bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical and Antioxidant Properties of Solvent and Enzyme-Assisted Extracts of Fucus vesiculosus and Porphyra dioica
Authors: Nova Paulo , Cunha Sara A. , Costa-Pinto Ana R. , Gomes Ana Maria
Published: July 2024

Extraction strategies impact the efficiency and nature of extracted compounds. This work assessed the chemical composition and antioxidant capacity of ethanolic, hydroethanolic, and aqueous versus enzyme-assisted extracts (isolated or with the sequential use of alcalase®, cellulase®, and viscozyme®) of the macroalgae Fucus vesiculosus (brown, Phaeophyceae) and Porphyra dioica (red, Rhodophyta. For both macroalgae, enzyme-assisted extraction (EAE) was the most efficient process compared to solvent-assisted extraction (SAE), independent of solvent. Fucus vesiculosus extraction yields were higher for EAE than for SAE (27.4% to 32.2% and 8.2% to 30.0%, respectively). Total phenolics content (TPC) was at least 10-fold higher in EAE extracts (229.2 to 311.3 GAE/gextract) than in SAE (4.34 to 19.6 GAE/gextract) counterparts and correlated well with antioxidant capacity (ABTS and ORAC methods), with EAE achieving values up to 8- and 2.6-fold higher than those achieved by SAE, respectively. Porphyra dioica followed F. vesiculosus’s trend for extraction yields (37.5% to 51.6% for EAE and 5.7% to 35.1% for SAE), TPC, although of a lower magnitude, (0.77 to 8.95 GAE/gextract for SE and 9.37 to 14.73 GAE/gextract for EAE), and antioxidant capacity. Aqueous extracts registered the highest DPPH values for both macroalgae, with 2.3 µmol TE/gextract and 13.3 µmol TE/gextract for F. vesiculosus and P. dioica, respectively. EAE was a more efficient process in the extraction of soluble protein and reducing sugars in comparison to SAE. Furthermore, an improved effect of enzyme-assisted combinations was observed for almost all analyzed parameters. This study shows the promising application of enzyme-assisted extraction for the extraction of valuable compounds from F. vesiculosus and P.dioica, making them excellent functional ingredients for a wide range of health and food industrial applications.
Assessing the Relationship between Physical Activity and Depression in Lawyers and Law Professionals: A Cross-Sectional Study
Authors: Cox Chapman , Thiese Matthew S. , Allen Joseph A.
Published: July 2024

Background: Law professionals are understudied, and little is known about their mental health. This cross-sectional study aimed to assess the relationship between the amount of physical activity and depressive symptoms. Methods: A sample of 681 law professionals completed a survey that assessed mental health conditions and activities to promote well-being. Crude and adjusted odds ratios (ORs) and 95% confidence interval (95% CI) were calculated for the relationship between the number of days of an hour or more of physical activity and two levels of depression from the patient health questionnaire-9 (PHQ-9). Results: Law professionals were found to have significantly more depressive symptoms, mild or worse and moderate or worse, when reporting 0 days of physical activity when compared to 7 days, with OR of 6.07 (95% CI 2.55–14.48) and 8.64 (95% CI 1.97–37.82) and adjusted ORs of 3.91 (95% CI 1.58–9.68) and 6.32 (95% CI 1.4–28.33), respectively. A dose response was also noted. Conclusions: There was a statistically significant relationship found between amounts of physical activity and depressive symptoms in law professionals. We recommend future research be conducted to better understand this relationship.
Gut Microbiota Composition Is Causally Linked to Multiple Sclerosis: A Mendelian Randomization Analysis
Authors: Zancan Valeria , Nasello Martina , Bigi Rachele , Reniè Roberta , Buscarinu Maria Chiara , Mechelli Rosella , Ristori Giovanni , Salvetti Marco , Bellucci Gianmarco
Published: July 2024

