bluuebunny/crossref_metadata_2025 huggingface dataset:
Histogram-Based Analysis of Low- and High-Grade Glioma and Its Surrounding Edema Using Arterial Spin Labeling Magnetic Resonance Imaging
Authors: Lindner Thomas , Dührsen Lasse , Kyselyova Anna Andriana , Entelmann Wiebke , Hau Luis , Fiehler Jens
Published: September 2023

A glioma is a type of intra-axial brain tumor originating from the glial cells. Making up about one-third of all brain tumors, a timely diagnosis alongside correct grading and subsequent therapy planning is crucial. Magnetic Resonance Imaging is an established method for the diagnosis of tumors. Arterial Spin Labeling (ASL) Perfusion Imaging allows for the non-contrast enhanced visualization of tumor hyper- or hypoperfusion. Commonly, cell swelling occurs around the tumor that causes edema, which subsequently puts healthy tissue at risk by potentially reducing regional perfusion. The patient collective in this study consists of 495 patients (501 scans) with histopathologically confirmed grade II-IV diffuse gliomas. The aim of this study was to evaluate the potential of histogram analysis of the ASL data to find biomarkers for the pathological diagnosis, grading, MGMT, and mutation status of the tumors as well as the analysis of tumor-surrounding edema. The analysis showed statistically significant results for the pathological diagnosis and grading but not for MGMT status or mutation. The differentiation between tumor and edema showed highly significant results yet did not show differences between edema and perfusion on the contralateral hemisphere.
Investigation of Wear and Friction Behavior of Cobalt-Chromium-Molybdenum Alloy Produced by Laser Powder Bed Fusion
Authors: Ramezani Maziar , Ripin Zaidi Mohd
Published: September 2023

This study investigates the sliding wear resistance of the Co-28Cr-6Mo alloy manufactured by laser-based powder bed fusion through linear reciprocating sliding wear tests. Varied loads and sliding speeds, in both dry and lubricated contact scenarios, were examined using a hardened steel ball as a counter material. Microhardness profiles were obtained to understand the alloy’s mechanical behavior, revealing consistent average microhardness values of 409.67 HV through the thickness and 404.05 HV along the wear track. The coefficient of friction (COF) indicated a decrease in COF with an increase in the applied normal load in both dry and lubricated contacts. On the other hand, the COF increased with higher sliding speeds, and remained stable under lubricated contact but fluctuated in dry conditions over time. Increased applied load and sliding speed correlated with escalated wear rates. Investigation of the impact of different lubricants on tribological properties highlighted synthetic gear oil as the most effective in reducing wear rate among tested lubricants. Wear mechanisms were elucidated through scanning electron microscopy and energy-dispersive X-ray spectroscopy. The presented results can serve as a guide for selecting suitable operating conditions and lubricants to reduce wear in Co-Cr-Mo alloy applications.
LCA-YOLOv8-Seg: An Improved Lightweight YOLOv8-Seg for Real-Time Pixel-Level Crack Detection of Dams and Bridges
Authors: Wu Yang , Han Qingbang , Jin Qilin , Li Jian , Zhang Yujing
Published: September 2023

Remotely operated vehicles (ROVs) and unmanned aerial vehicles (UAVs) provide a solution for dam and bridges structural health information acquisition, but problems like effective damage-related information extraction also occur. Vision-based crack detection methods can replace traditional manual inspection and achieve fast and accurate crack detection. This paper thereby proposes a lightweight, real-time, pixel-level crack detection method based on an improved instance segmentation model. A lightweight backbone and a novel efficient prototype mask branch are proposed to decrease the complexity of the model and maintain the accuracy of the model. The proposed method attains an accuracy of 0.945 at 129 frames per second (FPS). Moreover, our model has smaller volume, lower computational requirements and is suitable for low-performance devices.
Experimental Study on ELID Grinding of Silicon Nitride Ceramics for G5 Class Bearing Balls
Authors: Lee Seung-Min , Kim Ha-Neul , Ko Jae-Woong , Kwak Tae-Soo
Published: September 2023

