bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Detection of Pedestrians in Low-Light Conditions by Accentuating Gaussian–Sobel Edge Features from Depth Maps
Authors: Jung Minyoung , Cho Jeongho
Published: September 2024

Owing to the low detection accuracy of camera-based object detection models, various fusion techniques with Light Detection and Ranging (LiDAR) have been attempted. This has resulted in improved detection of objects that are difficult to detect due to partial occlusion by obstacles or unclear silhouettes. However, the detection performance remains limited in low-light environments where small pedestrians are located far from the sensor or pedestrians have difficult-to-estimate shapes. This study proposes an object detection model that employs a Gaussian–Sobel filter. This filter combines Gaussian blurring, which suppresses the effects of noise, and a Sobel mask, which accentuates object features, to effectively utilize depth maps generated by LiDAR for object detection. The model performs independent pedestrian detection using the real-time object detection model You Only Look Once v4, based on RGB images obtained using a camera and depth maps preprocessed by the Gaussian–Sobel filter, and estimates the optimal pedestrian location using non-maximum suppression. This enables accurate pedestrian detection while maintaining a high detection accuracy even in low-light or external-noise environments, where object features and contours are not well defined. The test evaluation results demonstrated that the proposed method achieved at least 1–7% higher average precision than the state-of-the-art models under various environments.
DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
Authors: Wu Aihua , Fu Liuxu
Published: September 2024

Existing RGB-depth semantic segmentation methods primarily rely on symmetric two-stream Convolutional Neural Networks (CNNs) to extract RGB and spatial features separately. However, these architectures have limitations in incorporating spatial features and efficiently fusing RGB and depth information. In this study, we propose a novel architecture called the DFormer-Based Cross-Attention Network (DBCAN), which utilizes DFormer as an encoder for feature extraction and integrates several modifications to address these challenges. While DFormer is leveraged for its strong feature extraction capabilities, our modifications in the decoder focus on improving cross-modal fusion and spatial feature incorporation. We introduce three modules in the decoding process: the Object-Region Generated Module (ORGM), the Feature-Region Relation Module (FRRM), and the Spatial-Semantic Fusion Module (SSFM), which enhance feature interaction and segmentation accuracy. Experimental results on the NYUDepthv2 and SUN-RGBD datasets demonstrate that DBCAN achieves state-of-the-art performance, highlighting the effectiveness of our architectural enhancements in overcoming the limitations of existing models.
Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI
Authors: Amoiridou Dimitra , Gkiatis Kostakis , Kakkos Ioannis , Garganis Kyriakos , Matsopoulos George K.
Published: September 2024

Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.
Geovisualization of Buildings: AI vs. Procedural Modeling
Authors: Nikçi Rexhep , Župan Robert , Racetin Ivana
Published: September 2024

Procedural modeling offers significant advantages over traditional methods of geovisualizing 3D building models, particularly in its use of scripts or machine language for model description. This approach is highly suitable for computer processing and allows for the rapid rendering of entire building models and cities, especially when the buildings are not highly diverse, thus fully leveraging the strengths of procedural modeling. The first hypothesis is that buildings in the real world are mostly different and they should still be able to be displayed through procedural modeling procedures, and the second hypothesis is that this can be achieved in several ways. The first hypothesis suggests that real-world buildings, despite their diversity, can still be effectively represented through procedural modeling. The second hypothesis explores various methods to achieve this representation. The first approach involves recognizing the basic characteristics of a building from photographs and creating a model using machine learning. The second approach utilizes artificial intelligence (AI) to generate detailed building models based on comprehensive input data. A script is generated for each building, making reverse procedural modeling in combination with AI an intriguing field of study, which is explored in this research. To validate this method, we compare AI-generated building models with manually derived models created through traditional procedural modeling techniques. The research demonstrates that integrating AI and machine learning techniques with procedural modeling significantly improves the efficiency and accuracy of generating 3D building models. Specifically, the use of convolutional neural networks (CNNs) for image-to-geometry translation, and Generative Adversarial Networks (GANs) for texture generation, showed promising results in creating detailed and realistic 3D structures. This research is significant as it introduces a novel methodology that bridges the gap between traditional procedural modeling and modern AI-driven techniques. It offers a robust solution for automated 3D building modeling, potentially revolutionizing the fields of urban planning and architectural design by enabling more efficient and accurate digital representations of complex building geometries.
Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method
Authors: Ma Jinlin , Wan Yuetong , Ma Ziping
Published: September 2024

Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images.
GIS-Based Agricultural Land Use Favorability Assessment in the Context of Climate Change: A Case Study of the Apuseni Mountains
Authors: Săvan Gabriela , Păcurar Ioan , Roșca Sanda , Megyesi Hilda , Fodorean Ioan , Bilașco Ștefan , Negrușier Cornel , Bara Lucian Vasile , Filipov Fiodor
Published: September 2024

With an emphasis on the effects of climate change, this study offers a thorough GIS-based assessment of land use favorability in the Apuseni Mountains. The Apuseni Mountains, a region characterized by its biodiversity and complex terrain, are increasingly vulnerable to the impacts of climate change, which threaten both natural ecosystems and human activities. The territory of 11 territorial administrative units was selected for the investigation because it shows more of an anthropogenic influence due to the migration of people to mountainous areas following the COVID-19 pandemic, which increased the amount of anthropogenic pressure in this area. Factors that describe the climate of the study area, the soil characteristics, and the morphometric characteristics of the relief were used to create a classification for the present on classes of favorability and restrictiveness for the plots of land, using a quantitative GIS model to determine the favorability of the land for the main crops and agricultural uses. The current land favorability was thus initially obtained, taking into account current temperature and precipitation values and using the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios for the 2020–2099 time frame. The results indicate a variation in the statistical classification of the land for different favorability classes, a decrease of 4.7% for the high favorability class for pastures, an estimated decrease of 4.4% for grassland, and in the case of orchards, the situation reflects a fluctuating variation. There is a decrease of 6.4% in the case of the very low favorability class according to SSP2-4.5 (in the case of reaching an average temperature of 12.7 °C and an annual precipitation of 895 mm), and in case of high and very high favorability, there is an increase in plots falling into better high favorability classes of up to 0.7%.
Peri-Implant Tissue Stability: A Series of Five Case Reports on an Innovative Implant Design
Authors: Bianchini Marco Aurélio , Escobar Mario , Galarraga-Vinueza Maria Elisa , Balduino Thalles Yurgen , Gehrke Sergio Alexandre
Published: September 2024

Background/Aim: The stability of peri-implant tissues is crucial for the long-term success of dental implant treatments. A new cervical implant design has been developed to address the challenges associated with peri-implant tissue stability, featuring a concave cervical portion to increase tissue volume in this area. The present study aimed to clinically evaluate the effectiveness of the new cervical implant design in maintaining peri-implant tissue stability. Materials and Methods: Five clinical cases involving completely edentulous patients were selected, in which 25 implants were installed. The marginal bone level around each implant was assessed at three different time points—T0: immediately after the prosthesis installation, T1: 6 months post installation, and T2: at the last control visit, up to 38 months later. Measurements were taken to analyze changes in marginal bone levels (MBLs) and the keratinized mucosa (KM) over time. Furthermore, the keratinized mucosa (KM) around the implants was evaluated. Results: The mean and standard deviation values of the marginal bone levels at each time point were as follows—T0: 0.59 ± 0.55 mm; T1: 1.41 ± 0.59 mm; T2: 1.76 ± 0.69 mm. Statistical analysis showed significant differences across the time points (ANOVA p < 0.0001). The overall mean KM values were 3.85 mm for T1 and T2, showing the stability of the peri-implant soft tissues at ≥1-year controls. Conclusion: Within the limitations of the present study, the results showed that the Collo implants presented measured MBL values increasing within the time range analyzed in each case but within the normal values cited in the literature for these types of rehabilitation treatments. However, the measured KM values presented, in all cases, an average above the values referenced in the literature as a minimum for maintaining the health of the peri-implant tissues.
A Survey on Penetration Path Planning in Automated Penetration Testing
Authors: Chen Ziyang , Kang Fei , Xiong Xiaobing , Shu Hui
Published: September 2024

