bluuebunny/crossref_metadata_2025 huggingface dataset:
Planning-Level Optimisation of Headway Regularity
Authors: Pitka Pavle , Simeunović Milan , Miličić Milica , Kovačević Tatjana , Simeunović Milja , Marinković Dragan , Ćojbašić Žarko
Published: July 2024

Headway variability has a negative impact on the public transport passengers’ perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them in practice. On the other hand, planning-level methods can be used to increase the resilience of public passenger transport (PPT) to the accumulation of headway disturbances. As this is typically done from the operator’s perspective, the passengers’ perspective tends to be overlooked, motivating the current work. In this article, an optimisation procedure for evaluating the viability of diametrical line splitting in terms of passenger travel time and headway regularity is proposed. The aim is to increase the robustness/resistance of the PPT system to the propagation of headway disturbances without reducing the service quality. The developed optimisation procedure was validated by applying it to real data pertaining to an urban PPT line. The results show that there is a positive correlation between the transport demand and the effects of the optimisation procedure, whereby an increase in the primary headway disturbance increases the sensitivity of the optimisation procedure to the transport demand.
Antiplatelet Activity of Phenolic Compounds-Fortified Merlot Wine and Pure Phenolic Compounds
Authors: Rodriguez Lyanne , Muñoz-Bernal Óscar A. , Fuentes Eduardo , Alvarez-Parrilla Emilio , Palomo Iván
Published: July 2024

Red wines and their pomace are valuable sources of phenolic compounds (PCs), which have been proposed as potential contributors to their cardioprotective effect through the inhibition of platelet aggregation. The antiplatelet activity of an extract depends on its chemical composition, specifically the presence of certain phenolic compounds, as well as the interactions between them affecting biological activity. In order to assess the effect on platelet aggregation, we investigated the effect of the grape pomace PC enrichment of a Merlot wine, as well as the effect of the five major phenolic compounds present in wine extracts: caffeic acid, gallic acid, quercetin, epicatechin, and catechin. We analyzed how their combination influenced platelet aggregation. We found that the fortified wine sample with the highest PC content (W8) exhibited a potent antiplatelet effect in aggregation and platelet activation assays induced by the agonists TRAP-6, collagen, and ADP, with its activity being most potent against the latter agonist (78 ± 4%). Among the evaluated phenolic compounds, quercetin showed the highest antiplatelet potential against all three agonists studied, while gallic acid showed minimal antiplatelet effect. These findings suggest that the cardioprotective effect of wines is related to their chemical composition and the synergy among phenolic compounds. However, further research is required to fully understand the underlying mechanisms and clinical relevance of this activity.
Geomechanical Analysis of the Main Roof Deformation in Room-and-Pillar Ore Mining Systems in Relation to Real Induced Seismicity
Authors: Chlebowski Dariusz , Burtan Zbigniew
Published: July 2024

Rockbursts represent one of the most serious and severe natural hazards emerging in underground copper mines within the Legnica–Glogow Copper District (LGCD) in Poland. The contributing factor determining the scale of this event is mining-induced seismicity of the rock strata. Extensive expertise of the copper mining practitioners clearly indicates that high-energy tremors are the consequence of tectonic disturbances or can be attributed to stress/strain behaviour within the burst-prone roof strata. Apparently, seismic activity is a triggering factor; hence, attempts are made by mine operators to mitigate and control that risk. Underlying the effective rockburst control strategy is a reliable seismicity forecast, taking into account the causes of the registered phenomena. The paper summarises the geomechanics analyses aimed to verify the actual seismic and rockburst hazard levels in one of the panels within the copper mine Rudna (LGCD). Two traverses were designated at the face range and comparative analyses were conducted to establish correlations between the locations of epicentres of registered tremors and anomaly zones obtained via analytical modelling of changes in stress/strain behaviours within the rock strata. The main objective of this study was to evaluate the likelihood of activating carbonate/anhydrite layers within the main roof over the excavation being mined, with an aim to verify the potential causes and conditions which might have triggered the registered high-energy events. Special attention is given to two seismic events giving rise to rockbursts in mine workings. Results seem to confirm the adequacy and effectiveness of solutions provided by mechanics of deformable bodies in the context of forecasting the scale and risk of dynamic phenomena and selecting the appropriate mitigation and control measures in copper mines employing the room-and-pillar mining system.
Highly Efficient Blue Host Compound Based on an Anthracene Moiety for OLEDs
Authors: Kwon Hyukmin , Oh Seyoung , Park Sangwook , Park Sunwoo , Lee Hayoon , Park Jongwook
Published: July 2024

