47 compounds in SALE and SAVE, respectively, with 9,12-octadecadienoic acid (Z, Z)-, n-hexadecanoic acid and 4S,6R-dimethyl-7R-hydroxynonan-3-one identified as the most abundant compounds in both extracts. The binding energy of the top five ligands from S. afzelii ranged from −7.7 to −6.6 kcal/mol, outperforming that of orlistat (−4.4 kcal/mol). The ligands and orlistat had similar binding poses stabilised by hydrogen and π interactions with CYS299, SER301, CYS304, ASN425 and VAL426. These compounds were predicted to possess promising pharmacokinetic, lipophilic and hydrophilic properties. These results offer insights into the traditional use of S. afzelii for treating obesity and valuable information on potential drug candidates that can be optimised for combating this disease.
The Volatolome of Chromhidrosis
Authors: Radan Mila , Plavčić Anđela
Published: November 2024

This study investigates the volatolome in an individual with chromhidrosis, utilizing solid-phase microextraction (SPME) and pentane extraction, followed by gas chromatography–mass spectrometry (GC-MS), to identify key volatile organic compounds (VOCs). A total of 31 compounds were identified, including aldehydes, fatty acid esters, and benzoic acid derivatives. SPME was more effective in capturing highly volatile compounds, while pentane extraction primarily isolated lipophilic substances such as squalene and cholesterol. The findings suggest that lipid peroxidation and metabolic dysregulation contribute to the formation of lipofuscin, the pigment responsible for colored sweat. Additionally, the detection of 9-octadecenamide and benzoic acid derivatives highlights the role of oxidative processes in chromhidrosis.
A Revisit to Effervescence-Assisted Microextraction of Non-Polar Organic Compounds Using Hydrophobic Magnetic Nanoparticles—Application to the Determination of UV Filters in Natural Waters
Authors: Toti Efthymia , Gouma Vasiliki , Karagianni Vasiliki I. , Giokas Dimosthenis L.
Published: November 2024

In this work, we revisited the method of effervescence-assisted microextraction, aiming to assess the effects of the process of effervescence on the extraction efficiency of organic compounds. We used a magnetic nano-sorbent material composed of stearic acid-coated cobalt-ferrite magnetic nanoparticles as an adsorbent and dispersed it in water using 12 combinations of acid and base mixtures at two different mass ratios. The solution pH, the ionic strength, and the duration of effervescence were calculated and correlated to the extraction efficiency of nonpolar UV filters from aqueous samples as model organic compounds. Our findings provide a general perspective into the influence of the process of effervescence on extraction efficiency. Based on these findings, we developed and optimized a new analytical method for extracting UV filters from water samples using HPLC-UV as a detector. Under the optimum experimental conditions (0.2 g fumaric acid/0.1 g Na2CO3, 50 mg of magnetic nanoparticles and methanol as an elution solvent assisted by vortex agitation for 5 min) the method was found to afford good linearity in the calibration curves expanding by two orders of magnitude, satisfactory reproducibility and repeatability (1.8–11.1%), and high recoveries (78.4–127.1%). This research provides a new perspective on the influence of the process of effervescence on the extraction efficiency of nonpolar organic compounds and introduces a new method for extracting UV filters from aqueous media.
Digital Competencies as Predictors of Academic Self-Efficacy: Correlations and Implications for Educational Development
Authors: González-Prida Vicente , Chuquin-Berrios Julima Gisella , Moreno-Menéndez Fabricio Miguel , Sandoval-Trigos Jesús César , Pariona-Amaya Diana , Gómez-Bernaola Kesler Osmar
Published: November 2024

This study focuses on determining the correlation between digital competencies and academic self-efficacy of students in universities in the Selva Central (Central Jungle) region of Peru to establish the significance of these factors in academic achievement. This study employs a quantitative, hypothesis-testing design and is both fundamental and co-relational in nature; 84 students from Universidad de la Selva Central were selected. Validated questionnaires were used to gather data on the participants’ DCs including information literacy, communication, content creation, security, and problem-solving skills, as well as the academic self-efficacy of the participants, with all having reliability scores of 0.823 and 0.895, respectively, for the Spearman correlation coefficient. By using statistical analysis, it was established that there is a stronger positive relationship between academic self-efficacy and every dimension of the digital competencies; this was particularly the case with communication and collaboration, rho = 0.853, and digital content creation, rho = 0.863. As such, there is evidence that increasing digital literacy may help improve the learners’ self-confidence in achieving academic success and address the problem of early school leaving, as well as improve competitiveness in the European Union labor market. This study suggests that appropriate curricula and/or workshops to strengthen these effectiveness- and efficiency-related competencies be disseminated to education stakeholders and the findings published in relevant outlets. This research therefore fits within the larger body of literature on the inclusion of digital literacy within tertiary education, information science, and technology.
Artificial Intelligence as an Opportunity for Journalism: Insights from the Brazilian and Portuguese Media
Authors: Canavilhas João , Ioscote Fabia , Gonçalves Adriana
Published: November 2024