This study has focused on analyzing the impact of material characteristics and grinding conditions on the surface roughness in ELID grinding of ceramic materials intended for bearing balls. The main research objective was to examine the feasibility of achieving the required surface roughness for G5 class bearing balls through a high-efficiency and high-precision ELID grinding process. Three types of silicon nitride specimens and two types of grinding wheels with cBN and diamond abrasives were prepared for the experiments. An HP (high-pressure) specimen was fabricated through high-temperature and high-pressure sintering at 1700 °C for 2 h, containing a composition of Y2O3 and MgO in Si3N4, while GPS 1hr and GPS 6hr specimens were prepared using gas-pressure sintering for 1 h and 6 h, respectively. From the experimental results, it has been confirmed through surface morphology and surface roughness analysis that material characteristics and grinding parameters affect the surface roughness of silicon nitride ceramics during the grinding process. The surface ground with a #2000 diamond wheel is at a level that can satisfy the required surface roughness, 0.014 um or less in G5 class bearing balls. Based on the analysis of surface morphology and roughness in grinding processes, the #325 cBN wheel exhibited excellent performance in rough grinding, while the #2000 diamond wheel demonstrated highly effective surface finishing performance, indicating that the combination of these two abrasives can be effectively utilized for high-efficiency and high-precision nanosurface machining of silicon nitride ceramics.
Exploring Arterial Wave Frequency Features for Vascular Age Assessment through Supervised Learning with Risk Factor Insights
Authors: Ipar Eugenia , Cymberknop Leandro J. , Armentano Ricardo L.
Published: September 2023

With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.
A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
Authors: Luo Yihang , Gong Bei , Zhu Haotian , Guo Chong
Published: September 2023

The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.
Comprehensive Calculation Method of Semantic Similarity of Transport Infrastructure Ontology Concept Based on SHO-BP Algorithm
Authors: Bao Tuyu , Chen Kun , Zhang Hao , Zhang Zheng , Ai Qingsong , Yan Junwei
Published: September 2023

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.
Developing a New Procedural Binary Particle Swarm Optimization Algorithm to Estimate Some Properties of Local Concrete Mixtures
Authors: Alsaleh Fatima , Hammami Mohammad Bassam , Wardeh George , Al Adday Feras
Published: September 2023

Artificial intelligence techniques have lately been used to estimate the mechanical properties of concrete to reduce time and financial expenses, but these techniques differ in their processing time and accuracy. This research aims to develop a new procedural binary particle swarm optimization algorithm (NPBPSO) by making some modifications to the binary particle swarm optimization algorithm (BPSO). The new software has been created based on some fresh state properties (slump, temperature, and grade of cement) obtained from several ready-mix concrete plants located in Aleppo, Syria to predict the density and compressive strength of the regional concrete mixtures. The numerical results obtained from NPBPSO have been compared with the results from BPSO and artificial neural network ANN. It has been found that BPSO and NPBPSO are both predicting the compressive strength of concrete with less number of iterations and more accuracy than ANN (0.992 and 0.998 correlation coefficient in BPSO and NPBPSO successively and 0.875 in ANN). In addition, NPBPSO is better than BPSO as it prevents the algorithm from falling into the problem of local solutions and reaches the desired optimal solution faster than BPSO. Moreover, NPBPSO improves the accuracy of obtained compressive strength values and density by 30% and 50% successively.
A Chinese–Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
Authors: Liu Canglan , Silamu Wushouer , Li Yanbing
Published: September 2023

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity, as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese–Kazakh (Arabic script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively. By combining the generation of pseudo-parallel corpora through phrase replacement with the application of R-Drop regularization, there is a significant advancement in machine translation performance for low-resource languages.
Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
Authors: Asefa Semere Araya , Shim Sangmin , Seong Myeongsu , Lee Dasol
Published: September 2023

Chirality, the absence of mirror symmetry, is predominant in nature. The chiral nature of the electromagnetic field behaves differently with chiral matter for left circularly polarized and right circularly polarized light. The chiroptical behavior in the sensing of naturally occurring chiral objects is weak, and improving the chiroptical response enhances the chiral sensing platform. This review covers the fundamental concepts of chiral metasurfaces and various types of single- and multi-layered chiral metasurfaces. In addition, we discuss tunable and deep-learning-based chiral metasurfaces. Tunability is achieved by manipulating the meta-atom’s property in response to external stimuli for applications such as optical modulation, chiral photonics, advanced sensing, and adaptive optics. Deep-learning modeling techniques, such as CNNs and GANs, offer efficient learning of the complex relationships in data, enabling the optimization and accurate prediction of chiral metasurface properties. The challenges in the design and fabrication of chiral metasurface include achieving broadband performance and scalability and addressing material limitations. Chiral metasurface performance is evaluated by optical rotation, circular dichroism enhancement, and tunability, which are quantified through the spectroscopic measurement of circular dichroism and optical rotation. Chiral metasurface progress enables applications, including metaholography, metalenses, and chiral sensing. Chiral sensing improves the detection of pharmaceuticals and biomolecules, increasing the sensitivity and accuracy of analytical diagnostics.
Towards the Development of a Z-Scheme FeOx/g-C3N4 Thin Film and Perspectives for Ciprofloxacin Visible Light-Driven Photocatalytic Degradation
Authors: Fendrich Murilo , Bajpai Om Prakash , Edla Raju , Molinari Alessandra , Ragonese Paola , Maurizio Chiara , Orlandi Michele , Miotello Antonio
Published: September 2023