We designed and successfully synthesized a novel anthracene-based host material, 10-(4-(naphthalen-1-yl)phenyl)-9-(naphthalen-3-yl)anthracene (2-NaAn-1-PNa). The 2-NaAn-1-PNa exhibited a PL max at 440 nm in film state and demonstrated a wide band gap of 2.95 eV, indicating its suitability as a blue host material. In an OLED device using 2-NaAn-1-PNa as the host and 3Me-1Bu-TPPDA as the dopant, real blue emission was achieved with an electroluminescence maximum (ELmax) at 460 nm and Commission Internationale de L’Eclairage coordinates of (0.133, 0.141). The device exhibited excellent EL characteristics, with an external quantum efficiency of 8.3% and a current efficiency of 9.3 cd/A at 10 mA/cm2. Furthermore, the efficiency roll-off was limited to only 1.9% up to 4000 nits, demonstrating exceptional performance.
Analysis of the Impact of Forces in Hanger Rods on Power Boiler Operation
Authors: Duda Piotr , Felkowski Łukasz , Duda Andrzej
Published: July 2024

Hanger rods intended for boiler suspensions on a metal structure require a special design approach that considers the total weight of the boiler structure that is to be carried. Unfortunately, a large reserve in the rods’ carrying capacity does not always guarantee the failure-free operation of a boiler. For this reason, it is very important to monitor the forces acting in the rods. Any negligence in this matter might cause a local excess of stresses in the boiler pressure part and result in periodic shutdowns of the boiler. Therefore, in order not to let this happen, adequate computational methods have to be developed. To date, the methods described in the literature and in design requirements have concentrated, first of all, on analyses of the boiler individual subassemblies or on small boiler units supported from the bottom at just a few points. The aim of this paper was to conduct a numerical analysis for a real high-capacity power boiler. The computations were performed based on experimentally measured forces in the hanger rods, and the results were verified by comparing the areas of stress concentration against the area where the boiler structure was regularly damaged.
Three-Dimension Inversion of Magnetic Data Based on Multi-Constraint UNet++
Authors: Jiao Jian , Zeng Xiangcheng , Liu Hui , Yu Ping , Lin Tao , Zhou Shuai
Published: July 2024

The three-dimension (3D) inversion of magnetic data is an effective method of recovering underground magnetic susceptibility distributions using magnetic anomaly data. The conventional regularization inversion method has good data fitting; however, its inversion model has the problem of a poor model-fitting ability due to a low depth resolution. The 3D inversion method based on deep learning can effectively improve the model-fitting accuracy, but it is difficult to guarantee the data-fitting accuracy of the inversion results. The loss function of traditional deep learning 3D inversion methods usually adopts the metric of the absolute mean squared error (MSE). In order to improve the accuracy of the data fitting, we added a forward-fitting constraint term (FFit) on the basis of the MSE. Meanwhile, in order to further improve the accuracy of the model fitting, we added the Dice coefficient to the loss function. Finally, we proposed a multi-constraint deep learning 3D inversion method based on UNet++. Compared with the traditional single-constraint deep learning method, the multi-constraint deep learning method has better data-fitting and model-fitting effects. Then, we designed corresponding test models and evaluation metrics to test the effectiveness and feasibility of the method, and applied it to the actual aeromagnetic data of a test area in Suqian City, Jiangsu Province.
A New Hybrid Approach for Product Management in E-Commerce
Authors: Yüregir Hacire Oya , Özşahin Metin , Akcan Yetgin Serap
Published: July 2024

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based–genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm.
Effects of Valve, Armature, and Armature Pin Guidance on Diesel Injector Performance
Authors: Işıklı Fırat , Şentürk Gökhan , Sürmen Ali
Published: July 2024

