bluuebunny/crossref_metadata_2025 huggingface dataset:
Subsoiling Before Wheat Sowing Enhances Grain Yield and Water Use Efficiency of Maize in Dryland Winter Wheat and Summer Maize Double Cropping System Under One-Off Irrigation Practice During the Wheat Season
Authors: Peng Yanmin , Zhao Kainan , Zhang Jun , Ren Kaiming , Zhang Junhao , Guo Jinhua , Wang Rongrong , Xiao Huishu , Jiang Peipei , Xu Ninglu , Huang Ming , Wu Jinzhi , Li Youjun
Published: February 2025

The winter wheat and summer maize double cropping system is the primary cropping pattern for wheat and maize in dryland areas of China. The management of tillage in this system is typically conducted before wheat sowing. However, few studies have validated and quantified the impact of tillage methods before wheat sowing and irrigation practices during the wheat season on the yield formation and water use efficiency of summer maize. Therefore, this study hypothesized that subsoiling before wheat sowing improves maize yield and WUE by enhancing soil moisture retention and plant development. A three-year field experiment with a two-factor split-plot design was conducted at the junction of the Loess Plateau and the Huang-Huai-Hai Plain in China for validation, from 2019 to 2022. Three tillage methods before wheat sowing (RT: rotary tillage; PT: plowing, SS: subsoiling) were assigned to the main plots, and two irrigation practices during wheat growing season (W0: zero-irrigation; W1: one-off irrigation) were assigned to subplots. We measured the soil moisture, grain yield, dry matter accumulation, nitrogen (N), phosphorus (P), and potassium (K) accumulation, and water use efficiency of summer maize. The results indicated that subsoiling before wheat sowing increased soil water storage at the sowing of summer maize, thereby promoting dry matter and nutrient accumulation. Compared to rotary tillage and plowing, subsoiling before wheat sowing increased grain yield and water use efficiency of maize by an average of 19.5% and 21.8%, respectively. One-off irrigation during the wheat season had negative effects on pre-sowing soil water storage and maize productivity in terms of yield and dry matter accumulation. However, subsoiling before wheat sowing can mitigate these negative effects of one-off irrigation. Correlation analysis and path model results indicated that tillage methods before wheat sowing had a greater impact on soil water storage and maize productivity than irrigation practices during wheat growing season. The most direct factor affecting maize yield was dry matter accumulation, whereas the most direct factor affecting water use efficiency was nutrient accumulation. The technique for order preference by similarity to an ideal solution (TOPSIS) comprehensive evaluation indicated that subsoiling before wheat sowing was superior for achieving high maize yield and water use efficiency under the practice of one-off irrigation during the wheat season. These findings offer practical guidance for optimizing soil water use and maize productivity in drylands.
Preservation of Soil Ruins After High Temperatures: Water Absorption and Compressive Strength
Authors: Li Miao , Zhang Yifan , Du Zhilin , Zheng Yao , Liu Senbiao , Luo Hongjie , Abdul Waraz , Zhu Jianfeng
Published: February 2025

Soil ruins hold historical significance and serve as witnesses to past civilizations. High temperatures cause soil moisture to evaporate and soil to shrink, leading to cracking issues and making preservation a global challenge. P(AA-AM-AMPS) (Acrylic acid -Acrylamide-2-Acrylamide-2-methy-propenesulfonic acid) composites solve this problem by combining absorbent materials with loess. In this study, P(AA-AM-AMPS) was prepared using a solution method. The water absorption, water retention, air permeability, and compressive strength of P(AA-AM-AMPS) in loess were studied. The results show that after adding P(AA-AM-AMPS) to loess, there is no obvious color difference compared with the blank sample, and the air permeability of the reinforced loess is basically the same as that of the blank soil. After aging resistance tests, the surface cracking of soil clods is significantly reduced compared to that of the blank samples, and their compressive strength improved from 1.8 N/mm2 to 2.6 N/mm2.
Nanofibrous Membrane-Based Stretchable Electrochemical Sweat Sensor for pH Detection
Authors: Zhang Longzhou , Ma Baoyuan , Xu Zhiguang , Zhao Yan
Published: February 2025

Wearable, non-invasive sweat sensors capable of continuously monitoring the pH of sweat, which is a key indicator related to metabolism and homeostasis level, are highly desirable for personal health management. However, ensuring the stability and accuracy of these sensors can be challenging when the body is in motion. In this work, we prepared a stretchable nanofibrous membrane-based electrochemical pH-sensing electrode by embedding carbon nanotubes (MWCNT) and silver nanowires (AgNWs) into an elastic electrospun nanofibrous membrane, followed by polyaniline electrodeposition. The as-prepared pH-sensing electrode showed a high sensitivity of 82.53 mV/pH and high accuracy in ionic solutions with pH ranging from 3 to 7. Notably, the electrode maintained stable sensing performance under deformations, including torsion, bending, and tensile strains up to 30%. Even after 1000 cycles of stretching at a 30% tensile strain, the detection sensitivity remained above 70 mV/pH, indicating its potential application as a wearable electrochemical sensor for monitoring sweat pH in personal health management.
Multidimensional In Situ Characterization of Surface and Free Nanobubbles in Oxidic Nanofluids
Authors: Wollmann Annett , Benker Bernd , Olszok Vinzent , Weber Alfred P.
Published: February 2025