Accumulating evidence links the microbial communities inhabiting the gut to the pathophysiological processes underlying multiple sclerosis (MS). However, most studies on the microbiome in MS are correlative in nature, thus being at risk of confounding and reverse causality. Mendelian randomization (MR) analyses allow the estimation of the causal relationship between a risk factor and an outcome of interest using genetic variants as proxies for environmental exposures. Here, we performed a two-sample MR to assess the causality between the gut microbiome and MS. We extracted genetic instruments from summary statistics from three large genome-wide association studies (GWASs) on the gut microbiome (18,340, 8959, and 7738 subjects). The exposure data were derived from the latest GWAS on MS susceptibility (47,429 patients and 68,374 controls). We pinpointed several microbial strains whose abundance is linked with enhanced MS risk (Actinobacteria class, Bifidobacteriaceae family, Lactobacillus genus) or protection (Prevotella spp., Lachnospiranaceae genus, Negativibacillus genus). The largest risk effect was seen for Ruminococcus Torques (OR, 2.89, 95% C.I. 1.67–5, p = 1.51 × 10−4), while Akkermansia municiphila emerged as strongly protective (OR, 0.43, 95% C.I. 0.32–0.57, p = 1.37 × 10−8). Our findings support a causal relationship between the gut microbiome and MS susceptibility, reinforcing the relevance of the microbiome–gut–brain axis in disease etiology, opening wider perspectives on host–environmental interactions for MS prevention.
Climate Change Stressors, Phosphate Limitation, and High Irradiation Interact to Increase Alexandrium minutum Toxicity and Modulate Encystment Rates
Authors: Sixto Marta , Riobó Pilar , Rodríguez Francisco , Díaz Patricio A. , Figueroa Rosa I.
Published: July 2024

The changes in the cell physiology (growth rate, cell size, and cell DNA content), photosynthetic efficiency, toxicity, and sexuality under variable light and nutrient (phosphates) conditions were evaluated in cultures of the dinoflagellate Alexandrium minutum obtained from a red tide in the Ría de Vigo (NW Spain). The cells were grown at low (40 and 150 µE m−2 s−1), moderate (400 µE m−2 s−1), and high (800 µE m−2 s−1) light intensities in a medium with phosphate (P+) and without (P−). Cultures were acclimated to the irradiance conditions for one week, and the experiment was run for ~1 month. The cell size and DNA content were monitored via flow cytometry. Two different clonal strains were employed as a monoculture (in a P− or P+ medium) or, to foster sexuality and resting cyst formation, as a mixed culture (only in a P− medium). A. minutum growth was favored by increasing light intensities until 400 µE m−2 s−1. The DNA content analyses indicated the accumulation of S-phase cells at the highest light intensities (400 and 800 µE m−2 s−1) and therefore the negative effects on cell cycle progression. Only when the cells were grown in a P− medium did higher light intensities trigger dose-dependent, significantly higher toxicities in all the A. minutum cultures. This result suggests that the toxicity level is responsive to the combined effects of (high) light and (low) P stress. The cell size was not significantly affected by the light intensity or P conditions. The optimal light intensity for resting cyst formation was 150 µE m−2 s−1, with higher irradiances reducing the total encystment yield. Encystment was not observed at the lowest light intensity tested, indicative of the key role of low-level irradiance in gamete and/or zygote formation, in contrast to the stressor effect of excessive irradiance on planozygote formation and/or encystment.
The Association of Vitamin D Levels and Dental Caries in Older Adults: A Cross-Sectional Study
Authors: Hung Man , Mohajeri Amir , Sadri Mahsa , Khodabandeh Elahe , Zeitoun Ibrahim , Lipsky Martin
Published: July 2024

Introduction: Most research examining vitamin D and dental caries focuses on children and younger adults. This study investigated the association between vitamin D levels and dental caries in older adults using data from the United States National Health and Nutrition Examination Survey from 2011 to 2016. Methods: Data were analyzed from 2723 participants aged 65 years and older who completed both dental examinations and serum 25(OH)D tests. Dental caries assessments included the decayed, missing, and filled teeth (DMFT) index and the presence of untreated dental caries. Vitamin D levels were measured as serum 25(OH)D concentrations and categorized as severely deficient (<25 nmol/L), deficient (25–49.9 nmol/L), insufficient (50–74.9 nmol/L), and normal (≥75 nmol/L). Logistic regression and Poisson regression models were used to assess the association between vitamin D levels and dental caries, adjusting for demographic factors. Results: The mean DMFT score was 17.73 ± 8.34, with 35.1% of participants having untreated dental caries. Vitamin D deficiency was associated with a 1.44 times higher likelihood of untreated caries (95% CI: 1.15, 1.81), which weakened after adjustment for demographic factors (adjusted OR: 1.23, 95% CI: 0.97, 1.55). Severe vitamin D deficiency correlated with a 1.13 times higher DMFT score (95% CI: 1.06, 1.20), with the association remaining similar after adjustment (adjusted RR: 1.12, 95% CI: 1.05, 1.20). Significant differences in vitamin D levels were observed across gender, race/ethnicity, and country of birth. Conclusions: This study suggests the potential importance of adequate vitamin D levels for maintaining dental health among older adults. Vitamin D deficiency is associated with a higher risk of poorer DMFT scores. Public health strategies that include vitamin D screening and supplementation, particularly for high-risk groups, may improve oral health outcomes in the older adult population. Further research is needed to elucidate the mechanisms by which vitamin D influences dental health and the potential for vitamin D supplementation to reduce the burden of dental caries in older adults.
Computational Design of Novel Tau-Tubulin Kinase 1 Inhibitors for Neurodegenerative Diseases
Authors: Ahamad Shahzaib , Junaid Iqbal Taliy , Gupta Dinesh
Published: July 2024

