bluuebunny/crossref_metadata_2025 huggingface dataset:
Demand for Information for Wildland Fire Management
Authors: Frisvold George B. , Zhang Ning , Crimmins Michael A. , Ferguson Daniel , Maxwell Charles
Published: November 2024

Significant resources have been devoted to increasing the supply of data and information products for wildland fire management. There has been comparatively less emphasis on understanding the demand for these products. There are large differences in the number of information sources that fire managers use in decision making. We developed a value-of-information model for wildland fire managers to formulate hypotheses about what factors drive these differences. Data from a comprehensive internet survey targeting a well-defined population of the Southwest wildland fire managers are used to test these hypotheses. Results are generally consistent with hypotheses generated from the value-of-information model. Multiple regression results suggest information use increases with the number of decisions that managers make and is greater during fire season than before. Information use is affected by a manager’s level of education, age, experience, job type, the agencies they work for, and the multi-agency dispatch centers where they work. Agency and dispatch center effects explain more of the variation in information use than differences in the respondents’ personal characteristics. To better understand fire manager demand for information, future research could explore in more detail what specific attributes of agencies and dispatch centers affect use of information for wildland fire management.
Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects
Authors: Alejo Sánchez Daniellys , Schalm Olivier , Álvarez Cruz Arianna , Hernández Rodríguez Erik , Martínez Laguardia Alain , Kairuz Cabrera David , Morales Pérez Mayra C.
Published: November 2024

The study investigates methods to enhance the reliability of NO2 monitoring using low-cost electrochemical sensors to measure gaseous pollutants in air by addressing the impacts of temperature and relative humidity. The temperature within a plastic container was controlled using an internal mica heater, an external hot air blower, or cooling packs, while relative humidity was adjusted using glycerine solutions. Findings indicated that the auxiliary electrode signal is susceptible to temperature and moderately affected by relative humidity. In contrast, the working electrode signal is less affected by temperature and relative humidity; however, adjustments are still required to determine gas concentrations accurately. Tests involving on/off cycles showed that the auxiliary electrode signal experiences exponential decay before stabilizing, requiring the exclusion of initial readings during monitoring activities. Additionally, calibration experiments in zero air allowed the determination of the compensation factor nT across different temperatures and humidity levels. These results highlight the importance of compensating for temperature and humidity effects to improve the accuracy and reliability of NO2 measurements using low-cost electrochemical sensors. This refinement makes the calibration applicable across a broader range of environmental conditions. However, the experiments also show a lack of repeatability in the zero air calibration.
SAL Method Applied in Grid Forecasting Product Verification with Three-Source Fusion Product
Authors: Su Debin , Zhong Jinhua , Xu Yunong , Lv Linghui , Liu Honglan , Fan Xingang , Han Lin , Wang Fuzeng
Published: November 2024

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved.
A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework
Authors: Khattak Afaq , Zhang Jianping , Chan Pak-wai , Chen Feng , H. Almaliki Abdulrazak
Published: November 2024

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide.
Spatiotemporal Gait Asymmetries Remain Unaffected by Increased Load Carriage in Professional Intervention Police Officers
Authors: Rožac Davor , Kasović Mario , Knjaz Damir
Published: November 2024

Background: Although evidence indicates that load carriage may have an influence on walking patterns, the specific impacts of progressively increased loads on spatial and temporal gait asymmetries remain underexplored. Therefore, the primary aim of this study was to examine whether an increased load carriage had an effect on spatiotemporal gait asymmetries among intervention police officers. Methods: For the purpose of this study, 96 male intervention police officers were recruited and assessed under four load conditions: (i) “No load”, (ii) “a 5 kg load”, (iii) “a 25 kg load”, and (iv) “a 45 kg load”. Spatial and temporal gait parameters were measured using a pedobarographic platform (Zebris FDM). The spatial and temporal gait parameters, along with the ground reaction forces beneath different foot regions, were examined. The gait asymmetry for each parameter was calculated using the formula (xright − xleft)/0.5 × (xright + xleft)*100%, where “x” represents the numerical value of each parameter for the left and right sides of the body. Results: The findings indicated no statistically significant differences in the spatiotemporal parameters, nor ground reaction force gait asymmetries between the left and right foot, during walking under a progressively increased load carriage. Additionally, the parameter values for both the left and right sides of the body remained consistent, with a high intercorrelation observed across all of the loading conditions. The gait speed and ground reaction forces, which served as covariates, did not significantly change the spatiotemporal gait asymmetries. Conclusions: In summary, this study demonstrates that an increased load carriage did not lead to a progressive rise in spatiotemporal gait asymmetries in professional intervention police officers. However, further examination using an advanced 3-D gait analysis and an assessment of physiological patterns and adaptations is recommended to identify and confirm the key factors influencing gait asymmetry.
Upper Crossed Syndrome and Scapulae Upper-Trapping: A Mesotherapy Protocol in Cervicoscapulobrachial Pain—The 8:1 Block
Authors: Pires Luyddy , Santos Napoliane , Lana João Vitor , de Macedo Alex Pontes , Costa Fábio Ramos , Azzini Gabriel Ohana Marques , Mosaner Tomas , de Moraes Ferreira Jorge Daniel , Santos Gabriel Silva , Medeiros Arthur , Pereira José Alexandre Reale , Lana José Fábio
Published: November 2024