The efficiency of froth flotation drastically drops towards ultrafine particles. Some improvements may be possible using smaller bubbles and high degrees of turbulence, however, reaching their limits in the nanometer particle range. Therefore, an approach is presented where the nanoparticles themselves produce nanobubbles that remain attached and allow, in combination with small bubbles, for the direct flotation of nanoparticles. Here, the formation and the fate of these surface nanobubbles are investigated directly in the dispersed systems for the first time. The required differentiation between free and attached nanobubbles is realized by combining light scattering and extinction measurements. With this combination, it was also possible to study the formation of the attached nanobubbles and the strength of their mechanical attachment to the particles. The successful formation of attached nanobubbles is also confirmed with measurements of the settling velocities. Surprisingly, stable surface nanobubbles can be formed even on hydrophilic particles if the surface contains enough concave sites.
A Concept for On-Road Inter-Laboratory Correlation Exercises with Portable Emission Measurement Systems (PEMS)
Authors: Trikka Maria , Valentini Sara , Cotogno Giulio , Canevari Pierluigi , Melas Anastasios , Clairotte Michaël , Otura García Marcos , Giechaskiel Barouch
Published: February 2025

Portable emission measurement systems (PEMS) are used onboard vehicles to determine the on-road real driving emissions of the vehicles for research or regulatory purposes. The assessment of a PEMS is carried out in a laboratory comparing it with laboratory grade systems (i.e., validation test). This procedure is described in the European Commission Regulation (EU) 2017/1151 and there are limits that must be respected (permissible tolerances). A few inter-laboratory studies evaluated PEMS in the laboratories of different institutes. However, there are no on-road inter-laboratory studies of PEMS because there is no reference instrument available and the source (i.e., emissions of the vehicle) fluctuates significantly due to the variation of the trip characteristics, driver behavior, and environmental conditions, making meaningful evaluation challenging. Here, we present a concept of how such inter-laboratory studies could take place. The concept is that a ‘reference PEMS’ is evaluated first in the laboratory of one of the participating institutes. Then, the ‘reference PEMS’, with a reference vehicle (optionally) is sent to the other institutes to compare their ‘test PEMS’ with the ‘reference PEMS’ on-road. The difference (absolute or relative) of the two PEMS, corrected for any ‘bias’ of the ‘reference PEMS’, is used for the assessment of the ‘test PEMS’ (i.e., comparison with the permissible tolerances) or any statistical analysis (e.g., z-scores). Ideally, the selected reference PEMS should have negligible ‘bias’ (e.g., due to calibration uncertainties, drift), and for this reason, a thorough investigation at the beginning of the exercise is highly recommended. A statistical analysis can be made to confirm if there is bias. Using the differences (absolute or relative) of PEMS, the source (vehicle emissions) variability is cancelled out. The differences can then be compared with the permissible tolerances of the regulation, but up to 40% higher deviations should still be acceptable. We demonstrate the concept with experiments in our institute.
Multitemporal Analysis of Declassified Keyhole Imagery’ for Landuse Change Detection in China (1960~1984): A Python-Based Spatial Coverage and Automation Workflow
Authors: Li Hao , Wang Tao , Yao Weiqi , Liu Huanjun , Song Chunyu , Sun Jinyu
Published: February 2025

Keyhole imagery, acquired between the 1960s and 1980s, offers a unique opportunity to study land use changes prior to the era of modern remote sensing. This study evaluates the potential of free-download Keyhole imagery within China to detect land use changes over five 5-year periods (1960–1984). Using metadata and spatial analysis tools in Python 3.12, we classified images into three resolution categories (meter-level, five-meter-level, and ten-meter-level) and analyzed their spatial distribution and repeated coverage. Results show that 26.5%, 58.9%, and 34.0% of areas were capable of detecting at least one land-use change event for the respective resolution categories. The T3 period (1970–1974) exhibited the greatest diversity of imagery combinations among the five periods. However, uneven spatial and temporal coverage, particularly in western and rural regions, limits the ability of free Keyhole imagery to conduct continuous multi-temporal analysis, and collaboration with paid Keyhole imagery could fill gaps in coverage and improve the accuracy of land use change detection. The study highlights the potential of Keyhole imagery for historical land use research while underscoring the need for methodological refinements to address data limitations. The shared Python scripts and metadata processing techniques could also support other land-use change research using Keyhole imagery globally.
ATIS-Driven 3DCNet: A Novel Three-Stream Hyperspectral Fusion Framework with Knowledge from Downstream Classification Performance
Authors: Zhang Quan , Long Jian , Li Jun , Li Chunchao , Si Jianxin , Peng Yuanxi
Published: February 2025