Penetration Testing (PT) is an effective proactive security technique that simulates hacker attacks to identify vulnerabilities in networks or systems. However, traditional PT relies on specialized experience and costs extraordinary time and effort. With the advancement of artificial intelligence technologies, automated PT has emerged as a promising solution, attracting attention from researchers increasingly. In automated PT, penetration path planning is a core task that involves selecting the optimal attack paths to maximize the overall efficiency and success rate of the testing process. Recent years have seen significant progress in the field of penetration path planning, with diverse methods being proposed. This survey aims to comprehensively examine and summarize the research findings in this domain. Our work first outlines the background and challenges of penetration path planning and establishes the framework for research methods. It then provides a detailed analysis of existing studies from three key aspects: penetration path planning models, penetration path planning methods, and simulation environments. Finally, this survey offers insights into the future development trends of penetration path planning in PT. This paper aims to provide comprehensive references for academia and industry, promoting further research and application of automated PT path planning methods.
Weightless Model Predictive Control for Permanent Magnet Synchronous Motors with Extended State Observer
Authors: Geng Quanfu , Liu Quanhui , Zheng Weiguang
Published: September 2024

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.
Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
Authors: Liu Zhongzhong , Liu Yuxuan , Wang Zonghao , Huang Wentao
Published: September 2024

It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows: (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt failure increase zone. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching four to five times the initial permeability. (3) When the static loading stage and dynamic load are constant, the rate of change in coal permeability decreases with increasing adsorption amounts. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in the static loading stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static loading stages and decreased sensitivity with greater adsorption amounts.
Development of a Novel Transonic Fan Casing Making Use of Rapid Prototyping and Additive Manufacturing
Authors: Cusator Andrew , Key Nicole L.
Published: September 2024

Additive manufacturing (AM) presents significant cost savings and lead time reductions because of features inherent to the manufacturing process. The technology lends itself to rapid prototyping due to the streamlined workflow of quickly implementing design changes. Compared to traditional machining, AM techniques are simpler in execution for design engineers because they do not require detailed engineering drawings and they typically make use of the nominal geometry in computer models. A novel transonic fan casing assembly has been developed that makes use of AM inserts surrounding the rotor to provide an opportunity to cost-effectively change the corresponding flowpath. The rapid prototyping design philosophy developed from this work will allow for numerous experimental studies into the effects that different design parameters of casing geometries have on fan aerodynamic performance. A fan stage representative of a small turbofan engine was successfully tested with smooth-walled, additively manufactured inserts as a baseline case for future configurations. Before installing the 3D printed casing assembly, computational thermal stress analysis was performed to reduce the risk in implementation due to the demanding environment associated with the rotor. AM components and materials typically have nonlinear mechanical properties, adding to the complexity of the structural analysis. As part of the research, steady aerodynamic performance was measured over the entire relevant operating range of the fan.
Kuratsuki Bacteria Interactions with Sake Yeast and Effect on Taste
Authors: Nishida Hiromi
Published: September 2024

Various microorganisms, referred to as kuratsuki microorganisms, inhabit each sake brewery. Previously, kuratsuki yeasts had been used for sake production in each sake brewery. Kuratsuki lactic acid bacteria have been used to produce kimoto, a fermentation starter. Kuratsuki non-lactic acid bacteria were examined to evaluate their potential roles and effects in sake production. The addition of kuratsuki bacteria to the sake-making process can change the flavor and taste of the sake. This change was observed in both the coculture experiments between sake yeast and kuratsuki bacteria and the sake making tests with and without kuratsuki bacteria. The comprehensive gene expression analysis of sake yeast cocultured with kuratsuki bacteria showed that 1.2% of the yeast genes were upregulated and 1.0% were downregulated following the addition of kuratsuki bacteria. This indicates that the change in flavor and taste of sake due to the addition of kuratsuki bacteria was caused by the interaction between sake yeast and kuratsuki bacteria. To understand the implications of kuratsuki bacteria in sake production, it is essential to study the interactions between sake yeast and kuratsuki bacteria.
Calibration of Typhoon Track Forecasts Based on Deep Learning Methods
Authors: Tao Chengchen , Wang Zhizu , Tian Yilun , Han Yaoyao , Wang Keke , Li Qiang , Zuo Juncheng
Published: September 2024