Artificial Intelligence (AI) has been emerging as a topic of significant interest, attracting the attention of the public and leading to an increase in research and on media coverage of this technology. This article examines how the Brazilian and Portuguese media represent AI in journalism and the challenges it poses. Using digital methods, this study analysed 60 news articles published between June 2022 and June 2024. The data were collected through an anonymous search on Google News, and the content was analysed using sentiment analysis with the PTNews software, followed by a similarity analysis using the Iramuteq software. The results show a predominantly positive sentiment towards AI in journalism, with 91.8% of articles highlighting its benefits, such as increased efficiency and the automation of routine tasks. However, concerns about disinformation, ethical implications, and the potential erosion of journalistic credibility were less emphasised. The analysis also identified key themes, including AI’s dual role as both an enabler and a threat to journalism, the importance of human oversight, and the challenges of newsroom adaptation. The findings suggest that the Brazilian and Portuguese media generally present AI as an opportunity for journalism, often downplaying the associated risks and ethical challenges.
Fathers’ Experiences of Juggling Work and Family Life in Abu Dhabi Workplaces
Authors: Dickson Martina , Midraj Jessica , McMinn Melissa , Sukkar Hala , Alharthi Maram , Read Barbara
Published: November 2024

In academic literature, fathers often cite barriers related to their employment, which may halt or negatively influence their level of involvement with their children. This is sometimes reported in relation to practices and policies around leave, such as for new-born children or child illness, and flexibility in leaving the workplace to attend key events in children’s lives. Research has also shown that fathers receiving validation of their paternal status in the workplace is an important factor for fathers’ emotional well-being, work life balance, and self-efficacy. In this study, we look at the needs and priorities of fathers working in the capital of the United Arab Emirates (UAE), Abu Dhabi. We used in-depth one-on-one interviews with employed fathers (n = 50) in order to create a detailed picture of their lived experiences, and with human resources personnel (n = 3) to understand if/how fathers are supported in the workplace. We discuss the implications of these experiences and present fathers’ suggestions for employers and workplaces to provide further support to nurture working conditions in which a work–life balance and employee well-being are more likely to be achieved.
Science, Technology, Engineering, and Mathematics Aspirations Among Refugee Families: A Comparative, Multiethnic, Qualitative Analysis
Authors: Hailu Meseret F. , Hovsepian Setrag , Ibrahim Mohammed , Atieh Bruno , Mohamed Saida , Chhetri Nalini , Judson Eugene
Published: November 2024

In this original research manuscript, we examined how gender, race, and ethnic heritage shape the science, technology, engineering, and mathematics (STEM)-related and higher education aspirations of different communities of refugee youth and families participating in university–community organization partnerships. Families from various ethnic-based community organizations in Arizona (serving Bhutanese, Burundian, Congolese, Somali, and Syrian people) participated in this study. Using social cognitive career theory as our conceptual framework and a qualitative research approach, we conducted interviews and focus groups with 27 families over two years to better understand their experiences. We found that the youth and families in our study experienced the following: (1) a recognition of the value of STEM education; (2) the value of consistent support from parents and community members; and (3) appreciation for the practical interventions provided by the university–community partnership. Additionally, families communicated conflicting perceptions of the salience of race and gender. We concluded by discussing the implications of the study findings for higher education scholarship and practice.
Navigating Heir Disputes over the New American South: Confederate Memorials and Media Framing of Black Mayoral Leadership Against Symbols of White Authoritarianism
Authors: King-Meadows Tyson , Agarwal Vishakha , Nalubula Priscilla Nakandi
Published: November 2024

Contrary to what other mayors had done to deal with calls to remove Confederate monuments in their cities, the first Black woman mayor of Charlotte, North Carolina appointed a 2020 commission to evaluate and make recommendations for dealing with the monument controversy. As the state’s largest city and “international gateway” to the New South, Charlotte had long wrestled with tensions over cultural memory. Utilizing a mixed methods “embedded design” case study approach, this article examines quantitative and qualitative data, including an analysis of newspaper articles from The Charlotte Observer and The Raleigh News & Observer, to ascertain public reaction to the commission. Results show that media accounts often framed the city’s monument controversy as reflecting the locale’s new sociodemographic reality, a euphemism for lingering conflicts in the jurisdiction over cultural memory, heritage claims, electoral representation, race, and monumentality.
Spatiotemporal Evolution and Convergence Analysis of Urban Economic Resilience in China—A Case Study of Jiangsu Province
Authors: Ma Xin , Jia Fen
Published: November 2024