Reconstructing high-resolution hyperspectral images (HR-HSIs) by fusing low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) is a significant challenge in image processing. Traditional fusion methods focus on visual and statistical metrics, often neglecting the requirements of downstream tasks. To address this gap, we propose a novel three-stream fusion network, 3DCNet, designed to integrate spatial and spectral information from LR-HSIs and HR-MSIs. The framework includes two dedicated branches for extracting spatial and spectral features, alongside a hybrid spatial–spectral branch (HSSI). The spatial block (SpatB) and the spectral block (SpecB) are designed to extract spatial and spectral details. The training process employs the global loss, spatial edge loss, and spectral angle loss for fusion tasks, with an alternating training iteration strategy (ATIS) to enhance downstream classification by iteratively refining the fusion and classification networks. Fusion experiments on seven datasets demonstrate that 3DCNet outperforms existing methods in generating high-quality HR-HSIs. Superior performance in downstream classification tasks on four datasets proves the importance of the ATIS. Ablation studies validate the importance of each module and the ATIS process. The 3DCNet framework not only advances the fusion process by leveraging downstream knowledge but also sets a new benchmark for classification-oriented hyperspectral fusion.
A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar
Authors: Wang Bo , Lai Tao , Wang Qingsong , Huang Haifeng
Published: February 2025

To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system.
Estimating Leaf Chlorophyll Fluorescence Parameters Using Partial Least Squares Regression with Fractional-Order Derivative Spectra and Effective Feature Selection
Authors: Zhuang Jie , Wang Quan
Published: February 2025

Chlorophyll fluorescence (ChlF) parameters serve as non-destructive indicators of vegetation photosynthetic function and are widely used as key input parameters in photosynthesis–fluorescence models. The rapid acquisition of the spatiotemporal dynamics of ChlF parameters is crucial for enhancing remote sensing applications and improving carbon cycle modeling. While hyperspectral reflectance offers a promising data source for estimating ChlF parameters, previous studies have relied primarily on spectral indices derived from specific datasets, which often lack robustness. In this study, we simultaneously monitored ChlF parameters and spectral reflectance in leaves from different species, growth stages, and canopy positions within a temperate deciduous forest. We developed a data-driven partial least squares regression (PLSR) model by integrating fractional-order derivative (FOD) spectral transformation with multiple feature selection methods to predict ChlF parameters. The results demonstrated that FOD spectra effectively improved prediction accuracy compared to conventional PLSR attempts. Among the feature selection algorithms, the least absolute shrinkage and selection operator (LASSO) and stepwise regression (Stepwise) methods outperformed others. Furthermore, the LASSO-based PLSR model that used low-order (<1) FOD spectra achieved high predictive performance for NPQ (R2 = 0.60, RPD = 1.60, NRMSE = 0.16), ΦP (R2 = 0.73, RPD = 1.94, NRMSE = 0.11), ΦN (R2 = 0.62, RPD = 1.62, NRMSE = 0.12), and ΦF (R2 = 0.54, RPD = 1.48, NRMSE = 0.15). These findings suggest that the integration of FOD spectral transformation and appropriate feature selection enables the simultaneous estimation of multiple ChlF parameters, providing valuable insights for the retrieval of ChlF parameters from hyperspectral data.
An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery
Authors: Liang Menglei , Zhang Ying , Chen Liangfu , Tao Jinhua , Fan Meng , Yu Chao
Published: February 2025

Methane is a potent greenhouse gas that significantly contributes to global warming, making the accurate quantification of methane emissions essential for climate change mitigation. The traditional matched filter (MF) algorithm, commonly used to derive methane enhancement from hyperspectral satellite data, is limited by its tendency to underestimate methane plumes, especially at higher concentrations. To address this limitation, we proposed a novel approach—the multi-level matched filter (MLMF)—which incorporates unit absorption spectra matching using a radiance look-up table (LUT) and applies piecewise regressions for concentrations above specific thresholds. This methodology offers a more precise distinction between background and plume pixels, reducing noise interference and mitigating the underestimation of high-concentration emissions. The effectiveness of the MLMF was validated through a series of tests, including simulated data tests and controlled release experiments using satellite observations. These validations demonstrated significant improvements in accuracy: In radiance residual tests, relative errors at high concentrations were reduced from up to −30% to within ±5%, and regression slopes improved from 0.89 to 1.00. In simulated data, the MLMF reduced root mean square error (RMSE) from 1563.63 ppm·m to 337.09 ppm·m, and R² values improved from 0.91 to 0.98 for Gaussian plumes. In controlled release experiments, the MLMF significantly enhanced emission rate estimation, improving R2 from 0.71 to 0.96 and reducing RMSE from 92.32 kg/h to 16.10 kg/h. By improving the accuracy of methane detection and emission quantification, the MLMF presents a significant advancement in methane monitoring technologies. The MLMF’s superior accuracy in detecting high-concentration methane plumes enables better identification and quantification of major emission sources. Its compatibility with other techniques and its potential for integration into real-time operational monitoring systems further extend its applicability in supporting evidence-based climate policy development and mitigation strategies.
A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection
Authors: Liu Jie , He Jinpeng , Chen Huaixin , Yang Ruoyu , Huang Ying
Published: February 2025

