bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Characterization of Liposomal-Based Carriers for the Encapsulation of Rosa canina Fruit Extract: In Vitro Gastrointestinal Release Behavior
Authors: Jovanović Aleksandra A. , Balanč Bojana , Petrović Predrag M. , Volić Mina , Micić Darko , Živković Jelena , Šavikin Katarina P.
Published: September 2024

The increasing demand for natural compounds as an alternative to synthetic antioxidants and conservans has led to the utilization of secondary plant metabolites in the food industry, as these bioactive compounds possess great antioxidative and antimicrobial properties without side effects on human health. Despite this, the sensitivity of plant-derived compounds is a restrictive factor in terms of their full potential. The current research aimed to characterize rosehip-fruit-extract-loaded liposomes (non-treated and UV-irradiated) in terms of their density, surface tension, viscosity, chemical composition (FTIR and HPLC analyses), and thermal behavior. In the storage stability study, the vesicle size, polydispersity index (PDI), zeta potential, conductivity, and mobility of the liposomes were monitored. FTIR analysis confirmed that the plant compounds were successfully loaded within the carrier, while no chemical reaction between the rosehip fruit extract and phospholipids was detected. The results of the HPLC analysis evidence the high potential for liposomal encapsulation to protect sensitive bioactives in the rosehip fruit extract from the degrading effect of UV irradiation. The size of the rosehip-fruit-extract-encapsulated liposomes increased on the seventh day of storage from 250 nm to 300 nm, while the zeta potential values were between −21 mV and −30 mV in the same period and further stabilized over 60 days of monitoring. In Vitro release studies in water and simulated gastrointestinal fluids showed that the presence of enzymes and bile salts (in intestinal fluid) enhanced the rosehip–polyphenol permeability from liposomes (70.3% after 6 h) compared with their release in water after 24 h and in gastric fluid after 4 h (38.9% and 41.4%, respectively). The obtained results indicate that the proliposome method was an effective method for rosehip fruit extract liposomal encapsulation and for the delivery of these plant-derived bioactives in foods.
High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision
Authors: Zhang Qi-Yuan , Fan Ke-Jun , Tian Zhixi , Guo Kai , Su Wen-Hao
Published: September 2024

The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in each pod, and obtain soybean phenotypes. The YOLOv8-Repvit model yielded the most optimal recognition results, with an R2 coefficient value of 0.96 for both pods and beans, and the RMSE values were 2.89 and 6.90, respectively. Moreover, a novel algorithm was devised to efficiently differentiate between the main stem and branches of soybean plants, called the midpoint coordinate algorithm (MCA). This was accomplished by linking the white pixels representing the stems in each column of the binary image to draw curves that represent the plant structure. The proposed method reduces computational time and spatial complexity in comparison to the A* algorithm, thereby providing an efficient and accurate approach for measuring the phenotypic characteristics of soybean plants. This research lays a technical foundation for obtaining the phenotypic data of densely overlapped and partitioned mature soybean plants under field conditions at harvest.
The Variability of Berry Parameters Could Be an Indicator of the Potential Quality of the Vineyard
Authors: Travanic-Fuentes Zlavek , Gutiérrez-Gamboa Gastón , Moreno-Simunovic Yerko
Published: September 2024