An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability.
A New Method Proposed for the Estimation of Exposure to Atmospheric Pollution through the Analysis of Black Pigments on the Lung Surface
Authors: Waked Dunia , Veras Mariana Matera , Saldiva Paulo Hilário Nascimento , Takano Ana Paula Cremasco
Published: September 2024

Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of pollutant levels, considering proximity to major roads, or through personal exposure monitoring with portable sensors. However, these approaches fall short in identifying individual exposure values over a lifetime. It is well established that individuals living in large urban areas inhale atmospheric particles containing carbonaceous components, resulting in the deposition of black pigments in lung tissue, known as black carbon or anthracosis. This study aims to detail the procedures for assessing the deposition of such pigments, which serve as an estimate of an individual’s exposure to atmospheric pollution particles. Data collection involves administering detailed questionnaires and capturing lung images in the autopsy room. The analysis is based on macroscopic quantification of black pigments, supplemented by an evaluation of personal habits and the clinical histories of the individuals. This method of estimating lifetime exposure to inhaled particles provides a valuable tool for understanding the correlation between urban living and its potential health effects.
Emissions of Polychlorinated Dibenzo-p-Dioxins/Dibenzofurans during Coffee Roasting: Exploring the Influence of Roasting Methods and Formulations
Authors: Lin Li-Man , Chen I-Jen , Huang Bo-Wun , Cheruiyot Nicholas Kiprotich , Chang-Chien Guo-Ping
Published: September 2024

Polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F) emissions during the roasting of green Arabica coffee and coffee formulations containing alcohol, sugar, and honey were investigated in this study. Fast and slow roasting methods, which took 5.62 and 9.65 min to achieve a light roast, respectively, were used to evaluate the emissions. The concentrations in the flue gas during the fast roasting of green coffee (0.0296 ng Nm−3 and 0.00364 ng WHO-TEQ Nm−3) were 13.9% and 70.5% higher than during slow roasting, respectively. However, this was only the trend for some formulations, and no significant differences in concentrations were found between the methods at p = 0.05. Thus, the slow roasting method might not necessarily reduce formation when additives are included. The emission factors were 2.86 ng kg−1 and 0.352 ng WHO-TEQ kg−1, and 4.17 ng kg−1 and 0.176 ng WHO-TEQ kg−1 for the fast and slow roasting of green coffee, respectively. Further investigations are warranted to understand the formation mechanism, assess potential health risks, and explore mitigation strategies. These findings have implications for both coffee processing industries and regulatory bodies, as understanding the impact of roasting methods and additives could inform the development of cleaner production practices and targeted emission reduction policies.
Hydrocarbon Traps for the Air Intake System: Component Test Rig and SHED Test Procedure for Determining Their Efficiencies
Authors: Brunnermeier Matthias
Published: September 2024

Hydrocarbon traps in the air intake system (AIS) are a common method for controlling evaporative emissions from the air intake path. Several different systems are available, but there is no standard method for determining their efficiencies. Therefore, a component test rig for hydrocarbon traps was developed. Some optimizations were necessary to achieve emission characteristics observed in engine measurements. Using this setup, several measurements were performed on four different hydrocarbon traps. The results were in reasonable agreement with those from engine measurements. Two different hydrocarbon (HC) traps were selected for further studies. In these studies, the repeatability and the dependency of the emission mass level were investigated. Furthermore, the hydrocarbon concentration in the air filter box was determined using point source flame ionization detector (FID) sampling and a metal oxide semiconductor (MOS) sensor. The data showed a correlation with the emission mass determined in a sealed housing emission determination (SHED) test.
Radon Equilibrium Factor and the Assessment of the Annual Effective Dose at Underground Workplaces
Authors: Grygier Agata , Skubacz Krystian
Published: September 2024

