bluuebunny/crossref_metadata_2025 huggingface dataset:
Sign Language Dataset for Automatic Motion Generation
Authors: Villa-Monedero María , Gil-Martín Manuel , Sáez-Trigueros Daniel , Pomirski Andrzej , San-Segundo Rubén
Published: November 2023

Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes for each sign (specified in HamNoSys, a transcription system developed at the University of Hamburg, Hamburg, Germany) and the corresponding motion information. The motion information includes sign videos and the sequence of extracted landmarks associated with relevant points of the skeleton (including face, arms, hands, and fingers). The dataset includes signs from three different subjects in three different positions, performing 754 signs including the entire alphabet, numbers from 0 to 100, numbers for hour specification, months, and weekdays, and the most frequent signs used in Spanish Sign Language (LSE). In total, there are 6786 videos and their corresponding phonemes (HamNoSys annotations). From each video, a sequence of landmarks was extracted using MediaPipe. The dataset allows training an automatic system for motion generation from sign language phonemes. This paper also presents preliminary results in motion generation from sign phonemes obtaining a Dynamic Time Warping distance per frame of 0.37.
The Visual Advantage Effect in Comparing Uni-Modal and Cross-Modal Probabilistic Category Learning
Authors: Sun Xunwei , Fu Qiufang
Published: November 2023

People rely on multiple learning systems to complete weather prediction (WP) tasks with visual cues. However, how people perform in audio and audiovisual modalities remains elusive. The present research investigated how the cue modality influences performance in probabilistic category learning and conscious awareness about the category knowledge acquired. A modified weather prediction task was adopted, in which the cues included two dimensions from visual, auditory, or audiovisual modalities. The results of all three experiments revealed better performances in the visual modality relative to the audio and audiovisual modalities. Moreover, participants primarily acquired unconscious knowledge in the audio and audiovisual modalities, while conscious knowledge was acquired in the visual modality. Interestingly, factors such as the amount of training, the complexity of visual stimuli, and the number of objects to which the two cues belonged influenced the amount of conscious knowledge acquired but did not change the visual advantage effect. These findings suggest that individuals can learn probabilistic cues and category associations across different modalities, but a robust visual advantage persists. Specifically, visual associations can be learned more effectively, and are more likely to become conscious. The possible causes and implications of these effects are discussed.
Towards a Simulation-Assisted Prediction of Residual Stress-Induced Failure during Powder Bed Fusion of Metals Using a Laser Beam: Suitable Fracture Mechanics Models and Calibration Methods
Authors: Panzer Hannes , Wolf Daniel , Bachmann Andreas , Zaeh Michael Friedrich
Published: November 2023

In recent years, Additive Manufacturing (AM) has emerged as a transformative technology, with the process of Powder Bed Fusion of Metals using a Laser Beam (PBF-LB/M) gaining substantial attention for its precision and versatility in fabricating metal components. A major challenge in PBF-LB/M is the failure of the component or the support structure during the production process. In order to locate a possible residual stress-induced failure prior to the fabrication of the component, a suitable failure criterion has to be identified and implemented in process simulation software. In the work leading to this paper, failure criteria based on the Rice-Tracey (RT) and Johnson-Cook (JC) fracture models were identified as potential models to reach this goal. The models were calibrated for the nickel-based superalloy Inconel 718. For the calibration process, a conventional experimental, a combined experimental and simulative, and an AM-adapted approach were applied and compared. The latter was devised to account for the particular phenomena that occur during PBF-LB/M. It was found that the JC model was able to capture the calibration data points more precisely than the RT model due to its higher number of calibration parameters. Only the JC model calibrated by the experimental and AM-adapted approach showed an increased equivalent plastic failure strain at high triaxialities, predicting a higher cracking resistance. The presented results can be integrated into a simulation tool with which the potential fracture location as well as the cracking susceptibility during the manufacturing process of PBF-LB/M parts can be predicted.
A Trajectory Tracking and Local Path Planning Control Strategy for Unmanned Underwater Vehicles
Authors: Zhang Xun , Wang Ziqi , Chen Huijun , Ding Hao
Published: November 2023

The control strategy of an underdriven unmanned underwater vehicle (UUV) equipped with front sonar and actuator faults in a continuous task environment is investigated. Considering trajectory tracking and local path planning in complex-obstacle environments, we propose a task transition strategy under the event-triggered mechanism and design the corresponding state space and action space for the trajectory tracking task under the deep reinforcement learning framework. Meanwhile, a feed-forward compensation mechanism is designed to counteract the effects of external disturbances and actuator faults in combination with a reduced-order extended state observer. For the path planning task under the rapidly exploring random tree (RRT) framework, a reward component and angular factors are introduced to optimize the growth and exploration points of the extended tree under the consideration of the shortest distance, optimal energy consumption, and steering angle constraints. The effectiveness of the proposed method was verified through continuous task simulations of trajectory tracking and local path planning.
Investigation on the Erosion Characteristics of Liquid–Solid Two-Phase Flow in Tee Pipes Based on CFD-DEM
Authors: Hong Shiming , Peng Guangjie , Yu Dehui , Chang Hao , Wang Xikun
Published: November 2023