Upper Crossed Syndrome (UCS), described by Vladimir Janda, is characterized by postural changes involving the cervical spine and trunk, leading to biomechanical limitations and cervicoscapulobrachial pain. This study proposes a mesotherapy protocol, termed the 8:1 block, to address cervicoscapulobrachialgia by targeting the scapulae and associated musculature. The scapula, central to shoulder girdle kinematics, often exhibits dyskinesis and muscular imbalances, notably the pattern referred to as scapular upper trapping (SUT). SUT involves scapular elevation, medial rotation, and shoulder protraction, contributing to cervicobrachial pain. The protocol includes a comprehensive assessment of muscle tone changes and biomechanical considerations, highlighting the importance of the scapula in upper limb movement and posture. Key anatomical changes involve tightened upper trapezius, levator scapulae, and pectoralis minor muscles, with weakened middle trapezius and serratus anterior. The mesotherapy approach targets these imbalances through specific injection points to alleviate muscle tension and correct postural deviations. Case studies from our clinic demonstrate the protocol’s effectiveness in reducing pain and restoring scapular biomechanics. Patients reported significant improvements in pain relief and functional outcomes, underscoring the clinical utility of the 8:1 block in treating cervicoscapulobrachialgia. This protocol offers a feasible, cost-effective intervention that enhances the efficacy of traditional therapeutic exercises by addressing underlying muscular and biomechanical dysfunctions. In conclusion, the 8:1 block mesotherapy protocol provides a novel approach to managing cervicoscapulobrachial pain by focusing on scapular biomechanics and muscle tension. Further studies are needed to validate these findings and refine the protocol for broader clinical application.
Unlocking the Potentials of Large Language Models in Orthodontics: A Scoping Review
Authors: Zheng Jie , Ding Xiaoqian , Pu Jingya Jane , Chung Sze Man , Ai Qi Yong H. , Hung Kuo Feng , Shan Zhiyi
Published: November 2024

(1) Background: In recent years, large language models (LLMs) such as ChatGPT have gained significant attention in various fields, including dentistry. This scoping review aims to examine the current applications and explore potential uses of LLMs in the orthodontic domain, shedding light on how they might improve dental healthcare. (2) Methods: We carried out a comprehensive search in five electronic databases, namely PubMed, Scopus, Embase, ProQuest and Web of Science. Two authors independently screened articles and performed data extraction according to the eligibility criteria, following the PRISMA-ScR guideline. The main findings from the included articles were synthesized and analyzed in a narrative way. (3) Results: A total of 706 articles were searched, and 12 papers were eventually included. The applications of LLMs include improving diagnostic and treatment efficiency in orthodontics as well as enhancing communication with patients. (4) Conclusions: There is emerging research in countries worldwide on the use of LLMs in orthodontics, suggesting an upward trend in their acceptance within this field. However, the potential application of LLMs remains in its early stage, with a noticeable lack of extensive studies and tailored products to address specific clinical needs.
Ustekinumab Biosimilars
Authors: Carmona-Rocha Elena , Puig Lluís
Published: November 2024