Background: Berry quality potential from a single vineyard is mainly defined based on some physicochemical parameters and subjective assessments. In this way, berry maturity variability would be a key factor affecting berry quality. Methods: This trial aimed to study the effects of the maturity variability of berries harvested from plots of low (~37,080 kg ha−1), middle (~12,545 kg ha−1), and high (~1476 kg ha−1) quality potential on berry and wine physicochemical parameters of Cabernet Sauvignon in two consecutive seasons. The quality potential of the plots was defined by the winemakers considering mostly yield per hectare and the final price of their wines. Results: The berry heterogeneous maturity of soluble solids and berry weight in Cabernet Sauvignon was confirmed. The coefficient of variability (CV) of berry weight of high-quality plots was high at véraison and decreased as ripening progressed, reaching CV of 19.9% at harvest. Low-quality plots showed the lowest CV of berry weight in all the studied dates, whereas high-quality plots presented the lowest CV in soluble solids content of berries, reaching a 5.1% of variability at harvest. The physicochemical parameters showed that high-quality plots were characterized by high levels of soluble solids and phenolic maturity parameters, whereas samples from low-quality plots reached high berry weight and malic acid content. Berry differences among the physicochemical parameters determined wine quality, which allowed for plots to be classified by their potential quality at harvest. Conclusions: Studying maturity variability of soluble solids and berry weight will allow for sampling to be sectorized within a vineyard to reduce the extremes of maturity that would affect wine quality and productive goals of winemakers.
Streamlining of Simple Sequence Repeat Data Mining Methodologies and Pipelines for Crop Scanning
Authors: Geethanjali Subramaniam , Kadirvel Palchamy , Anumalla Mahender , Hemanth Sadhana Nithyananth , Annamalai Anandan , Ali Jauhar
Published: September 2024

Genetic markers are powerful tools for understanding genetic diversity and the molecular basis of traits, ushering in a new era of molecular breeding in crops. Over the past 50 years, DNA markers have rapidly changed, moving from hybridization-based and second-generation-based to sequence-based markers. Simple sequence repeats (SSRs) are the ideal markers in plant breeding, and they have numerous desirable properties, including their repeatability, codominance, multi-allelic nature, and locus specificity. They can be generated from any species, which requires prior sequence knowledge. SSRs may serve as evolutionary tuning knobs, allowing for rapid identification and adaptation to new circumstances. The evaluations published thus far have mostly ignored SSR polymorphism and gene evolution due to a lack of data regarding the precise placements of SSRs on chromosomes. However, NGS technologies have made it possible to produce high-throughput SSRs for any species using massive volumes of genomic sequence data that can be generated fast and at a minimal cost. Though SNP markers are gradually replacing the erstwhile DNA marker systems, SSRs remain the markers of choice in orphan crops due to the lack of genomic resources at the reference level and their adaptability to resource-limited labor. Several bioinformatic approaches and tools have evolved to handle genomic sequences to identify SSRs and generate primers for genotyping applications in plant breeding projects. This paper includes the currently available methodologies for producing SSR markers, genomic resource databases, and computational tools/pipelines for SSR data mining and primer generation. This review aims to provide a ‘one-stop shop’ of information to help each new user carefully select tools for identifying and utilizing SSRs in genetic research and breeding programs.
In Vitro Gastrointestinal Digestion of Corn-Oil-in-Water Pickering Emulsions: Influence of Lignin-Containing Cellulose Nanofibrils Loading
Authors: Wang Langhong , Liu Lin , Li Jun , Liao Jianming , Li Bin , Jiao Wenjuan , Guo Shasha
Published: September 2024

There is a growing trend in incorporating biomass-based engineered nanomaterials into food products to enhance their quality and functionality. The zeta potential, droplet size, microstructure, and content of free fatty acid (FFA) release were determined to investigate the influence of a plant-derived particle stabilizer, i.e., lignin-containing cellulose nanofibrils (LCNFs). Remarkable differences were observed during digestion stages, which were found to be correlated with the concentrations of LCNFs. The gradual FFA release in the small intestine stage from LCNF-coated lipid droplets was monitored over time, with a final lowest release of FFAs amounting to 26.3% in the emulsion containing 20.0% (v/v) of the dispersed phase stabilized by 3 mg/mL of LCNFs. This release can be attributed to the physical barrier at lipid droplet surfaces and the network effect created by the free LCNFs in the continuous phase. This work provides a foundation for the potential application of nature-derived LCNF materials in reducing fat absorbance.
Damage Evaluation of Unconsolidated Sandstone Particle Migration Reservoir Based on Well–Seismic Combination
Authors: Wang Zhao , Yin Hanjun , Tang Haoxuan , Hou Yawei , Yu Hang , Liu Qiang , Tang Hongming , Jia Tianze
Published: September 2024