Thermally synthesized graphitic carbon nitride (g-C3N4) over pulsed laser deposition (PLD) produced urchin-like iron oxide (FeOx) thin films were fabricated via in situ and ex situ processes. Materials characterisation revealed the formation of the graphitic allotrope of C3N4 and a bandgap Eg for the combined FeOx/g-C3N4 of 1.87 and 1.95 eV for each of the different fabrication strategies. The in situ method permitted to develop a novel petal-like morphology, whereas for the ex situ method, a morphological mixture between FeOx bulk and g-C3N4 was observed. Given the improved optical and morphological properties of the in situ film, it was employed as a proof of concept for the direct photocatalysis and photo-Fenton removal of ciprofloxacin antibiotic (CIP) under visible light irradiation. Improved photocatalytic activity (rate constant k = 8.28 × 10−4 min−1) was observed, with further enhancement under photo-Fenton conditions (k = 2.6 × 10−3 min−1), in comparison with FeOx + H2O2 (k = 1.6 × 10−3 min−1) and H2O2 only (k = 1.3 × 10−4 min−1). These effects demonstrate the in situ methodology as a viable route to obtain working heterojunctions for solar photocatalysis in thin-film materials, rather than the more common powder materials.
Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
Authors: Latrech Basma , Yacoubi Samir , Hermassi Taoufik , Slatni Adel , Jarray Fathia , Pouget Laurent
Published: September 2023

As a semi-arid Mediterranean country, Tunisia is affected by the impacts of climate change, particularly the coastal regions like the Cap-Bon. Irregular rainfall, rising temperatures and the recurrence of extreme events are all indicators that affect ecosystems and populations and make them more vulnerable to the influence of climatic variables. Therefore, an analysis of the trends of climate variables can contribute to facilitating the development of effective adaptation strategies. In this matter, this study was conducted to assess the homogeneity and trends of minimum and maximum air temperature (Tmin and Tmax) and precipitation (P) in the Cap-Bon region. Daily data were collected from the meteorological station of Nabeul for the period of 1982–2020. Pettitt and SNHT tests for homogeneity were applied to identify the breakpoints in multi-time scales of Tmax, Tmin and P data series. The Mann–Kendall (MK) test was used to detect the change in the time-series trend. A modified Mann–Kendall (mMK) test was used to remove the autocorrelation effect from the data series. Both the MK and mMK tests were used at the 5% significant level. The magnitude of the climatic trend was estimated using the non-parametric Sen’s slope estimator. Contrary to Tmin and P, the results of the homogeneity tests revealed the existence of significant breakpoints in the annual, seasonal and monthly Tmax time series. For most cases, the breakpoint occurred around the year 2000. For Tmin, significant breakpoints were recorded in March and April, while a significant shift in the P time series was detected in December. The Mann–Kendall results show a significant warming trend in annual Tmax, with magnitudes equal to 0.065 and 0.045 °C/year before and after the breakpoint, respectively. Nevertheless, non-significant tendencies were observed in the annual Tmin and P time series. On the monthly time scale, Tmax exhibited a significant upward trend in June and August, before the observed breakpoints, with Sen’s slope values equal to 0.065 and 0.045 °C/year, respectively. Regarding the Tmin data, a significant positive trend was observed in July at a rate of 0.033 °C/year.
A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
Authors: Cho U Jin , Jang Dongjun , Jeon Youhyeong , Kim Taeha , Jo Beomsu , Kim Ryangha , Kim Younglae , Kwon Min-Woo
Published: September 2023