Ustekinumab is a fully human IgG1k monoclonal antibody that binds with high affinity and specificity to the p40 subunit of interleukins (IL-) 12 and 23, inhibiting their activity by preventing binding to their receptors. The European extension of the patent (Supplementary Protection Certificate) of ustekinumab expired on 20 July 2024. Biosimilar alternatives to ustekinumab are now an additional option for treating patients. The efficacy data for this drug in moderate-to-severe psoriasis obtained both from clinical trials and indirect comparisons through meta-analyses, are superior to those of etanercept and adalimumab, and its safety profile is more favorable than that of tumor necrosis factor (TNF) inhibitors. Several ustekinumab biosimilars have already been approved by regulatory agencies: between October 2023 and October 2024, Wezlana® (Amgen ABP 654), Uzpruvo® (Alvotech AVT04) and Pyzchiva® (Samsung/Bioepis SB17) have been approved by both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). SteQeyma® (Celltrion Healthcare CT-P43) was approved by the EMA in August 2024. Otulfi® (Fresenius Kabi/Formycon) was approved by the FDA in October 2024. Several other potential biosimilar candidates are under development, including BAT2206 (Bio-Thera), DMB-3115 (Dong-A ST), QX001S (Qyuns Therapeutic), BFI-751 (BioFactura), NeuLara (Neuclone), ONS3040 (Oncobiologics), and BOW090 (Epirus Biopharmaceuticals). In most cases, these monoclonal antibodies are expressed in cell lines (e.g., Chinese Hamster Ovary, CHO) different from those used for the originator (Sp2/0 spleen cell murine myeloma); of note, the cell line of origin is not a requirement for biosimilarity in the totality-of-evidence comparison exercise and may facilitate the production and reduce the immunogenicity of biosimilars originated in CHO cultures. This narrative review summarizes the available data on characteristics of the full comparability exercises and comparative clinical trials of these drugs.
Advancements in Multiple Myeloma Research: High-Throughput Sequencing Technologies, Omics, and the Role of Artificial Intelligence
Authors: Gutiérrez-González Alejandra , Del Hierro Irene , Cariaga-Martínez Ariel Ernesto
Published: November 2024

Multiple myeloma is a complex and challenging type of blood cancer that affects plasma cells in the bone marrow. In recent years, the development of advanced research techniques, such as omics approaches—which involve studying large sets of biological data like genes and proteins—and high-throughput sequencing technologies, has allowed researchers to analyze vast amounts of genetic information rapidly and gain new insights into the disease. Additionally, the advent of artificial intelligence tools has accelerated data analysis, enabling more accurate predictions and improved treatment strategies. This review aims to highlight recent research advances in multiple myeloma made possible by these novel techniques and to provide guidance for researchers seeking effective approaches in this field.
Possible Potentiating Effects of Combined Administration of Alcohol, Caffeine, and Nicotine on In Vivo Dopamine Release in Addiction-Related Circuits Within the CNS of Rats
Authors: Costas-Ferreira Carmen , Barreiro-Chapela Martiño , Durán Rafael , Faro Lilian R. Ferreira
Published: November 2024

Background: Studies that assess the effects of the interaction of psychoactive substances on dopamine release, the key neurotransmitter in the neurochemical and behavioral effects related to drug consumption, are crucial to understand both their roles and the dysfunctions they produce in the central nervous system. Objective: We evaluated the effects of individual and combined administration of the three most widely consumed psychoactive substances in the world, ethanol, caffeine, and nicotine, on dopaminergic neurotransmission in three brain regions of rats related to addiction: the prefrontal cortex (PFC), the nucleus accumbens (NAcc), and the dorsal striatum. Methods: The dopamine levels were measured in vivo by cerebral microdialysis associated with HPLC-ED. Results: We observed that local administration of a single concentration of caffeine (5 mM) or nicotine (5 mM) significantly increased the dopamine levels in all three areas studied, while ethanol (300 mM) increased them in the NAcc and striatum. Perfusion of nicotine + caffeine produced a synergistic effect in both the NAcc and striatum, with increases in the in vivo dopamine release greater than the sum of the effects of both substances. When administering the combination of nicotine + caffeine + ethanol, we observed an additive effect in the NAcc, while in the PFC we observed a synergistic effect. Conclusions: Our results support the stimulating effects of caffeine, nicotine, and ethanol on the brain reward system. In addition, we also observed that the administration of different mixtures of these substances produces synergistic and additive effects on the release of dopamine in the mesocortical and nigrostriatal systems.
Role of the Egr2 Promoter Antisense RNA in Modulating the Schwann Cell Chromatin Landscape
Authors: Martinez Moreno Margot , Karambizi David , Hwang Hyeyeon , Fregoso Kristen , Michles Madison J. , Fajardo Eduardo , Fiser Andras , Tapinos Nikos
Published: November 2024