The equilibrium factor F is one of the parameters that should be considered when assessing the effective dose based on radon activity concentration. Since the equilibrium factor in various environments ranges theoretically from a value close to 0 to 1, it is expected that dose assessment based on one recommended coefficient value may lead to an underestimation or overestimation of the dose. That is why it is essential to measure this quantity if the basis for dose assessment is the radon concentration and not the concentration of radon decay products. The equilibrium factors were determined based on measurements of radon activity concentration and potential alpha energy concentration and varied from 0.15 to 0.94, with an arithmetic mean of 0.55. The average effective dose calculated for the employee taking into account these values was 31 mSv, assuming an annual working time of 1800 h. In turn, the average effective dose calculated for the equilibrium factor of 0.2 as recommended by the International Commission on Radiological Protection (ICRP) was equal to 13 mSv.
Risk Assessment of Community-Scale High-Temperature and Rainstorm Waterlogging Disasters: A Case Study of the Dongsi Community in Beijing
Authors: Xing Pei , Yang Ruozi , Du Wupeng , Gao Ya , Xuan Chunyi , Zhang Jiayi , Wang Jun , Bai Mengxin , Dang Bing , Xiong Feilin
Published: September 2024

With the advancement of urbanization and acceleration of global warming, extreme weather and climate events are becoming increasingly frequent and severe, and climate risk continues to rise. Each community is irreplaceable and important in coping with extreme climate risk and improving urban resilience. In this study, the Dongsi Community in the functional core area of Beijing was explored, and the risk assessment of high temperatures and rainstorm waterlogging was implemented at the community scale. Local navigation observations were integrated into a theoretical framework for traditional disaster risk assessment. The risk assessment indicator system for community-scale high-temperature and rainstorm waterlogging disasters was established and improved from a microscopic perspective (a total of 22 indicators were selected from the three dimensions of hazard, exposure, and vulnerability). Geographic Information Systems (GIS) technology was used to integrate geographic information, meteorological, planning, municipal, socioeconomic and other multisource information layers, thus enabling more detailed spatial distribution characteristics of the hazard, exposure, vulnerability, and risk levels of community-scale high temperatures and rainstorm waterlogging to be obtained. The results revealed that the high-risk area and slightly high-risk area of high-temperature disasters accounted for 13.5% and 15.1%, respectively. The high-risk area and slightly high-risk area of rainstorm waterlogging disasters accounted for 9.8% and 31.6%, respectively. The high-risk areas common to high temperatures and waterlogging accounted for 3.9%. In general, the risk of high-temperature and rainstorm waterlogging disasters at the community scale showed obvious spatial imbalances; that is, the risk in the area around the middle section of Dongsi Santiao was the lowest, while a degree of high temperatures or rainstorm waterlogging was found in other areas. In particular, the risk of high-temperature and rainstorm waterlogging disasters along Dongsi North Street, the surrounding areas of Dongsi Liutiao, and some areas along the Dongsi Jiutiao route was relatively high. These spatial differences were affected to a greater extent by land cover (buildings, vegetation, etc.) and population density within the community. This study is a useful exploration of climate risk research for resilient community construction, and provides scientific support for the planning of climate-adaptive communities, as well as the proposal of overall adaptation goals, action frameworks, and specific planning strategies at the community level.
Issues Concerning the Mechanisms of Bone Conduction
Authors: Geal-Dor Miriam , Adelman Cahtia , Sohmer Haim
Published: September 2024