The valve, armature, and armature pin are critical factors influencing the hydraulic pressure differences in diesel injectors, and are essential for injection and backflow quantity control. These components play crucial roles in enhancing energy efficiency and reducing engine emissions. This experimental study investigated the effects of clearance between the valve, armature, and armature pin guidance. Forty-nine 2000 bar common-rail injectors (Bosch) were tested in calibrated stations. Injection quantities were assessed at both minimum and maximum operational pressures. Backflow rates were specifically examined at maximum pressure. A correlation matrix was created using Python to analyze the relationship between inputs and outputs, identifying dominant characteristics that define injector behavior. Increased injector precision correlated with reduced fuel consumption and enhanced energy efficiency. The study found that the effect of clearance between the armature and armature pins was more significant than that between the valve and armature. Injection quantities were observed to increase with pressure, and no critical difference in injection quantities was noted among different diameter groups at the minimum pressure point. Backflow quantities were consistent within groups when the armature–armature pin and valve–armature clearances were minimized.
Electricity Sales Package Decision Making Using Two-Stage Density Clustering and Minimum Adjustment Distance Consensus
Authors: Wang Kesheng , Hu Xinyu , Ma Yuanqian
Published: July 2024

In the decision making of electricity sales packages, it is usually the specific situation of similar customers that provides the basis of a decision-making plan for target customer package selection, so it is particularly important to integrate the opinions of similar customers. Therefore, a multi-attribute group decision-making method for an electricity sales package is proposed, which is based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. Firstly, in order to provide support for identifying similar customers among target customers, a sample customer set clustering method is proposed, which is based on a customer portrait label system and TSDC. Secondly, based on the entropy method, the attribute weight of the electricity sales package is determined. Based on the weight and the multi-attribute group decision-making consensus process, the minimum adjustment distance consensus of the sample customers’ fuzzy evaluation matrix for the electricity sales package is proposed. Then, a full-ranking decision method for an electricity sales package based on target customer satisfaction is proposed. Finally, customers in a certain area of China are selected as an example. This example is used to verify the accuracy and effectiveness of the decision-making method of electricity sales package proposed in this paper.
SNNtrainer3D: Training Spiking Neural Networks Using a User-Friendly Application with 3D Architecture Visualization Capabilities
Authors: Jurj Sorin Liviu , Nouri Sina Banasaz , Strutwolf Jörg
Published: July 2024

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model’s behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners.
Colour Stability between In-House 3D-Printed Resin Brackets and Conventionally Aesthetic Brackets: An In Vitro Study
Authors: Brucculeri Luca , Pellitteri Federica , Falconi Virginia , Palone Mario , Lombardo Luca
Published: July 2024

Background: To evaluate colour stability in artificial saliva by mechanically simulating brushing between in-house 3D-printed resin brackets (IH3DBs) and commercially available brackets. Methods: The samples consist of four sets of ten aesthetic brackets each supplied by four different manufacturers: clear Damon, Synovate C, Crystal and in-house 3D printed brackets (IH3DBs). The brackets were immersed in a plastic tank containing artificial saliva maintained at a constant temperature of 37 °C with a 65-minute brushing system. Staining changes at T0 (pre-brushing) and T1 (post-brushing) were measured with a spectrophotometer according to the VITA and Master scale and brightness values. Statistical analysis compared the colour changes with the Wilcoxon test and in case of significance, severity was investigated. The significance level considered was p < 0.05. Results: The IH3DBs shows statistically significant differences for both scales and brightness values. The Damon and the Crystal brackets report a statistically significant difference only for brightness. The Synovate C bracket shows no statistically significant differences. Conclusions: The IH3DB produced chromaticity differences for the VITA and Master scale possibly due to the surface roughness created during the printing process. However, the IH3DBs together with Damon Clear improved brightness, due to the mechanical action of brushing.
An Evaluation of the Fire Safety of Waste Paper-Based Internal Finishing Materials Combined with Expandable Graphite According to Changes in Magnesium Hydroxide Content
Authors: Park Dongin , Kim Yongjoo , Rie Dongho
Published: July 2024