Recently, hydrogen (H2) energy has attracted attention among eco-friendly energy sources because H2 energy is eco-friendly, energy-efficient, and abundant in nature. However, when the concentration of H2 in the atmosphere is more than 4%, H2 has a risk of explosion. H2 is a colorless, tasteless, and odorless gas that is difficult to detect with human senses. Therefore, developing an optimized hydrogen sensor is essential. Palladium (Pd) has good reactivity to hydrogen. Molybdenum disulfide (MoS2) has high carrier mobility, sensitive reactivity to toxic gases, and high surface-area-to-volume ratio. Therefore, we proposed hydrogen sensors that use Pd and MoS2. The main fabrication processes include MoS2 deposition through CVD and Pd deposition through DC sputtering. In this study, we utilized Pd and MoS2 to enable sensing at room temperature. By optimizing the Pd to a nanoparticle structure with an expansive surface area of 4 nm, we achieved a fast response time of 4–5 s and an enhanced yield through a simplified structure. Hydrogen sensors inherently exhibit sensitivity to various environmental factors. To address these challenges, technologies such as machine learning can be incorporated. Emphasizing low-power consumption and various application compatibilities becomes pivotal to promoting commercialization across diverse industries.
Drying Technologies in Food Processing
Authors: Nowacka Małgorzata , Dadan Magdalena , Tylewicz Urszula
Published: September 2023

Recently, consumers are paying more attention to healthy diets and often seek products with a high number of bioactive compounds, such as fruit and vegetables [...]
Nonlocal Mechanistic Models in Ecology: Numerical Methods and Parameter Inferences
Authors: Ellefsen Erin , Rodríguez Nancy
Published: September 2023

Animals utilize their surroundings to make decisions on how to navigate and establish their territories. Some species gather information about competing groups by observing them from a distance, detecting scent markings, or relying on memories of encounters with rival populations. Gathering such information involves a nonlocal process, prompting the development of mechanistic models that incorporate nonlocal terms to explore species movement. These models, however, pose analytical and computational challenges. In this study, we focus on a multi-species advection–diffusion model that incorporates nonlocal advection. To efficiently compute solutions for this system involving a large number of interacting species, we introduce a numerical scheme using spectral methods. Additionally, we examine the influence of various parameters and interaction potentials on population densities. Our investigation aims to provide a method to identify the primary factors driving species movements, and we validate our approach using synthetic data.
Injuries in French High-Level and National-Level Women Artistic Gymnastics: One-Year Prevalence and Associated Factors
Authors: Charpy Séréna , Billard Pierre , Dandrieux Pierre-Eddy , Chapon Joris , Tondut Jeanne , Edouard Pascal
Published: September 2023

Objective: The aims of this study were (1) to determine the one-year prevalence of injuries and their characteristics and (2) to analyze potential injury risk factors in high-level/national-level women artistic gymnasts. Methods: Competitive women artistic gymnasts training more than 10 h per week in a national training center and a TOP 12 club in France were invited to participate in a retrospective study through an online questionnaire about their self-reported injuries and potential injury risk factors (e.g., previous injuries, diseases, weight modification) during the 2020/2021 season. Results: A total of 88 gymnasts between 9 and 23 years old, who trained on average 22.0 ± 6.2 h per week, answered the questionnaire. Ninety-five injuries were reported by 62 (70.5%) of the 88 gymnasts. The one-year injury prevalence was 70.5% (95% CI 60.9 to 80.0%). Gymnasts had, on average, 1.1 ± 1.0 injuries per year. The most common reported injury location was the knee (25.3%), and the most common reported injury type was the ligament (26.3%). Having an injury during the previous season (OR = 9.06; CI 95% 2.66 to 36.73; p = 0.0007) and using a prevention program (OR = 3.97; CI 95% 1.46 to 11.15; p = 0.007) were associated with a higher risk of injury in the multivariate analysis. Conclusions: More than 70% of French high-level/national-level gymnasts had at least one injury during the 2020/2021 season. This high injury rate supports the need to promote injury prevention. However, in the present study, using a prevention program was associated with a higher risk of injury. There is thus a need to improve injury prevention strategies to counter the high injury risk and its potential consequences.
High Precision Control System for Micro-LED Displays
Authors: Chen Yufeng , Zheng Xifeng , Cao Hui , Wang Yang , Cheng Hongbin , Chen Junchang , Huang Shuo , Li Jingxu , Huang Deju , Chen Yu
Published: September 2023