Air and bone conduction thresholds are used to differentiate between conductive and sensori-neural hearing losses because bone conduction is thought to bypass the conductive apparatus, directly activating the inner ear. However, the suggested bone conduction mechanisms involve the outer and middle ears. Also, normal bone conduction thresholds have been reported in cases of lesions to the conduction pathway. Therefore, further investigation of bone conduction mechanisms is required.
Greedoids and Violator Spaces
Authors: Kempner Yulia , Levit Vadim E.
Published: September 2024

This research explores the interplay between violator spaces and greedoids—two distinct theoretical frameworks developed independently. Violator spaces were introduced as a generalization of linear programming, while greedoids were designed to characterize combinatorial structures where greedy algorithms yield optimal solutions. These frameworks have, until now, existed in isolation. This paper bridges the gap by showing that greedoids can be defined using a modified violator operator. The established connections not only deepen our understanding of these theories but also provide a new characterization of antimatroids.
Clinical Investigation of Bioelectric Toothbrush for Dentin Hypersensitivity Management: A Randomized Double-Blind Study
Authors: Kang Hyun-Kyung , Kim Yu-Rin , Lee Ji-Young , Kim Da-Jeong , Kim Young-Wook
Published: September 2024

Background: The objective of this study was to evaluate how effectively the bioelectric toothbrush can alleviate dentin hypersensitivity (DHS) by using electrostatic forces to remove biofilm from the tooth surface. Methods: This study divided inpatients of a preventative dental clinic between March and October 2023 into the following two groups: a bioelectric toothbrush group (BET, n = 25) and a non-bioelectric toothbrush group (NBET, n = 18) as a control group. This was a randomized double-blind, placebo-controlled trial study. A survey, the number of hypersensitive teeth, the O’Leary index, the visual analogue scale (VAS), and the Schiff Cold Air Sensitivity Scale (SCASS) were also investigated. Results: When fluoride toothpaste was applied with a bioelectric toothbrush, the subjects’ VAS and SCASS scores reflecting symptoms of hyperesthesia significantly decreased over time, as did the number of hypersensitive teeth and the O’Leary index. Moreover, the bioelectric toothbrush was confirmed to be effective in removing dental plaque. Conclusions: Dental clinics must actively promote bioelectric toothbrushes and fluoride toothpaste for patients suffering from hyperesthesia and pain. Furthermore, these items can be suggested as preventative oral care products to patients with potential hyperesthesia.
Advanced Engineering Technology in Orthopedic Research
Authors: Cheng Rongshan , Wang Huizhi , Cheng Cheng-Kung
Published: September 2024

Musculoskeletal injuries are increasing in conjunction with the aging of populations and the rising frequency of exercise [...]
Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding
Authors: Tan Xiyue , Wang Dan , Xu Meng , Chen Jiaming , Wu Shuhan
Published: September 2024

Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain–computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.
Thermal Evaluation of Bone Drilling: Assessing Drill Bits and Sequential Drilling
Authors: Rugova Sihana , Abboud Marcus
Published: September 2024

Sequential drilling is a common practice in dental implant surgery aimed at minimizing thermal damage to bone. This study evaluates the thermal effects of sequential drilling and assesses modifications to drilling protocols to manage heat generation. We utilized a custom drill press and artificial bone models to test five drill bits under various protocols, including sequential drilling with different loads, spindle speeds, and peck drilling. Infrared thermography recorded temperature changes during the drilling process, with temperatures monitored at various depths around the osteotomy. The results reveal sequential drilling does not eliminate the thermal damage zone it creates (well over 70 °C). It creates harmful heat to surrounding bone that can spread up to 10 mm from the osteotomy. The first drill used in sequential drilling produces the highest temperatures (over 100 °C), and subsequent drill bits cannot remove the thermal trauma incurred; rather, they add to it. Modifying drill bit design and employing proper drilling techniques, such as reducing drilling RPM and load, can reduce thermal trauma by reducing friction. Inadequate management of heat can lead to prolonged recovery, increased patient discomfort, and potential long-term complications such as impaired bone-to-implant integration and chronic conditions like peri-implantitis. Ensuring healthy bone conditions is critical for successful implant outcomes.
Manufacturing Process Affects Coagulation Kinetics of Ortho-R, an Injectable Chitosan–Platelet-Rich Plasma Biomaterial for Tissue Repair
Authors: Chevrier Anik , Lavertu Marc
Published: September 2024

