bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications
Authors: Rahman Md Minhazur , Joha Md Ibne , Nazim Md Shahriar , Jang Yeong Min
Published: December 2024

The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum R2 score of 82.04%, the showing proposed model’s superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT’s reliability in real-time environmental and power forecasting.
Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
Authors: Thongtam Nuttawan , Sinthupinyo Sukree , Chandrachai Achara
Published: December 2024

With the continuous growth of Industry 4.0 (I4.0), the industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for the sustainable development of organizations. Machinery is a critical component and key to the success of production in a smart industrial factory. Minimizing unplanned downtime (UPDT) poses a significant challenge in designing an effective maintenance system. In the era of Industry 4.0, the most widely adopted maintenance frameworks are intelligent maintenance systems (IMSs), which integrate predictive maintenance with computerized systems. IMSs are intelligent tools designed to efficiently plan maintenance cycles for each machine component in a smart factory. This research presents the application of a search algorithm named state space search (SSS) in conjunction with a newly designed IMS, aimed at optimizing maintenance routines by identifying the optimal timing for maintenance cycles. The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. The new IMS concept provides a summary of the total maintenance cost and the remaining lifetime of the equipment. By integrating with SSS algorithms, the IMS presents optimized maintenance cycle solutions to the maintenance manager, focusing on minimizing costs while maximizing the remaining lifetime of the equipment. Moreover, the SSS algorithms take into account the risks associated with maintenance routines, following factory standards such as failure mode and effects analysis (FMEA). This approach is well suited to smart factories and helps to reduce UPDT.
Bridging Data Distribution Gaps: Test-Time Adaptation for Enhancing Cross-Scenario Pavement Distress Detection
Authors: Hou Yushuo , Li Yishun , Du Mengyun , Li Lunpeng , Wu Difei , Yu Jiang
Published: December 2024

Automatic pavement distress detection using deep learning has revolutionized maintenance efficiency, but deploying models in new, unseen scenarios presents significant challenges due to shifts in data distribution. Traditional transfer learning requires extensive labeled data from the new domain, which is both time-consuming and costly. This paper proposes a test-time adaptation (TTA) framework that addresses feature distribution biases across different scenes, including differences in background, perspective, and environmental conditions. It adapts models at inference time without requiring additional labeled data, making it a promising solution for cross-scenario applications. The framework dynamically adapts the model to these biases by generating domain-specific prior knowledge, applying perspective correction, and generating global attention maps to reduce focus on irrelevant elements. We evaluate the framework on a cross-scene dataset that includes pavement images from three countries and four perspectives. In unsupervised settings, the TTA framework improves detection accuracy by 20.6%, achieving 93.09% of the accuracy obtained through transfer learning with 10,000 labeled images. Compared to traditional transfer learning, our framework reduces the reliance on high-quality labeled data while achieving similar performance gains. Experimental results also demonstrate the framework’s adaptability across various deep learning detection models, offering a scalable solution for rapid deployment and cross-scenario application of pavement distress detection systems.
Outpatient Ductal Steroid Irrigations as an Adjuvant Treatment to Sialendoscopy in Recurrent Inflammatory Obstructive Sialadenitis
Authors: Torretta Sara , Pignataro Lorenzo , Libonati Antonio , Gaffuri Michele , Capaccio Pasquale
Published: December 2024

The aim of this study is to retrospectively investigate the effectiveness of outpatient ductal steroid irrigations (DSIs) as an adjuvant treatment in recurrent inflammatory obstructive sialadenitis (RIOS). A retrospective chart review of prospectively recruited RIOS patients was randomly assigned to group A (i.e., interventional sialendoscopy) or group B (i.e., interventional sialendoscopy with outpatient DSIs). The statistical analysis detected any postoperative difference between groups in terms of the number and severity (attested by 0–10 VAS pain) of episodes of swelling. Interventional sialendoscopy and DSIs were effective and well tolerated in all 122 patients, and a significantly reduced number of salivary gland swelling episodes (p-value = 0.01) was documented in group B compared to group A at any follow-up assessment. When the specific aetiology was considered, repeated DSIs were more effective than interventional sialendoscopy alone in patients with type 1 or 3 stenoses and in those without mucous plugs. Our results confirm the safety and effectiveness of interventional sialendoscopy (both as a single-modality and a multimodal approach) in a large series of adult patients with RIOS at short-, medium-, and long-term analyses. The superiority of a combined therapeutic protocol, with a positive effect still detectable 12 months after the end of the treatment, was attested as well.
Aflatoxin Inactivation in Gamma-Ray-Irradiated Almonds
Authors: Bozinou Eleni , Athanasiadis Vassilis , Samanidis Iordanis , Govari Maria , Govaris Alexander , Sflomos Konstantinos , Lalas Stavros I.
Published: December 2024