Inflammable building finishing materials act as a major cause of fire propagation, and they, therefore, pose significant risks to life and can lead to property damage. To replace such flammable building finishing materials, many countries have established regulations limiting their use, which has led to extensive research on the development of flame-retardant building finishing materials. Such methods have included adding flame retardants to construction materials to reduce the heat release rate and total heat release. The present study aimed to enhance the fire performance of cellulose-based architectural finishing materials by creating a dual flame-retardant mixture using expandable graphite and magnesium hydroxide added to recycled paper waste. Specimen fabrication involves using a pressing method to apply uniform pressure to compress the mixture in a mold. The total heat release (THR), CO, and CO2 production of the produced specimens were measured using a cone calorimeter while varying the magnesium hydroxide additive ratio. The combustion gases were measured through NES 713 experiments to determine any changes in the Toxic Index corresponding to variations in the magnesium hydroxide content. The experiment results established a correlation between the magnesium hydroxide additive ratio and the total heat release, as well as the existence of variations in CO and CO2 production for the dual flame-retardant recycled paper material. A database for combustion gases was also obtained. It was confirmed that the fire performance was improved by confirming that the total heat release decreased by 52% from the previous one in the magnesium hydroxide content of 30 g, and it was confirmed that the inflection points of the Toxic Index value due to the change in CO and CO2 gas production occurred in the magnesium hydroxide content of 20 g due to the improvement of the fire performance. Through the ISO 5660-1 experiment data, we have secured data that can be used as foundational information for performance-oriented fire risk assessments, thereby ensuring the fire safety of cellulose materials that are vulnerable to fire.
Research on the Training and Application Methods of a Lightweight Agricultural Domain-Specific Large Language Model Supporting Mandarin Chinese and Uyghur
Authors: Pan Kun , Zhang Xiaogang , Chen Liping
Published: July 2024

In the field of Natural Language Processing (NLP), the lack of support for minority languages, especially Uyghur, the scarcity of Uyghur language corpora in the agricultural domain, and the lightweight nature of large language models remain prominent issues. This study proposes a method for constructing a bilingual (Uyghur and Chinese) lightweight specialized large language model for the agricultural domain. By utilizing a mixed training approach of Uyghur and Chinese, we extracted Chinese corpus text from agricultural-themed books in PDF format using OCR (Optical Character Recognition) technology, converted the Chinese text corpus into a Uyghur corpus using a rapid translation API, and constructed a bilingual mixed vocabulary. We applied the parameterized Transformer model algorithm to train the model for the agricultural domain in both Chinese and Uyghur. Furthermore, we introduced a context detection and fail-safe mechanism for the generated text. The constructed model possesses the ability to support bilingual reasoning in Uyghur and Chinese in the agricultural domain, with higher accuracy and a smaller size that requires less hardware. It (our work) addresses issues such as the scarcity of Uyghur corpora in the agricultural domain, mixed word segmentation and word vector modeling in Uyghur for widespread agricultural languages, model lightweighting and deployment, and the fragmentation of non-relevant texts during knowledge extraction from small-scale corpora. The lightweight design of the model reduces hardware requirements, facilitating deployment in resource-constrained environments. This advancement promotes agricultural intelligence, aids in the development of specific applications and minority languages (such as agriculture and Uyghur), and contributes to rural revitalization.
Meander Structure Analysis Techniques Using Artificial Neural Networks
Authors: Belova-Plonienė Diana , Krukonis Audrius , Abromavičius Vytautas , Serackis Artūras , Urbanavičius Vytautas , Katkevičius Andrius
Published: July 2024

Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time (td), reflection coefficient (S11), and transmission coefficient (S21) in the 0–10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic td was predicted with a 3.3 ps average MAE. The characteristic S21 was predicted with a 0.64 dB average MAE, and S11 was predicted with a 2.47 dB average MAE. The TDNN allowed the average MAEs to be reduced to 0.9 ps, 0.11 dB, and 1.63 dB, respectively. Using a CNN, the average MAEs were 27.5 ps, 0.44 dB, and 1.36 dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min.
Antibiotic Resistance in the Farming Environment
Authors: Karwowska Ewa
Published: July 2024

Bacterial resistance to antibiotics is now an extremely important safety and health issue. Much of the research on this phenomenon focuses on its clinical aspects, while current findings confirm that it is only one of a number of potential sources of bacteria and genes responsible for drug resistance. There are many indications that one of the main contributors to this issue is currently agriculture and that this applies virtually worldwide. Increased awareness of issues of rational use of antibiotics in husbandry practice entails increased interest in phenomena related to the spread of antibiotic resistance in the environment, their specifics, and the scale of the existing threat. This work, based on current research, analyzed selected aspects of the use of antibiotics in animal production, the presence of antibiotic-resistant microorganisms in farm animals and in waste from agricultural production, in particular from animal breeding farms, the determinants of antibiotic resistance in farming practices and the potential threats related to emissions and spread of antibiotic resistance factors in the environment, including the possibility of transfer of resistant bacteria and resistance genes to humans.
The Risk Assessment of the Security of Electronic Health Records Using Risk Matrix
Authors: Alarfaj Khalid A. , Rahman M. M. Hafizur
Published: July 2024