The tau-tubulin kinase 1 (TTBK1) protein is a casein kinase 1 superfamily member located at chromosome 6p21.1. It is expressed explicitly in the brain, particularly in the cytoplasm of cortical and hippocampal neurons. TTBK1 has been implicated in the phosphorylation and aggregation of tau in Alzheimer’s disease (AD). Considering its significance in AD, TTBK1 has emerged as a promising target for AD treatment. In the present study, we identified novel TTBK1 inhibitors using various computational techniques. We performed a virtual screening-based docking study followed by E-pharmacophore modeling, cavity-based pharmacophore, and ligand design techniques and found ZINC000095101333, LD7, LD55, and LD75 to be potential novel TTBK1 lead inhibitors. The docking results were complemented by Molecular Mechanics/Generalized Born Surface Area (MMGBSA) calculations. The molecular dynamics (MD) simulation studies at a 500 ns scale were carried out to monitor the behavior of the protein toward the identified ligands. Pharmacological and ADME/T studies were carried out to check the drug-likeness of the compounds. In summary, we identified a new series of compounds that could effectively bind the TTBK1 receptor. The newly designed compounds are promising candidates for developing therapeutics targeting TTBK1 for AD.
Optical and Scintillation Properties of Tb-Doped Gadolinium Pyrosilicate Single Crystals
Authors: Kantuptim Prom , Kato Takumi , Nakauchi Daisuke , Pattanaboonmee Nakarin , Kawaguchi Noriaki , Watanabe Kenichi , Chewpraditkul Weerapong , Yanagida Takayuki
Published: July 2024

Gadolinium pyrosilicate (GPS, Gd2Si2O7) single crystals with different doping concentrations of Tb (0.1–2.0 mol%) are successfully fabricated using the floating-zone technique. In this work, the dependence of Tb-doping concentration on the photoluminescence (PL) and scintillation properties of Tb-doped GPS (Tb:GPS) has been investigated. The PL emission contour graph shows multiple emissions, with the strongest emissions at 378 nm for 0.1% and 0.5% Tb-doping and 544 nm for 1.0% and 2.0% Tb-doping, corresponding to Tb3+ 4f-4f transitions. The PL lifetimes of the specimens range from 4.89 to 5.22 ms. The scintillation spectra exhibit comparable wavelength and intensity trends to the PL emission. The scintillation lifetimes of the specimens range from 2.41 to 3.88 ms. The Tb:GPS specimens demonstrate a relatively excessive afterglow level, with Af20 values ranging from 1640 to 7250 ppm and Af40 values ranging from 136 to 362 ppm. Using recently developed pulse height measurement for millisecond decay scintillators, under excitation at 662 keV γ-rays, the 1.0% Tb:GPS specimen exhibits the highest scintillation light yield among all other specimens at 95,600 ph/MeV, making Tb:GPS one of the highest light yield oxide scintillators.
Understanding Water Utilization Mechanisms in Degrading Bamboo Shoots: A Cytological and Physiological Study
Authors: Hu Tianyi , Wu Zhengchun , Deng Meng , Liu Haiwen , Xiao Jiao , Wei Qiang , Yu Fen
Published: July 2024

Degradation of shoots, characterized by stunted growth and signs of water deficit, is common in bamboo stands. However, the specific mechanisms underlying water utilization in degrading shoots remain unclear. This study sought to address this gap by harvesting bamboo shoots and culms of Phyllostachys edulis ‘Pachyloen’, employing cytological and physiological techniques to compare water utilization mechanisms between healthy and degrading shoots, and investigating the water supply to bamboo shoots by the parent bamboo. The water pressure in the degrading shoots was markedly lower compared to that of the healthy shoots, and it declined as the degradation progressed, resulting in reduced water content and the cessation of guttation in the degrading shoots. In conditions of water deficit, the percentage of free water in bamboo shoots decreased while the percentages of bound and semi-bound water increased, with the proportion of semi-bound water reaching as high as 88.13% in the late stages of degradation. The water potential of parent bamboo culms of different ages varied at different times of the day and during different growth stages of bamboo shoots, showing a strong association with the development of bamboo shoots. Conversely, the correlation between changes in the water potential of bamboo shoots and their degradation patterns was found to be comparatively minimal. The weakening of the connection between the bamboo shoots and the parent bamboo culms may play a significant role in the degradation of the bamboo shoots. This is evidenced by a decrease in the fluorescence intensity of the nucleus in bamboo shoots and the degradation of genetic material. This study lays the foundation for future research into the mechanisms of bamboo shoot degradation.
Exudate and Propolis from Escallonia pulverulenta: Phytochemical Characterization and Antibacterial Activity
Authors: Jorquera Bairon , Valenzuela-Barra Gabriela , Mayorga Ailin , Mejía Jessica , Núñez Gabriel , Gómez Miguel , Montenegro Gloria , Vera Quezada Waleska E. , Echeverría Javier , Costa de Camargo Adriano , Lino von Poser Gilsane , Bridi Raquel
Published: July 2024