In recent years, numerous advanced lightweight models have been proposed for salient object detection (SOD) in optical remote sensing images (ORSI). However, most methods still face challenges such as performance limitations and imbalances between accuracy and computational cost. To address these issues, we propose SggNet, a novel semantic- and graph-guided lightweight network for ORSI-SOD. The SggNet adopts a classical encoder-decoder structure with MobileNet-V2 as the backbone, ensuring optimal parameter utilization. Furthermore, we design an Efficient Global Perception Module (EGPM) to capture global feature relationships and semantic cues through limited computational costs, enhancing the model’s ability to perceive salient objects in complex scenarios, and a Semantic-Guided Edge Awareness Module (SEAM) that leverages the semantic consistency of deep features to suppress background noise in shallow features, accurately predict object boundaries, and preserve the detailed shapes of salient objects. To further efficiently aggregate multi-level features and preserve the integrity and complexity of overall object shape, we introduce a Graph-Based Region Awareness Module (GRAM). This module incorporates non-local operations under graph convolution domain to deeply explore high-order relationships between adjacent layers, while utilizing depth-wise separable convolution blocks to significantly reduce computational cost. Extensive quantitative and qualitative experiments demonstrate that the proposed model achieves excellent performance with only 2.70 M parameters and 1.38 G FLOPs, while delivering an impressive inference speed of 108 FPS, striking a balance between efficiency and accuracy to meet practical application needs.
Erosion and Accretion Characteristics of the Muddy Coast in the Central Coastal Area of Jiangsu Province Based on Long-Term Remote Sensing Monitoring
Authors: Pan Qiqi , Zhang Dong , Xu Min , Zhang Zhuo , Gu Yunjuan
Published: February 2025

Owing to the abundant land resources in the intertidal zone, the central coastal area of Jiangsu Province, China, has implemented large-scale activities such as tidal flat reclamation, aquaculture, and harbor construction, which have strongly affected the local hydrodynamic environment and the evolution of the mudflat. In this study, based on the 1984–2022 multisource remote sensing image data, an enhanced waterline method (EWM) combined with an average slope method (ASM) were adopted to obtain the spatial–temporal evolution characteristics of the continental coastline and intertidal zone in central Jiangsu Province for six typical years, exhibiting the coastal variations at critical year intervals in response to former large-scale coastal development and subsequent coastal zone protection. Results showed that the coastlines significantly advanced toward the sea. The deposited coast moved toward the seaside at an annual rate of 85.91 m, and the reclaimed coast advanced toward the seaside at a yearly rate of 129.25 m, which were dominated by natural siltation and reclamation activities of mudflats. In the past forty years, the coast’s erosion and siltation transition node has gradually moved southward from the Sheyang Estuary to the Simaoyou Estuary. Affected by reclamation and coastal erosion, the most drastic changes in the slope of the erosive intertidal zone occurred in the section from Binhai Port to the Biandan Estuary, ranging from 2‰ to 14‰. The silted coastal section from the Sheyang Estuary to the Xinyang Estuary increased in average slope from 0.89‰ to 2.43‰ as a result of the continuous intensification of erosion. The area of the intertidal mudflat decreased by 47.76% from 1378.59 to 720.11 km2, whereas the mean width of the intertidal zone decreased by 48.02%, from 5518.44 m to 2868.36 m. This study provides current situations of the dynamic changes in the muddy coast of the central Jiangsu coast, which could be a comparison and reference for the sustainable development, utilization, and protection of similar muddy coasts globally.
Sensor-Based Assessment of Mental Fatigue Effects on Postural Stability and Multi-Sensory Integration
Authors: Sun Yao , Sun Yingjie , Zhang Jia , Ran Feng
Published: February 2025