The healthcare industry has been shifting toward electronic health records to improve operations, reduce overhead expenditure, and provide better healthcare. Electronic health records (EHRs) are supposed to offer the same levels of confidentiality and privacy as paper records, which have been used for decades. However, this is not the case, as the technology used to access, transmit, and store records poses a high risk to patients and healthcare organizations. Employees are a big risk to EHRs, as they use their devices to access information about a patient and discuss such records with other employees. Healthcare professionals also access patients’ records illegally. Such security loopholes have a high impact on EHRs, as people with malicious intent can use the records to access their financial records or blackmail them. External access to EHRs by cyber attackers poses the highest risk to the records and patients, as attackers are primarily driven by financial gain. On the contrary, internal access to data, though unethical, does not pose a grave danger to patients, as the employees mainly discuss the cases within themselves without any financial incentive to access the data. The current research provides a risk analysis of EHRs, the source of security problems, the impact of the risks involved, and risk management best practices that healthcare organizations can use to protect patients’ data.
A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
Authors: Cai Jiangwei , Zhou Tan , Xu Yishen , Zhu Xin
Published: July 2024

We have designed a high-resolution magnetic field imaging system using 256 unpackaged Hall elements. These unpackaged Hall elements are arranged in a Hall linear array, and the distance between adjacent elements reaches 255 µm. The sensitivity of the unpackaged Hall element array can be adjusted using a computer to measure magnetic environments with different magnetic field strengths. High-resolution magnetic field images of 256 × 256 pixels can be generated by moving the array using the X–Y axis motorized rail. This spatial resolution can reach 99.61 pixels per inch (ppi). This rail allows for the spatial resolution of the system to be further increased to 199.22 ppi by using a special movement route. In the experiments, we employ this system to image magnetized metal scissors, and the result displays the structural features of the scissor surface. We also detected the magnetic suction wireless charging coil inside an Apple phone. The image obtained shows the shape of the coil and the gap between the magnets. The high-resolution magnetic imaging system displays the magnetic characteristics of the sample very well and easily obtains information about small-shaped structures and defects on the sample surface. This provides the system with potential in several fields such as quality inspection, security, biomedicine, and detection imaging.
In-Orbit Image Motion Compensation Technology for Long-Integration Time Space Cameras Using a Two-Axis Pointing Platform
Authors: Che Xin , Hu Chunhui , Qi Xinda , Ma Zelong , Li Wenxian , Niu Haipeng , Yan Changxiang
Published: July 2024

Space cameras play a pivotal role in various fields, such as astronomical exploration. When operating in orbit, these cameras encounter the relative motion between the target and the camera during the exposure, resulting in image motion, which affects the imaging quality. Therefore, it is necessary to compensate for this image motion. This paper investigates the in-orbit image motion compensation (IMC) method for space cameras with a long integration time based on a two-axis pointing platform. Firstly, the mechanical design of the camera is introduced. Secondly, the in-orbit IMC model for the camera is analyzed, and the angular motion needed for compensating for the image motion by the two-axis pointing platform are derived. Factors influencing the compensation accuracy are also analyzed. The effectiveness of the IMC model is verified through simulations. Finally, the in-orbit experimental results indicate that the energy concentration of the target star images obtained exceeded 70%, demonstrating excellent performance in space cameras and effectively enhancing imaging quality using IMC technology.
Investigation of the Shear and Pore Structure Characteristics of Rubber Fiber-Reinforced Expansive Soil
Authors: Yang Zhongnian , Wang Rongchang , Shi Wei , Sun Zhenxing , Ling Xianzhang
Published: July 2024

In recent years, many researchers have evaluated the sustainable use of waste tire rubber as an aggregate in soil. Its effectiveness has been widely acknowledged. The main objective of this work is to study the influence of rubber fibers on shear strength and pore structure characteristics in relation to expansive soil. In this context, we conducted a series of experiments that were carried out on reinforced expansive soil with rubber fiber contents of 0, 5, 10, 15, and 20%. The results show that the shear strength and maximum dilatation angle increase gradually with rubber fiber content. Due to the pore water pressure and creep effects, the deviator stress and effective cohesion of the samples under the consolidated drained conditions were higher than those under the undrained conditions. The converse was true for the internal angle. The addition of an appropriate amount of (5–10%) rubber fiber can effectively inhibit the development of soil cracks and reduce the porosity of the samples. The results obtained can highlight the beneficial effects of rubber fiber, which is highly desirable in many backfill applications.
A Residual Deep Learning Method for Accurate and Efficient Recognition of Gym Exercise Activities Using Electromyography and IMU Sensors
Authors: Mekruksavanich Sakorn , Jitpattanakul Anuchit
Published: July 2024