Tee pipes are widely utilized in pipeline transportation, especially in subsea production systems and the chemical industry. The purpose of this research is to study the influences of gravity direction, conveying parameters and particle properties on the erosion distribution in tee junctions. Investigation using the CFD-DEM coupled method is conducted on the flow mechanisms and erosion characteristics in a tee junction under different flow conditions. Firstly, numerical calculations of liquid–solid two-phase flow in a vertical cylindrical pipe are performed, and the comparison between simulation results and experimental results is carried out. Then, the verified lift and erosion models are used for the numerical calculations of tee pipes. Flow mechanisms and erosion characteristics are numerically investigated through analysis of the velocity profiles, streamlines, and erosion contours. The results indicate that the gravity direction has a nonnegligible influence on the cross-sectional velocity profiles, particularly under the condition of high velocity and high particle concentration. The region with the maximum erosion rate occurred at the branch pipes, about three fourths of the pipe diameter away from the tee junction.
Simulation-Driven Design Optimization of a Destroyer-Type Vessel via Multi-Fidelity Supervised Active Learning
Authors: Spinosa Emanuele , Pellegrini Riccardo , Posa Antonio , Broglia Riccardo , De Biase Mario , Serani Andrea
Published: November 2023

The paper presents the use of a supervised active learning approach for the solution of a simulation-driven design optimization (SDDO) problem, pertaining to the resistance reduction of a destroyer-type vessel in calm water. The optimization is formulated as a single-objective, single-point problem with both geometrical and operational constraints. The latter also considers seakeeping performance at multiple conditions. A surrogate model is used, based on stochastic radial basis functions with lower confidence bounding, as a supervised active learning approach. Furthermore, a multi-fidelity formulation, leveraging on unsteady Reynolds-averaged Navier–Stokes equations and potential flow solvers, is used in order to reduce the computational cost of the SDDO procedure. Exploring a five-dimensional design space based on free-form deformation under limited computational resources, the optimal configuration achieves a resistance reduction of about 3% at the escape speed and about 6.4% on average over the operational speed range.
A Floating-Waste-Detection Method for Unmanned Surface Vehicle Based on Feature Fusion and Enhancement
Authors: Li Yong , Wang Ruichen , Gao Dongxu , Liu Zhiyong
Published: November 2023

Unmanned surface vehicle (USV)-based floating-waste detection presents significant challenges. Due to the water surface’s high reflectivity, there are often light spots and reflections in images captured by USVs. Furthermore, floating waste often consists of numerous small objects that prove difficult to detect, posing a robustness challenge for object-detection networks. To address these issues, we introduce a new dataset collected by USV, FloatingWaste-I, which accounts for the effects of light in various weather conditions, including sunny, cloudy, rainy and nighttime scenarios. This dataset comprises two types of waste: bottles and cartons. We also propose the innovative floating-waste-detection network, YOLO-Float, which incorporates a low-level representation-enhancement module and an attentional-fusion module. The former boosts the network’s low-level representation capability while the latter fuses the highest- and lowest-resolution feature map to improve the model robustness. We evaluated our method by using both the public dataset FloW-img and our FloatingWaste-I dataset. The results confirm YOLO-Float’s effectiveness, with an AP of 44.2% on the FloW-img dataset, surpassing the existing YOLOR, YOLOX and YOLOv7 by 3.2%, 2.7% and 3.4%, respectively.
Hydroelastic Responses of a Submersible Ring Structure for Offshore Seaweed Cultivation under Wave Action
Authors: Nguyen Huu Phu , Wang Chien Ming , von Herzen Brian , Huang Chenxuan
Published: November 2023

This paper investigates the hydroelastic response of a submersible circular ring structure, designed for offshore seaweed cultivation, under wave action and during the submergence process. The ring structure comprises two circular HDPE pipes connected to each other by equally spaced brackets. The structure carries seaweed grow-out lines, and is kept in position by a mooring-line system used for fish pens. The HDPE collar is equipped with multiple inlet and outlet valves, allowing it to be submerged to avoid strong waves and to be raised to the water surface when the strong waves die down. The software AquaSim was used for the hydroelastic analysis of the moored structure. It is found that we can significantly reduce the von Mises stresses in the ring structure as well as the mooring-line forces by submerging. However, the structure can experience significant increase in stress during the submergence process due to bending from combined wave action and non-uniform distribution of filled water in the ring structure. This stress increase may cause structural damage or even failure. Therefore, it is important to submerge the ring structure in calm waves ahead of predicted storms and to control the distribution of seawater into the ring structure. For the latter, it is best to use at least two inlet valves and two outlet valves to minimize the likelihood of damage of the ring structure during the submergence process.
New Techniques in Marine Aquaculture
Authors: Ma Zhenhua , Qin Jianguang
Published: November 2023