Aflatoxins are foodborne toxins that occur naturally in various crops because of fungal contamination, particularly from two strains, namely Aspergillus flavus and Aspergillus parasiticus. Given their adverse properties, which are teratogenic, mutagenic, and carcinogenic, aflatoxins present a significant public health concern. Consequently, efforts are underway to inactivate aflatoxins and inhibit the growth of these fungi to prevent toxin formation. Since chemical treatments for food products are undesirable or even restricted in some countries, alternative approaches are also implemented. This study investigated gamma-ray (γ-ray) irradiation as a potential method for reducing aflatoxin levels. Specifically, solutions of aflatoxins B1, B2, G1, and G2 were irradiated with doses of 1, 2, 4, and 8 kGy using a cobalt-60 irradiation source. Following γ-irradiation, a notable reduction in aflatoxin levels was observed, particularly for types B1 and G1, which process higher toxicity. This finding suggests γ-irradiation as a feasible method for aflatoxin deactivation. Additionally, as a proof of concept, almond samples spiked with aflatoxins and A. flavus were irradiated. The results showed a decrease in both aflatoxin levels and microbial load in these samples. Overall, these findings indicate that γ-irradiation is a promising approach to aflatoxin reduction, microbial decontamination, and the potential extension of almonds’ shelf life.
Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
Authors: Wang Huiwen , Lim Ying Terk , Xie Shenming , Yi Wen
Published: December 2024

Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation.
Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
Authors: Aldoğan Cemre Fazilet , Aksu Koray , Demirel Hande
Published: December 2024

Ship detection holds great value regarding port management, logistics operations, ship security, and other crucial issues concerning surveillance and safety. Recently, ship detection from optical satellite imagery has gained popularity among the research community because optical images are easily accessible with little or no cost. However, these images’ quality and quantity of feature details are bound to their spatial resolution, which often comes in medium-low spatial resolution. Accurately detecting ships requires images with richer texture and resolution. Super-resolution is used to recover features in medium-low resolution images, which can help leverage accuracy in ship detection. In this regard, this paper quantitatively and visually investigates the effectiveness of super-resolution in enabling more accurate ship detection in medium spatial resolution images by comparing Sentinel-2A images and enhanced Sentinel-2A images. A collection of Sentinel-2A images was enhanced four times with a Real-ESRGAN model that trained PlanetScope images with high spatial resolution. Separate ship detections with YOLOv10 were implemented for Sentinel-2A images and enhanced Sentinel-2A images. The visual and metric results of both detections were compared to demonstrate the contributory effect of enhancement on the ships’ detection accuracy. Ship detection on enhanced Sentinel-2A images has a mAP50 and mAP50-95 value of 87.5% and 68.5%. These results outperformed the training process on Sentinel-2A images with a mAP value increase of 2.6% for both mAP50 and mAP50-95, demonstrating the positive contribution of super-resolution.
Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD
Authors: Fan Xiaowei , Wang Ruimiao , Yang Yi , Wang Jingang
Published: December 2024

In order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer–Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation forecasting fusion model based on the Newton–Raphson optimization algorithm (NRBO) and Variational Modal Decomposition (VMD). Firstly, the principle of the VMD technique and the Gray Wolf Optimization (GWO) algorithm’s key parameter optimization method for VMD are introduced. Then, the Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the body of the prediction model, followed by explaining the principle of the NRBO algorithm. And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. The algorithm sets up a multi-model comparison experiment, and the results show that the prediction model proposed in this paper has the best prediction accuracy and the optimal evaluation index.
A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
Authors: Xu Hui , Ma Kaixue , Li Shuantao , Liu Gaojian
Published: December 2024

We propose a double-sided layout design method with high reliability for multi-beam phased array transmitter modules based on high-temperature co-fired ceramic technology. Efficient transmission of microwave signals and low-frequency signals is realized by using multi-layer wiring and an inter-layer transition. The proposed Ka-band transmitter module exhibits a gain of ≥26 dB, an output power of ≥21 dBm, and an overall efficiency of the component of ≥40%. The RMS accuracy of phase shift and attenuation is better than 3° and 0.5 dB, respectively. The proposed design method addresses the problems of high integration, lightness, and miniaturization in transmit/receive module design, and shows a good performance of heat dissipation performance and sealing capability. It can be easily expanded to large-scale phased arrays.
Predictive Factors of Inpatient Rehabilitation Stay and Post-Discharge Burden of Care After Joint Replacement for Hip and Knee Osteoarthritis: A Retrospective Study on 1678 Patients
Authors: Pennestrì Federico , Tosto Valentina , Pelosi Catia , Grippa Dario , Negrini Stefano , Kiekens Carlotte , Sarasso Elisabetta , Banfi Giuseppe , Cordani Claudio ,   the PREPARE Project Group
Published: December 2024