The accurate and efficient recognition of gym workout activities using wearable sensors holds significant implications for assessing fitness levels, tailoring personalized training regimens, and overseeing rehabilitation progress. This study introduces CNN-ResBiGRU, a novel deep learning architecture that amalgamates residual and hybrid methodologies, aiming to precisely categorize gym exercises based on multimodal sensor data. The primary goal of this model is to effectively identify various gym workouts by integrating convolutional neural networks, residual connections, and bidirectional gated recurrent units. Raw electromyography and inertial measurement unit data collected from wearable sensors worn by individuals during strength training and gym sessions serve as inputs for the CNN-ResBiGRU model. Initially, convolutional neural network layers are employed to extract unique features in both temporal and spatial dimensions, capturing localized patterns within the sensor outputs. Subsequently, the extracted features are fed into the ResBiGRU component, leveraging residual connections and bidirectional processing to capture the exercise activities’ long-term temporal dependencies and contextual information. The performance of the proposed model is evaluated using the Myogym dataset, comprising data from 10 participants engaged in 30 distinct gym activities. The model achieves a classification accuracy of 97.29% and an F1-score of 92.68%. Ablation studies confirm the effectiveness of the convolutional neural network and ResBiGRU components. The proposed hybrid model uses wearable multimodal sensor data to accurately and efficiently recognize gym exercise activity.
Extracellular Overexpression of a Neutral Pullulanase in Bacillus subtilis through Multiple Copy Genome Integration and Atypical Secretion Pathway Enhancement
Authors: Dong Wenkang , Fu Xiaoping , Zhou Dasen , Teng Jia , Yang Jun , Zhen Jie , Zhao Xingya , Liu Yihan , Zheng Hongchen , Bai Wenqin
Published: July 2024

Neutral pullulanases, having a good application prospect in trehalose production, showed a limited expression level. In order to address this issue, two approaches were utilized to enhance the yield of a new neutral pullulanase variant (PulA3E) in B. subtilis. One involved using multiple copies of genome integration to increase its expression level and fermentation stability. The other focused on enhancing the PulA-type atypical secretion pathway to further improve the secretory expression of PulA3E. Several strains with different numbers of genome integrations, ranging from one to four copies, were constructed. The four-copy genome integration strain PD showed the highest extracellular pullulanase activity. Additionally, the integration sites ytxE, ytrF, and trpP were selected based on their ability to enhance the PulA-type atypical secretion pathway. Furthermore, overexpressing the predicated regulatory genes comEA and yvbW of the PulA-type atypical secretion pathway in PD further improved its extracellular expression. Three-liter fermenter scale-up production of PD and PD-ARY yielded extracellular pullulanase activity of 1767.1 U/mL at 54 h and 2465.1 U/mL at 78 h, respectively. Finally, supplementing PulA3E with 40 U/g maltodextrin in the multi-enzyme catalyzed system resulted in the highest trehalose production of 166 g/L and the substrate conversion rate of 83%, indicating its potential for industrial application.
Bridging the Gap: Integrating 3D Bioprinting and Microfluidics for Advanced Multi-Organ Models in Biomedical Research
Authors: De Spirito Marco , Palmieri Valentina , Perini Giordano , Papi Massimiliano
Published: July 2024

Recent advancements in 3D bioprinting and microfluidic lab-on-chip systems offer promising solutions to the limitations of traditional animal models in biomedical research. Three-dimensional bioprinting enables the creation of complex, patient-specific tissue models that mimic human physiology more accurately than animal models. These 3D bioprinted tissues, when integrated with microfluidic systems, can replicate the dynamic environment of the human body, allowing for the development of multi-organ models. This integration facilitates more precise drug screening and personalized therapy development by simulating interactions between different organ systems. Such innovations not only improve predictive accuracy but also address ethical concerns associated with animal testing, aligning with the three Rs principle. Future directions include enhancing bioprinting resolution, developing advanced bioinks, and incorporating AI for optimized system design. These technologies hold the potential to revolutionize drug development, regenerative medicine, and disease modeling, leading to more effective, personalized, and humane treatments.
A New Method to Design and Manufacture a Low-Cost Custom-Made Template for Mandible Cut and Repositioning Using Standard Plates in BSSO Surgery
Authors: Di Brigida Liliana , Cortese Antonio , Cataldo Emilio , Naddeo Alessandro
Published: July 2024