Background: Schwann cells (SCs) and their plasticity contribute to the peripheral nervous system’s capacity for nerve regeneration after injury. The Egr2/Krox20 promoter antisense RNA (Egr2-AS) recruits chromatin remodeling complexes to inhibit Egr2 transcription following peripheral nerve injury. Methods: RNA-seq and ATAC-seq were performed on control cells, Lenti-GFP-transduced cells, and cells overexpressing Egr2-AS (Lenti-AS). Egr2 AS-RNA was cloned into the pLVX-DsRed-Express2-N1 lentiviral expression vector (Clontech, Mountain View, CA, USA), and the levels of AS-RNA expression were determined. Ezh2 and Wdr5 were immunoprecipitated from rat SCs and RT-qPCR was performed against AS-Egr2 RNA. ChIP followed by DNA purification columns was used to perform qPCR for relevant promoters. Hi-C, HiC-DC+, R, Bioconductor, and TOBIAS were used for significant and differential loop analysis, identifications of COREs and CORE-promotor loops, comparisons of TF activity at promoter sites, and identification of site-specific TF footprints. OnTAD was used to detect TADs, and Juicer was used to identify A/B compartments. Results: Here we show that a Neuregulin-ErbB2/3 signaling axis mediates binding of the Egr2-AS to YY1Ser184 and regulates its expression. Egr2-AS modulates the chromatin accessibility of Schwann cells and interacts with two distinct histone modification complexes. It binds to EZH2 and WDR5 and enables targeting of H3K27me3 and H3K4me3 to promoters of Egr2 and C-JUN, respectively. Expression of the Egr2-AS results in reorganization of the global chromatin landscape and quantitative changes in the loop formation and contact frequency at domain boundaries exhibiting enrichment for AP-1 genes. In addition, the Egr2-AS induces changes in the hierarchical TADs and increases transcription factor binding scores on an inter-TAD loop between a super-enhancer regulatory hub and the promoter of mTOR. Conclusions: Our results show that Neuregulin-ErbB2/3-YY1 regulates the expression of Egr2-AS, which mediates remodeling of the chromatin landscape in Schwann cells.
Methods for Identifying Epilepsy Surgery Targets Using Invasive EEG: A Systematic Review
Authors: Ivankovic Karla , Principe Alessandro , Zucca Riccardo , Dierssen Mara , Rocamora Rodrigo
Published: November 2024

Background: The pre-surgical evaluation for drug-resistant epilepsy achieves seizure freedom in only 50–60% of patients. Efforts to identify quantitative intracranial EEG (qEEG) biomarkers of epileptogenicity are needed. This review summarizes and evaluates the design of qEEG studies, discusses barriers to biomarker adoption, and proposes refinements of qEEG study protocols. Methods: We included exploratory and prediction prognostic studies from MEDLINE and Scopus published between 2017 and 2023 that investigated qEEG markers for identifying the epileptogenic network as the surgical target. Cohort parameters, ground truth references, and analytical approaches were extracted. Results: Out of 1789 search results, 128 studies were included. The study designs were highly heterogeneous. Half of the studies included a non-consecutive cohort, with sample sizes ranging from 2 to 166 patients (median of 16). The most common minimum follow-up was one year, and the seizure onset zone was the most common ground truth. Prediction studies were heterogeneous in their analytical approaches, and only 25 studies validated the marker through post-surgical outcome prediction. Outcome prediction performance decreased in larger cohorts. Conversely, longer follow-up periods correlated with higher prediction accuracy, and connectivity-based approaches yielded better predictions. The data and code were available in only 9% of studies. Conclusions: To enhance the validation qEEG markers, we propose standardizing study designs to resemble clinical trials. This includes using a consecutive cohort with long-term follow-up, validating against surgical resection as ground truth, and evaluating markers through post-surgical outcome prediction. These considerations would improve the reliability and clinical adoption of qEEG markers.
Rituximab Administration to Treat Nephrotic Syndrome in Children: 2-Year Follow-Up
Authors: Ivanov Dmytro , Weber Lutz T. , Levtchenko Elena , Vakulenko Liudmyla , Ivanova Mariia , Zavalna Iryna , Lagodych Yelizaveta , Boiko Ninel
Published: November 2024