The primary factor constraining the performance of unconsolidated sandstone reservoirs is blockage from particle migration, which reduces the capacity of liquid production. By utilizing logging, seismic, core–testing, and oil–well production data, the reservoir damage induced by particle migration in the Bohai A oilfield was characterized and predicted through combined well–seismic methods. This research highlights the porosity, permeability, median grain diameter, and pore structure as the primary parameters influencing reservoir characteristics. Based on their permeability differences, reservoirs can be categorized into Type I (permeability ≥ 800 mD), Type II (400 mD < permeability < 800 mD), and Type III (permeability ≤ 400 mD). The results of the core displacement experiments revealed that, compared to their initial states, the permeability change rates for Type I and Type II reservoirs exceeded 50%, whereas the permeability change rate for Type III reservoirs surpassed 200%. Furthermore, by combining this quantitative relationship model with machine learning techniques and well–seismic methods, the distribution of permeability change rates caused by particle migration across the entire region was successfully predicted and validated against production data from three oil wells. In addition, to build a reliable deep learning model, a sensitivity analysis of the hyperparameters was conducted to determine the activation function, optimizer, learning rate, and neurons. This method enhances the prediction efficiency of reservoir permeability changes in offshore oilfields with limited coring data, providing important decision support for reservoir protection and field development.
Influence of Plasticizers Concentration on Thermal, Mechanical, and Physicochemical Properties on Starch Films
Authors: Sirbu Elena-Emilia , Dinita Alin , Tănase Maria , Portoacă Alexandra-Ileana , Bondarev Andreea , Enascuta Cristina-Emanuela , Calin Catalina
Published: September 2024

The increasing demand for sustainable packaging materials has driven the exploration of biodegradable alternatives to synthetic plastics. This study investigates the thermal and mechanical properties of starch-based films plasticized with varying concentrations of glycerol and sorbitol. Cornstarch films were prepared with glycerol and sorbitol plasticizers in different ratios, and their physical characteristics, including swelling index, water solubility, and thermal stability, were assessed using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and tensile testing. The results indicate that the incorporation of plasticizers significantly influenced the films’ properties. Films with higher glycerol content exhibited greater flexibility and solubility, while sorbitol-plasticized films showed enhanced thermal stability. The combination of both plasticizers yielded films with balanced properties suitable for food packaging applications. This study demonstrates the potential of glycerol and sorbitol as effective plasticizers in developing biodegradable starch-based films, offering a promising alternative to conventional plastic packaging.
A New Framework for Generating Indoor 3D Digital Models from Point Clouds
Authors: Gao Xiang , Yang Ronghao , Chen Xuewen , Tan Junxiang , Liu Yan , Wang Zhaohua , Tan Jiahao , Liu Huan
Published: September 2024

Three-dimensional indoor models have wide applications in fields such as indoor navigation, civil engineering, virtual reality, and so on. With the development of LiDAR technology, automatic reconstruction of indoor models from point clouds has gained significant attention. We propose a new framework for generating indoor 3D digital models from point clouds. The proposed method first generates a room instance map of an indoor scene. Walls are detected and projected onto a horizontal plane to form line segments. These segments are extended, intersected, and, by solving an integer programming problem, line segments are selected to create room polygons. The polygons are converted into a raster image, and image connectivity detection is used to generate a room instance map. Then the roofs of the point cloud are extracted and used to perform an overlap analysis with the generated room instance map to segment the entire roof point cloud, obtaining the roof for each room. Room boundaries are defined by extracting and regularizing the roof point cloud boundaries. Finally, by detecting doors and windows in the scene in two steps, we generate the floor plans and 3D models separately. Experiments with the Giblayout dataset show that our method is robust to clutter and furniture point clouds, achieving high-accuracy models that match real scenes. The mean precision and recall for the floorplans are both 0.93, and the Point–Surface Distance (PSD) and standard deviation of the PSD for the 3D models are 0.044 m and 0.066 m, respectively.
Determination of Microtopography of Low-Relief Tidal Freshwater Forested Wetlands Using LiDAR
Authors: Shukla Tarini , Tang Wenwu , Trettin Carl C. , Chen Shen-En , Allan Craig
Published: September 2024

