bluuebunny/crossref_metadata_2025 huggingface dataset:
Superplastic Forming/Diffusion Bonding of TA15 Titanium Alloy for Manufacturing Integrated Solid/Hollow Four-Layer Grid Lightweight Structure Components
Authors: Han Zheng , Xing Yuhan , Liu Taiying , Zhang Ning , Jiang Shaosong , Lu Zhen
Published: December 2024

In recent years, the excellent mechanical properties and lightweight characteristics of multi-layer hollow components have led to a surge in research focused on their forming processes. This growing interest has greatly advanced technological progress in aerospace and other related fields. In this paper, the metal flow behavior of TA15 titanium alloy at different temperatures from 840 °C to 940 °C and different strain rates from 0.001 s−1 to 0.1 s−1 was studied. Utilizing the finite element method, this study examined the local stress concentration, total strain distribution, thickness thinning characteristics, and pressure loading control during the superplastic forming process of the component. The integrated solid/hollow four-layer grid lightweight structural parts were successfully fabricated using the superplastic forming/diffusion bonding (SPF/DB) process. The quality of the components was evaluated using X-ray and ultrasonic C-scan detection methods. The results show that the maximum elongation of the alloy is 1340% at 900 °C/0.001 s−1. When the temperature is too high, the grain size increases remarkably, and the elongation decreases. Based on the finite element simulation results, 900 °C is the best superplastic forming temperature. Under this temperature parameter, the maximum thinning rate of the core sheet is 39.7%, the SPF time is 10,000 s, the maximum thinning rate of the face sheet is 9.8%, and the SPF time is 2400 s. In addition, the solid block has a minimal effect on the thinning of the core sheet. The grid exhibits obvious stress concentration and thinning at its rounded corners, while the thickness distribution in other areas remains relatively uniform. The nondestructive testing results confirmed that the ribs of the component are fully formed, with no missing or broken ribs. The grid exhibits good geometry and high-quality diffusion bonding. The average thickness at key positions of the component is 1.84 mm, with the minimum thickness being 1.7 mm. As the size of the grid cavity decreases, the thickness of the component tends to increase gradually. The maximum error between the simulated and measured values is 4.47%, indicating good accuracy in the simulation. Additionally, the thickness distribution of the component is relatively uniform.
SERS-Based Local Field Enhancement in Biosensing Applications
Authors: Xie Yangdong , Xu Jiling , Shao Danyang , Liu Yuxin , Qu Xuzhou , Hu Songtao , Dong Biao
Published: December 2024

Surface-enhanced Raman scattering (SERS) stands out as a highly effective molecular identification technique, renowned for its exceptional sensitivity, specificity, and non-destructive nature. It has become a main technology in various sectors, including biological detection and imaging, environmental monitoring, and food safety. With the development of material science and the expansion of application fields, SERS substrate materials have also undergone significant changes: from precious metals to semiconductors, from single crystals to composite particles, from rigid to flexible substrates, and from two-dimensional to three-dimensional structures. This report delves into the advancements of the three latest types of SERS substrates: colloidal, chip-based, and tip-enhanced Raman spectroscopy. It explores the design principles, distinctive functionalities, and factors that influence SERS signal enhancement within various SERS-active nanomaterials. Furthermore, it provides an outlook on the future challenges and trends in the field. The insights presented are expected to aid researchers in the development and fabrication of SERS substrates that are not only more efficient but also more cost-effective. This progress is crucial for the multifunctionalization of SERS substrates and for their successful implementation in real-world applications.
Plant-Derived Compounds in Hemp Seeds (Cannabis sativa L.): Extraction, Identification and Bioactivity—A Review
Authors: Tanase Apetroaei Virginia , Istrati Daniela Ionela , Vizireanu Camelia
Published: December 2024