Background: Steroid-sensitive nephrotic syndrome (SSNS) and steroid-resistant nephrotic syndrome (SRNS) significantly affect children’s quality of life. There are frequent relapses in SSNS and progression in SRNS. IPNA guidelines suggest that monoclonal antibodies like rituximab (RTX) are promising treatments. Objective: This study aims to evaluate the long-term efficacy and safety of rituximab administration in children with SSNS, encompassing FRNS and SDNS, and SRNS over a two-year follow-up period, facilitating individualized management. Methods: We conducted an open-label, multicenter, randomized, and patient-oriented study (RICHNESS), involving children aged 3–18 with SRNS (18) and SSNS (11) undergoing 2 years continuous RTX therapy. The primary outcome was complete/partial remission (CR/PR), as defined by IPNA/KDIGO guidelines, at 6, 12, 18, and 24 months on RTX; secondary outcomes included adverse events. Key endpoints included the estimated glomerular filtration rate (eGFR), the albumin-to-creatinine ratio (ACR), CD20 levels, IgG levels, and the incidence of infections. Kidney biopsies were performed in 94% of SRNS patients. RTX was administered every 6–9 months, depending on CD20 levels, IgG levels, and the presence of infections. The eGFR and ACR were assessed every 6 months. Results: Some 31 children were selected for RTX treatment. Overall, 2 experienced severe allergic reactions, leading to their exclusion from the final analysis of 29 children. In the SSNS group, all children achieved and maintained complete remission within 2 years. Remission rates in the SRNS group ranged from 39% (RR 0.78; 95% CI: 16.4–61.4%, NNT 9) at the 6th month to 72% (RR 1.44; 95% CI: 51.5–92.9%) over the 2-year follow-up period due to continuous RTX therapy. The median duration of RTX use was 26.1 months, with a median cumulative dose of 1820 mg/m2. Adverse reactions and complications were presented by mild infusion-related reactions in 3 children (10.3%), severe allergic reactions in 2 children (6.2%), hypogammaglobulinemia in 7 children (24%), infections in 3 children (10.3%), severe destructive pneumonia in 1 child, recurrent respiratory infections in 2 children, and neutropenia in 1 child (3.44%). Conclusions: RTX was tolerated well, and proved highly effective as a steroid-sparing agent, offering potential in terms of stopping relapses and minimizing steroid-related side effects. It also demonstrated efficacy in slowing progression in SRNS, indicating potential for use in ACR reduction and renal function restoration, but requires careful use given potential severe allergic reactions and infectious complications. Further studies should focus on long-term cost-effectiveness and deferred side effects.
Tea Bud Detection Model in a Real Picking Environment Based on an Improved YOLOv5
Authors: Li Hongfei , Kong Min , Shi Yun
Published: November 2024

The detection of tea bud targets is the foundation of automated picking of premium tea. This article proposes a high-performance tea bud detection model to address issues such as complex environments, small target tea buds, and blurry device focus in tea bud detection. During the spring tea-picking stage, we collect tea bud images from mountainous tea gardens and annotate them. YOLOv5 tea is an improvement based on YOLOv5, which uses the efficient Simplified Spatial Pyramid Pooling Fast (SimSPPF) in the backbone for easy deployment on tea bud-picking equipment. The neck network adopts the Bidirectional Feature Pyramid Network (BiFPN) structure. It fully integrates deep and shallow feature information, achieving the effect of fusing features at different scales and improving the detection accuracy of focused fuzzy tea buds. It replaces the independent CBS convolution module in traditional neck networks with Omni-Dimensional Dynamic Convolution (ODConv), processing different weights from spatial size, input channel, output channel, and convolution kernel to improve the detection of small targets and occluded tea buds. The experimental results show that the improved model has improved precision, recall, and mean average precision by 4.4%, 2.3%, and 3.2%, respectively, compared to the initial model, and the inference speed of the model has also been improved. This study has theoretical and practical significance for tea bud harvesting in complex environments.
Animal-Morphing Bio-Inspired Mechatronic Systems: Research Framework in Robot Design to Enhance Interplanetary Exploration on the Moon
Authors: Cornejo José , García Cena Cecilia E. , Baca José
Published: November 2024