Propolis is a complex mixture formed from exudates that bees collect from plants and then mix with beeswax and their own salivary enzymes. Chilean propolis is characterized by the presence of phenolic compounds, which are considered responsible for the biological activities. The endemic species Escallonia pulverulenta (Ruiz and Pav.) Pers. [Escalloniaceae] is a recognized source of exudate to produce propolis. This study reports for the first time the chemical profile and antibacterial activity of E. pulverulenta exudate and leaves, as well as two samples of Chilean propolis. Palynological and morphological analysis showed the presence of E. pulverulenta as one of the main species in the propolis samples. UPLC-MS/MS analyses enabled the identification of phenolic acids in the leaves and in the propolis. Conversely, flavonoids are mainly present in exudates and propolis. Quercetin is the most abundant flavonol in the exudate, with similar concentrations in the propolis samples. Nevertheless, the main compound present in both samples of propolis was the flavanone pinocembrin. The antibacterial results obtained for exudate and propolis have shown a similar behavior, especially in the inhibition of Streptococcus pyogenes. These results show the importance of the exudates collected by the bees in the chemical composition and antibacterial capacity of propolis.
Kazakhstan Has an Unexpected Diversity of Medicinal Plants of the Genus Acorus (Acoraceae) and Could Be a Cradle of the Triploid Species A. calamus
Authors: Sokoloff Dmitry D. , Degtjareva Galina V. , Valiejo-Roman Carmen M. , Severova Elena E. , Barinova Sophia , Chepinoga Victor V. , Kuzmin Igor V. , Sennikov Alexander N. , Shmakov Alexander I. , Skaptsov Mikhail V. , Smirnov Sergey V. , Remizowa Margarita V.
Published: July 2024

The Acorus calamus group, or sweet flag, includes important medicinal plants and is classified into three species: A. americanus (diploid), A. verus (tetraploid), and A. calamus (sterile triploid of hybrid origin). Members of the group are famous as components of traditional Indian medicine, and early researchers suggested the origin of the sweet flag in tropical Asia. Subsequent research led to an idea of the origin of the triploid A. calamus in the Amur River basin in temperate Asia, because this was the only region where both diploids and tetraploids were known to co-occur and be capable of sexual reproduction. Contrary to this hypothesis, triploids are currently very rare in the Amur basin. Here, we provide the first evidence that all three species occur in Kazakhstan. The new records extend earlier data on the range of A. verus for c. 1800 km. Along the valley of the Irtysh River in Kazakhstan and the adjacent Omsk Oblast of Russia, A. verus is recorded in the south, A. americanus in the north, and A. calamus is common in between. We propose the Irtysh River valley as another candidate for a cradle of the triploid species A. calamus. It is possible that the range of at least one parent species (A. americanus) has contracted through competition with its triploid derivative species, for which the Irtysh River floods provide a tool for downstream range expansion. We refine our earlier data and show that the two parent species have non-overlapping ranges of variation in a quantitative metric of leaf aerenchyma structure.
Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm–Backpropagation Neural Network
Authors: Xia Dawei , Li Ling , Zhang Buting , Li Min , Wang Can , Gong Zhijie , Shagali Abdulmajid Abdullahi , Jiang Long , Hu Song
Published: July 2024