The growing demand for plant-based protein and natural food ingredients has further fueled interest in exploring hemp seeds (Cannabis sativa L.) as a sustainable source of and nutrition. In addition to the content of proteins and healthy fats (linoleic acid and alpha-linolenic acid), hemp seeds are rich in phytochemical compounds, especially terpenoids, polyphenols, and phytosterols, which contribute to their bioactive properties. Scientific studies have shown that these compounds possess significant antioxidant, antimicrobial, and anti-inflammatory effects, making hemp seeds a promising ingredient for promoting health. Since THC (tetrahydrocannabinol) and CBD (cannabidiol) are found only in traces, hemp seeds can be used in food applications because the psychoactive effects associated with cannabis are avoided. Therefore, the present article reviews the scientific literature on traditional and modern extraction methods for obtaining active substances that meet food safety standards, enabling the transformation of conventional foods into functional foods that provide additional health benefits and promote a balanced and sustainable diet. Also, the identification methods of biologically active compounds extracted from hemp seeds and their bioactivity were evaluated. Mechanical pressing extraction, steam distillation, solvent-based methods (Soxhlet, maceration), and advanced techniques such as microwave-assisted and supercritical fluid extraction were evaluated. Identification methods such as high-performance liquid chromatography (HPLC) and mass spectrometry (MS) allowed for detailed chemical profiling of cannabinoids, terpenes, and phenolic substances. Optimizing extraction parameters, including solvent type, temperature, and time, is crucial for maximizing yield and purity, offering the potential for developing value-added foods with health benefits.
New Triterpenoids from the Leaves of Heritiera littoralis and Their Anti-Inflammatory Activity
Authors: Ren Chenyang , Liang Xiaoqin , Pi Rui , Xin Jiwen , Yang Bo , Zheng Qingfang , Li Yanqin , Li Jun , Zhang Yanjun
Published: December 2024

Six new triterpenoids, heritieras C–H (1–6), along with thirteen known triterpenoids (7–19), were isolated from the leaves of Heritiera littoralis. Their structures were identified by spectroscopic analysis, including 1D and 2D nuclear magnetic resonance (NMR), high-resolution electrospray ionization mass spectrometry (HRESIMS), and by comparison with the literature. Anti-inflammatory activity of the isolates was evaluated using the lipopolysaccharide (LPS) stimulated RAW 264.7 cell model. Among the isolated triterpenoid, compounds 1, 12, 16, 17, and 18 demonstrated inhibitory activity against nitric oxide (NO) release, in which compound 18 exhibited the best activity with an IC50 value of 18.13 μM. The potential anti-inflammatory mechanism was investigated using molecular docking. The triterpenoids from H. littoralis could be served as potential candidates for the development of new anti-inflammatory agents.
A Spontaneous Complexation–Exfoliation Strategy for a Flexible Anode Towards Superior Durable and Ultrafast Lithium-Ion Batteries
Authors: Chu Heying , Zhang Jingchuan , Zhao Pengsen , Li Yong , Liu Zhaoxia , Zhang Hongzhou
Published: December 2024

Transition metal oxides are considered promising anode materials for high performance flexible electrodes due to their abundant reserves and excellent specific capacity. However, their inherent low conductivity, large volume effect, and poor cycling performance limit their applications. Herein, we report a novel “spontaneous complexation and exfoliation” strategy for the fabrication of flexible MnO NCs@rGO thin-film electrodes, which overcomes the aforementioned drawbacks and pushes the mechanical flexibility and lithium-ion (Li+) storage performance to a higher level. The combination of large-area few-layer reduced graphene oxide (rGO) films and ultrafine MnO nanocrystals (MnO NCs) provides a high density of electrochemical active sites. Notably, the layer-by-layer embedded structure not only enables the MnO NCs@rGO electrodes to withstand various mechanical deformations but also produces a strong synergistic effect of enhanced reaction kinetics by providing an enlarged electrode/electrolyte contact area and reduced electron/ion transport resistance. The elaborately designed flexible MnO NCs@rGO anode provides a specific capacity of about 1220 mAh g−1 over 1000 cycles, remarkable high-rate capacity (50.0 A g−1), and exceptional cycling stability. Finally, the assembled flexible lithium-ion full cells achieve zero capacity loss during repeated large-angle bending, demonstrating immense potential as a high-performance flexible energy storage device. This work provides valuable insights into unique structural designs for durable and ultra-fast lithium ion batteries.
Rapid Detection of Aluminium and Iron Impurities in Lithium Carbonate Using Water-Soluble Fluorescent Probes
Authors: Wu Hong-Mei , Cheng Huai-Gang , Zhu Zi-Wen , Cui Li
Published: December 2024