Ortho-R (ChitogenX Inc., Kirkland, QC, Canada) is an injectable combination drug–biologic product that is used as an adjunct to augment the standard of care for the surgical repair of soft tissues. The drug product comprises lyophilized chitosan, trehalose and calcium chloride, and it is dissolved in platelet-rich plasma (PRP), a blood-derived biologic, prior to injection at the surgical site where it will coagulate. The first step of the Ortho-R manufacturing process involves dissolving the chitosan in hydrochloric acid. The purpose of this study was to investigate the effect of increasing the amount of acid used to dissolve the chitosan on final drug product performance, more specifically, on the chitosan–PRP coagulation kinetics. Chitosans were solubilized in hydrochloric acid, with concentrations adjusted to obtain between 60% and 95% protonation of the chitosan amino groups. Freeze-dried Ortho-R was solubilized with PRP, and coagulation was assessed using thromboelastography (TEG). The clotted mixtures were observed with histology. Clot reaction time (TEG R) increased and clot maximal amplitude (TEG MA) decreased with protonation levels as pH decreased. Chitosan distribution was homogeneous in chitosan–PRP clots at the lowest protonation levels, but it accumulated toward the surface of the clots at the highest protonation levels as pH decreased. These changes in coagulation kinetics, clot strength and chitosan distribution induced by high protonation of the chitosan amino groups were partially reversed by adding sodium hydroxide to the dissolved chitosan component in order to decrease pH. Careful control of manufacturing processes is critical, and it is important to consider the impact of each manufacturing step on product performance.
ABNet: AI-Empowered Abnormal Action Recognition Method for Laboratory Mouse Behavior
Authors: Chen Yuming , Guo Chaopeng , Han Yue , Hao Shuang , Song Jie
Published: September 2024

The automatic recognition and quantitative analysis of abnormal behavior in mice play a crucial role in behavioral observation experiments in neuroscience, pharmacology, and toxicology. Due to the challenging definition of abnormal behavior and difficulty in collecting training samples, directly applying behavior recognition methods to identify abnormal behavior is often infeasible. This paper proposes ABNet, an AI-empowered abnormal action recognition approach for mice. ABNet utilizes an enhanced Spatio-Temporal Graph Convolutional Network (ST-GCN) as an encoder; ST-GCN combines graph convolution and temporal convolution to efficiently capture and analyze spatio-temporal dynamic features in graph-structured data, making it suitable for complex tasks such as action recognition and traffic prediction. ABNet trains the encoding network with normal behavior samples, then employs unsupervised clustering to identify abnormal behavior in mice. Compared to the original ST-GCN network, the method significantly enhances the capabilities of feature extraction and encoding. We conduct comprehensive experiments on the Kinetics-Skeleton dataset and the mouse behavior dataset to evaluate and validate the performance of ABNet in behavior recognition and abnormal motion detection. In the behavior recognition experiments conducted on the Kinetics-Skeleton dataset, ABNet achieves an accuracy of 32.7% for the top one and 55.2% for the top five. Moreover, in the abnormal behavior analysis experiments conducted on the mouse behavior dataset, ABNet achieves an average accuracy of 83.1%.
The Use of Artificial Intelligence in Caries Detection: A Review
Authors: Al-Khalifa Khalifa S. , Ahmed Walaa Magdy , Azhari Amr Ahmed , Qaw Masoumah , Alsheikh Rasha , Alqudaihi Fatema , Alfaraj Amal
Published: September 2024

Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.
Toward Digital Periodontal Health: Recent Advances and Future Perspectives
Authors: Soheili Fatemeh , Delfan Niloufar , Masoudifar Negin , Ebrahimni Shahin , Moshiri Behzad , Glogauer Michael , Ghafar-Zadeh Ebrahim
Published: September 2024