In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model’s output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather conditions, the prediction error metrics showcased superior performance: the mean absolute percentage error (MAPE) consistently remained below 13%, meeting the stringent requirement of the power grid’s tolerance level below 20%. Moreover, the normalized root mean square error (NRMSE) ranged between 6% and 9%, while the coefficient of determination (R2) exceeded 0.9. These underscored the remarkable prediction accuracy achieved by the model. Under rainy and snowy weather conditions, although MAPE slightly increased to the range of 14% to 20% compared to that of scenarios without rain and snow, it still adhered to the stringent requirement. NRMSE varied between 4.5% and 8%, and R2 remained consistently above 0.9, indicative of satisfactory model performance even in adverse weather conditions. The successful application of the proposed model in predicting hourly photovoltaic power generation output during winter in Henan Province bears significant practical implications for the advancement and integration of renewable energy technologies.
Review of Hydrogen-Driven Power-to-X Technology and Application Status in China
Authors: Zhai Yunchu , He Yong , Shao Jiaming , Zhang Weiling , Tong Xiaofan , Wang Zhihua , Weng Wubin
Published: July 2024

Given China’s ambition to realize carbon peak by 2030 and carbon neutralization by 2060, hydrogen is gradually becoming the pivotal energy source for the needs of energy structure optimization and energy system transformation. Thus, hydrogen combined with renewable energy has received more and more attention. Nowadays, power-to-hydrogen, power-to-methanol, and power-to-ammonia are regarded as the most promising three hydrogen-driven power-to-X technologies due to the many commercial or demonstration projects in China. In this paper, these three hydrogen-driven power-to-X technologies and their application status in China are introduced and discussed. First, a general introduction of hydrogen energy policies in China is summarized, and then the basic principles, technical characteristics, trends, and challenges of the three hydrogen-driven power-to-X technologies are reviewed. Finally, several typical commercial or demonstration projects are selected and discussed in detail to illustrate the development of the power-to-X technologies in China.
Impact Analysis of Vegetation FVC Changes and Drivers in the Ring-Tarim Basin from 1993 to 2021
Authors: Xi Lei , Qi Zhao , Cao Xiaoming , Cui Mengcun , Zou Jiaxiu , Feng Yiming
Published: July 2024

As an ecologically sensitive area with significant desertification problems, the Ring-Tarim Basin has a fragile ecological environment that is vulnerable to both natural and anthropogenic factors. Accurate long-term vegetation observations are ecologically, socially, and economically important for desertification control. In this study, based on the ground-measured data and the fractional vegetation cover (FVC) inversion dataset obtained by the image element dichotomy method, we used the methods of slope-trend analysis and multiple-regression residual analysis to analyze the spatial and temporal characteristics of the vegetation cover in the desertified area of the Ring-Tarim Basin. At the same time, we assessed the impacts of climate change and human activities on vegetation changes and the contribution of driving forces. The results showed that (1) The annual mean value of FVC in the growing season in the Ring-Tarim Basin generally showed a fluctuating and increasing trend during the period of 1993–2021; a decreasing trend during 1993–1999, with a change rate of −0.13 × 10−2a−1; and the fastest increasing trend during 2010–2021, with a change rate of 0.23 × 10−2a−1. (2) The effects of climate change and human activities on FVC changes in the growing season had great spatial heterogeneity. The areas where climate change and human activities had no significant effect on FVC changes in the growing season accounted for 86.25% and 77.91%, respectively, the areas where climate and human activities promoted FVC increase in the growing season accounted for 10.53% and 16.37%, respectively, and the areas where climate and human activities inhibited FVC increase in the growing season accounted for 3.22% and 5.72%, respectively. (3) About 76.9% of the FVC changes in the area around the Ring-Tarim Basin were caused by climate change and human activities. In addition to the eastern part of the study area, the vegetation cover of the oases in the west, north, and south generally showed an increasing trend, and the increasing area was proportional to the distribution density of the oasis cities. The trend of vegetation change in the area of the oasis and the fringes of the oasis was drastic. The contribution and inhibition of human activities to FVC, and the driving force of FVC change were greater than that of climate change. More than half of the area had an anthropogenic contribution of more than 60%, indicating that China’s ecological projects have had a significant effect on vegetation change in the extreme arid regions.
Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression
Authors: Sharma Prakriti , Villegas-Diaz Roberto , Fennell Anne
Published: July 2024

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines.
Spatial Information Enhancement with Multi-Scale Feature Aggregation for Long-Range Object and Small Reflective Area Object Detection from Point Cloud
Authors: Li Hanwen , Tao Huamin , Deng Qiuqun , Xiao Shanzhu , Zhou Jianxiong
Published: July 2024