The global demand for end-stage hip and knee osteoarthritis surgical treatment is rising, as is the need of optimal postoperative rehabilitation. Patient stratification is key to provide rehabilitation professionals and policy makers with real-life data in support of early discharge planning and continuous care provision. The aim of this retrospective, observational study was to investigate which factors can predict the burden of care at discharge (BCD) and the inpatient rehabilitation length of stay (LOS) based on a set of demographic, societal, clinical and organizational data collected from a high-volume orthopedic hospital. We included 45.306 variables from 1678 patients. All variables were initially tested individually using a linear regression model for inpatient rehabilitation LOS and a logistic regression model for BCD. Variables that resulted significant (p < 0.05) were subsequently considered in a single, comprehensive linear regression model, or a single, logistic regression model, respectively. Age, living with a family, occupational status, baseline Barthel Index and duration of surgery were predictors of inpatient rehabilitation LOS and BCD. Sex, primary or secondary osteoarthritis, American Society of Anesthesiologists score, body mass index, transfusion, biological risk, type of anesthesia, day of surgery, numeric pain rating scale and baseline cognitive function at baseline were not. Including specific patient comorbidities, surgical access technique and chronic use of pharmacological therapy can improve the predictive power of the model.
Modified Kapok Fibers (Ceiba pentandra (L.) Gaerth) for Oil Spill Remediation
Authors: Oliveira Leonardo M. T. M. , Fonseca Eduardo J. S. , Bernardo Vanderson B. , Zanta Carmem L. P. S. , Oliveira Laís F. A. M. , Oliveira Jennifer N. S. R. de , Souza Samuel T. de , Duarte José Leandro da Silva
Published: December 2024

The search for efficient oil spill remediation techniques leads to the use of physical, chemical, and biological methods, featuring both natural and modified lipophilic materials. Kapok fibers (Ceiba pentandra (L.) Gaerth) have been studied as a result of their unusual natural oil sorption capacity, which can be improved even further through physical or chemical treatments that augment their rugosity and alter their functional properties. Furthermore, the exact role of fiber morphology is not completely clear regarding the sorption process. Hence, this study investigated the efficacy of kapok fibers using different treatments, based on chemical and physical approaches, and characterized using advanced techniques (FTIR, SEM, AFM), aiming to improve the understanding of application possibilities in oil contamination scenarios. The results indicate that treatments using a low thermal intensity and low concentration do not lead to variation in sorption properties nor in surface structural features. Fiber rugosity varied from 4.40 to 12.35 nm, whereas an excessive increase in roughness was observed when the material was subject to more extreme conditions, such as a temperature of 120 °C and high concentrations (2.0 M) of both acid or alkali, accompanied by a loss in functionality and affecting the material sorption capacity. Thus, the study provides conditions to suggest that these treatments are not necessary for this type of material when inserted into sorption processes.
Analysis of Vegetation Coverage Changes and Influencing Factors in Aksu, Xinjiang, China (2000–2020): A Comparative Study of Climate Factors and Urban Development
Authors: Feng Zhimin , Xin Haiqiang , Liu Hairong , Wang Yong , Wang Junhai
Published: December 2024

The ecological environment is fundamental to human survival and development, and China has seen a historical shift from localized to widespread improvements in its ecological conditions. Aksu, a typical ecologically sensitive region in Xinjiang, China, is significant for the study of vegetation dynamics and their driving factors, which is crucial for ecological conservation. This study evaluates the spatiotemporal changes in vegetation coverage in Aksu from 2000 to 2020 using long-term Normalized Difference Vegetation Index (NDVI) data and trend analysis. Additionally, this study explores key factors influencing vegetation changes through correlation analysis with temperature, precipitation, and nighttime light data. The results indicate the following: (1) vegetation coverage in Aksu exhibits significant spatial heterogeneity, with annual NDVI increasing at a rate of 0.83% per year (p < 0.05); (2) the influence of temperature and precipitation on NDVI was weakly correlated from 2000 to 2020; and (3) a strong positive correlation was found between nighttime light intensity and NDVI, suggesting that urban development plays a dominant role in vegetation change, while temperature and precipitation have comparatively minor impacts. The findings provide a scientific basis for ecological conservation and sustainable development in the region.
Cmpk2 Gene and Protein Expression in Saliva or Salivary Glands of Dyslipidemic Mice
Authors: Zhang Baiyan , Kawamoto Akiyo , Nakagawa Masato , Honda Yoshitomo , Takahashi Kazuya
Published: December 2024