In recent years, the importance of marine aquaculture has been increasing globally, and new technologies are playing a significant role in this trend [...]
Multiple Container Terminal Berth Allocation and Joint Operation Based on Dueling Double Deep Q-Network
Authors: Li Bin , Yang Caijie , Yang Zhongzhen
Published: November 2023

In response to the evolving challenges of the integration and combination of multiple container terminal operations under berth water depth constraints, the multi-terminal dynamic and continuous berth allocation problem emerges as a critical issue. Based on computational logistics, the MDC-BAP is formulated to be a unique variant of the classical resource-constrained project scheduling problem, and modeled as a mixed-integer programming model. The modeling objective is to minimize the total dwelling time of linerships in ports. To address this, a Dueling Double DQN-based reinforcement learning algorithm is designed for the multi-terminal dynamic and continuous berth allocation problem A series of computational experiments are executed to validate the algorithm’s effectiveness and its aptitude for multiple terminal joint operation. Specifically, the Dueling Double DQN algorithm boosts the average solution quality by nearly 3.7%, compared to the classical algorithm such as Proximal Policy Optimization, Deep Q Net and Dueling Deep Q Net also have better results in terms of solution quality when benchmarked against the commercial solver CPLEX. Moreover, the performance advantage escalates as the number of ships increases. In addition, the approach enhances the service level at the terminals and slashes operation costs. On the whole, the Dueling Double DQN algorithm shows marked superiority in tackling complicated and large-scale scheduling problems, and provides an efficient, practical solution to MDC-BAP for port operators.
Influence of Beach Slope on Morphological Changes and Sediment Transport under Irregular Waves
Authors: Dionísio António Sara , van der Werf Jebbe , Horstman Erik , Cáceres Iván , Alsina José , van der Zanden Joep , Hulscher Suzanne
Published: November 2023

This paper presents new data from large-scale wave flume experiments. It shows the beach profile evolution and sediment transport for two different bed slopes (1:15 and 1:25), and three irregular high-energy erosive wave conditions and one low-energy accretive wave condition. The bulk cross-shore net sediment transport was investigated for the total active profile and for the surf and swash zone separately. It is shown that the steep slope is morphologically more active than the gentle slope, with faster and more pronounced morphological changes and larger sediment transport rates. For both slopes, the total and surf zone net sediment transport were offshore-directed for erosive waves and onshore-directed for the accretive wave condition. However, the net swash zone transport for the erosive wave conditions was offshore-directed for the steep slope and onshore-directed for the gentle slope. The direction and magnitude of the total and surf zone sediment transport correlate well with the slope-corrected Dean criterion with increasing offshore-directed sediment transport (erosion) observed for increasing wave energy and bed slope. This relation does not hold for the swash zone sediment transport along the gentle slope, suggesting that swash zone sediment transport processes are not well captured when using a simple predictor such as the (modified) Dean number. Differences in sediment transport in the swash for the different slopes are likely influenced by differences in incoming wave energy, wave–swash interactions and the relative importance of long- and short-waves.
Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning
Authors: Gao Wei , Han Mengxue , Wang Zhao , Deng Lihui , Wang Hongjian , Ren Jingfei
Published: November 2023

A UUV can perform tasks such as underwater surveillance, reconnaissance, surveillance, and tracking by being equipped with sensors and different task modules. Due to the complex underwater environment, the UUV must have good collision avoidance planning algorithms to avoid various underwater obstacles when performing tasks. The existing path planning algorithms take a long time to plan and have poor adaptability to the environment. Some collision-avoidance planning algorithms do not take into account the kinematic limitations of the UUV, thus placing high demands on the performance and control algorithms of UUV. This article proposes a PPO−DWA collision avoidance planning algorithm for the UUV under static unknown obstacles, which is based on the proximal policy optimization (PPO) algorithm and the dynamic window approach (DWA). This algorithm acquires the obstacle information from forward-looking sonar as input and outputs the corresponding continuous actions. The PPO−DWA collision avoidance planning algorithm consists of the PPO algorithm and the modified DWA. The PPO collision avoidance planning algorithm is only responsible for outputting the continuous angular velocity, aiming to reduce the difficulty of training neural networks. The modified DWA acquires obstacle information and the optimal angular velocity from the PPO algorithm as input, and outputs of the linear velocity. The collision avoidance actions output by this algorithm meet the kinematic constraints of UUV, and the algorithm execution time is relatively short. The experimental data demonstrates that the PPO−DWA algorithm can effectively plan smooth collision-free paths in complex obstacle environments, and the execution time of the algorithm is acceptable.
Integration of Metabolomes and Transcriptomes Provides Insights into Morphogenesis and Maturation in Morchella sextelata
Authors: Zhang Chen , Shi Xiaofei , Zhang Jiexiong , Zhang Yesheng , Liu Wei , Wang Wen
Published: November 2023