This paper proposes a Field Programmable Gate Array (FPGA)-based control system to implement micro-light-emitting diode (micro-LED) real-time display. The control system includes the interface control, video processing, memory management, image data transmission, control signal generation and correction. Then, we implement the micro-LED real-time display via memory management. We propose the brightness correction to achieve high grey-scale and high uniformity display. The LEDs are mounted on the glass substrate prepared using low-temperature polysilicon (LTPS) technology, and then we find the 24 × 46 pixels micro-LED panel. And the control system has been successfully applied to the panel of glass-based micro-LED displays. A new grey control method is proposed in this work, which can effectively improve the refresh rate of the micro-LED displays. The high grey-scale refresh rate is 2100 Hz, and the low grey-scale refresh rate is 300 Hz. The uniformity of the panel is increased to 85% after brightness correction.
Explicit Method in the Seismic Assessment of Unreinforced Masonry Buildings through Plane Stress Elements
Authors: Nochebuena-Mora Elesban , Mendes Nuno , Calixto Valentina , Oliveira Sandra
Published: September 2023

The complex nonlinear behaviour of unreinforced masonry (URM), along with the interaction between structural elements, still represents a challenge for the seismic assessment of existing URM buildings. A large variety of mathematical tools have been developed in the last decades to address the issue. The numerical work herein presented attempts to provide some insights into the use of FEM models to obtain reliable results from nonlinear dynamic analyses conducted with explicit methods. Through plane stress elements, two in-plane mechanisms were studied to identify optimal parameters for unreinforced masonry elements subjected to dynamic actions. The results were then compared with outcomes generated by an implicit solver. Subsequently, these parameters were used in nonlinear dynamic analyses on a building section for the seismic assessment in both unreinforced and reinforced conditions. The element type, hourglass control, damping, and bulk viscosity influence the dynamic response, mainly when the nonlinearities become larger. The hourglass control techniques employ a scaling factor to suppress the occurrence of spurious modes. Values ranging from 0.01 to 0.03 have shown effective results. When the stiffness-damping parameter for Rayleigh damping is of a similar order of magnitude or lower than the time increment without damping, the time increment remained in feasible ranges for performing analysis. Additionally, the bulk viscosity can stabilise the response without causing substantial alterations to the time increment if the values are under 1.00.
Cyberphysical System Modeled with Complex Networks and Hybrid Automata to Diagnose Multiple and Concurrent Faults in Manufacturing Systems
Authors: Velazquez Alejandro , Martell Fernando , Sanchez Irma Y. , Paredes Carlos A.
Published: September 2023

Cyber–physical systems use digital twins to provide advanced monitoring and control functions, including self-diagnosis. The digital twin is often conceptualized as a 3D model, but mathematical models implemented in numerical simulations are required to reproduce the dynamical and functional characteristics of physical systems. In this work, a cyber–physical system scheme is proposed to monitor and diagnose failures. The virtual system, embedded at the supervisory control level, combines concepts from complex networks and hybrid automata to detect failures in the hardware components and in the execution of the sequential logic control. An automated storage and retrieval system is presented as a case study to show the applicability of the proposed scheme. The functional test and the obtained results validate the implemented system that is shown to be capable of fault diagnosis and location in real time. The online execution of the digital twin present several advantages for diagnosing multiple concurrent failures in sensors, actuators, and the control unit. This approach can be incorporate into diverse manufacturing systems.
Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
Authors: da Silva Renan Favarão , Melani Arthur Henrique de Andrade , Michalski Miguel Angelo de Carvalho , de Souza Gilberto Francisco Martha
Published: September 2023

Proper maintenance planning is critical for maintenance management to contribute to increasing availability, ensuring quality requirements, and controlling the safety and environmental risks associated with physical assets. As supporting tools for developing maintenance strategies, Reliability-Centered Maintenance (RCM) and Risk-Based Maintenance (RBM) methods are currently used in several organizations. Nevertheless, these strategies are often approached separately although they are complementary. In this context, this paper proposes a novel method that effectively integrates RCM and RBM by adapting the traditional RCM method to incorporate risk management into maintenance planning decision-making to support maintenance management. The proposed Reliability and Risk Centered Maintenance (RRCM) method allows organizations to determine maintenance plans that ensure the reliability of the physical assets while considering and prioritizing the risks associated with their potential functional failures. The proposed method was demonstrated through a case study considering the operational context of a hydroelectric power plant. The results show the ability of RRCM to assist in the development and implementation of maintenance plans oriented to reliability, risk, and cost.
A Multi-Adversarial Joint Distribution Adaptation Method for Bearing Fault Diagnosis under Variable Working Conditions
Authors: Cui Zhichao , Cao Hui , Ai Zeren , Wang Jihui
Published: September 2023

Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, this paper designs a transfer learning model named the multi-adversarial joint distribution adaptation network (MAJDAN) to achieve effective fault diagnosis across operating conditions. MAJDAN uses a one-dimensional lightweight convolutional neural network (1DLCNN) to directly extract features from the original bearing vibration signal. Combining the distance-based domain-adaptive method, maximum mean difference (MMD), with the multi-adversarial network will simultaneously reduce the conditional and marginal distribution differences between the domains. As a result, MAJDAN can efficiently acquire domain-invariant feature information, addressing the challenge of cross-domain bearing fault diagnosis. The effectiveness of the model was verified based on two sets of different bearing vibration signals, and one-to-one and one-to-many working condition migration task experiments were carried out. Simultaneously, various levels of noise were introduced to the signal to enable analysis and comparison. The findings demonstrate that the suggested approach achieves exceptional diagnostic accuracy and exhibits robustness.
Evaluation of Cushioning Effect and Human Injury According to Occupant Posture and Use of Air Mat in Case of Elevator Fall
Authors: Kim Eui-Soo
Published: September 2023

Because of the function of facilities that transport individuals and luggage to elevated locations, poor maintenance and human errors by users and workers can result in many elevator accidents annually. In particular, given the characteristics of an elevator used by an unspecified number of people, in the event of a fall accident due to wire rope cutting, an occupant’s body may be fatally injured, thereby causing substantial human damage. To minimize human injury, it is important to reduce the relative impact speed between the car and the pit by utilizing the buffer effect according to the role of the safety device and the posture of occupants. The AIS (Abbreviated Injury Scale) is an anatomical-based coding system established by the Association for the Advancement of Automotive Medicine that classifies and describes the severity of injuries. In this study, through human injury analysis, safety device operation such as speed governor and buffer when cutting elevator wire rope, occupant’s behavior, and air mat usage were used to derive force and torque values for the injured part, and then determine the amount of impact and degree of injury to the human body using the multibody model provided by the MADYMO program and AIS by analyzing the buffering effect of the impact.
An Evaluation Study on the Analysis of People’s Domestic Routines Based on Spatial, Temporal and Sequential Aspects
Authors: Arribas Velasco Aitor , McGrory John , Berry Damon
Published: September 2023

The concept of collecting data on people’s domestic routines is not novel. However, the methods and processes used to decipher these raw data and transform them into useful and appropriate information (i.e., sequence, duration, and timing derived from monitoring domestic routines) have presented challenges and are the focus of numerous research groups. But how are the results of the decoded transposition received, interpreted and used by the various professionals (e.g., occupational therapists and architects) who consume the information? This paper describes the inclusive evaluation process undertaken, which involved a selected group of stakeholders including health carers, engineers and end-users (not the occupants themselves, but more so the care team managing the occupant). Finally, our study suggests that making accessible key spatial and temporal aspects derived from people’s domestic routines can be of great value to different professionals. Shedding light on how a systematic approach for collecting, processing and mapping low-level sensor data into higher forms and representations can be a valuable source of knowledge for improving the domestic living experience.
Wear Prediction Method of Differential Planetary Roller Screws Considering the Ambient Temperature Variations
Authors: Gu Weikai , Li Chaoyang , Miao Jiacheng , Wang Peng , Chen Bingkui
Published: September 2023

As a key component in fields such as high-precision machine tools, aerospace and weaponry, the thermal deformations in a differential planetary roller screw (DPRS) caused by the changes in ambient temperatures can lead to uneven load distribution and increased wear, which can seriously affect its transmission accuracy, efficiency, and service life. Therefore, the temperature rise of a DPRS at different ambient temperatures was studied using the block thermal network method, and the thermal deformations were calculated. A load distribution model of DPRS considering thermal deformation was developed, and a wear prediction method combined with surface roughness was proposed. The results show that the increase in ambient temperature has an obvious negative effect on the load distribution and wear depth of DPRS. Moreover, the DPRS temperature rise, load distribution and wear depth dramatically change with the variation of parameters such as load, speed and thread pitch. A DPRS durability test device that considers changes in ambient temperature was developed. Experiments with temperature rise and DPRS wear under different ambient temperatures were conducted to validate the theoretical analysis model.
Research and Simulation of Kinematics and Dynamics of Tracked Support Equipment Based on Multi-Body Dynamics
Authors: Xin Changqing , Shi Yongkui , Hao Jian , Nie Junlong , Liu Qiuyue
Published: September 2023