The real-time measurement of the content of impurities such as iron and aluminium ions is one of the keys to quality evaluation in the production process of high-purity lithium carbonate; however, impurity detection has been a time-consuming process for many years, which limits the optimisation of the production of high-purity lithium carbonate. In this context, this work explores the possibility of using water-soluble fluorescent probes for the rapid detection of impurity ions. Salicylaldehyde was modified with the hydrophilic group dl-alanine to synthesise a water-soluble Al3+ fluorescent probe (Probe A). Moreover, a water-soluble Fe3+ fluorescent probe (Probe B) was synthesised from coumarin-3-carboxylic acid and 3-hydroxyaminomethane. Probe A and Probe B exhibited good stability in the pH range of 4–9 in aqueous solutions, high sensitivity, as well as high selectivity for Al3+ and Fe3+; the detection limits for Al3+ and Fe3+ were 1.180 and 1.683 μmol/L, whereas the response times for Al3+ and Fe3+ were as low as 10 and 30 s, respectively. Electrostatic potential (ESP) analysis and density functional theory calculations identified the binding sites and fluorescence recognition mechanism; theoretical calculations showed that the enhanced fluorescence emission of Probe A when detecting Al3+ was due to the excited intramolecular proton transfer (ESIPT) effect, whereas the fluorescence quenching of Probe B when detecting Fe3+ was due to the electrons turning off fluorescence when binding through the photoelectron transfer (PET) mechanism.
Tackling Old Challenges in Microalgal Biotechnology: The Role of Photobioreactors to Advance the Technology Readiness Level
Authors: Deprá Mariany Costa , Dias Rosangela Rodrigues , Zepka Leila Queiroz , Jacob-Lopes Eduardo
Published: December 2024

Microalgae biotechnology has taken the world by storm. However, despite its great potential promise, it still cannot be considered a fully consolidated technology due to a crucial challenge: the low rates of biomass productivity. To overcome this hurdle, photobioreactors have been developed as an innovative solution, promising to increase the efficiency of microalgae cultures by providing optimized conditions. However, the results obtained with these systems do not always meet initial expectations, and their large-scale implementation faces complex technical challenges. In light of this, the present review addresses the main aspects related to the design and engineering of photobioreactors, highlighting their potentialities and limitations in overcoming the critical challenges of microalgal biotechnology. Furthermore, we discuss the current technological readiness level and the commercial readiness index of microalgae-based bioproducts from the perspective of industrial-scale production.
Study on Drying of Municipal Sludge and Pollutants Release Characteristics
Authors: Xu Yun , Zhang Qian , Xu Jingxin , Sun Xueli , Chen Heng
Published: December 2024

With the rapid development of China’s economy, urban domestic sewage and industrial wastewater treatment efficiency has improved, resulting in a significant increase in sludge production. Thermal drying is essential for reducing, safely disposing of, and resourcefully utilizing sludge. However, this drying process inevitably releases harmful pollutants, posing potential environmental risks that necessitate careful management. This work focused on the thermal drying of municipal sludge at five temperature intervals (90–210 °C) and examined the impact of calcium oxide on sludge drying properties. The results indicated that higher temperatures increased sludge drying rates, with optimal efficiency achieved at a 15% calcium oxide addition. Online detection of NH3, H2S, CO, and CH4 in the exhaust gas revealed that pollutant generation was temperature-dependent. While calcium oxide addition had no significant effect on CH4 and CO emissions, it significantly inhibited the generation of H2S and NH3. This work provided crucial insights into optimizing sludge treatment, which improved drying efficiency and mitigated the release of hazardous pollutants, thereby reducing potential environmental and health risks associated with sludge disposal.
A Review of Synchronous Fixed-Frequency Microgrid Droop Control Systems Based on Global Positioning System
Authors: Li Kuan , Li Yudun , Fan Rongqi , Liu Zehao , Lu Maozeng
Published: December 2024

Microgrids, as a new type of power supply network that connects distributed energy sources with power loads, can operate in both grid-connected and islanded states. It has the advantages of high reliability and flexible configuration. When the microgrid operates in islanding mode, ensuring voltage and frequency stability becomes a primary focus of research. This paper provides a brief overview of the master-slave control and peer-to-peer control strategies used in microgrids, analyzing the advantages and disadvantages of each approach. The application of droop control strategies to microgrid converters is emphasized. This research analyzes the implementation of droop control strategies in addressing microgrid frequency and power offsets. Given the advantages of the synchronized fixed-frequency droop control method, the authors provide a detailed overview of this strategy, which is based on the global satellite navigation system (GPS). On this basis, a comprehensive comparison of various synchronous frequency control methods is conducted, analyzing the advantages and disadvantages of each approach. Finally, the research findings in this area are summarized, and the future development trends of research in this field are discussed and anticipated.
Sensory Feedback of Grasp Security by Direct Neural Stimulation Improves Amputee Prediction of Object Slip
Authors: Smiles Andrew B. , Earley Eric J. , Jiang Ning , Ortiz-Catalan Max
Published: December 2024