The microtopography of tidal freshwater forested wetlands (TFFWs) impacts biogeochemical processes affecting the carbon and nitrogen dynamics, ecological parameters, and habitat diversity. However, it is challenging to quantify low-relief microtopographic features that might only vary by a few tens of centimeters. We assess the high-resolution fine-scale microtopographic features of a TFFW with terrestrial LiDAR and aerial LiDAR to test a method appropriate to quantify microtopography in low-relief forested wetlands. Our method uses a combination of water-level and elevation thresholding (WALET) to delineate hollows in terrestrial and aerial LiDAR data. Close-range remote sensing technologies can be used for microtopography in forested regions. However, the aerial and terrestrial LiDAR technologies have not been used to analyze or compare microtopographic features in TFFW ecosystems. Therefore, the objectives of this study were (1) to characterize and assess the microtopography of low-relief tidal freshwater forested wetlands and (2) to identify optimal elevation thresholds for widely available aerial LiDAR data to characterize low-relief microtopography. Our results suggest that the WALET method can correctly characterize the microtopography in this area of low-relief topography. The microtopography characterization method described here provides a basis for advanced applications and scaling mechanistic models.
SA-SatMVS: Slope Feature-Aware and Across-Scale Information Integration for Large-Scale Earth Terrain Multi-View Stereo
Authors: Chen Xiangli , Diao Wenhui , Zhang Song , Wei Zhiwei , Liu Chunbo
Published: September 2024

Satellite multi-view stereo (MVS) is a fundamental task in large-scale Earth surface reconstruction. Recently, learning-based multi-view stereo methods have shown promising results in this field. However, these methods are mainly developed by transferring the general learning-based MVS framework to satellite imagery, which lacks consideration of the specific terrain features of the Earth’s surface and results in inadequate accuracy. In addition, mainstream learning-based methods mainly use equal height interval partition, which insufficiently utilizes the height hypothesis surface, resulting in inaccurate height estimation. To address these challenges, we propose an end-to-end terrain feature-aware height estimation network named SA-SatMVS for large-scale Earth surface multi-view stereo, which integrates information across different scales. Firstly, we transform the Sobel operator into slope feature-aware kernels to extract terrain features, and a dual encoder–decoder architecture with residual blocks is applied to incorporate slope information and geometric structural characteristics to guide the reconstruction process. Secondly, we introduce a pixel-wise unequal interval partition method using a Laplacian distribution based on the probability volume obtained from other scales, resulting in more accurate height hypotheses for height estimation. Thirdly, we apply an adaptive spatial feature extraction network to search for the optimal fusion method for feature maps at different scales. Extensive experiments on the WHU-TLC dataset also demonstrate that our proposed model achieves the best MAE metric of 1.875 and an RMSE metric of 3.785, which constitutes a state-of-the-art performance.
GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data
Authors: Zhang Xiang , Xie Shuai , Zhang Yiping , Song Qinghai , Filippa Gianluca , Qi Dehua
Published: September 2024

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.
Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation
Authors: Feng Wei , Bu Fan , Wu Puxia , Dauphin Gabriel , Quan Yinghui , Xing Mengdao
Published: September 2024

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was 7.2% higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached 98.8%. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated.
Enhancing Bioluminescence Imaging of Cultured Tissue Explants Using Optical Telecompression
Authors: Myung Jihwan
Published: September 2024