Aiming at addressing the problems of low production efficiency and low safety factors in coal mines, this study designed a new type of support equipment and verified its theoretical reliability through the analysis of driving theory. The analysis was carried out through kinematics and dynamics, the position coordinates of the prototype at different times were analyzed using the RPY angles, and the spatial coordinate system of the prototype was established. Then, the position coordinates at different times could be solved by establishing the plane kinematics equations of the prototype. A 14-degree-of-freedom dynamic system model was established to reflect the longitudinal dynamic characteristics of the traveling mechanism of the prototype, and the differential equations of the traveling mechanism were established via the Lagrange dynamic equation method. Finally, the dynamic stability of the prototype under different working conditions was studied by using multi-body dynamic simulation technology. The research results show that the prototype meets the design requirements, has good reliability, realizes the mechanization, automation and high-efficiency production of coal mines, and realizes safe underground production. The research results provide effective solutions for ensuring the sustainable development of coal resources and the safe production of coal mines.
Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR
Authors: Wang Wencong , Li Gang , Liu Shuwei , Yang Qiang
Published: September 2023

With the development of science and technology, self-driving technology is gradually being applied to automobile semi-trailer trains. Aiming at the problem that it is challenging to plan the traveling trajectory of an autonomous semi-trailer train, a trajectory planning algorithm based on a constrained iterative linear quadratic regulator is proposed. The constrained iterative linear quadratic regulator solves the problem that the iterative constrained linear quadratic regulator cannot deal with inequality constraints by transforming inequality constraints into interior point penalty functions to deal with all kinds of inequality constraints in the trajectory planning problem. When dealing with the obstacle avoidance problem, the body contour is modeled approximately according to the actual shape of the vehicle, and the obstacle avoidance constraints are transformed into inequality constraints. Considering the unique body structure of a semi-trailer train, the articulation angle constraint function is designed according to different vehicle speeds. Simulation experimental results show that the algorithm can plan safe driving trajectories for semi-trailer trains in complex traffic scenarios.
Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method
Authors: Dzulkefli Farah Syazana , Abdul Latiff Abdul Halim , Hashim Hazwan Syahmi , Majdi Amir Mustaqim , Rusmanugroho Herurisa , Li Junxiao
Published: September 2023

A major challenge for the oil and gas industry is expediting the exploration geophysics activity. It is necessary to produce fast and accurate image of the targeted hydrocarbon reservoir, yet it involves extensive dataset due to the target location lies in deeper subsurface. Therefore, target-oriented oil and gas-reservoir imaging is currently seen as more attractive than conventional full-volume migration, in terms of computation efficiency. Through the integration of seismic redatuming with Kirchhoff migration, we presented a target-oriented imaging workflow that can effectively reduce computational cost while simultaneously enhancing the accuracy of the migrated image. Kirchhoff migration is renowned for being one of the simplest and flexible migration techniques, making it straightforward to implement. However, this migration approach is typically avoided in the presence of complex geology, as it tends to introduce artifacts in the images, thereby disrupting the interpretation beneath the complex overburden. Therefore, this paper highlighted the advantages of utilizing seismic redatuming to retrieve data beneath the overburden, followed by the utilization of the redatumed data as input for Kirchhoff migration. With an improved signal to noise ratio, the primary reflection events were amplified, providing clearer representation of the subsurface structure within the hydrocarbon reservoir or targeted area. Additionally, we utilized the benefits of Kirchhoff migration and explored the feasibility of performing patch-based migration in several areas of interest beneath the salt layer. This approach aimed to reduce both the turnaround time and the associated seismic migration costs. The developed workflow then was subsequently put to the test and applied to both a sub-salt SEG Advanced Modeling (SEAM) model and field data situated in East Malaysia. Based on the outcomes of the target-oriented migration, the proposed methodology demonstrated its capability to generate better quality and instant migrated images within the designated region. Furthermore, the feasibility of target-oriented imaging with Kirchhoff migration was substantiated through the integration of seismic redatuming, resulting in significant benefits towards seismic processing, imaging, and interpretation.
Comprehensive Study of Arabic Satirical Article Classification
Authors: Assiri Fatmah , Himdi Hanen
Published: September 2023