Over the past 50 years, the space race has potentially grown due to the development of sophisticated mechatronic systems. One of the most important is the bio-inspired mobile-planetary robots, actually for which there is no reported one that currently works physically on the Moon. Nonetheless, significant progress has been made to design biomimetic systems based on animal morphology adapted to sand (granular material) to test them in analog planetary environments, such as regolith simulants. Biomimetics and bio-inspired attributes contribute significantly to advancements across various industries by incorporating features from biological organisms, including autonomy, intelligence, adaptability, energy efficiency, self-repair, robustness, lightweight construction, and digging capabilities-all crucial for space systems. This study includes a scoping review, as of July 2024, focused on the design of animal-inspired robotic hardware for planetary exploration, supported by a bibliometric analysis of 482 papers indexed in Scopus. It also involves the classification and comparison of limbed and limbless animal-inspired robotic systems adapted for movement in soil and sand (locomotion methods such as grabbing-pushing, wriggling, undulating, and rolling) where the most published robots are inspired by worms, moles, snakes, lizards, crabs, and spiders. As a result of this research, this work presents a pioneering methodology for designing bio-inspired robots, justifying the application of biological morphologies for subsurface or surface lunar exploration. By highlighting the technical features of actuators, sensors, and mechanisms, this approach demonstrates the potential for advancing space robotics, by designing biomechatronic systems that mimic animal characteristics.
A Multi-Robot Task Allocation Method Based on the Synergy of the K-Means++ Algorithm and the Particle Swarm Algorithm
Authors: Yuan Youdong , Yang Ping , Jiang Hanbing , Shi Tiange
Published: November 2024

Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after the task assignment, which makes the cooperative operation of multiple robots inefficient, this paper puts forward a multi-robot task assignment method based on the synergy of the K-means++ algorithm and the particle swarm optimization (PSO) algorithm. According to the processing capability of the robots, the K-means++ algorithm that limits the maximum number of clusters is used to cluster the target points of the task. The clustering results are assigned to the multi-robot system using the PSO algorithm based on the distances between the robots and the centers of the clusters, which divides the multi-robot task assignment problem into a multiple traveling salesmen problem. Then, the PSO algorithm is used to optimize the ordering of the task sets in each cluster for the multiple traveling salesmen problem. An experimental verification platform is established by building a simulation and physical experiment platform utilizing the Robot Operating System (ROS). The findings indicate that the proposed algorithm outperforms both the clustering-based market auction algorithm and the non-clustering particle swarm algorithm, enhancing the efficiency of collaborative operations among multiple robots.
EchoPT: A Pretrained Transformer Architecture That Predicts 2D In-Air Sonar Images for Mobile Robotics
Authors: Steckel Jan , Jansen Wouter , Huebel Nico
Published: November 2024

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.
Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control
Authors: AlQahtani Nouf Jubran , Al-Naib Ibraheem , Ateeq Ijlal Shahrukh , Althobaiti Murad
Published: November 2024

The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain–computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.
Chemical Heating for Minimally Instrumented Point-of-Care (POC) Molecular Diagnostics
Authors: Mauk Michael G. , Ansah Felix , El-Tholoth Mohamed
Published: November 2024

The minimal instrumentation of portable medical diagnostic devices for point-of-care applications is facilitated by using chemical heating in place of temperature-regulated electrical heaters. The main applications are for isothermal nucleic acid amplification tests (NAATs) and other enzymatic assays that require elevated, controlled temperatures. In the most common implementation, heat is generated by the exothermic reaction of a metal (e.g., magnesium, calcium, or lithium) with water or air, buffered by a phase-change material that maintains a near-constant temperature to heat the assay reactions. The ability to incubate NAATs electricity-free and to further to detect amplification with minimal instrumentation opens the door for fully disposable, inexpensive molecular diagnostic devices that can be used for pathogen detection as needed in resource-limited areas and during natural disasters, wars, and civil disturbances when access to electricity may be interrupted. Several design approaches are reviewed, including more elaborate schemes for multiple stages of incubation at different temperatures.
Dielectric Hybrid Optimization Model Based on Crack Damage in Semi-Rigid Base Course
Authors: Huang Zhiyong , Xu Guoyuan , Yu Huayang , Xiong Xuetang , Zang Bo
Published: November 2024

To accurately predict the relative permittivity of cement-stabilized base materials, a study on the dielectric mixing model for cracked base materials was conducted. Based on the electromagnetic mixing theory of multiphase composites, a comprehensive dielectric mixing model of cement-stabilized base materials was derived. The volume ratios and relative permittivity values of the specimen constituents in different cracking states of the cement-stabilized base were determined using industrial CT and a Percometer relative permittivity meter, with comprehensive consideration given to the effects of different initial porosities and crack widths on the dielectric properties. Based on the volumetric and dielectric properties of the base material specimens in both intact and cracked states, as well as the error analysis between the predicted and measured values of the relative permittivity constant, the u-optimal solution of the dielectric mixing model for cement-stabilized base material was determined to be 1. Consequently, an optimization dielectric mixing model for semi-rigid base course materials in a cracked state was developed. The optimization model proposed is suitable for predicting the dielectric properties of cement-stabilized base material with crack widths generally greater than 3 mm during the service life of semi-rigid base course in engineering practice.
Experimental Measurements of Explosion Effects Propagating in the Real Geological Environment—Correlation with Small-Scale Model
Authors: Papán Daniel , Brozová Emma , Papánová Zuzana
Published: November 2024