Objective: Mental fatigue (MF) induced by prolonged cognitive tasks poses significant risks to postural stability, yet its effects on multi-sensory integration remain poorly understood. Method: This study investigated how MF alters sensory reweighting and postural control in 27 healthy young males. A 45 min incongruent Stroop task was employed to induce MF, validated via subjective Visual Analog Scale (VAS) scores and psychomotor vigilance tests. Postural stability was assessed under four sensory perturbation conditions (O-H: no interference; C-H: visual occlusion; O-S: proprioceptive perturbation; C-S: combined perturbations) using a Kistler force platform. Center of pressure (COP) signals were analyzed through time-domain metrics, sample entropy (SampEn), and Discrete Wavelet Transform (DWT) to quantify energy distributions across sensory-related frequency bands (visual: 0–0.1 Hz; vestibular: 0.1–0.39 Hz; cerebellar: 0.39–1.56 Hz; proprioceptive: 1.56–6.25 Hz). Results: MF significantly reduced proprioceptive energy contributions (p < 0.05) while increasing vestibular reliance under O-S conditions (p < 0.05). Time-domain metrics showed no significant changes in COP velocity or displacement, but SampEn decreased under closed-eye conditions (p < 0.001), indicating reduced postural adaptability. DWT analysis highlighted MF’s interaction with visual occlusion, altering cerebellar and proprioceptive energy dynamics (p < 0.01). Conclusion: These findings demonstrate that MF disrupts proprioceptive integration, prompting compensatory shifts toward vestibular and cerebellar inputs. The integration of nonlinear entropy and frequency-domain analyses advances methodological frameworks for fatigue research, offering insights into real-time sensor-based fatigue monitoring and balance rehabilitation strategies. This study underscores the hierarchical interplay of sensory systems under cognitive load and provides empirical evidence for optimizing interventions in high-risk occupational and clinical settings.
Ship–Bridge Collision Real-Time Alarming Method Based on Cointegration Theory
Authors: Zhong Wanwen , Liu Deling , Xie Chunhui , Zhang Kuijun , Zhan Wenkai , Cao Maosen , Zhang Yufeng
Published: February 2025

Ship–bridge collisions in inland waterways pose a serious threat to bridge infrastructure, often resulting in structural damage and jeopardizing safety. Despite the widespread deployment of collision warning systems, these systems fail to function effectively due to factors such as weather conditions, equipment malfunctions, and human error. Current alarming technologies, such as wavelet-based methods, are limited by poor real-time performance, high sensitivity to noise, and low localization accuracy, which hinder their practical application. This paper proposes an innovative Kalman filter–cointegration alarming (KFCA) technology, combining cointegration theory with Kalman filtering to achieve precise and real-time collision detection. Through numerical simulation, KFCA is validated, with the results summarized as follows: (i) KFCA effectively recognizes ship–bridge collisions under an SNR of 60, 70, and 80 dB; and (ii) it accurately identifies impact locations on the bridge based on sensor arrangement indices. Compared to existing methods, KFCA offers significant advantages in real-time response, noise resistance, and localization accuracy. This technology provides an efficient solution for bridge management departments, enabling the timely and accurate detection of ship–bridge collisions, thereby enhancing bridge safety and reducing secondary disasters.
An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis
Authors: Liu Jixin , Deng Liwei , Cao Yue , Wen Chenglin , Song Zhihuan , Liu Mei , Cui Xiaowei
Published: February 2025

To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. Subsequently, the rolling bearing failure signals are filtered and denoised using the optimized CYCBD, producing a denoised signal. Ultimately, the noise-reduced signal is fed into the BiLSTM model to realize the classification of faults. The experimental findings demonstrate the suggested approach’s strong noise reduction performance and high diagnostic accuracy. The optimized CYCBD–BiLSTM improves the accuracy by approximately 9.89% compared with other methods when the signal-to-noise ratio (SNR) reaches −9 dB, and it can be effectively used for diagnosing rolling bearing faults under noisy backgrounds.
A Comparative Study of Plantar Pressure and Inertial Sensors for Cross-Country Ski Classification Using Deep Learning
Authors: Polo-Rodríguez Aurora , Escobedo Pablo , Martínez-Martí Fernando , Marcen-Cinca Noel , Carvajal Miguel A. , Medina-Quero Javier , Martínez-García María Sofía
Published: February 2025

This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar pressure, foot angles, and acceleration. A deep learning model based on CNN and LSTM was trained on various sensor combinations, ranging from two specific pressure sensors to a full multisensory array per foot incorporating 4 pressure sensors and an inertial measurement unit with accelerometer, magnetometer, and gyroscope. Results demonstrate an encouraging performance with plantar pressure sensors and classification accuracy closer to inertial sensing. The proposed approach achieves a global average accuracy of 94% to 99% with a minimal sensor setup, highlighting its potential for low-cost and precise technique classification in cross-country skiing and future applications in sports performance analysis.
GPC-YOLO: An Improved Lightweight YOLOv8n Network for the Detection of Tomato Maturity in Unstructured Natural Environments
Authors: Dong Yaolin , Qiao Jinwei , Liu Na , He Yunze , Li Shuzan , Hu Xucai , Yu Chengyan , Zhang Chengyu
Published: February 2025