Salivary biomarkers are promising molecules for diagnosing systemic diseases. Cytidine/uridine monophosphate kinase 2 (CMPK2) is associated with various systemic diseases. However, little is known about the role of the CMPK2 gene in saliva and dyslipidemia. This study investigated the relationship between serum lipid levels and Cmpk2 mRNA expression in the saliva of dyslipidemic mice. Additionally, immunofluorescence staining was employed to assess the localization of the CMPK2 protein in the submandibular gland. Two types of dyslipidemic mice were utilized: mice fed a high-fat and high-cholesterol (HFHC) diet and genetically dyslipidemic ApoE-deficient mice. The mice at 9 to 46 weeks were analyzed for serum lipid levels, Cmpk2 mRNA expression in saliva, and CMPK2 protein localization in the submandibular glands. Both dyslipidemic mice displayed elevated low-density lipoprotein cholesterol and total cholesterol in serum. ApoE-deficient mice apparently exhibited increased Cmpk2 expression in saliva. Immunofluorescence staining indicated that CMPK2 proteins were primarily localized in the serous acini, potentially associated with the secretion of Cmpk2 mRNA in saliva. These findings suggest that Cmpk2 mRNA increases and is detectable in the saliva of dyslipidemic mice, providing a viable experimental model to assess the potential use of CMPK2 as a biomarker for dyslipidemia.
Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area
Authors: Gao Xiaoqing , Liu Yunzhu , Gao Cheng , Qing Dandan , Wang Qian , Cai Yulong
Published: December 2024

In this study, the JiaoGang Basin in the Yangtze River Delta plains of the river network area was the research object. A basin water level simulation model was constructed based on the physical mechanism model and Mike software, and the parameters were calibrated and validated. Based on the dataset produced by the physical model, three types of ML models, Support Vector Machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), were constructed, trained, validated, and compared with the physical model. The results showed that the physical mechanism model met the water level simulation accuracy requirements at most stations. In the training and validation periods, the RF water level simulation and GBDT water level simulation models had root mean square errors (RMSEs) of all stations less than 0.25 and the Nash–Sutcliffe coefficient (NSE) of all stations was greater than 0.7. The physical mechanism model and ML water level simulation models can simulate the water level in the JiaoGang Basin better. The RF and GBDT models considerably outperform the physical mechanism model in terms of the peak simulation errors and peak present time errors, and the fluctuations of the ML water level simulation models (RMSE and NSE) are minor compared to those of the physical mechanism model.
Corrosion Detection and Grading Method for Hydraulic Metal Structures Based on an Improved YOLOv10 Sequential Architecture
Authors: Cheng Haodong , Kang Fei
Published: December 2024

Herein, we present a method for detecting and determining the corrosion level of hydraulic metal structure surfaces through images while reducing the difficulty of dataset annotation. To achieve accurate detection of corrosion targets, the MobileViTv3 block is integrated into YOLOv10, resulting in the proposed YOLOv10-vit for corrosion target detection. Based on YOLOv10-vit, the YOLOv10-vit-cls classification network is introduced for corrosion level determination. This network leverages the pre-trained parameters of YOLOv10-vit to more quickly learn the features of different corrosion levels. To avoid subjective factors in the corrosion level annotation process and reduce annotation difficulty, a cascaded corrosion detection architecture combining YOLOv10-vit and YOLOv10-vit-cls is proposed. Finally, based on the proposed corrosion detection architecture, we achieve accurate corrosion detection and level determination for hydraulic metal structures.
Impact of Temperature and Humidity on Key Mechanical Properties of Corrugated Board
Authors: Mrówczyński Damian , Gajewski Tomasz , Cornaggia Aram , Garbowski Tomasz
Published: December 2024