This research focuses on comparing small-scale and full-scale measurements of wave propagation from explosions by using scaling relationships to find significant correlations between the two. The study investigates how seismic waves generated by explosions behave in the geological environment. The research covers various aspects such as the development of the model, the explosive materials used, measurement methods, evaluation techniques, and relevant software. A scientific approach based on the principle of backward Fourier transform was used to process and evaluate the data, which helps to filter the frequencies. One of the important calculations discussed is the determination of the attenuation coefficient, which helps to describe how waves attenuate as they pass through a material. The research also deals with dynamic scaling, using the dynamic exponent as a scaling factor to provide a better understanding of the behavior of waves at different scales. By comparing real in situ data with results from small-scale models, the study provides a robust framework for predicting the effects of explosions in complex geological environments. The research results show a high correlation coherence of the statistical data files of up to 4.1%. For dynamic tasks and model scaling, an important result can be pointed out, namely the approximately fourfold decrease in the exponents of the dependence on the distance from the excitation source and the amplitudes between P-waves (0.4316) and R-waves (0.1219). Conclusions are targeted at the possibility of correlating three types of results: small-scale simulations, numerical simulations, and a real full-scale experiment.
Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning
Authors: Gao Pengfei , Song Yuanyuan , Wang Jian , Yang Zhiyong , Wang Kai , Yuan Yongyu
Published: November 2024

The chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (YN), the replacement ratio of recycled coarse aggregates (r), the bending load level (L), the carbonation time (t) and temperature (T) of RAC, and the replacement ratio of carbonated recycled fine aggregates (f), were considered to conduct a chloride penetration test on RAC. Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. The results showed that the predicted values of all four models were in good agreement with the experimental values, and the XGBoost model had the best prediction performance on the testing set. Based on the XGBoost model, the LIME method was adopted to solve the interpretability problem in the prediction process. The importance ranking of IFs on the electric flux was r > t > f > T > L > YN. A graphical user interface (GUI) was developed based on Python 3.8 software to facilitate the use of machine learning models for the chloride ion permeability resistance of RAC. The research results can provide an accurate prediction of the electric flux of RAC.
Improved Building Information Modeling Based Method for Prioritizing Clash Detection in the Building Construction Design Phase
Authors: Bitaraf Iman , Salimpour Ali , Elmi Pedram , Shirzadi Javid Ali Akbar
Published: November 2024

The rising complexity of construction projects and the industry’s commitment to sustainable practices have driven the extensive adoption of Building Information Modeling (BIM) technology. A core function of BIM is the early identification and resolution of clashes during the design phase, which serves to mitigate costly rework and delays in the construction process. This study presents an advanced method for classifying and prioritizing hard clashes between structural components and mechanical, electrical, and plumbing (MEP) systems. Employing the Best-Worst Method (BWM), this research assigned specific weights to structural and MEP elements based on expert evaluations. Six parameters were incorporated into this prioritization framework: the weights determined by the BWM, outputs from Navisworks software (v2021), the ratio of MEP volume to floor volume, the functional purpose of each floor, and the number of adjacent elements. A custom-developed plugin for Autodesk Navisworks integrated these parameters, enabling real-time automated clash prioritization. Clashes were ranked by criticality through a calculation involving the six parameters, which enhanced the efficiency of clash detection by optimizing time and cost considerations during the design phase. Case study results indicate that beams and columns represent the most critical structural elements, while ducts are identified as the most significant MEP elements. The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation.
“It Was a Downward Spiral”: A Qualitative Study of Young Adult Cancer Survivors’ Experiences with Cognitive and Mental Health
Authors: Tometich Danielle B. , Hersh Christina , Maconi Melinda L. , Fulton Hayden J. , Martinez Tyson Dinorah , Zambrano Kellie , Hasan Syed , Welniak Taylor , Rodriguez Yvelise , Bryant Crystal , Gudenkauf Lisa M. , Li Xiaoyin , Reed Damon R. , Oswald Laura B. , Galligan Andrew , Small Brent J. , Jim Heather S. L.
Published: November 2024