Effective fruit identification and maturity detection are important for harvesting and managing tomatoes. Current deep learning detection algorithms typically demand significant computational resources and memory. Detecting severely stacked and obscured tomatoes in unstructured natural environments is challenging because of target stacking, target occlusion, natural illumination, and background noise. The proposed method involves a new lightweight model called GPC-YOLO based on YOLOv8n for tomato identification and maturity detection. This study proposes a C2f-PC module based on partial convolution (PConv) for less computation, which replaced the original C2f feature extraction module of YOLOv8n. The regular convolution was replaced with the lightweight Grouped Spatial Convolution (GSConv) by downsampling to reduce the computational burden. The neck network was replaced with the convolutional neural network-based cross-scale feature fusion (CCFF) module to enhance the adaptability of the model to scale changes and to detect many small-scaled objects. Additionally, the integration of the simple attention mechanism (SimAM) and efficient intersection over union (EIoU) loss were implemented to further enhance the detection accuracy by leveraging these lightweight improvements. The GPC-YOLO model was trained and validated on a dataset of 1249 mobile phone images of tomatoes. Compared to the original YOLOv8n, GPC-YOLO achieved high-performance metrics, e.g., reducing the parameter number to 1.2 M (by 59.9%), compressing the model size to 2.7 M (by 57.1%), decreasing the floating point of operations to 4.5 G (by 45.1%), and improving the accuracy to 98.7% (by 0.3%), with a detection speed of 201 FPS. This study showed that GPC-YOLO could effectively identify tomato fruit and detect fruit maturity in unstructured natural environments. The model has immense potential for tomato ripeness detection and automated picking applications.
Cellulose/TiO2 Humidity Sensor
Authors: Devesa Susana , Benzarti Zohra , Costa Madalena , Cavaleiro Diogo , Faia Pedro , Carvalho Sandra
Published: February 2025

Resistivity-type humidity sensors, which detect changes in electrical resistance in response to variations in environmental humidity, have garnered significant interest due to their widespread application in industry, agriculture, and daily life. These sensors rely on diverse materials for fabrication, but their increasing variety has contributed to the accumulation of electronic waste. As a biodegradable polymer, cellulose offers unique advantages, including a naturally hydrophilic structure and a large specific surface area. These properties enable cellulose to reduce e-waste generation while facilitating the efficient adsorption of water molecules. However, despite these benefits, humidity sensors based solely on cellulose often suffer from poor sensitivity due to its limited hydrophilicity and non-adjustable structure. To overcome these limitations, the development of composite materials emerges as a promising solution for enhancing the performance of cellulose-based humidity sensors. Combining the complementary properties of cellulose and TiO2, this work presents the development of a cellulose/TiO2 composite humidity sensor through a sustainable approach. The resulting composite material exhibits significantly improved sensitivity compared with a sensor fabricated purely from cellulose. To achieve this, TiO2 nanoparticles were incorporated into cellulose extracted from potato peels, and the composite film was fabricated using the casting method. The sensor’s performance was evaluated by analyzing the dependence of its complex impedance, measured over a frequency range between 2 kHz and 10 MHz, while varying relative humidity (RH).
Lightweight Explicit 3D Human Digitization via Normal Integration
Authors: Liu Jiaxuan , Wu Jingyi , Jing Ruiyang , Yu Han , Liu Jing , Song Liang
Published: February 2025

In recent years, generating 3D human models from images has gained significant attention in 3D human reconstruction. However, deploying large neural network models in practical applications remains challenging, particularly on resource-constrained edge devices. This problem is primarily because large neural network models require significantly higher computational power, which imposes greater demands on hardware capabilities and inference time. To address this issue, we can optimize the network architecture to reduce the number of model parameters, thereby alleviating the heavy reliance on hardware resources. We propose a lightweight and efficient 3D human reconstruction model that balances reconstruction accuracy and computational cost. Specifically, our model integrates Dilated Convolutions and the Cross-Covariance Attention mechanism into its architecture to construct a lightweight generative network. This design effectively captures multi-scale information while significantly reducing model complexity. Additionally, we introduce an innovative loss function tailored to the geometric properties of normal maps. This loss function provides a more accurate measure of surface reconstruction quality and enhances the overall reconstruction performance. Experimental results show that, compared with existing methods, our approach reduces the number of training parameters by approximately 80% while maintaining the generated model’s quality.
Exploring Cannabidiol’s Therapeutic Role in Colorectal Cancer: Network Pharmacology and Molecular Docking Insights
Authors: Guzmán-Flores Juan Manuel , Martínez-Esquivias Fernando , Alviz-Amador Antistio , Avilés-Rodríguez Guadalupe Thonanzyn , García-Azuela Michel Fabricio
Published: February 2025