In this study, a new methodology for designing and creating a custom-made template for maxillofacial surgery has been developed. The custom-made template can be used both for cutting and repositioning of the mandible arches for executing a BSSO (bilateral sagittal split osteotomy) treatment. The idea was developed in order to give the possibility of using a custom-made template with standard plates, thus reducing long times, high costs and low availability of custom-made plates; this represents the proof of novelty of the proposed template, based on a well-established methodology. The methodology was completely developed in the CAD virtual environment and, after the surgeons’ assessment, an in-vitro experiment by a maxillofacial surgeon was performed in order to check the usability and the versatility of the system, thanks to the use of additive manufacturing technologies. When computer-aided technologies are used for orthognathic surgery, there are significant time and cost savings that can be realised, as well as improved performance. The cost of the whole operation is lower than the standard one, thanks to the use of standard plates. To carry out the procedures, the proposed methodology allows for inexpensive physical mock-ups that enable the BSSO procedure to be performed.
Assessing the Impact of PRESERFLO MicroShunt on Intraocular Pressure in Porcine Eyes Ex Vivo Using Infusion Pump System
Authors: Masdipa Andi , Kaidzu Sachiko , Tanito Masaki
Published: July 2024

To evaluate the effectiveness of the PRESERFLO MicroShunt (PFM) in reducing intraocular pressure (IOP) ex vivo in porcine eyes using an infusion pump system and to simulate various IOP conditions, In this study, porcine eyes received increasing flows between 2 and 20 μL/min. IOP measurements were taken under conditions with and without the PFM [PFM (+) and PFM (−), respectively]. In the PFM (−) group, IOP increased from 7.4 mmHg to 46.3 mmHg as the flow rate increased from 2 μL/min to 20 μL/min. The rate of IOP reduction (%ΔIOP) rose with increasing flow rates, although the absolute IOP values achieved with the PFM insertion also increased. The correlation between IOPs in the PFM (−) conditions and the %ΔIOP was modeled as %ΔIOP = 22.4 Ln [PFM(−) IOP] − 41.7. According to this equation, IOP reduction by PFM insertion is 0% at IOPs of 6.4 mmHg or lower. IOP reductions of 10%, 20%, 30%, and 40% were observed when the pre-insertion IOPs were 10.1, 15.7, 24.6, and 38.4 mmHg, respectively. Achievable post-insertion IOP levels of ≤21 mmHg, ≤18 mmHg, ≤15 mmHg, and ≤12 mmHg corresponded to the initial IOPs of 33 mmHg, 26 mmHg, 20 mmHg, and 14.8 mmHg, respectively. In conclusion, the PFM effectively reduced IOP within a specific range of IOP values in an ex vivo experimental system. In clinical situations, the PFM is unlikely to be effective at low IOP levels. At higher levels, the PFM reduces IOP, but it may be insufficient to achieve the target IOP.
Deep Learning-Based Automated Measurement of Murine Bone Length in Radiographs
Authors: Rong Ruichen , Denton Kristin , Jin Kevin W. , Quan Peiran , Wen Zhuoyu , Kozlitina Julia , Lyon Stephen , Wang Aileen , Wise Carol A. , Beutler Bruce , Yang Donghan M. , Li Qiwei , Rios Jonathan J. , Xiao Guanghua
Published: July 2024

Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes.
Machine Learning and Graph Signal Processing Applied to Healthcare: A Review
Authors: Calazans Maria Alice Andrade , Ferreira Felipe A. B. S. , Santos Fernando A. N. , Madeiro Francisco , Lima Juliano B.
Published: July 2024

Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.
Cerebral Microbleeds Associate with Brain Endothelial Cell Activation-Dysfunction and Blood–Brain Barrier Dysfunction/Disruption with Increased Risk of Hemorrhagic and Ischemic Stroke
Authors: Hayden Melvin R.
Published: July 2024