A well-known issue for social media sites consists of the hazy boundaries between malicious false news and protected speech satire. In addition to the protective measures that lessen the exposure of false material on social media, providers of fake news have started to pose as satire sites in order to escape being delisted. Potentially, this may cause confusion to the readers as satire can sometimes be mistaken for real news, especially when their context or intent is not clearly understood and written in a journalistic format imitating real articles. In this research, we tackle the issue of classifying Arabic satiric articles written in a journalistic format to detect satirical cues that aid in satire classification. To accomplish this, we compiled the first Arabic satirical articles dataset extracted from real-world satirical news platforms. Then, a number of classification models that integrate a variety of feature extraction techniques with machine learning, deep learning, and transformers to detect the provenance of linguistic and semantic cues were investigated, including the first use of the ArabGPt model. Our results indicate that BERT is the best-performing model with F1-score reaching 95%. We also provide an in-depth lexical analysis of the formation of Arabic satirical articles. The lexical analysis provides insights into the satirical nature of the articles in terms of their linguistic word uses. Finally, we developed a free open-source platform that automatically organizes satirical and non-satirical articles in their correct classes from the best-performing model in our study, BERT. In summary, the obtained results found that pretrained models gave promising results in classifying Arabic satirical articles.
Osteoarthritis—The Role of Mesenchymal Stem Cells in Cartilage Regeneration
Authors: Gherghel Robert , Macovei Luana Andreea , Burlui Maria-Alexandra , Cardoneanu Anca , Rezus Ioana-Irina , Mihai Ioana Ruxandra , Rezus Elena
Published: September 2023

Osteoarthritis (OA) is a condition that can cause substantial pain, loss of joint function, and a decline in quality of life in patients. Numerous risk factors, including aging, genetics, and injury, have a role in the onset of OA, characterized by structural changes within the joints. Most therapeutic approaches focus on the symptoms and try to change or improve the structure of the joint tissues. Even so, no treatments have been able to stop or slow the progression of OA or give effective and long-lasting relief of symptoms. In the absence of disease-modifying drugs, regenerative medicine is being investigated as a possible treatment that can change the course of OA by changing the structure of damaged articular cartilage. In regenerative therapy for OA, mesenchymal stem cells (MSCs) have been the mainstay of translational investigations and clinical applications. In recent years, MSCs have been discovered to be an appropriate cell source for treating OA due to their ability to expand rapidly in culture, their nontumorigenic nature, and their ease of collection. MSCs’ anti-inflammatory and immunomodulatory capabilities may provide a more favorable local environment for the regeneration of injured articular cartilage, which was thought to be one of the reasons why they were seen as more suited for OA. In addition to bone marrow, MSCs have also been isolated from adipose tissue, synovium, umbilical cord, cord blood, dental pulp, placenta, periosteum, and skeletal muscle. Adipose tissue and bone marrow are two of the most essential tissues for therapeutic MSCs. Positive preclinical and clinical trial results have shown that, despite current limitations and risks, MSC-based therapy is becoming a promising approach to regenerative medicine in treating OA.
Application of Virtual Reality in Learning Quantum Mechanics
Authors: Tarng Wernhuar , Pei Ming-Che
Published: September 2023

Quantum mechanics is a physical theory that describes the behavior of microscopic matter. According to quantum theory, a microscopic particle may be described as either a particle or a wave, called wave–particle duality. Many students in high school or college (BC level) find it difficult to imagine that microscopic particles have both particle and wave properties. This is mainly caused by the scale of the world they see since quantum mechanics deals with things that are too small, while the wave and particle phenomena at the microscopic scale are difficult to understand, measure, or verify in the real world. In this study, virtual reality technology was used to develop teaching modules on quantum mechanics, allowing learners to see the particles and wave phenomena of electrons and photons in the microscopic world through interactive operation in virtual experiments. A teaching experiment was conducted by recruiting 60 high school students as research subjects. The control group (30 students) used physics textbooks, and the experimental group (30 students) used the virtual teaching modules for learning quantum mechanics. The analysis results show that the experimental group’s learning effectiveness is higher than the control group. The questionnaire results show that students were satisfied with the learning experience using virtual teaching modules with high learning motivation and low cognitive load because virtual reality can visualize the abstract concepts of wave–particle duality and help them understand quantum mechanics.