Background: Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. Methods: We propose an approach for upper limb prosthetic grasp security feedback, delivered to the wearer through direct nerve stimulation proportional to the likelihood of objects slipping from grasp. This proportional feedback is based on a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed pulling tasks with their prosthetic hand grasping an object at predetermined grip forces, attempting to pull the object with as much force as possible without slip. These trials were performed with two different prediction notification paradigms. Results: At lower grasp forces, where slip was more likely, a strong, single impulse notification of impending slip reduced the incidence of object slip by a median of 32%, but the maximum achieved pull forces did not change. At higher grasp forces, where slip was less likely, the maximum achieved pull forces increased by a median of 19% across participants when provided with a stimulation strength inversely proportional to the grasp security, but slip incidence was unchanged. Conclusions: These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb that can be incorporated into motor planning and ultimately prevent unanticipated object slips.
CBGS-YOLO: A Lightweight Network for Detecting Small Targets in Remote Sensing Images Based on a Double Attention Mechanism
Authors: Wu Zhenyuan , Wu Di , Li Ning , Chen Wanru , Yuan Jie , Yu Xiangyue , Guo Yongqiang
Published: December 2024

With the continuous progress of remote sensing technology, the demand for means of detecting small targets in remote sensing images is escalating. The significance of detecting small targets in remote sensing images lies in enhancing the ability to identify small and elusive targets and the detection accuracy against complex backgrounds, holding significant application value in military reconnaissance, environmental monitoring, and disaster early-warning systems. Firstly, the minuteness of certain targets in relation to the entire image in which they occur, particularly when the camera is situated at a higher altitude, renders them difficult to detect. Secondly, the varying background and lighting conditions in remote sensing images further complicate the detection task. Conventional target detection methods are frequently incapable of addressing these complexities, resulting in a reduction in detection accuracy and an increase in false alarms. Hence, in this paper, we propose a lightweight remote-sensing image target detection network model, CBGS-YOLO, created by introducing the Ghost module to decrease the model parameters, applying the SPD-Conv module to optimize downsampling, and integrating the convolutional block attention module to enhance detection accuracy. The experimental outcomes demonstrate that CBGS-YOLO outperforms other models when applied to the DB_Licenta and USOD datasets, significantly enhancing detection performance for small targets. Compared with YOLOv9, this model can reduce the number of parameters from 7.10 M to 5.12 M, and the average precision (mAP) is effectively improved. The model strengthens the ability to identify small targets against complex backgrounds while maintaining lightweight properties and possesses remarkable application prospects and practical value.
Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm
Authors: Barim Menekse S. , Lu Ming-Lun , Feng Shuo , Hayden Marie A. , Werren Dwight
Published: December 2024

The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model. The models were evaluated using data from 360 lifting trials performed by 10 subjects (5 males and 5 females) with an average age of 51.50 (±9.83) years. The accuracy of the two models was compared against data collected by a laboratory-based motion capture system as a function of 12 ACGIH lifting risk zones and 3 grouped risk zones (low, medium, and high). Results showed that only the ratio + length model provides acceptable estimates of lifting risk with an average of 69% accuracy level for predicting one of the 3 grouped zones and a higher rate of 92% for predicting the high lifting zone.
Automatic Parking Trajectory Planning Based on Warm Start Nonlinear Dynamic Optimization
Authors: Ren Hongbin , Niu Yaqi , Li Yunong , Yang Lin , Gao Hongliang
Published: December 2024