Background/Objectives: Cancer-related cognitive impairment (CRCI) is common and distressing for young adult (YA) cancer survivors; however, subjective CRCI is often attributed to psychological distress rather than true cognitive change. Thus, we aimed to generate hypotheses regarding potential common biobehavioral mechanisms of CRCI and psychological distress. Methods: We conducted semi-structured interviews with YA cancer survivors in peak years for frontal neurocognitive development (ages 18–30), and we asked them to describe their experiences with cognitive and mental health changes during and after treatment for their cancer. An applied thematic analysis was used to identify themes. Results: The participants were 20 YA cancer survivors who were, on average, 23 years old at diagnosis and 26 years old when interviewed. Five themes emerged regarding the connection between cognitive and mental health: depressed mood after diagnosis that decreased mental engagement, cancer-related anxiety consumed cognitive resources during treatment, a bidirectional “downward spiral” during treatment, frustration with forgetfulness, and self-criticism about long-term cognitive limitations. Additionally, participants described receiving minimal information about evidence-based strategies for managing CRCI. Conclusions: Future research is needed to examine common neurological mechanisms among symptoms of depression, anxiety, and CRCI and to address gaps in survivorship care for YA survivors with CRCI. Cognitive–behavioral interventions may address both cognitive and mental health.
Comparing the Efficacies of Electrospun ZnO and TiO2 Nanofibrous Interlayers for Electron Transport in Perovskite Solar Cells
Authors: Zafar Abdullah , Iqbal Waqar , Khan Shahzaib , Alhodaib Aiyeshah , Fatima Mahvish
Published: November 2024

ZnO and TiO2 are both well-known electron transport materials. Their comparison of performance is considered advantageous and novel. Therefore, a viable electrospinning route was considered for the development of highly polycrystalline TiO2 and ZnO nanofibers as an electron transport material (ETM) for perovskite solar cells. The materials were well-characterized in terms of different analytical techniques. The X-ray diffraction detected polycrystalline structural properties corresponding to TiO2 and ZnO. Morphological analysis by scanning electron microscopy revealed that the nanofibers are long, uniform, and polycrystalline, having a diameter in the nanometer range. Optoelectronic properties showed that TiO2 and ZnO exhibit absorption values in the ultraviolet and visible ranges, and band gap values for TiO2 and ZnO were 3.3 and 3.2 eV, respectively. TiO2 bandgap and semiconductor nature were more compatible with Electron Transport Layer (ETL) compared to ZnO. Electrical studies revealed that TiO2 nanofibers have enhanced values of conductivity and sheet carrier mobility compared to ZnO nanofibers. Therefore, higher photovoltaic conversion efficiency was achieved for TiO2 nanofibers (10.4%) compared to ZnO (8.5%).
Pediatric Cranial Vault Lesions: A Tailored Approach According to Bony Involvement
Authors: Barbotti Arianna , Vetrano Ignazio G. , Casali Cecilia , Galbiati Tommaso F. , Mariani Sabrina , Porto Edoardo , Erbetta Alessandra , Chiaravalli Stefano , Valentini Laura G.
Published: November 2024

Background: Cranial vault lesions are common in children, with dermoid and epidermoid cysts being the most frequent. Management is debated due to their slow growth, but early resection can prevent complications and provide a definitive histological diagnosis, which is sometimes linked to systemic diseases. Methods: A retrospective study of children treated surgically for cranial vault tumors from January 2011 to April 2023 was conducted. The data collected included age, gender, symptoms, comorbidities, lesion location, radiological features, surgical techniques, histopathology, and recurrence rates. Results: Eighty-eight children (mean age: 3.5 years, mean tumor size: 1.21 cm) underwent surgery. The most common locations were the frontal and occipital bones. The main diagnoses were dermoid cysts, myofibroma, and Langerhans cell histiocytosis. Gross total resection was achieved in 64 cases with simple skin incisions. In 13 cases, small cranioplasties with bone cement were used. Craniotomy and cranioplasty with autologous bone grafting were performed for 11 patients with lesions larger than 2 cm and full skull thickness erosion. Conclusions: Early resection is recommended for complete removal with minimally invasive surgery and to ensure histological diagnosis. For lesions larger than 2 cm with full skull erosion, cranioplasty with autologous bone is the preferred technique.