Long-term observation of single-cell oscillations within tissue networks is now possible by combining bioluminescence reporters with stable tissue explant culture techniques. This method is particularly effective in revealing the network dynamics in systems with slow oscillations, such as circadian clocks. However, the low intensity of luciferase-based bioluminescence requires signal amplification using specialized cameras (e.g., I-CCDs and EM-CCDs) and prolonged exposure times, increasing baseline noise and reducing temporal resolution. To address this limitation, we implemented a cost-effective optical enhancement technique called telecompression, first used in astrophotography and now commonly used in digital photography. By combining a high numerical aperture objective lens with a magnification-reducing relay lens, we significantly increased the collection efficiency of the bioluminescence signal without raising the baseline CCD noise. This method allows for shorter exposure times in time-lapse imaging, enhancing temporal resolution and enabling more precise period estimations. Our implementation demonstrates the feasibility of telecompression for enhancing bioluminescence imaging for the tissue-level network observation of circadian clocks.
The FeetMe® Insoles System: Repeatability, Standard Error of Measure, and Responsiveness
Authors: Martin Nathan , Leboeuf Fabien , Pradon Didier
Published: September 2024

Background: Three-dimensional motion analysis using optoelectronic cameras and force platforms is typically used to quantify gait disorders. However, these systems have various limitations, particularly when assessing patients in an ecological environment. To address these limitations, several wearable devices have been developed. However, few studies have reported metrological information regarding their repeatability and sensitivity to change. Methods: A healthy adult performed 6 min walking tests with FeetMe® system insoles under different walking conditions overground and on a treadmill. The standard error of measurement (SEM), the minimum detectable differences (MDDs), and the effect size (ES) were calculated for spatio-temporal parameters, and the ground reaction force was calculated from the 16,000 steps recorded. Results: SEM values were below 3.9% for the ground reaction force and below 6.8% for spatio-temporal parameters. ES values were predominantly high, with 72.9% of cases between overground and treadmill conditions with induced asymmetry, and 64.5% of cases between treadmill conditions with and without induced asymmetry exhibiting an ES greater than 1.2. The minimum detectable differences ranged from 4.5% to 10.7% for ground reaction forces and 2.1% to 18.9% for spatio-temporal parameters. Conclusion: Our study demonstrated that the FeetMe® system is a reliable solution. The sensitivity to change showed that these instrumented insoles can effectively reflect patient asymmetry and progress.
Regulating Modality Utilization within Multimodal Fusion Networks
Authors: Singh Saurav , Saber Eli , Markopoulos Panos P. , Heard Jamison
Published: September 2024

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network’s utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method’s average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications.
Electroencephalography Neurofeedback Training with Focus on the State of Attention: An Investigation Using Source Localization and Effective Connectivity
Authors: Casagrande Wagner Dias , Nakamura-Palacios Ester Miyuki , Frizera-Neto Anselmo
Published: September 2024

Identifying brain activity and flow direction can help in monitoring the effectiveness of neurofeedback tasks that aim to treat cognitive deficits. The goal of this study was to compare the neuronal electrical activity of the cortex between individuals from two groups—low and high difficulty—based on a spatial analysis of electroencephalography (EEG) acquired through neurofeedback sessions. These sessions require the subjects to maintain their state of attention when executing a task. EEG data were collected during three neurofeedback sessions for each person, including theta and beta frequencies, followed by a comprehensive preprocessing. The inverse solution based on cortical current density was applied to identify brain regions related to the state of attention. Thereafter, effective connectivity between those regions was estimated using the Directed Transfer Function. The average cortical current density of the high-difficulty group demonstrated that the medial prefrontal, dorsolateral prefrontal, and temporal regions are related to the attentional state. In contrast, the low-difficulty group presented higher current density values in the central regions. Furthermore, for both theta and beta frequencies, for the high-difficulty group, flows left and entered several regions, unlike the low-difficulty group, which presented flows leaving a single region. In this study, we identified which brain regions are related to the state of attention in individuals who perform more demanding tasks (high-difficulty group).
Health Community 4.0: An Innovative Multidisciplinary Solution for Tailored Healthcare Assistance Management
Authors: Jayousi Sara , Barchielli Chiara , Alaimo Marco , Guarducci Sara , Caputo Stefano , Paffetti Marzia , Zoppi Paolo , Mucchi Lorenzo
Published: September 2024