This research explores how temperature and relative humidity impact the mechanical properties of corrugated cardboard. Samples were treated under a range of controlled climate conditions in a climate chamber to simulate varying environmental exposures. Following this conditioning, we performed a series of mechanical tests: the Edge Crush Test (ECT) to assess compressive strength, four-point Bending Tests (BNTs) in both the Machine (MD) and Cross Directions (CD) to evaluate bending stiffness, Sample Torsion Tests (SSTs) for shear stiffness, and Transverse Shear Tests (TSTs) to measure torsional rigidity. By comparing results across these tests, we aim to determine which mechanical property shows the highest sensitivity to changes in humidity levels. Findings from this study are expected to offer valuable insights into the environmental adaptability of corrugated board, particularly for applications in packaging and storage, where climate variability can affect material performance and durability. Such insights will support the development of more robust and adaptable packaging solutions optimised for specific climate conditions.
Synergistic Effects of a Rotating Magnetic Field and Pulsed Light on Key Quality Characteristics of Refrigerated Pork: A Novel Approach to Shaping Food Quality
Authors: Duma-Kocan Paulina , Rudy Mariusz , Gil Marian , Stanisławczyk Renata , Krajewska Anna , Dziki Dariusz , Saletnik Bogdan
Published: December 2024

The combined effects of pulsed light and a rotating magnetic field on the quality of raw pork loin stored under refrigerated conditions were studied. Muscles from the same carcass were divided into six distinct portions. Three portions were designated as untreated control samples, while the other three underwent experimental procedures involving exposure to pulsed light and a rotating magnetic field. Comprehensive laboratory analyses were conducted at specific intervals during the storage period to evaluate changes and assess the impact of storage duration on the samples. The results demonstrated that the combined use of a magnetic field and pulsed light significantly extended the shelf life of raw pork. A significant (p < 0.05) reduction in total microbial count was observed in treated samples compared to the control group throughout all storage periods. The treatment also improved all sensory attributes and reduced purge loss during refrigerated storage. Additionally, the applied treatment significantly (p < 0.05) lowered the hardness and rigidity on day 10, as well as the chewiness of the pork loin on days 1 and 10 of storage. The novelty and innovation of this study lie in the application of a rotating magnetic field combined with a pulsed light beam to enhance the properties of raw pork. This approach resulted in a synergistic effect, notably decelerating the deterioration of meat quality, extending its shelf life, and reducing energy consumption during processing. These outcomes hold significant potential for environmental, economic, and social benefits.
The Effects of Plyometric Training on the Performance of Three Types of Jumps and Jump Shots in College-Level Male Basketball Athletes
Authors: Huang Wei-Yang , Wu Cheng-En , Huang Hsuan
Published: December 2024

Recent studies have shown that lower-limb plyometric training can effectively enhance muscle strength and explosiveness, which are particularly important for improving jumping ability. The purpose of this study was to evaluate the effects of plyometric training on vertical, lateral, and horizontal jumping abilities, and their subsequent impact on basketball shooting performance and sports injury prevention. A quasi-experimental design was used, recruiting 30 male college-level basketball players from Taiwan, who were randomly assigned to an experimental group (n = 15) and a control group (n = 15). Both groups participated in 2 h of basketball training daily, while the experimental group additionally engaged in plyometric training twice a week. The results revealed significant improvements in the experimental group in several key areas, including rate of force development (RFD), ground reaction force (GRF), jump height, jump distance, and both horizontal and vertical forces, across vertical, lateral, and horizontal jumps. Specifically, vertical jumps required the highest ground reaction force, followed by lateral jumps, with horizontal (step-back) jumps requiring the least. The optimal angles for the resultant force during take-off were found to be between 66.1° and 66.8° for lateral jumps, and between 56.2° and 57.2° for step-back jumps, while vertical jumps did not show significant variation in take-off angle. In terms of basketball performance, the experimental group demonstrated significantly better post-test results in all three types of jump shots, with the highest accuracy observed in the vertical jump shot, followed by the lateral jump shot, and the lowest in the step-back jump shot. Furthermore, the experimental group experienced a substantial reduction in sports injury rates, with the injury rate decreasing to 6%. These findings indicate that plyometric training not only enhances jumping performance, but also contributes to injury prevention by strengthening lower-limb muscles. This study provides a theoretical basis for coaches to develop comprehensive training programs that improve athletic performance and reduce injury risk.
Polynomial Regression-Based Predictive Expert System for Enhancing Hydraulic Press Performance over a 5G Network
Authors: Jankovič Denis , Pipan Miha , Šimic Marko , Herakovič Niko
Published: December 2024