True morels (Morchella, Pezizales) are a popular edible and medicinal fungus with great nutritional and economic value. The dynamics and regulatory mechanisms during the morphogenesis and maturation of morels are poorly understood. In this study, the metabolomes and transcriptomes of the mycelium (MY), primordium differentiation (PR), young fruiting body (YFB), and mature fruiting body (MFB) were comprehensively analyzed to reveal the mechanism of the morphogenesis and maturation of Morchella sextelata. A total of 748 differentially expressed metabolites (DEMs) and 5342 differentially expressed genes (DEGs) were detected, mainly enriched in the carbohydrate, amino acid, and lipid metabolism pathways, with the transition from the mycelium to the primordium being the most drastic stage at both the metabolic and transcriptional levels. The integrated metabolomics and transcriptomics highlighted significant correlations between the DEMs and DEGs, and specific amino acid and nucleotide metabolic pathways were significantly co-enriched, which may play key roles in morphological development and ascocarp maturation. A conceptual model of transcriptional and metabolic regulation was proposed during morphogenesis and maturation in M. sextelata for the first time, in which environmental factors activate the regulation of transcription factors, which then promote metabolic and transcriptional regulation from vegetative to reproductive growth. These results provide insights into the metabolic dynamics and transcriptional regulation during the morphogenesis and maturation of morels and valuable resources for future breeding enhancement and sustainable artificial cultivation.
Desmoid Tumors in the Active Surveillance Era: Evaluation of Treatment Options and Pain Relief in a Single-Center Retrospective Analysis
Authors: Rastrelli Marco , Chiusole Benedetta , Cavallin Francesco , Del Fiore Paolo , Angelini Andrea , Cerchiaro Maria Chiara , Ruggieri Pietro , Sbaraglia Marta , Mocellin Simone , Brunello Antonella
Published: November 2023

In patients with desmoid tumors (DTs), active surveillance has been increasingly preferred over surgery, while treatment (including pharmacological therapy, radiotherapy, and/or surgery) is performed in cases with confirmed disease progression. This study aimed to evaluate event-free survival and pain management according to different treatment strategies. We evaluated event-free survival, including recurrence after initial surgical treatment or changes in the therapeutic management after initial non-surgical treatment and pain management according to different treatment strategies. All patients referred for DT in 2001–2021 at our institutions were stratified into four groups: those treated surgically prior to 2012 (SGPre12) or after 2012 (SGPost12), those treated pharmacologically (MG), and those under active surveillance (ASG). An event was defined as recurrence after initial surgical treatment or a change in therapeutic management. Overall, 123 patients were included in the study: 28 in SGPre12, 41 in SGPost12, 38 in MG, and 16 in ASG. Pharmacological treatment resolved painful symptoms in 16/27 (60%) patients (p = 0.0001). The median follow-up duration was 40 months (IQR 23–74). Event-free survival at 1, 3, and 5 years was: 85%, 70%, and 62% in SGPre12; 76%, 58%, and 49% in SGPost12; 49%, 31%, and 31% in MG; and 45%, 45%, and 45% in ASG. Our findings support the role of active surveillance as initial management, as demonstrated by the fact that about half the patients did not experience any progression, while surgery can be reserved as a first-line approach for selected patients. In terms of pain relief, medical therapy led to symptom resolution in more than half the cases.
Simulation-Based Study on the Effect of Green Roofs on Summer Energy Performance in Melbourne
Authors: Jamei Elmira , Thirunavukkarasu Gokul , Abuseif Majed , Seyedmahmoudian Mehdi , Mekhilef Saad , Stojcevski Alex , Chau Hing-Wah
Published: November 2023