This paper presents a co-designed solution aimed at improving community healthcare assistance management. To enhance patients’ self-awareness of their health status, encouraging active participation in care process, and to support nurses in delivering patient-centered care, we have developed an innovative platform with a highly customized app. This platform was designed using a multi-disciplinary, bottom-up approach. Patient data collection and processing facilitate the automation of care timeline planning, based on real-time patients’ needs and the available resources. To achieve this goal, different components have been considered: real-time health data collection and processing, patient care planning, decision support for nurses, secure communication for data transmission, and user-friendly interfaces to ensure easy access to platform functionalities.
Contrastive Learning-Based Personalized Tag Recommendation
Authors: Zhang Aoran , Yu Yonghong , Li Shenglong , Gao Rong , Zhang Li , Gao Shang
Published: September 2024

Personalized tag recommendation algorithms generate personalized tag lists for users by learning the tagging preferences of users. Traditional personalized tag recommendation systems are limited by the problem of data sparsity, making the personalized tag recommendation models unable to accurately learn the embeddings of users, items, and tags. To address this issue, we propose a contrastive learning-based personalized tag recommendation algorithm, namely CLPTR. Specifically, CLPTR generates augmented views of user–tag and item–tag interaction graphs by injecting noises into implicit feature representations rather than dropping nodes and edges. Hence, CLPTR is able to greatly preserve the underlying semantics of the original user–tag or the item–tag interaction graphs and avoid destroying their structural information. In addition, we integrate the contrastive learning module into a graph neural network-based personalized tag recommendation model, which enables the model to extract self-supervised signals from user–tag and item–tag interaction graphs. We conduct extensive experiments on real-world datasets, and the experimental results demonstrate the state-of-the-art performance of our proposed CLPTR compared with traditional personalized tag recommendation models.
Dress Code Monitoring Method in Industrial Scene Based on Improved YOLOv8n and DeepSORT
Authors: Zou Jiadong , Song Tao , Cao Songxiao , Zhou Bin , Jiang Qing
Published: September 2024

Deep learning-based object detection has become a powerful tool in dress code monitoring. However, even state-of-the-art detection models inevitably suffer from false alarms or missed detections, especially when handling small targets such as hats and masks. To overcome these limitations, this paper proposes a novel method for dress code monitoring using an improved YOLOv8n model, the DeepSORT tracking, and a new dress code judgment criterion. We improve the YOLOv8n model through three means: (1) a new neck structure named FPN-PAN-FPN (FPF) is introduced to enhance the model’s feature fusion capability, (2) Receptive-Field Attention convolutional operation (RFAConv) is utilized to better capture the difference in information brought by different positions, and a (3) Focused Linear Attention (FLatten) mechanism is added to expand the model’s receptive field. This improved YOLOv8n model increases mAP while reducing model size. Next, DeepSORT is integrated to obtain instance information across multi-frames. Finally, we adopt a new judgment criterion to conduct real-scene dress code monitoring. The experimental results show that our method effectively identifies instances of dress violations, reduces false alarms, and improves accuracy.
A Survey on Reduction of Energy Consumption in Fog Networks—Communications and Computations
Authors: Kopras Bartosz , Idzikowski Filip , Bogucka Hanna
Published: September 2024

Fog networking has become an established architecture addressing various applications with strict latency, jitter, and bandwidth constraints. Fog Nodes (FNs) allow for flexible and effective computation offloading and content distribution. However, the transmission of computational tasks, the processing of these tasks, and finally sending the results back still incur energy costs. We survey the literature on fog computing, focusing on energy consumption. We take a holistic approach and look at energy consumed by devices located in all network tiers from the things tier through the fog tier to the cloud tier, including communication links between the tiers. Furthermore, fog network modeling is analyzed with particular emphasis on application scenarios and the energy consumed for communication and computation. We perform a detailed analysis of model parameterization, which is crucial for the results presented in the surveyed works. Finally, we survey energy-saving methods, putting them into different classification systems and considering the results presented in the surveyed works. Based on our analysis, we present a classification and comparison of the fog algorithmic models, where energy is spent on communication and computation, and where delay is incurred. We also classify the scenarios examined by the surveyed works with respect to the assumed parameters. Moreover, we systematize methods used to save energy in a fog network. These methods are compared with respect to their scenarios, objectives, constraints, and decision variables. Finally, we discuss future trends in fog networking and how related technologies and economics shall trade their increasing development with energy consumption.
Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring
Authors: Bricout Augustin , Leleux Philippe , Acco Pascal , Escriba Christophe , Fourniols Jean-Yves , Soto-Romero Georges , Floquet Rémi
Published: September 2024