In this paper, we propose an optimal parking path planning method based on numerical solving, which leverages the concept of the distance between convex sets. The obstacle avoidance constraints were transformed into continuous, smooth nonlinear constraints using the Lagrange dual function. This approach enables the determination of a globally optimal parking path while satisfying vehicular kinematic constraints. To address the inefficiency typically associated with numerical solving, a warm start strategy was employed for the optimization variables: first, the Hybrid A* algorithm was utilized to generate the initial path values; next, a velocity planning problem was formulated to obtain initial velocity values; and finally, converted convex optimization problems were used to compute the initial dual variables. The optimality of the proposed method was validated through a real car test with ACADO as a solver in three typical parking scenarios. The results demonstrate that the proposed method achieved smoother parking paths in real time.
Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems
Authors: Alanazi Meshari D. , Albekairi Mohammed , Abbas Ghulam , Alanazi Turki M. , Kaaniche Khaled , Elsayed Gehan , Mercorelli Paolo
Published: December 2024

An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in SCs is exposed to adversary risk, resulting in privacy issues. Privacy issues impact the contingent mobility and localization of the ITS path. This paper introduces a novel resilient privacy preserving (RPP) method through presumed secrecy (PS) to provide a robust privacy measure. The privacy of the progressive communication sessions is preserved based on the previous security depletion levels. The interruptions in traffic data-related communication sessions are recurrently identified, and re-handoffs are recommended with dodged transfer learning. The empirical results indicate a 25% reduction in computational overhead and a 30% enhancement in privacy protection over conventional methods, demonstrating the model’s efficacy in secure ITS communication. Compared with existing methods, the proposed approach decreases security depletion rates by 15% across varying traffic densities, underscoring ITS resilience in high-interaction scenarios.
Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System
Authors: Özkahraman Ali , Ölmez Tamer , Dokur Zümray
Published: December 2024

Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain–Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model.
Three-Dimensionally Printed Mini Air Scrubbing Cartridges Based on Nano-Graphite for Air Pollution Monitoring
Authors: Zampetti Emiliano , Ammiraglia Mattia , Conti Marco , Montiroli Cassandra , Papa Paolo , Bianconi Daniele , Macagnano Antonella
Published: December 2024

Ecosystems and environments are impacted by atmospheric pollution, which has significant effects on human health and climate. For these reasons, devices for developing portable and low-cost monitoring systems are required to assess human exposure during daily life. In the last decade, the advancements of 3D printing technology have pushed researchers to exploit, in different fields of applications, the advantages offered, such as rapid prototyping and low-cost replication of complex sample treatment devices. In this work, we present the fabrication and testing of 3D printed cartridges based on both commercial photopolymer and a modified version with the intrusion of nano graphite. The air scrubbing performances towards some volatile organic compounds have been investigated, inserting the cartridges into a low-cost monitoring system using a photoionization sensor. In particular, the cartridges were tested in the presence of concentrations of ethanol, benzene, and toluene to evaluate the abatement percentage with and without their use. Although the results have shown that all cartridges abated ethanol and toluene, the abatement of benzene increased 20 times in the case of cartridges based on modified resin with nano graphite. These results could enable their employment to reduce the concentration of interfering compounds in low-cost monitoring systems.
Vibration Serviceability Assessment of Floor Structures: Simulation of Human–Structure–Environment Interactions Using Agent-Based Modeling
Authors: Shahabpoor Erfan , Berari Bernard , Pavic Aleksandar
Published: December 2024

A rapidly growing body of experimental evidence in the literature shows that the effects of humans interacting with vibrating structures, other humans, and their surrounding environment can be critical for reliable estimation of structural vibrations. The Interaction-based Vibration Serviceability Assessment framework (I-VSA) was proposed by the authors in 2017 to address this, taking into account human-structure dynamic interactions (HSI) to simulate the structural vibrations experienced by each occupant/pedestrian. The I-VSA method, however, had limited provisions to simulate simultaneously multiple modes of structure in HSI, to simulate human-human and human-environment interactions, and the movement pattern of the occupants/pedestrians. This study proposes a new Agent-based Vibration Serviceability Assessment framework, termed AVSA, to address the following limitations: (a) allowing for multiple modes of structure to be simulated simultaneously, (b) to simulate effects of vibrations on gait parameters and walking pattern/routes, and (c) to simulate human-environment interactions, and movement patterns for any desired interior layout and use case. The AVSA framework was used to simulate the response and to assess the vibration serviceability of a lightweight floor under a combination of sitting and walking traffic, where three vertical modes of vibrations were engaged simultaneously. The results of the simulations show that for all tests, the experimental Cumulative Distribution Functions of the vibrations experienced by the participants are within the 95% confidence interval predicted by the AVSA method. The proposed method provides a generic and flexible framework to simulate simultaneously different interaction modalities, different human tasks and postures, and multiple modes of structure and the human body.
A Modular AI-Driven Intrusion Detection System for Network Traffic Monitoring in Industry 4.0, Using Nvidia Morpheus and Generative Adversarial Networks
Authors: Chiriac Beatrice-Nicoleta , Anton Florin-Daniel , Ioniță Anca-Daniela , Vasilică Bogdan-Valentin
Published: December 2024

Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.
Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection
Authors: Jachymczyk Urszula , Knap Paweł , Lalik Krzysztof
Published: December 2024

In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost. To address these issues, a structured feature selection method based on correlation analysis supplemented with comprehensive visual evaluation was proposed. Unlike generic dimensionality reduction techniques, this approach preserves critical domain-specific information and avoids misinterpretation of fault indicators. By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. The proposed method for reducing condition indicators successfully decreased their number by approximately 3.27 times, simultaneously significantly reducing computational time of prediction, reaching up to 50% reduction for complex models. In doing so, we lowered computational demands and improved classification efficiency without compromising accuracy for ML models. Although feature reduction did not similarly benefit the metrics for DL models, these findings highlight that well-chosen, domain-relevant condition indicators can streamline data input and deliver interpretable, cost-effective PdM solutions suitable for industrial applications.
AllianceBlockchain in the Governance Innovation of Internet Hospitals
Authors: Wang Xiaofeng , Yue Xiaoguang , Sajid Ahthasham , Tariq Noshina
Published: December 2024

The rise of Internet hospitals has significant issues associated with data security and governance in managing sensitive patient data. This paper discusses an alliance blockchain (i.e., a private blockchain) model for governance innovation in internet hospitals with an improved encryption methodology. We compare our proposed model, improved Rivest–Shamir–Adleman (RSA) encryption, integrated into the blockchain framework. Improved RSA achieves impressive improvements in all key metrics by increasing the throughput by 24.7% and lowering the latency by 19.8% compared to the base model. Thus, the improved model is more optimized for processing transactions related to healthcare data. Memory usage was also reduced by 14.3%. While encryption time remained pretty close, the decryption time remarkably improved by 97.5%. IoT sensors are one of the foundations for Internet hospitals that produce consistent patient data streams, such as physiological and environmental metrics. The proposed alliance blockchain model enables the secure and efficient real-time management of this sensor data. These results demonstrate the capability of alliance blockchain and cryptographic upgrades in creating safe and efficient governance frameworks for Internet hospitals.
Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics
Authors: Sanders Gabriel J. , Skodinski Stacie , Peacock Corey A.
Published: December 2024

The purpose was to create a systematic approach for analyzing data to improve predictive models for fatigue and neuromuscular performance in volleyball, with potential applications in other sports. The study aimed to assess whether average, peak, or peak-to-average ratios of countermovement jump (CMJ) force plate metrics exhibit stronger correlations and determine which metric most effectively predicts performance. Data were obtained from nine division I female volleyball athletes over a season, recording daily jump loads (total jumps, jump counts >38.1 cm (Jumps 38+), and >50.8 cm (Jumps 50+) in height) and comparing these with CMJ force metrics recorded the next day, both average and peak. Correlations and regressions were utilized to assess the relationship and predictive value for jump loads on CMJ test data. The findings revealed that the most significant (p < 0.001 for all) negative correlations (r ranged from −0.384 to −0.529) occurred between Jumps 50+ and the average CMJ test variables. Furthermore, there were no significant relationships between jump loads and peak-to-average ratios (p ≥ 0.233). Average CMJ force metrics and Jumps 50+ provide slightly more predictive (up to 28% of variability) potential for fatigue modeling of neuromuscular performance.
Novel Design on Knee Exoskeleton with Compliant Actuator for Post-Stroke Rehabilitation
Authors: Wu Lin , Wang Chao , Liu Jiawei , Zou Benjian , Chakrabarty Samit , Bao Tianzhe , Xie Sheng Quan
Published: December 2024