Green roofs are increasingly recognised as a crucial urban solution, addressing climate change, enhancing energy efficiency, and promoting sustainable architecture in densely populated areas. In this manuscript, the research study delves into the influence of green roofs on energy consumption, focusing on the Treasury Place building in Melbourne, Australia. The utilisation of DesignBuilder and EnergyPlus simulations was explored. Various green roof parameters such as the Leaf Area Index (LAI), plant height, soil moisture, and tree coverage were optimised and compared against base case scenarios. The key findings indicate an optimal LAI of 1.08 for maximum energy savings, with diminishing returns beyond an LAI of 2.5. The soil moisture content was most effective, around 50%, while a plant height of approximately 0.33 m optimised energy reduction. The introduction of 50% canopy tree coverage provided temperature regulation, but increased soil moisture due to trees and their influence on wind flow had an adverse energy impact. These results emphasise the necessity for precise green roof representation and parameter optimisation for maximum energy efficiency. This research offers essential insights for those in urban planning and building design, endorsing green roofs as a pivotal solution for sustainable urban environments.
The Expression of Illegal Urbanism in the Urban Morphology and Landscape: The Case of the Metropolitan Area of Seville (Spain)
Authors: López-Casado David , Fernández-Salinas Víctor
Published: November 2023

Illegal urbanism has been, and still is, an inherent aspect of the metropolitan fabric of many Spanish cities shaped throughout the twentieth century. The morphology of these complex spaces, with origins in organic growth processes, reflects illegal housing developments through discordant, poorly connected networks, often with significant landscape and environmental impacts. The metropolitan area of Seville, comprising almost 5000 km2 and more than 1,500,000 inhabitants, is a paradigmatic space where these facts can be traced, especially in its western sector, where the strong urbanisation dynamics of the last decades have reached and engulfed many of these housing developments. This work establishes an analysis and proposes typologies of the different repercussions of the process in this territory. It does so by drawing on, among other sources, two regional inventories on illegal urbanism, which allow the application of a diachronic perspective and fieldwork with information synchronised to the present time. The main conclusion is that, in addition to explaining the complex metropolitan networks in which they appear (justifying singular forms, asymmetries, and morphologies), the illegal housing developments analysed are an important element for weighing the sociopolitical characteristics of the municipalities in which they are inserted.
Interaction of Urban Configuration, Temperature, and De Facto Population in Seoul, Republic of Korea: Insights from Two-Stage Least-Squares Regression Using S-DoT Data
Authors: Park Minkyung , Kim Heechul
Published: November 2023

Climate change exacerbates thermal experiences in urban environments, affecting the frequency of social activities in public spaces. As climate change is expected to have a greater influence on thermal comfort, effective integration of climatic knowledge and urban design is required. However, there is a lack of knowledge regarding urban configurations that are resistant to temperature and promote urban vitality. This study aimed to explore the correlation between urban configuration, thermal environment, and urban vitality. We categorized the urban configuration of Seoul and analyzed the urban configuration type that impacts urban vitality and temperature. We used the number of the de facto population to measure urban vitality. The two-stage least-squares (2-SLS) model was used to address endogeneity concerns related to configuration, temperature, and de facto population. This study shows that de facto population is influenced by both urban configuration type and temperature. Effective design strategies for maintaining de facto population while responding to climate change include a combination of small blocks and high height-to-width ratios (H/W). In contrast, open-space urban configurations negatively impact de facto population. In high-density and high-H/W areas, de facto population increased due to shading effects but decreased when the critical value was exceeded. Urban configurations with high density and deep urban canyons have greater de facto population in winter than in summer.
Exploring the Effects of Climate Change on Farming System Choice: A Farm-Level Space-for-Time Approach
Authors: Ribeiro Paulo Flores , Santos José Lima
Published: November 2023

Climate change is expected to affect the agricultural sector in ways that are often unclear to predict. If in the short- and medium-terms farmers may adapt to climate change by adjusting their agricultural practices, in the long-term, these adjustments may become insufficient, forcing farmers to change their farming systems. The extent and direction in which these farming system transitions will occur is still a subject that is underexplored in the literature. We propose a new framework to explore the effect of climate change on the choice of farming system while controlling the effect of other drivers that are also known to influence the farming system choice. Using a spatially explicit farming system choice model developed by a previous study in an extensive agricultural region of southern Portugal, we applied a space-for-time approach to simulate the effect of climate change on the future dynamics of the farming systems in the study area. The results suggest that climate change will force many farmers to change the farming system in a foreseeable future. The extent of the projected changes in farming systems is likely to trigger significant social, economic, and environmental impacts, which should require early attention from policy makers.
Brain–Heart Axis: Brain-Derived Neurotrophic Factor and Cardiovascular Disease—A Review of Systematic Reviews
Authors: Fioranelli Massimo , Garo Maria Luisa , Roccia Maria Grazia , Prizbelek Bianca , Sconci Francesca Romana
Published: November 2023