In industrial applications, hydraulic presses maintain workloads by controlling the hydraulic cylinder to extend and retract, ensuring optimum tracking performance in terms of position and force. Dealing with nonlinear and multinode systems, such as hydraulic systems, often requires an advanced approach that frequently includes machine learning and artificial intelligence methods. Introducing an adaptive control system to significantly improve the response of hydraulic presses is a challenge. Therefore, a polynomial regression model predictive control (PR-MPC) mechanism is proposed in this paper to compensate for external disturbances such as the forming processes and friction dynamics. Using polynomial regression modeling and least squares optimization, the approach produces highly accurate data-driven models with an R2 value of 0.948 to 0.999. The simplicity of polynomial regression facilitates the integration of smart algorithms into an expert system with additional decision-making rules. Remote adaptive control integrated within a 5G network is based on I 4.0 distributed system guidelines that provide insights into the behavior of the hydraulic press. The results of real-time experiments have shown that the PR-MPC mechanism integrated into the expert system reduces the absolute response error of the hydraulic press by up to 98.7% compared to the initial control system with a PID regulation.
Drone Rider: Foot Vibration Stimuli to Enhance Immersion and Flight Sensation in VR
Authors: Shimato Kazuya , Goto Yuta , Okamoto Shogo
Published: December 2024

Simulating the experience of flight is a key objective of virtual reality (VR) technology. To enhance the sense of flying and immersion, we developed Drone Rider, a VR system that simulates free-flight atop a drone. In this study, we investigated whether delivering vibratory stimuli to the user’s feet could improve these sensations. While high-frequency drone propeller vibrations typically induce sensory numbness, alternative vibration patterns were explored. In Experiment 1, participants rated 13 different vibration patterns derived from various mechanical sounds, such as those from chainsaws, motorcycles, and washing machines. The motorcycle-based vibrations were most effective in enhancing both the sense of flight and immersion. In Experiment 2, we synthesized new vibration patterns by superimposing the highest-rated vibrations from Experiment 1, but no combination outperformed the original motorcycle vibration. These findings suggest that vibrations with multiple components below 100 Hz may reduce sensory adaptation and enhance the sense of flight and immersion in VR. This work provides valuable insights for developers aiming to optimize haptic feedback in VR flight simulators.
The Effect of Whitening and Daily Dentifrices on Red Wine Staining in Different Types of Composite Resins
Authors: Krajangta Nantawan , Angkananuwat Chayanit , Leelaponglit Seelassaya , Saelor Panyapat , Ngamjarrussriwichai Natthasit , Klaisiri Awiruth
Published: December 2024

Injectable resin composites offer flowability while providing enhanced mechanical properties, making them suitable for both posterior and anterior restorations. However, their susceptibility to discoloration and the efficacy of toothpaste in removing stains have not been extensively studied. This study aimed to evaluate the color stability of injectable (G-ænial Universal Injectable), flowable (3M™ Filtek™ Supreme Flowable, 3M, St. Paul, MN, USA), and conventional composite (3M™ Filtek™ Z350 XT Universal Restorative, 3M, MN, USA) after red wine exposure and to compare the stain removal efficacy of whitening (Colgate® Optic White™ Volcanic Mineral Whitening Toothpaste, Colgate-Palmolive, Chonburi, Thailand) versus daily (Colgate® Cavity Protection Toothpaste, Colgate-Palmolive, Chonburi, Thailand) dentifrice. Disc-shaped (7 mm in diameter and 2 mm in height) samples (n = 30) in A3 shade were prepared using injectable, flowable, and conventional composites. The samples were polymerized for 40 s on each side using a curing device set at 1500 mW/cm2 and immersed in red wine for 7 days. After staining, they were brushed with either whitening or daily dentifrice using 4950 strokes. Color measurements were taken at baseline (T1), after staining (T2), and after brushing (T3), using a spectrophotometer. Statistical analysis was performed using ANOVA. Results showed that conventional composites exhibited the highest color change (ΔET1-T2 = 5.50) compared to injectable (ΔET1-T2 = 3.60) and flowable composites (ΔET1-T2 = 3.36). While whitening dentifrice facilitated a greater recovery of discoloration in stained samples (ΔET1-T3 = 1.38–3.67) compared to daily dentifrice (ΔET1-T3 = 2.01–4.28), the observed difference was not statistically significant. In conclusion, all composite types are prone to staining from red wine, and both whitening and daily dentifrices show no difference in efficacy in reducing discoloration.
An Integrated Risk Management Model for Performance Assessment of Airport Pavements: The Case of Istanbul Airport
Authors: Seven Eyyüp , Yardım Mustafa Sinan
Published: December 2024