Background: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, and current treatments have significant side effects. Cannabidiol (CBD), a compound derived from Cannabis sativa, has demonstrated promising anticancer properties. However, further investigation is required to elucidate its underlying molecular mechanisms. Methods: Network pharmacology and molecular docking analysis approaches were utilized. Molecular targets of CBD and CRC-associated genes were identified using the Swiss Target Prediction, Malacards, and DisGeNet databases. Protein–protein interactions were analyzed using the STRING and Cytoscape. Ontology enrichment was conducted using ShinyGO, and gene expression and immune infiltration were evaluated with UALCAN and TISIDB. Results: We found 95 common genes between CRC and CBD targets. Six major genes (ANXA5, IGF1R, JAK2, MAPK8, MDM2, and PARP1) were particularly interesting due to their high connectivity and role in relevant metabolic pathways. The results of the molecular docking analysis indicated that CBD interacts favorably with these genes, modulating critical pathways such as RAS/MAPK and PI3K-AKT/FoxO, which are involved in cell proliferation, apoptosis, and cell cycle regulation. ANXA5 and JAK2 were identified as particularly relevant, as they correlated significantly with immune cell infiltration, suggesting a role in the immunoregulation of the tumor microenvironment. Conclusions: CBD has the potential to modulate key molecular processes in CRC through specific pathways and core genes, presenting itself as a possible complementary therapy to improve efficacy and reduce the adverse effects of conventional treatments.
Sexual Violence Against Men: Impacts on Individual Victims, Significant Others, and the Community in the Eastern Region of Congo
Authors: Yagi Ines
Published: February 2025

Conflict-related sexual violence remains a systematic tool employed in warfare and terrorism to undermine communities, as recognized by the UN Security Council in Resolutions 1820 (2008) and 2242 (2015). Sexual violence has been a persistent issue throughout the history of conflict, war, and human existence. However, the victimization of men and boys remains insufficiently acknowledged and reported. This under-recognition can be attributed to several factors, such as societal stigma, the topic’s sensitive nature, prevailing stereotypes, and cultural influences. Male sexual violence is recognized as a critical public health concern because of its profound, immediate, and lasting effects on the victims, their loved ones, the community, and society at large. This paper examines the social and relational consequences of such violence on the individual victims, their significant others, and the wider society. The analysis will draw upon data gathered from the author’s doctoral thesis conducted in the Eastern region of the Democratic Republic of the Congo in 2021, as well as recent research on this critical issue to enrich the discussion.
Afghan and Arab Refugee International Medical Graduate Brain Waste: A Scoping Review
Authors: Pirzada Ahmad Fahim , Chaban Zaina , Guggenbickler Andrea Michelle , Mokhtabad Amrei Seyedeh Ala , Sulikhanyan Arliette Ariel , Afzal Laila , Hakim Rashim , Koga Patrick Marius
Published: February 2025

The forced migration of tens of thousands of refugee doctors exacerbates a phenomenon referred to as “brain waste”. Based on the Arksey and O’Malley model, this scoping review conducted in SCOPUS, ProQuest, CINAHL, and ERIC via EBSCO examines three decades of peer-reviewed literature (1990–2022) on resettled Afghan and Arab refugee International Medical Graduates (rIMGs) attempting, most often unsuccessfully, relicensing/professional reentry in the USA, Canada, the EU, Australia, and New Zealand. The search identified 760 unique citations, of which only 16 met the inclusion/exclusion criteria. Included publications explored (1) systemic and personal barriers to rIMG professional reentry and (2) existing supporting reentry programs and policy recommendations. The findings point to inconsistencies in evaluating medical education credentials and to racial profiling, inequities, and discrimination in residency interviews. The support provided by some programs was perceived as inadequate, confusing, biased, and gendered. The rIMG personal barriers identified included refugees’ unique limitations and life adversities. The review grasps a collection of isolated support programs with widely varying learning performance, unclear buy-in from residency program directors, and weak policy impacts. This analysis highlights the need for legislated and standardized rIMG reentry support programs to reduce physician shortages, health disparities, and, ultimately, IMG brain waste.
The Propensity for Parenthood in Italy Between Cultural Orientations, Individual Choices, and Welfare Policies
Authors: Mangone Emiliana , Masullo Giuseppe , Cubeddu Francesca , Matteo Miriam
Published: February 2025

The birth rate data in Italy continue to be discouraging. They are taking on the nature of a structural problem given the continuous decline in population, which is neither accompanied by significant structural policies to promote parenthood, nor supporting the births of fertile generations. This study uses a mixed methods approach and was started in 2022 with the creation of six focus groups in the southern Italian regions of Basilicata, Campania, Calabria, Molise, Puglia, and Sicily. Images taken from the “Fertility Day” social communication campaign were used as a visual stimulus to initiate the discussion. In 2024, it continued with a survey based on a pilot study at a hospital involving pregnant women in southern Italy. The results show that the choice to become parents is not only interconnected with either socio-economic issues or the presence/absence of services and incentives but also relates to emerging cultural models that need to be better understood. On this basis, the dual role of institutions in promoting policies therefore becomes fundamental. On the one hand, the “guarantors” of human, civil, and social rights, which have been developed and established over the centuries, are significant. On the other hand, the “promoters” of parental support pathways are needed.
Asymmetric Effect of Natural Resource Exploitation on Climate Change in Resource-Rich African Countries
Authors: Hassan Adewale Samuel
Published: February 2025