Accurate and comprehensive 3D objects detection is important for perception systems in autonomous driving. Nevertheless, contemporary mainstream methods tend to perform more effectively on large objects in regions proximate to the LiDAR, leaving limited exploration of long-range objects and small objects. The divergent point pattern of LiDAR, which results in a reduction in point density as the distance increases, leads to a non-uniform point distribution that is ill-suited to discretized volumetric feature extraction. To address this challenge, we propose the Foreground Voxel Proposal (FVP) module, which effectively locates and generates voxels at the foreground of objects. The outputs are subsequently merged to mitigating the difference in point cloud density and completing the object shape. Furthermore, the susceptibility of small objects to occlusion results in the loss of feature space. To overcome this, we propose the Multi-Scale Feature Integration Network (MsFIN), which captures contextual information at different ranges. Subsequently, the outputs of these features are integrated through a cascade framework based on transformers in order to supplement the object features space. The extensive experimental results demonstrate that our network achieves remarkable results. Remarkably, our approach demonstrated an improvement of 8.56% AP on the SECOND baseline for the Car detection task at a distance of more than 20 m, and 9.38% AP on the Cyclist detection task.
Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images
Authors: Xu Weiming , Wang Juan , Wang Chengjun , Li Ziwei , Zhang Jianchang , Su Hua , Wu Sheng
Published: July 2024

The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder–decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model’s effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction.
Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland
Authors: Janowski Łukasz , Wróblewski Radosław
Published: July 2024

The digital representation of seafloor, a challenge in UNESCO’s Ocean Decade initiative, is essential for sustainable development support and marine environment protection, aligning with the United Nations’ 2030 program goals. Accuracy in seafloor representation can be achieved through remote sensing measurements, including acoustic and laser sources. Ground truth information integration facilitates comprehensive seafloor assessment. The current seafloor mapping paradigm benefits from the object-based image analysis (OBIA) approach, managing high-resolution remote sensing measurements effectively. A critical OBIA step is the segmentation process, with various algorithms available. Recent artificial intelligence advancements have led to AI-powered segmentation algorithms development, like the Segment Anything Model (SAM) by META AI. This paper presents the SAM approach’s first evaluation for seafloor mapping. The benchmark remote sensing dataset refers to Puck Lagoon, Poland and includes measurements from various sources, primarily multibeam echosounders, bathymetric lidar, airborne photogrammetry, and satellite imagery. The SAM algorithm’s performance was evaluated on an affordable workstation equipped with an NVIDIA GPU, enabling CUDA architecture utilization. The growing popularity and demand for AI-based services predict their widespread application in future underwater remote sensing studies, regardless of the measurement technology used (acoustic, laser, or imagery). Applying SAM in Puck Lagoon seafloor mapping may benefit other seafloor mapping studies intending to employ AI technology.
Image-Acceleration Multimodal Danger Detection Model on Mobile Phone for Phone Addicts
Authors: Wang Han , Ji Xiang , Jin Lei , Ji Yujiao , Wang Guangcheng
Published: July 2024

With the popularity of smartphones, a large number of “phubbers” have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract environmental description features from the rear-view image of the mobile phone. The behavior feature module uses acceleration time series as observation data, maps the acceleration observation data to a two-dimensional image space through GADFs (Gramian Angular Difference Fields), and extracts behavior description features through MobileNet, while utilizing statistical feature vectors to enhance the representation capability of behavioral features. Finally, in the recognition module, the environmental and behavioral characteristics are fused to output the type of hazardous state. Experiments indicate that the accuracy of the proposed model surpasses existing methods, and it possesses the advantages of compact model size (28.36 Mb) and fast execution speed (0.08 s), making it more suitable for deployment on mobile devices. Moreover, the developed image-acceleration multimodal phubber hazard recognition network combines the behavior of mobile phone users with surrounding environmental information, effectively identifying potential hazards for phubbers.
The Impact of Liquids and Saturated Salt Solutions on Polymer-Coated Fiber Optic Sensors for Distributed Strain and Temperature Measurement
Authors: Weisbrich Martin , Messerer Dennis , Holzer Frank , Trommler Ulf , Roland Ulf , Holschemacher Klaus
Published: July 2024

The application of distributed fiber optic strain and temperature measurement can be utilized to address a multitude of measurement tasks across a diverse range of fields, particularly in the context of structural health monitoring in the domains of building construction, civil engineering, and special foundation engineering. However, a comprehensive understanding of the influences on the measurement method and the sensors is essential to prevent misinterpretations or measurement deviations. In this context, this study investigated the effects of moisture exposure, including various salt solutions and a high pH value, on a distributed strain measurement using Rayleigh backscattering. Three fiber optic sensors with different coating materials and one uncoated fiber were exposed to five different solutions for 24 h. The study revealed significant discrepancies (∼38%) in deformation between the three coating types depending on the surrounding solution. Furthermore, in contrast to the prevailing literature, which predominantly describes swelling effects, a negative deformation (∼−47 με) was observed in a magnesium chloride solution. The findings of this study indicate that corresponding effects can impact the precision of measurement, potentially leading to misinterpretations. Conversely, these effects could be used to conduct large-scale monitoring of chemical components using distributed fiber optic sensing.
Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers
Authors: Zhang Chen , Deng Xiangyao , Ling Sai Ho
Published: July 2024