Background: The brain–heart axis is an intra- and bidirectional complex that links central nervous system dysfunction and cardiac dysfunction. In recent decades, brain-derived neurotrophic factor (BDNF) has emerged as a strategic molecule involved in both brain and cardiovascular disease (CVD). This systematic review of systematic reviews aimed to (1) identify and summarize the evidence for the BDNF genotype and BDNF concentration in CVD risk assessment, (2) evaluate the evidence for the use of BDNF as a biomarker of CVD recovery, and (3) evaluate rehabilitation approaches that can restore BDNF concentration. Methods: A comprehensive search strategy was developed using PRISMA. The risk of bias was assessed via ROBIS. Results: Seven studies were identified, most of which aimed to evaluate the role of BDNF in stroke patients. Only two systematic reviews examined the association of BDNF concentration and polymorphism in CVDs other than stroke. Conclusions: The overall evidence showed that BDNF plays a fundamental role in assessing the risk of CVD occurrence, because lower BDNF concentrations and rs6265 polymorphism are often associated with CVD. Nevertheless, much work remains to be carried out in current research to investigate how BDNF is modulated in different cardiovascular diseases and in different populations.
Progression-Free Survival Efficacy in Refractory/Relapsed Multiple Myeloma among Elderly Patients: A Systematic Review
Authors: Yang Tung-Lung , Lin Chin , Ho Ching-Liang , Huang Tzu-Chuan , Wu Yi-Ying , Jhou Hong-Jie , Chen Po-Huang , Lee Cho-Hao
Published: November 2023

Background: Over the last decade, many studies have assessed the efficacy of treatments for refractory/relapsed multiple myeloma (R/R MM). While combination therapies show greater efficacy than traditional methods, limited research has targeted elderly patients who might be less resilient to treatments. Our study aimed to evaluate treatment efficacy for these elderly patients. Methods: We carried out a comprehensive review of the literature using a systematic approach. Initially, 4966 citations were retrieved and subsequently narrowed down to 13 eligible randomized controlled trials (RCTs) through our systematic review process from databases like Embase, PubMed, and Cochrane Library from 1 January 2000 to 31 December 2022. Evidence was collated through a frequentist network meta-analysis, using the hazard ratio (HR) for evaluation. Results: Combined therapy of daratumumab, lenalidomide, and dexamethasone (DaraLenDex) was the preferred treatment for R/R MM elderly patients. Its strengths included an HR for progression-free survival (0.15; 95% CI: 0.09–0.25) and a 96% P-score. Conclusions: Our analysis suggests that, pending more comprehensive RCTs, DaraLenDex is the treatment with the highest efficacy for R/R MM in elderly patients.
Bearing Digital Twin Based on Response Model and Reinforcement Learning
Authors: Li Zhaorong , Wang Jiaoying , Ruan Diwang , Yan Jianping , Gühmann Clemens
Published: November 2023

In recent years, research on bearing fault modeling has witnessed significant advancements. However, the modeling of bearing faults using digital twins (DTs) remains an emerging area of exploration. This paper introduces a bearing digital twin developed by integrating a signal-based response model with reinforcement learning techniques. Initially, a signal-based model is constructed, comprising a unit fault impulse function and a decay oscillation function. This model illustrates the bearing’s acceleration response under fault conditions and acts as the environmental component within the bearing digital twin. Subsequently, a parameter estimation process identifies two critical parameters from the signal-based model: the load proportional factor and the decaying constant. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed as the agent for online learning of these parameters. The cosine similarity metric is employed to define the state and reward by comparing the real acceleration measurements with the simulation data generated by the digital twin. To validate the effectiveness of the digital twin, experimental data sourced from the three datasets are utilized. The results underscore the digital twin’s capacity to faithfully replicate the bearing’s acceleration response under diverse conditions, demonstrating a high degree of similarity in both the time and frequency domains.
1-(2,5-Dimethoxy-4-nitrophenyl)piperidine
Authors: Syeda Tanjia M. , Al-Shaidi Muadh R. , Basri Ibtihal , Merzouk Mahboub , Aldabbagh Fawaz
Published: November 2023

Treatment of the non-purified mixture of dinitro isomers obtained from the nitration of 1,4-dimethoxybenzene with piperidine led to the isolation of novel but minor adduct, 1-(2,5-dimethoxy-4-nitrophenyl)piperidine (2b) in 15% yield. Yields of nucleophilic aromatic substitution adducts are high when using purified 1,4-dimethoxy-2,5-dinitrobenzene (1b) with piperidine and pyrrolidine to give (2b) and 1-(2,5-dimethoxy-4-nitrophenyl)pyrrolidine (3b) in 76% and 82% yield, respectively.
On Drum Brake Squeal—Assessment of Damping Measures by Time Series Data Analysis of Dynamometer Tests and Complex Eigenvalue Analyses
Authors: Gräbner Nils , Schmid Dominik , von Wagner Utz
Published: November 2023