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the “BeeTogether” dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives’ extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony’s belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.
Characterization of Geopolymer Masonry Mortars Incorporating Recycled Fine Aggregates
Authors: Abuowda Elen , El-Hassan Hilal , El-Maaddawy Tamer
Published: September 2024

This study evaluates the characteristics of geopolymer masonry mortars (GMMs) made with slag–fly ash binder and up to 100% recycled fine aggregates (RFAs). For each RFA replacement rate, two types of GMMs, namely N and S types based on ASTM C91, were proportioned and tested for mechanical, physical, and durability properties. Results revealed that using geopolymeric binder enhanced the flow, water retention, compressive strength, sorptivity, and abrasion resistance of GMMs compared to cementitious counterparts but reduced the initial setting time by up to 75%. Subsequent RFA additions negatively affected the flow, setting time, density, water absorption, porosity, and bulk resistivity but enhanced the water retention, sorptivity, and abrasion resistance of GMM. It also reduced the compressive, pull-off, and flexural strengths by 36, 44, and 27%, respectively. Furthermore, S-type mortars exhibited improved bulk resistivity, sorptivity, and abrasion resistance compared to N-type counterparts. A multifunctional performance index deduced that the GMM mixes incorporating 100% RFAs were superior to geopolymeric or cementitious masonry mortars made with natural fine aggregates (NFAs). Such findings emphasize the sustainability of GMMs made with RFAs in masonry construction, eliminating the need for water curing while maintaining comparable or even superior properties compared to cement-based mortars made with NFAs.
Investigating the Role of Financial Development in Encouraging the Transition to Renewable Energy: A Fractional Response Model Approach
Authors: Alshagri Reem , Alsabhan Talal H. , Binsuwadan Jawaher
Published: September 2024

This paper aims to investigate the relationship between financial development and renewable energy consumption using a fractional response model. The study examines a sample of 34 advanced economies and 64 emerging markets and developing economies from 2008 to 2020. The findings from the fractional response model indicate that financial development has a positive impact on renewable energy consumption in advanced economies. However, in emerging and developing economies, financial development negatively affects the consumption of renewable energy. Additionally, the findings illustrate that financial development has a more pronounced positive impact in advanced economies. This effect is especially strong in countries with higher levels of financial development. On the other hand, in emerging and developing economies, the consumption of renewable energy is more strongly affected by the negative impact of financial development on countries with lower financial development.
Intelligent Development, Knowledge Breadth, and High-Tech Enterprise Innovation: The Moderating Role of Knowledge Absorptive Capacity
Authors: Zhang Jin , Ba Duoxun
Published: September 2024