Globally, cerebral microbleeds (CMBs) are increasingly being viewed not only as a marker for cerebral small vessel disease (SVD) but also as having an increased risk for the development of stroke (hemorrhagic/ischemic) and aging-related dementia. Recently, brain endothelial cell activation and dysfunction and blood–brain barrier dysfunction and/or disruption have been shown to be associated with SVD, enlarged perivascular spaces, and the development and evolution of CMBs. CMBs are a known disorder of cerebral microvessels that are visualized as 3–5 mm, smooth, round, or oval, and hypointense (black) lesions seen only on T2*-weighted gradient recall echo or susceptibility-weighted sequences MRI images. CMBs are known to occur with high prevalence in community-dwelling older individuals. Since our current global population is the oldest recorded in history and is only expected to continue to grow, we can expect the healthcare burdens associated with CMBs to also grow. Increased numbers (≥10) of CMBs should raise a red flag regarding the increased risk of large symptomatic neurologic intracerebral hemorrhages. Importantly, CMBs are also currently regarded as markers of diffuse vascular and neurodegenerative brain damage. Herein author highlights that it is essential to learn as much as we can about CMB development, evolution, and their relation to impaired cognition, dementia, and the exacerbation of neurodegeneration.
Verification of Criterion-Related Validity for Developing a Markerless Hand Tracking Device
Authors: Suwabe Ryota , Saito Takeshi , Hamaguchi Toyohiro
Published: July 2024

Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand movements. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit’s dependency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahrenheit and to assess their criterion-related validity. The analysis revealed moderate-to-high correlations between the two methods. Consistent results were also observed in the peak angle and velocity comparisons across the severity stages. Because Fahrenheit determines finger recovery status based on these measures, it has the potential to transfer this function to MediaPipe. This study highlighted the potential use of MediaPipe in paralysis estimation applications.
Aerodynamic Investigation on a Coaxial-Rotors Unmanned Aerial Vehicle of Bionic Chinese Parasol Seed
Authors: Gan Wenbiao , Wang Yunpeng , Wang Hongbo , Zhuang Junjie
Published: July 2024

Aerodynamic investigation of a bionic coaxial-rotors unmanned aerial vehicle (UAV) is performed. According to Chinese parasol seed features and flight requirements, the bionic conceptual design of a coaxial-rotors UAV is described. A solution procedure for the numerical simulation method, based on a multi-reference frame (MRF) model, is expressed, and a verification study is presented using the typical case. The aerodynamic design is conducted for airfoil, blade, and coaxial-rotors interference. The aerodynamic performance of the coaxial rotors is investigated by numerical simulation analysis. The rotor/motor integrated experiment verification is conducted to assess the performance of the coaxial-rotors UAV. The results indicate that the UAV has excellent aerodynamic performance and bionic configuration, allowing it to adapt to task requirements. The bionic UAV has a good cruise power load reach of 8.36 kg/kw, and the cruise flying thrust force is not less than 78 N at coaxial-rotor and rotor-balloon distance ratios of 0.39 and 1.12, respectively. It has the “blocks stability phenomenon” formed by the rotor downwash speed decreases and the balloon’s additional negative pressure. The present method and the bionic configuration provide a feasible design and analysis strategy for coaxial-rotors UAVs.
Unveiling the e-Servicescape of ChatGPT: Exploring User Psychology and Engagement in AI-Powered Chatbot Experiences
Authors: Kim Minseong
Published: July 2024

This study explores the psychological motivations that drive ChatGPT users to embrace and sustain the use of such technology based on the fundamental notion of the environmental psychology theory, including servicescapes. To do so, this study delves into the influence of ChatGPT’s e-servicescapes on users’ emotional states and intention to engage with ChatGPT for decision-making processes. This study conducted an online survey among ChatGPT users in the United States. Structural equation modeling revealed that negative emotions were significantly influenced by various e-servicescape sub-dimensions, including security, visual appeal, entertainment value, originality of design, and social factors. Positive emotions, on the other hand, were influenced by factors such as visual appeal, customization, interactivity, and relevance of information. Both positive and negative emotions significantly affected user satisfaction, which, in turn, shaped their behavioral intention to engage with ChatGPT. This study contributes to the understanding of digital environmental psychology and chatbots by extending the notion of e-servicescapes to the context of AI-based services. It underscores the significance of e-servicescapes in shaping user experiences and provides valuable insights for business scholars and marketing practitioners.