Knee joint disorders pose a significant and growing challenge to global healthcare systems. Recent advancements in robotics, sensing technologies, and artificial intelligence have driven the development of robot-assisted therapies, reducing the physical burden on therapists and improving rehabilitation outcomes. This study presents a novel knee exoskeleton designed for safe and adaptive rehabilitation, specifically targeting bed-bound stroke patients to enable early intervention. The exoskeleton comprises a leg splint, thigh splint, and an actuator, incorporating a series elastic actuator (SEA) to enhance torque density and provide intrinsic compliance. A variable impedance control method was also implemented to achieve accurate position tracking of the exoskeleton, and performance tests were conducted with and without human participants. A preliminary clinical study involving two stroke patients demonstrated the exoskeleton’s potential in reducing muscle spasticity, particularly at faster movement velocities. The key contributions of this study include the design of a compact SEA with improved torque density, the development of a knee exoskeleton equipped with a cascaded position controller, and a clinical test validating its effectiveness in alleviating spasticity in stroke patients. This study represents a significant step forward in the application of SEA for robot-assisted rehabilitation, offering a promising approach to the treatment of knee joint disorders.
Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM
Authors: Tang Jun , Chen Jie , Hu Miaojun , Hu Yao , Zhang Zixi , Xiao Liuming
Published: December 2024

Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical research, more and more evidence suggests that dynamic functional connectivity analysis can more comprehensively reveal the complex and variable characteristics of brain networks and their underlying mechanisms, thus providing more solid scientific support for computer-aided diagnosis of ASD. To overcome the lack of time-scale information in static functional connectivity analysis, in this paper, we proposes an innovative GNN-LSTM model, which combines the advantages of long short-term memory (LSTM) and graph neural networks (GNNs). The model captures the spatial features in fMRI data by GNN and aggregates the temporal information of dynamic functional connectivity using LSTM to generate a more comprehensive spatio-temporal feature representation of fMRI data. Further, a dynamic graph pooling method is proposed to extract the final node representations from the dynamic graph representations for classification tasks. To address the variable dependence of dynamic feature connectivity on time scales, the model introduces a jump connection mechanism to enhance information extraction between internal units and capture features at different time scales. The model achieves remarkable results on the ABIDE dataset, with accuracies of 80.4% on the ABIDE I and 79.63% on the ABIDE II, which strongly demonstrates the effectiveness and potential of the model for ASD detection. This study not only provides new perspectives and methods for computer-aided diagnosis of ASD but also provides useful references for research in related fields.
A Game Model and Fault Recovery Algorithm for SDN Multi-Domain
Authors: Xu Tao , Chen Chen , Hu Kaiming , Zhuang Yi
Published: December 2024

Software-defined networking (SDN) offers an effective solution for flexible management of Wireless Sensor Networks (WSNs) by separating control logic from sensor nodes. This paper tackles the challenge of timely recovery from SDN controller failures and proposes a game theoretic model for multi-domain controllers. A game-enhanced autonomous fault recovery algorithm for SDN controllers is proposed, which boasts fast fault recovery and low migration costs. Taking into account the remaining capacity of controllers and the transition relationships between devices, the target controller is first selected to establish a controller game domain. The issue of mapping the out-of-control switches within the controller game domain to the target controller is transformed into a linear programming problem for solution. A multi-population particle swarm optimization algorithm with repulsive interaction is employed to iteratively evolve the optimal mapping between controllers and switches. Finally, migration tasks are executed based on the optimal mapping results, and the role transition of the target controller is completed. Comparative experimental results demonstrate that, compared to existing SDN controller fault recovery algorithms, the proposed algorithm can balance the migration cost of switches and the load pressure on controllers while reducing propagation delay in SDN controllers, significantly decreasing the fault recovery time.
Digital Health Technologies for Optimising Treatment and Rehabilitation Following Surgery: Device-Based Measurement of Sling Posture and Adherence
Authors: Langford Joss , Barakat Ahmed , Daghash Engy , Singh Harvinder , Rowlands Alex V.
Published: December 2024

Background: Following shoulder surgery, controlled and protected mobilisation for an appropriate duration is crucial for appropriate recovery. However, methods for objective assessment of sling wear and use in everyday living are currently lacking. In this pilot study, we aim to determine if a sling-embedded triaxial accelerometer and/or wrist-worn sensor can be used to quantify arm posture during sling wear and adherence to sling wear. Methods: Four participants were asked to wear a GENEActiv triaxial accelerometer on their non-dominant wrist for four hours in an office environment, and, for two of those hours, they also wore a sling in which an additional GENEActiv accelerometer was secured. During sling wear, they were asked to move their arm in the sling through a series of pre-specified arm postures. Results: We found that upper arm angle and posture type during sling wear can be predicted from a sling sensor alone (R2 = 0.79, p < 0.001 and Cohen’s kappa = 0.886, respectively). The addition of a wrist-worn sensor did not improve performance. The optimisation of an existing non-wear algorithm accurately detected adherence (99.3%). Conclusions: the remote monitoring of sling adherence and the quantification of immobilisation is practical and effective with digital health technology.
Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11
Authors: Zhang Lei , Sun Zhipeng , Tao Hongjing , Wang Meng , Yi Weixun
Published: December 2024