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.
Complex Pathophysiology of Acute Kidney Injury (AKI) in Aging: Epigenetic Regulation, Matrix Remodeling, and the Healing Effects of H2S
Authors: Gupta Shreyasi , Mandal Subhadeep , Banerjee Kalyan , Almarshood Hebah , Pushpakumar Sathnur B. , Sen Utpal
Published: September 2024

The kidney is an essential excretory organ that works as a filter of toxins and metabolic by-products of the human body and maintains osmotic pressure throughout life. The kidney undergoes several physiological, morphological, and structural changes with age. As life expectancy in humans increases, cell senescence in renal aging is a growing challenge. Identifying age-related kidney disorders and their cause is one of the contemporary public health challenges. While the structural abnormalities to the extracellular matrix (ECM) occur, in part, due to changes in MMPs, EMMPRIN, and Meprin-A, a variety of epigenetic modifiers, such as DNA methylation, histone alterations, changes in small non-coding RNA, and microRNA (miRNA) expressions are proven to play pivotal roles in renal pathology. An aged kidney is vulnerable to acute injury due to ischemia-reperfusion, toxic medications, altered matrix proteins, systemic hemodynamics, etc., non-coding RNA and miRNAs play an important role in renal homeostasis, and alterations of their expressions can be considered as a good marker for AKI. Other epigenetic changes, such as histone modifications and DNA methylation, are also evident in AKI pathophysiology. The endogenous production of gaseous molecule hydrogen sulfide (H2S) was documented in the early 1980s, but its ameliorative effects, especially on kidney injury, still need further research to understand its molecular mode of action in detail. H2S donors heal fibrotic kidney tissues, attenuate oxidative stress, apoptosis, inflammation, and GFR, and also modulate the renin–angiotensin–aldosterone system (RAAS). In this review, we discuss the complex pathophysiological interplay in AKI and its available treatments along with future perspectives. The basic role of H2S in the kidney has been summarized, and recent references and knowledge gaps are also addressed. Finally, the healing effects of H2S in AKI are described with special emphasis on epigenetic regulation and matrix remodeling.
Myco-Biosynthesis of Silver Nanoparticles, Optimization, Characterization, and In Silico Anticancer Activities by Molecular Docking Approach against Hepatic and Breast Cancer
Authors: El-Naggar Noura El-Ahmady , Shweqa Nada S. , Abdelmigid Hala M. , Alyamani Amal A. , Elshafey Naglaa , Soliman Hoda M. , Heikal Yasmin M.
Published: September 2024

This study explored the green synthesis of silver nanoparticles (AgNPs) using the extracellular filtrate of Fusarium oxysporum as a reducing agent and evaluated their antitumor potential through in vitro and in silico approaches. The biosynthesis of AgNPs was monitored by visual observation of the color change and confirmed by UV–Vis spectroscopy, revealing a characteristic peak at 418 nm. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analyses showed spherical nanoparticles ranging from 6.53 to 21.84 nm in size, with stable colloidal behavior and a negative zeta potential of −15.5 mV. Selected area electron diffraction (SAED) confirmed the crystalline nature of the AgNPs, whereas energy-dispersive X-ray (EDX) indicated the presence of elemental silver at 34.35%. A face-centered central composite design (FCCD) was employed to optimize the biosynthesis process, yielding a maximum AgNPs yield of 96.77 µg/mL under the optimized conditions. The antitumor efficacy of AgNPs against MCF-7 and HepG2 cancer cell lines was assessed, with IC50 values of 35.4 µg/mL and 7.6 µg/mL, respectively. Molecular docking revealed interactions between Ag metal and key amino acids of BCL-2 (B-cell lymphoma-2) and FGF19 (fibroblast growth factor 19), consistent with in vitro data. These findings highlight the potential of biologically derived AgNPs as promising therapeutic agents for cancer treatment and demonstrate the utility of these methods for understanding the reaction mechanisms and optimizing nanomaterial synthesis.