Brake squeal—an audible high-frequency noise phenomenon in the range between 1 kHz and 15 kHz resulting from self-excited vibrations—is one of the main cost drivers while developing brake systems. Increasing damping is often a crucial factor in the context of self-excited vibrations. Countermeasures applied for preventing brake squeal have been investigated particularly for disk brakes in the past. However, in recent years, drum brakes have once again become more important, partly because of the issue of particle emissions. Concerning noise problems, drum brakes have a decisive advantage compared to disk brake systems in that the outer drum surface is freely accessible for applying damping devices. This paper focuses on the fundamental proving and evaluation of passive damping measures on a simplex drum brake system. To obtain a detailed understanding of the influence of additional damping on the squealing behavior of drum brakes, extensive experimental investigations are performed on a brake with an intentionally introduced high squealing tendency in the initial configuration. This made it possible to investigate the influence of different types of damping measures on their effectiveness. Techniques from the field of big data analysis and machine learning are tested to detect squeal in measured time series data. These techniques were remarkably reliable and made it possible to detect squeal efficiently even in data that was not generated on a traditional costly NVH brake dynamometer. To investigate whether the simulation method usually used for the simulation of brake squeal is applicable to depicting the influence of additional damping in drum brakes, a complex eigenvalue analysis was performed with Abaqus, and the results were compared with those from the experiments.
Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation
Authors: Vorasawad Konchanok , Park Myoungkuk , Kim Changwon
Published: November 2023

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.
Measurable Sensitivity for Semi-Flows
Authors: Quan Weizhen , Lu Tianxiu , Li Risong , Chen Yuanlin , Ding Xianfeng , Li Yongjiang
Published: November 2023

Sensitive dependence on initial conditions is a crucial characteristic of chaos. The concept of measurable sensitivity (MS) was introduced as a measure-theoretic version of sensitive dependence on initial conditions. Their research demonstrated that MS arises from light mixing, indicates a finite number of eigenvalues for a transformation, and is not present in the case of infinite measure preservation. Unlike the traditional understanding of sensitivity, MS carries up to account for isomorphism in the sense of measure theory, which ignores the function’s behavior on null sets and eliminates dependence on the chosen metric. Inspired by the results of James on MS, this paper generalizes some of the concepts (including MS) that they used in their study of MS for conformal transformations to semi-flows, and generalizes their main results in this regard to semi-flows.
Optimizing Lifetime of Internet-of-Things Networks with Dynamic Scanning
Authors: Choi Seung-Kyu , Kim Woo Hyun , Sohn Illsoo
Published: November 2023

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs.
Temperature-Controlled Laser Cutting of an Electrical Steel Sheet Using a Novel Fuzzy Logic Controller
Authors: Nguyen Dinh-Tu , Lin Yuan-Ting , Ho Jeng-Rong , Tung Pi-Cheng , Lin Chih-Kuang
Published: November 2023

A novel PID-type fuzzy logic controller (FLC) with an online fuzzy tuner was created to maintain stable in situ control of the cutting front temperature, aiming to enhance the laser process for thin non-oriented electrical steel sheets. In the developed controller, the output scaling factors and the universe of discourse were initially optimized using a hybrid of the particle swarm optimization and grey wolf optimization methods. The optimal parameters obtained were utilized in experiments involving the laser cutting of thin non-oriented electrical steel sheets, compared to an open-loop control system maintaining a constant cutting speed. The PID-type FLC with an online fuzzy tuner demonstrated a superior cutting quality, generating a smaller roundness and a reduced heat-affected zone (HAZ) through the in situ tuning of control parameters. Particularly, the HAZ width was significantly smaller than that reported in a previous study which used fuzzy gain scheduling for temperature control. Moreover, the cutting time was diminished by optimally adjusting the cutting speed using PID-type FLC with an online fuzzy tuner. Therefore, the accumulated heat in the steel sheet, particularly under high laser pulse frequencies, was effectively reduced, making it suitable for industrial applications.
Predicting Intensive Care Unit Patients’ Discharge Date with a Hybrid Machine Learning Model That Combines Length of Stay and Days to Discharge
Authors: Cuadrado David , Valls Aida , Riaño David
Published: November 2023