The advancement of medical imaging has profoundly impacted our understanding of the human body and various diseases. It has led to the continuous refinement of related technologies over many years. Despite these advancements, several challenges persist in the development of medical imaging, including data shortages characterized by low contrast, high noise levels, and limited image resolution. The U-Net architecture has significantly evolved to address these challenges, becoming a staple in medical imaging due to its effective performance and numerous updated versions. However, the emergence of Transformer-based models marks a new era in deep learning for medical imaging. These models and their variants promise substantial progress, necessitating a comparative analysis to comprehend recent advancements. This review begins by exploring the fundamental U-Net architecture and its variants, then examines the limitations encountered during its evolution. It then introduces the Transformer-based self-attention mechanism and investigates how modern models incorporate positional information. The review emphasizes the revolutionary potential of Transformer-based techniques, discusses their limitations, and outlines potential avenues for future research.
Recognition of Dynamic Emotional Expressions in Children and Adults and Its Associations with Empathy
Authors: Chiang Yu-Chen , Chien Sarina Hui-Lin , Lyu Jia-Ling , Chang Chien-Kai
Published: July 2024

This present study investigates emotion recognition in children and adults and its association with EQ and motor empathy. Overall, 58 children (33 5–6-year-olds, 25 7–9-year-olds) and 61 adults (24 young adults, 37 parents) participated in this study. Each participant received an EQ questionnaire and completed the dynamic emotion expression recognition task, where participants were asked to identify four basic emotions (happy, sad, fearful, and angry) from neutral to fully expressed states, and the motor empathy task, where participants’ facial muscle activity was recorded. The results showed that “happy” was the easiest expression for all ages; 5- to 6-year-old children performed equally well as adults. The accuracies for “fearful,” “angry,” and “sad” expressions were significantly lower in children than in adults. For motor empathy, 7- to 9-year-old children exhibited the highest level of facial muscle activity, while the young adults showed the lowest engagement. Importantly, individual EQ scores positively correlated with the motor empathy index in adults but not in children. In sum, our study echoes the previous literature, showing that the identification of negative emotions is still difficult for children aged 5–9 but that this improves in late childhood. Our results also suggest that stronger facial mimicry responses are positively related to a higher level of empathy in adults.
Spatio-Temporal Joint Optimization-Based Trajectory Planning Method for Autonomous Vehicles in Complex Urban Environments
Authors: Guo Jianhua , Xie Zhihao , Liu Ming , Dai Zhiyuan , Jiang Yu , Guo Jinqiu , Xie Dong
Published: July 2024

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio–temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments.
Agricultural Production Efficiency and Differentiation of City Clusters along the Middle Reaches of Yangtze River under Environmental Constraints
Authors: Wang Lei , Zhang Yi , Xia Jingyi , Wang Zilei , Zhang Wenjing
Published: July 2024

The improvement of overall agricultural efficiency in the city clusters along the middle reaches of the Yangtze River is crucial for promoting stable regional agricultural production and ensuring food security. This study employs the SBM (slack-based measure) model with the unexpected environmental outputoutputs, including agricultural surface pollution and agricultural carbon emissions, and the SFA (stochastic frontier approach) model to investigate the overall agricultural efficiency and its influencing factors in 31 prefecture-level cities in the middle reaches of the Yangtze River urban agglomeration from 2008 to 2021. The research findings indicate the following: (1) Without eliminating the impact of environmental variables, the overall agricultural efficiency in the middle reaches of the Yangtze River city clusters shows a rise–fall–stability trend and limited level. The scale of production input is relatively reasonable, but there is inefficiency in the utilization of factor resources. (2) The SFA model reveals that economic development, urbanization construction, industrial structure, and government influence have significant but different impacts on agricultural production factor input. Accelerating economic development is helpful for reducing excessive inputs of agricultural capital, labor, planting area, agricultural film, and irrigation. Increasing the level of urbanization can promote the efficient allocation of planting area and effective irrigation area. The improvement of industrialization level pushes the rational input of planting area and agricultural film, but it may also lead to excessive input of agricultural capital, labor, pesticides, and effective irrigation area. Expanding government influence can restrain the excessive use of pesticides. (3) After eliminating environmental variables, there is a low and slow declining trend of the overall agricultural efficiency over time. Neither production scale efficiency nor pure technical efficiency reached optimal levels; the former one is significantly lower than the latter. In terms of spatial distribution, there exists a “higher in the west and lower in the east” feature, with obvious and expanding regional efficiency differences and high-efficiency areas gradually concentrating in the Wuhan urban circle. In summary, this article puts forward the following suggestions: optimize the structure of the government’s support for agriculture, focusing on the construction of agricultural infrastructure and the support for green production in agriculture; improve the research and development and promotion of green production technology and encourage the establishment of the use of resources and recycling; and absorb the population of farmers who have been transferred to urban areas reasonably and orderly under the adjustment of industrial structure.
Simulation and Regulation of High-Quality Economic Development Based on System Dynamics and DPSIR Model
Authors: Fu Guomei , Tang Jiafu
Published: July 2024