Innovation serves as the cornerstone for high-quality development in high-tech enterprises, with intelligent development emerging as a central aspect of innovation efforts. However, how intelligent development promotes the innovative development of high-tech enterprises is still a topic of continuous debate and exploration. By integrating enterprise innovation theory and knowledge-based theory, this paper constructs a theoretical framework to examine the influence of intelligent development on high-tech enterprise innovation. Through an analysis of 694 listed high-tech enterprises on China’s manufacturing A-share market from 2013 to 2021, we empirically investigated the effects of mediating mechanisms and moderating effects of intelligent development on high-tech enterprise innovation. The results show that intelligent development significantly boosts high-tech enterprise innovation. Knowledge breadth plays a mediating role in the relationship between intelligent development and high-tech enterprise innovation, indicating that intelligent development promotes high-tech enterprise innovation by enhancing knowledge breadth. Additionally, knowledge absorptive capacity can strengthen the impact of knowledge breadth on high-tech enterprise innovation, that is, the stronger the knowledge absorptive capacity, the greater the impact of knowledge breadth on high-tech enterprise innovation. The conclusion of this paper provides a theoretical basis and practical guidance for high-tech enterprises regarding how to better use intelligent technology for innovation. Relevant enterprises can strengthen their knowledge management and mobility strategies and fully utilize the potential of intelligent technology to achieve more innovative and competitive development.
Towards Sustainable Development of the Old City: Design Practice of Alleyway Integration in Old City Area Based on Heritage Corridor Theory
Authors: Zhang Keyi , Liu Jia
Published: September 2024

Urbanization has increasingly led to the overlapping of old and new spaces in historic city areas, creating challenges in preserving valuable urban heritage. To meet the requirements of the effective protection, utilization, and sustainable development of these historical spaces, this study introduces a comprehensive framework grounded in the theory of heritage corridors. The proposed framework subdivides and connects fragmented historical spatial resources through the “point–line–plane” dimensions, fostering sustainability across the environmental, ecological, and cultural domains. To validate the effectiveness of the framework, we apply it to the historic city center of Wuxi. This case study demonstrates that integrating spatial resources within the old city enhances the overall quality of urban living spaces in the historic district. The findings suggest that the heritage corridor framework is a feasible and systematic approach for the sustainable renewal of historic urban areas. Furthermore, this study offers valuable insights for advancing Sustainable Development Goal 11 (SDG 11), particularly in promoting inclusive, safe, resilient, and sustainable cities.
Analyzing Energy Efficiency and Battery Supervision in Electric Bus Integration for Improved Urban Transport Sustainability
Authors: Szürke Szabolcs Kocsis , Saly Gábor , Lakatos István
Published: September 2024

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions.
The Impact of Green Purchase Intention on Compensatory Consumption: The Regulatory Role of Pro-Environmental Behavior
Authors: Chen Chao , Li Desheng , Qian Jingjing , Li Ziying
Published: September 2024

Green purchasing behavior refers to the potential of consumers to reduce the impact on the environment and the excessive loss of natural resources in the procurement process as far as possible under the premise of meeting their own needs. This behavior is not only helpful in alleviating environmental problems but also is an important way to achieve sustainable development. However, whether consumers will increase non-green or excessive compensatory consumption behaviors due to the “moral permission” psychological tendency, present after purchasing green products, is an important question in the current research. This study explores the effect of green purchase intention on compensatory consumption behavior, with special attention to the moderating role of pro-environmental behavior in this relationship. With the increasingly severe global environmental problems, green consumption, as a pro-environmental behavior, has gradually become the focus of social attention. By analyzing the relationship between consumers’ green purchase intention and their subsequent compensatory consumption behavior, this study further reveals the important role of pro-environment behavior in the consumption decision-making process.
Probabilistic Multi-Robot Task Scheduling for the Antarctic Environments with Crevasses
Authors: Kang Seokjin , Lee Heoncheol
Published: September 2024

This paper deals with the problem of multi-robot task scheduling in the Antarctic environments with crevasses. Because the crevasses may cause hazardous situations when robots are operated in the Antarctic environments, robot navigation should be planned to safely avoid the positions of crevasses. However, the positions of the crevasses may be inaccurately measured due to the lack of sensor performance, the asymmetry of sensor data, and the possibility of crevasses drifting irregularly as time passes. To overcome these uncertain and asymmetric problems, this paper proposes a probabilistic multi-robot task scheduling method based on the Nearest Neighbors Test (NNT) algorithm and the probabilistic modeling of the positions of crevasses. The proposed method was tested with a Google map of the Antarctic environments and showed a better performance than the Ant Colony Optimization (ACO) algorithm and the Genetic Algorithm (GA) in the context of total cost and computational time.