Effective management of airport pavements is essential for maintaining safety and operational efficiency in air travel. An airport pavement management system (APMS) operates at two levels: the network level, which monitors overall pavement performance across the airport, and the project level, which conducts detailed inspections of individual pavements. However, pavement assessments are often costly and labor intensive, necessitating the development of cost-effective and practical models. This paper introduces the Airport Pavement Integrated Risk Management (APIRM) model, which integrates pavement condition assessment criteria with safety risk management (SRM) methodologies. The model was applied at Istanbul Airport. By using APIRM, airports can prioritize high-risk areas, optimizing resource allocation and enhancing safety. The model encourages coordination among various airport departments, offering a holistic approach to pavement management that integrates maintenance requirements with safety considerations.
Automatic Gender Identification from Text
Authors: Younkin Vladimir , Litvak Marina , Rabaev Irina
Published: December 2024

The gender identification of authors in literary texts is a compelling research area at the intersection of computational linguistics and natural language processing, offering insights into historical biases and socio-cultural dynamics while enriching our understanding of literary traditions. This study is inspired by the historical context of women adopting male pseudonyms to navigate a male-dominated literary domain. By leveraging machine learning and state-of-the-art language models, we investigate the feasibility and accuracy of inferring an author’s gender from their writings. Our key contributions include (1) the creation of a large-scale, diverse dataset of literary texts spanning various literary epochs and (2) the evaluation of multiple classification models. Our experiments reveal that the best-performing model achieves an accuracy above 90%, highlighting the potential of computational methods to uncover stylistic and linguistic markers tied to gender. These findings open avenues for further research into stylistic and linguistic patterns across literary history and their relationship to authorial identity.
Optimization of Composite Structures with Thin Rigid Fibers Using Bioinspired Algorithms
Authors: Poteralski Arkadiusz , Szczepanik Mirosław , Fedeliński Piotr
Published: December 2024

The paper deals with an application of the artificial immune system (AIS) and the particle swarm optimizer (PSO) to the optimization of composites with thin rigid fibers. The boundary value problem is solved using the boundary element method (BEM). The numerical examples demonstrate the optimization of the distribution and orientation of fibers in a composite. The objective functions depend on effective elastic properties. Two separate independent optimization methods are used to confirm the correctness of the obtained results (AIS and PSO). The bioinspired approach shows that the method based on the artificial immune system or particle swarm optimizer is an efficient technique for solving computer-aided optimal design problems (maximalization of the Young’s modulus or Poisson’s ratio) and allows for the development of new materials.
Design and Analysis of Yoshimura Tubular Origami Mechanisms
Authors: Wang Chang , Xu Dongyang , Song Shanyuan , Zhao Yanzhi , Zhang Jianhua
Published: December 2024

The Yoshimura tubular origami mechanism possesses numerous advantageous properties and, when integrated with advanced material technologies, can be applied across various engineering disciplines. However, current research on Yoshimura origami predominantly focuses on centrally symmetric tubular origami mechanisms, which restricts the structural forms and motion patterns of these mechanisms. Drawing inspiration from the biological concept of “morphological variation,” we propose a novel tubular origami mechanism based on the Yoshimura pattern, which is the main contribution of this research. We analyze the Yoshimura planar crease elements and introduce both heterocellular and homocellular tubular origami mechanisms. Furthermore, we establish the origami topology matrices for the Yoshimura tubular origami mechanisms. This research also investigates complex motion forms that differ from traditional Yoshimura origami mechanisms, including macroscopic twisting and compound movements, thereby providing an intuitive design approach and extensive structural guidance for research in Yoshimura tubular origami engineering. Based on the tubular origami mechanism, we created an origami robot and investigate its motion characteristics.
Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat
Authors: Azurmendi Iker , Gonzalez Manuel , García Gustavo , Zulueta Ekaitz , Martín Elena
Published: December 2024

Deep learning, a subfield of artificial intelligence that uses neural networks with multiple layers, is rapidly changing healthcare. Its ability to analyze large datasets and extract relevant information makes it a powerful tool for improving diagnosis, treatment, and disease management. The integration of DL with pressure mats—which are devices that use pressure sensors to continuously and non-invasively monitor the interaction between patients and the contact surface—is a promising application. These pressure platforms generate data that can be very useful for detecting postural anomalies. In this paper we will discuss the application of deep learning algorithms in the analysis of pressure data for the detection of postural asymmetries in 139 patients aged 3 to 20 years. We investigated several main tasks: patient classification, hemibody segmentation, recognition of specific body parts, and generation of automated clinical reports. For this purpose, convolutional neural networks in their classification and regression modalities, the object detection algorithm YOLOv8, and the open language model LLaMa3 were used. Our results demonstrated high accuracy in all tasks: classification achieved 100% accuracy; hemibody division obtained an MAE of approximately 7; and object detection had an average accuracy of 70%. These results demonstrate the potential of this approach for monitoring postural and motor disabilities. By enabling personalized patient care, our methodology contributes to improved clinical outcomes and healthcare delivery. To our best knowledge, this is the first study that combines pressure images with multiple deep learning algorithms for the detection and assessment of postural disorders and motor disabilities in this group of patients.
AI-Powered Digital Twins and Internet of Things for Smart Cities and Sustainable Building Environment
Authors: Alnaser Aljawharah A. , Maxi Mina , Elmousalami Haytham
Published: December 2024