Increased external uncertainties and growing tensions within the urban economic system pose new challenges to the urban economy. How to improve the quality of urban economic development and enhance the resilience of urban economies has become a new goal for sustainable urban development. Therefore, taking Jiangsu Province as an example, this study aims to explore the evolutionary trend of urban economic resilience and provide valuable references for cultivating and enhancing urban economic resilience. Initially, a system of indicators is established based on three dimensions: resistance, adaptability, and resilience. Then, combined with the entropy method, coefficient of variation method, kernel density method, natural break-point method, and Theil index, the spatial and temporal differences of economic elasticity of 13 cities in Jiangsu Province from 2006 to 2021 are analyzed. Finally, exploratory spatial data analysis and spatial convergence models are applied to investigate the spatial correlation and convergence of urban economic resilience. The results show an upward trend in the economic resilience of the cities in Jiangsu Province, but with significant regional differences and agglomeration. A downward trend in the level of economic resilience is observed from Southern Jiangsu to Northern Jiangsu. The spatial convergence model suggests that the economic resilience growth of cities will gradually converge to the same stable level, and the regional differences have a trend of reduction. In the future, the Jiangsu region needs to strengthen regional cooperation, enhance the role of the central city to drive, and continuously improve the economic resilience of the city to promote high-quality economic development.
Integrating Digital Technologies and Alternate Reality Games for Sustainable Education: Enhancing Cultural Heritage Awareness and Learning Engagement
Authors: Lin Hao-Chiang Koong , Lu Li-Wen , Lu Ruei-Shan
Published: November 2024

As traditional cultural heritage education gradually reduces students’ exposure to the physical environment, it is more difficult to stimulate students’ awareness of and interest in local culture. The concepts of cultural preservation and sustainable development have not yet been effectively integrated into teaching. Therefore, this study developed an alternative reality game (ARG) combined with a learning scaffold to enable students to gain a deeper understanding of the history and cultural heritage of Taiwan during the Japanese rule period through an immersive learning experience and to explore and solve real-world problems in an interactive environment, which helped students understand the concepts of cultural preservation and sustainable development. The results of the study showed that the experimental group of students using the ARG integrated learning scaffold significantly outperformed the control group in terms of learning effectiveness and engagement. Students in the experimental group not only had a deeper understanding of the curriculum content but also showed higher behavioral and emotional engagement. In addition, students’ awareness of cultural preservation and sustainable development increased, which indicates that the materials developed in this study are effective in promoting their deeper understanding of local educational development and cultural identity. This study confirms that the combination of innovative technology and gamified instruction not only enhances students’ learning effectiveness but also promotes their understanding of cultural heritage. Future research should further optimize the use of technology to reduce the impact of operational difficulties on students’ learning experience and explore how alternative reality game (ARG) technology can be used in more subject areas.
Identifying Landscape Characteristics That Maximize Ecosystem Services Provision
Authors: Benedetti Yanina , Morelli Federico , Svitok Marek , Santolini Riccardo , Kadlecová Petra , Cavalli Alice , Strollo Andrea , Munafò Michele
Published: November 2024

Given global changes and the loss of ecosystem services, it is crucial to assess the effects of landscape characteristics on ecosystem service distribution for sustainable territory management. Italy’s diverse landscapes present an opportunity to study this effect. This study identified optimal elevation and landscape heterogeneity ranges that optimize four ecosystem service provisions across Italy. We mapped ecosystem services across Italy using generalized additive models (GAM) to assess their spatial relationships with landscape characteristics, such as elevation and heterogeneity, and specifically, we identified their optimal values concerning elevation and landscape heterogeneity. In Italy, agricultural production is concentrated at low altitudes, like the Po Valley, while the pre-Alps and Apennines regions at intermediate altitudes provide ecosystem services like timber production and carbon storage. However, elevation gradient and landscape heterogeneity significantly influence trade-offs between agricultural production and these services. The optimal altitude for timber production, carbon storage, and habitat quality is around 1500 m above sea level, while agricultural production peaks at the lowest and highest elevations. Our study shows landscape features’ significant role in supporting specific ecosystem services. This information is crucial for guiding land use planning and management decisions, especially under global land use and climate change.
Barriers to the Implementation of On-Grid Photovoltaic Systems in Ecuador
Authors: Mogrovejo-Narvaez Mateo , Barragán-Escandón Antonio , Zalamea-León Esteban , Serrano-Guerrero Xavier
Published: November 2024

Ecuador has significant solar potential, and the growing demand calls for sustainable energy solutions. Photovoltaic (PV) microgeneration in buildings is an ideal alternative. Identifying barriers to the widespread adoption of this technology is based on expert consultation and multi-criteria analysis, followed by proposals to overcome these challenges. The methodology of this study includes a systematic literature review (SLR), surveys of industry professionals, and statistical analysis of the collected data. The results highlight barriers such as the high initial cost, government-subsidized tariffs, bureaucratic processes and permits, ineffective regulations, limited awareness, lack of financing, distribution and operational network challenges, and insufficient government incentives. The proposed solutions suggest developing incentive policies to promote investment in PV microgeneration, training programs to enhance technical and cultural knowledge of solar energy, simplifying regulatory processes to facilitate project implementation, and providing accessible financing to reduce economic barriers. Additionally, the recommendations include the implementation of demonstration and outreach projects to showcase the feasibility and benefits of PV microgeneration, thus improving the social and technical acceptance of these systems. These actions aim to foster a faster and more effective energy transition in Ecuador.