The key to promoting high-quality economic development (HQED) is to identify the influencing factors and the complex relationships among them. Under the guidance of China’s new development concept, we systematically analyze the factors influencing the HQED based on the DPSIR (Driver-Pressure-State-Impact-Response) model. We also establish a system dynamics model and adopt Vensim PLE x64 software to simulate the impact of dynamic changes on HQED, which include innovation, coordination, green, openness, sharing systems, and their combinations. The results show that increasing green investment is the most effective, followed by enhancing regional coordination, enhancing foreign trade, and improving human capital. Among the studied factors, improving livelihood is the weakest. For multiple policy scenarios, the balanced scenario is the most efficient, followed by the environment-oriented, and the weakest is the openness-oriented scenario. Interestingly, combined policies are also more effective than single policies, which does not always apply. Furthermore, the effect of the policy combination is more obvious with the strengthening of the implementation intensity, which is important for policymakers.
Risk Management of Physical Assets Supported by Maintenance Performance Indicators
Authors: da Silva Renan Favarão , Melani Arthur Henrique de Andrade , Michalski Miguel Angelo de Carvalho , Souza Gilberto Francisco Martha de , Nabeta Silvio Ikuyo
Published: July 2024

Many asset-intensive organizations implement risk management strategies to mitigate potential hazards associated with physical asset failures, such as infrastructure deterioration or mechanical breakdown. As these physical assets’ risks can be treated with maintenance activities, properly evaluating the performance of maintenance management is of interest for risk management. Accordingly, this paper proposes a framework for the determination of Maintenance Performance Indicators (MPIs) to support the risk management of physical assets. The proposed framework included four main processes: Integrate performance evaluation guidelines, Review the maintenance management strategy, Define the performance indicators, and Assess maintenance across the MPIs. The ISO 55000 series for asset management and the Balanced Scorecard (BSC) approach were the guidelines considered. The ISO 55001 standard provides three assessment domains for performance evaluation: asset portfolio, asset management, and asset management system. The BSC approach identifies four performance evaluation perspectives that were integrated to address the requirements of one of these asset management domains. Then, the MPIs were defined for each of the performance evaluation domains in line with the maintenance management strategy toward the risk management of physical assets. Through a case study, the proposed framework was demonstrated considering the operational context of a Brazilian hydroelectric power plant. As a result, the proposed framework was shown to be consistent in systematically determining the MPIs that support risk management in organizations.
A Study on the Impact of Urbanization Level on Carbon Emissions Based on the STIRPAT Model: A Case Study of Five Major Urban Agglomerations in Northern China
Authors: Li Di , Han Peiyan , Li Kexin , Di Qianbin , Duan Ye
Published: July 2024

With the acceleration of urbanization, city clusters of different levels have emerged. They play a significant role in the process of carbon emissions. Based on the improved STIRPAT model and using the five most important city clusters in northern China as the research object, this study investigates carbon emissions (carbon emissions refer to CO2 emissions) from 2005 to 2021 and the urbanization factors that affect carbon emissions. The study found that energy urbanization has the most significant positive impact on carbon emissions from the overall perspective, while population urbanization has the most substantial negative effect. The total emissions of the Beijing–Tianjin–Hebei urban cluster far exceeding those of the other four clusters. The factor with the least impact on carbon emissions in the Beijing–Tianjin–Hebei city cluster, the Central Plains city cluster, and the Guanzhong Plain city cluster is energy urbanization. The impact of technological urbanization factors can be positive or negative, the role of spatial urbanization factors is not significant. The northern city clusters should start with transforming energy structure, promote industrial upgrading through innovation, encourage green production and consumption, increase the use of efficient scientific and technological products and services, and promote the development of new urbanization.