This systematic literature review explores the intersection of AI-driven digital twins and IoT in creating a sustainable building environment. A comprehensive analysis of 125 papers focuses on four major themes. First, digital twins are examined in construction, facility management, and their role in fostering sustainability and smart cities. The integration of IoT and AI with digital twins and energy optimization for zero-energy buildings is discussed. Second, the application of AI and automation in manufacturing, particularly in Industry 4.0 and cyber-physical systems, is evaluated. Third, emerging technologies in urban development, including blockchain, cybersecurity, and EEG-driven systems for sustainable buildings, are highlighted. The study underscores the role of data-driven approaches in flood resilience and urban digital ecosystems. This review contributes to sustainability by identifying how digital technologies and AI can optimize energy use and enhance resilience in both urban and industrial contexts.
Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning
Authors: Liapis Georgios , Vlahavas Ioannis
Published: December 2024

Conventional personality assessment methods depend on subjective input, while game-based AI predictive methods offer a dynamic and objective framework. However, training these models requires large and labeled datasets, which are challenging to obtain from real players with diverse personality traits. In this paper, we propose a multi-agent system using Deep Reinforcement Learning in a game environment to generate the necessary labeled data. Each agent is trained with custom reward functions based on the HiDAC system that encourages trait-aligned behaviors to emulate specific personality traits based on the OCEAN personality trait model. The Multi-Agent Posthumous Credit Assignment (MA-POCA) algorithm facilitates continuous learning, allowing agents to emulate behaviors through self-play. The resulting gameplay data provide diverse, high-quality samples. This approach allows for robust individual and team assessments, as agent interactions reveal the impact of personality traits on team dynamics and performance. Ultimately, this methodology provides a scalable, unbiased methodology for human personality evaluation in various settings, establishing new standards for data-driven assessment methods.
Circular Catalytic Hydrogen/Methanol Plate Burner with Stackable Clover Channels Supporting Rapid Start-Up and Stable Operation for Highly Efficient Reformer System
Authors: Tseng Fan-Gang , Hu Wen-Hsin , Li He-Jia , Wang Xiang-Jun
Published: December 2024

This study proposes a platinum catalytic plate burner with a clover-shaped microchannel design to reduce the maximum temperature difference (ΔTmax) and improve long-term hydrogen production (HP) performance in an autothermal methanol steam reforming (ATMSR) microreactor. The burner integrates with a plate reformer within a cylindrical adiabatic container. By optimizing catalyst arrangement and incorporating a parallel clover-type microchannel design, thermal gradients inside the burner are minimized, enabling better operation conditions for the plate reformer. Three Pt catalyst gradients (50/50, 40/60, and 30/70) reduce ΔTmax from 48.2 °C and 38.3 °C to 25.8 °C. Additionally, the startup time to 250 °C is reduced from 35, 25, and 14 min, respectively. The integration of the plate burner and reformer with the 30/70 catalyst type shows a higher methanol conversion rate (98%), better hydrogen yield, and lower CO selectivity compared to the 50/50 type. Long-term testing for 30 h shows a low catalyst degradation rate, making it suitable for sustained operation.
Coordination of Speaking Opportunities in Virtual Reality: Analyzing Interaction Dynamics and Context-Aware Strategies
Authors: Chen Jiadong , Gu Chenghao , Zhang Jiayi , Liu Zhankun , Ma Boxuan , Konomi Shin‘ichi
Published: December 2024

This study explores the factors influencing turn-taking coordination in virtual reality (VR) environments, with a focus on identifying key interaction dynamics that affect the ease of gaining speaking opportunities. By analyzing VR interaction data through logistic regression and clustering, we identify significant variables impacting turn-taking success and categorize typical interaction states that present unique coordination challenges. The findings reveal that features related to interaction proactivity, individual status, and communication quality significantly impact turn-taking outcomes. Furthermore, clustering analysis identifies five primary interaction contexts: high competition, intense interaction, prolonged single turn, high-status role, and low activity, each with unique turn-taking coordination requirements. This work provides insights into enhancing turn-taking support systems in VR, emphasizing contextually adaptive feedback to reduce speaking overlap and turn-taking failures, thereby improving overall interaction flow in immersive environments.