In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management. This article presents an enhanced algorithm based on YOLOv11n, referred to as GCB-YOLOv11. The proposed improvements are realized through three key aspects: Firstly, the traditional convolution is replaced with GSConv, which significantly enhances feature extraction capabilities while simultaneously reducing computational costs. Secondly, a novel C3K2_FE module was designed that integrates Faster_block and ECA attention mechanisms. This design aims to improve detection accuracy while also accelerating detection speed. Finally, the introduction of the Bi FPN mechanism in the Neck section optimizes the efficiency of multi-scale feature fusion and addresses issues related to feature loss and redundancy. The experimental results demonstrate that GCB-YOLOv11 exhibits strong performance on the dataset concerning mine personnel and safety helmets, achieving a mean average precision of 93.6%. Additionally, the frames per second reached 90.3 f·s−1, representing increases of 3.3% and 9.4%, respectively, compared to the baseline model. In addition, when compared to models such as YOLOv5s, YOLOv8s, YOLOv3 Tiny, Fast R-CNN, and RT-DETR, GCB-YOLOv11 demonstrates superior performance in both detection accuracy and model complexity. This highlights its advantages in mining environments and offers a viable technical solution for enhancing the safety of mine personnel.
Cardiac Function and Fatigue During Exoskeleton-Assisted Sit-to-Stand Maneuver and Walking in People with Stroke with Moderate to Severe Gait Disability: A Pilot Cross-Sectional Study
Authors: Kubilius Raimondas , Ruočkus Darius , Stonkuvienė Vitalija , Vareikaitė Rugilė , Cardini Rebecca , Bowman Thomas
Published: December 2024

Background. Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton. Methods. Five PwST were recruited in this pilot cross-sectional study. We observed three experimental conditions: walking without and with the UAN.GO exoskeleton and walking with the UAN.GO combined with the OPTIGO walker. Each condition included five trials related to ADLs such as sitting and walking. Results. No statistically significant difference was found between heart rate and R–R of ECG data while comparing all the observed conditions during each respective trial. The NASA Task Load Index did not show significant differences across all trials, except for a significant difference between Condition 2 and Condition 3 in Trial 4 (p = 0.043). However, walking and sit-to-stand tasks seem to be more challenging according to the NASA-TLX. Only one participant scored over 70 points on the System Usability Scale. The TSQ-WT scores for conditions 2 and 3 were 62 (56.5–72.5) and 70 (66.5–75) points, respectively. Conclusions. This study suggests that UAN.GO exoskeleton could be used for RAGT in PwST with disability without compromising cardiovascular function.
Study on the Evolution of the Urban Land Use and the Driving Mechanism from the Perspective of the “Productive–Living–Ecological” Spaces
Authors: Cheng Qian , Lu Yujia , Wang Tieliang , Lu Xiaofeng
Published: December 2024

The present research examined the “production–living–ecological space” (“PLES”) by using land use data, the ecological and environmental quality index model, growth and reduction based on the spectrum, the center of gravity migration model, and the optimal parameter geoprobe model to further evaluate adjustments to ecological quality and the driving mechanisms. The findings indicate that (1) spanning the years 2000–2020, the production space and living area in Panjin increased to 2093 km2 and 380 km2 respectively, and the ecological area of forest land and water area showed a decreasing trend. (2) The center of gravity of the urban living space and the industrial and mining production space shifted significantly. (3) The negative effect of the contribution rate was of greater value than the positive effect. Thus, it can be concluded that the negative effect of “PLES” is greater than the positive one. (4) The results for this region showed that the mean annual temperature was the strongest explanation for the spatial variation in natural factors, and that social factors such as population density also had a strong effect, so an interaction analysis was carried out to analyze the interaction between the two factors, which showed that the relationship between mean annual temperature and density of population had the strongest explanatory power.