This study investigated the asymmetric impact of natural resource exploitation on climate change in resource-rich African countries, based on panel data from 1980 to 2022. The dynamic common correlated effect (DCCE) and dynamic seemingly unrelated regression (DSUR) econometric techniques were employed to evaluate the long-term effects of positive shocks and negative shocks to natural resource exploitation. The findings revealed a positive relationship between both positive and negative shocks to natural resource exploitation and temperature, with increases in natural resource exploitation exerting a more intensified impact on temperature than decreases. In contrast, both positive and negative changes in natural resource exploitation are negatively related to precipitation, with an increased exploitation intensity having a more pronounced effect on rainfall patterns. The study also highlights the critical role of control variables such as GDP per capita, urban population, and total energy consumption in altering temperature and precipitation patterns. The findings underscore the importance of adopting sustainable natural resource extraction practices, integrating green technologies, and promoting collaboration across natural resource exploitation and renewable energy value chains to mitigate the negative impacts of natural resource exploitation.
Food Forward’s Fresh Approach: Can Meal Boxes Improve Food Security for Low-Income Communities Living in Healthy Food Priority Areas?
Authors: Katre Aparna , Raddatz Brianna , Swanson Britta , Turgeon Taylor , Dugan Alison
Published: February 2025

Food security continues to be an issue in the United States, especially in urban healthy food priority areas. Most interventions prioritize physical and economic access, just one of the four pillars of food security. We present the beginnings of a framework to help qualitatively assess an intervention’s contributions to various dimensions and sub-dimensions of food security and summarize the contributions of dominant interventions observed in the literature. We have followed Food Forward, a meal box social enterprise, since its conception in 2021. As a case study, we apply the framework and find that meal boxes have the potential to comprehensively address all dimensions of food security at the household level. Community involvement in strategic planning and operations can improve food utilization, and a financially sustainable social enterprise model can facilitate access, availability, and utilization stability. Future research is needed to develop the framework further by adding indicators to assess individual and collective contributions of community-based interventions to household food security. Longitudinal studies for innovative social business models for meal boxes are necessary to assess their contribution to the stability dimension of household food security.
Research on Design of Collective Housing with Air-Circulation Central Air-Conditioning System Based on Solar Energy Utilization
Authors: Shi Chuan , Wang Zhen , Liang Qian , Wan Ziwei , Ma Qingsong
Published: February 2025

The article presents an innovative design schema for air circulation within collective housing, which effectively reduces energy consumption and improves the indoor environment. It also solves the problem of the high operating and maintenance costs caused by the simultaneous installation of air conditioners and radiators. Employing dynamic energy consumption calculation software THERB for HAM, the energy-saving benefits of this design are simulated. The strategy involves capturing heat within the sunspace and transferring it to the conditioning chamber, from where the air is tempered and circulated throughout the habitable spaces to minimize heating. The findings suggest that by strategically using sunspace heat, heating energy can be significantly reduced by 43%. It helps to promote the development of sustainable building design. A comparative analysis of window materials in the sunspace, including single glazing, double glazing, and low-e double glazing, indicates that windows with enhanced insulation properties can substantially decrease the heating energy. Considering both energy efficiency and economic feasibility, low-e double glazing is identified as a particularly advantageous choice.
Evaluating the Current and Future of Corporation and Hotel Industry Performance by Analyzing CEO Messages Using the SBSC Framework Assessment
Authors: Kang Hyeon , Meng Fan , Na Hyung Jong
Published: February 2025

The hotel industry has faced significant challenges in both the short and long term, particularly due to the impact of COVID-19, highlighting the need for strategic adjustments to ensure sustainability and growth. This study investigates the strategic elements emphasized in CEO messages published on hotel company websites and their relationship with current and future corporate performance. Utilizing text mining techniques and the Sustainability Balanced Scorecard (SBSC) framework, this research classifies key strategies into financial, customer, internal business processes, learning and growth, social responsibility, and security and safety perspectives. Empirical analysis using the 2SLS regression model reveals several critical findings. First, strategies emphasizing financial and customer perspectives positively influence future corporate performance, though no significant impact is observed on current performance. Second, internal business processes and learning and growth perspectives show no statistically significant relationship with either current or future corporate performance. Third, social responsibility initiatives have an immediate positive effect on current performance, but their long-term impact is negligible. Finally, security and safety perspectives negatively affect both short-term and long-term corporate performance, largely due to the associated costs. However, additional network analysis demonstrates that security and safety factors are interconnected with other strategic elements, suggesting their complementary importance for overall operational stability. The study highlights the critical role of transparent information delivery through CEO messages as a tool for communicating corporate vision and strategy, offering insights into how stakeholders can utilize this information for decision-making. These findings provide valuable theoretical and managerial implications for sustainable hotel management in a post-pandemic environment.