Background: Accurate planning of the duration of stays at intensive care units is of utmost importance for resource planning. Currently, the discharge date used for resource management is calculated only at admission time and is called length of stay. However, the evolution of the treatment may be different from one patient to another, so a recalculation of the date of discharge should be performed, called days to discharge. The prediction of days to discharge during the stay at the ICU with statistical and data analysis methods has been poorly studied with low-quality results. This study aims to improve the prediction of the discharge date for any patient in intensive care units using artificial intelligence techniques. Methods: The paper proposes a hybrid method based on group-conditioned models obtained with machine learning techniques. Patients are grouped into three clusters based on an initial length of stay estimation. On each group (grouped by first days of stay), we calculate the group-conditioned length of stay value to know the predicted date of discharge, then, after a given number of days, another group-conditioned prediction model must be used to calculate the days to discharge in order to obtain a more accurate prediction of the number of remaining days. The study is performed with the eICU database, a public dataset of USA patients admitted to intensive care units between 2014 and 2015. Three machine learning methods (i.e., Random Forest, XGBoost, and lightGBM) are used to generate length of stay and days to discharge predictive models for each group. Results: Random Forest is the algorithm that obtains the best days to discharge predictors. The proposed hybrid method achieves a root mean square error (RMSE) and mean average error (MAE) below one day on the eICU dataset for the last six days of stay. Conclusions: Machine learning models improve quality of predictions for the days to discharge and length of stay for intensive care unit patients. The results demonstrate that the hybrid model, based on Random Forest, improves the accuracy for predicting length of stay at the start and days to discharge at the end of the intensive care unit stay. Implementing these prediction models may help in the accurate estimation of bed occupancy at intensive care units, thus improving the planning for these limited and critical health-care resources.
REKP: Refined External Knowledge into Prompt-Tuning for Few-Shot Text Classification
Authors: Dang Yuzhuo , Chen Weijie , Zhang Xin , Chen Honghui
Published: November 2023

Text classification is a machine learning technique employed to assign a given text to predefined categories, facilitating the automatic analysis and processing of textual data. However, an important problem is that the number of new text categories is growing faster than that of human annotation data, which makes many new categories of text data lack a lot of annotation data. As a result, the conventional deep neural network is forced to over-fit, which damages the application in the real world. As a solution to this problem, academics recommend addressing data scarcity through few-shot learning. One of the efficient methods is prompt-tuning, which transforms the input text into a mask prediction problem featuring [MASK]. By utilizing descriptors, the model maps output words to labels, enabling accurate prediction. Nevertheless, the previous prompt-based adaption approaches often relied on manually produced verbalizers or a single label to represent the entire label vocabulary, which makes the mapping granularity low, resulting in words not being accurately mapped to their label. To address these issues, we propose to enhance the verbalizer and construct the refined external knowledge into a prompt-tuning (REKP) model. We employ the external knowledge bases to increase the mapping space of tagged terms and design three refinement methods to remove noise data. We conduct comprehensive experiments on four benchmark datasets, namely AG’s News, Yahoo, IMDB, and Amazon. The results demonstrate that REKP can outperform the state-of-the-art baselines in terms of Micro-F1 on knowledge-enhanced text classification. In addition, we conduct an ablation study to ascertain the functionality of each module in our model, revealing that the refinement module significantly contributes to enhancing classification accuracy.
Unsupervised Classification under Uncertainty: The Distance-Based Algorithm
Authors: Ghanaiem Alaa , Kagan Evgeny , Kumar Parteek , Raviv Tal , Glynn Peter , Ben-Gal Irad
Published: November 2023

This paper presents a method for unsupervised classification of entities by a group of agents with unknown domains and levels of expertise. In contrast to the existing methods based on majority voting (“wisdom of the crowd”) and their extensions by expectation-maximization procedures, the suggested method first determines the levels of the agents’ expertise and then weights their opinions by their expertise level. In particular, we assume that agents will have relatively closer classifications in their field of expertise. Therefore, the expert agents are recognized by using a weighted Hamming distance between their classifications, and then the final classification of the group is determined from the agents’ classifications by expectation-maximization techniques, with preference to the recognized experts. The algorithm was verified and tested on simulated and real-world datasets and benchmarked against known existing algorithms. We show that such a method reduces incorrect classifications and effectively solves the problem of unsupervised collaborative classification under uncertainty, while outperforming other known methods.
Well-Posedness and Stability Results for Lord Shulman Swelling Porous Thermo-Elastic Soils with Microtemperature and Distributed Delay
Authors: Choucha Abdelbaki , Boulaaras Salah , Jan Rashid , AbaOud Mohammed , Alrajhi Rowaida
Published: November 2023

The Lord Shulman swelling porous thermo-elastic soil system with the effects of microtemperature, temperatures and distributed delay terms is considered in this study. The well-posedness result is established by the Lumer–Phillips corollary applied to the Hille–Yosida theorem. The exponential stability result is proven by the energy method under suitable assumptions.