bluuebunny/crossref_metadata_2025 huggingface dataset:
Can Kernel Uniformity Indices Be Used as Criteria for Variability Assessment of Wheat Breeding Lines?
Authors: Protasova Ioanna M. , Aniskina Tatiana S. , Gulevich Alexander A. , Shchuklina Olga A. , Baranova Ekaterina N.
Published: December 2024

Wheat is one of the main food crops, the value of which lies in the high content of protein and carbohydrates in the kernel. To improve the quality of kernel processing, it is desirable that all kernels are uniform in shape and quantitative parameters. However, the kernel technological properties are affected by agricultural technology, environmental conditions and genetic characteristics, for example, even within one ear; kernels vary in size and the degree of ripening. Therefore, the aim of this work is to test the relationship between the coefficients (indices) of kernel shape variability in winter wheat lines that were pre-selected in long-term competitive variety trials and to select the best variety accession for further targeted selection to improve the uniformity of kernels. This work examined seven lines and a control variety of winter wheat grown during 2022–2023. Sampling in the field experiment was carried out randomly. The variability of quantitative traits was assessed by the analysis of variance method. It is noted that symmetrical kernels are mainly characteristic of 188h, the intermediate position is occupied by Moskovskaya 56, 150h, 152h, 171h, 184h, 187h and variety sample 151h has clearly expressed asymmetry. Index 5 of the kernel cut has a strong correlation with gluten content (r = 0.74, p = 0.05), index 4 with kernel test weight (r = 0.84, p = 0.01), index 3 with a tillering coefficient (r = 0.83, p = 0.05) and index 1 with plant height (r = 0.81, p = 0.05). An inverse relationship was found for index 2 with kernel vitreousness (r = −0.74, p = 0.05). The kernel test weight has an inverse relationship with the grain area (r = −0.71, p = 0.05). Predictive regression equations on the relationship of plant height, tillering ratio, gluten content and indices are given. While limited by its one-year duration, this study reveals intriguing correlations between grain shape parameters and economically valuable traits in wheat, offering valuable insights for high-throughput phenotyping applications in rapidly advancing agricultural technologies. This article will be useful for breeding for kernel uniformity and, consequently, for increasing the kernels in the ear and the weight of 1000 seeds.
Exploring High Voltage Potential of 3D Printed Capacitors: A Filament-Based Comparison Through Dielectric Performance Analysis
Authors: Uydur Cihat Cagdas , Akin Firat
Published: December 2024

Recent advancements in 3D printing technology have enabled the rapid production of complex structures, yet the dielectric performance of 3D printing materials and their potential for manufacturing electrical components remain insufficiently studied. In this study, a capacitor rated at 10 kV with a capacitance of 1 nF was designed and developed for high-voltage applications. During the production of the capacitor, the insulating and conductive parts were fabricated using a 3D printer. While PLA, ABS, ASA, and PETG were employed as insulating materials, aluminum was chosen as the conductive part. Theoretical calculations and the finite element method were used to validate the measured capacitance of the equipment. The performance of the prototype capacitor was analyzed through partial discharge inception voltages (PDIV), dissipation factor (tanδ), and breakdown voltage measurements. Dissipation factor measurements were performed at 2 and 4 kV voltages in the 50–400 Hz frequency range. The performance of employed materials was comparatively analyzed through experimental and simulation results. Finally, the impact of different insulating materials on the dielectric performance of the prototype capacitors was evaluated.
Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
Authors: Badreldin Hazem , M. Hassan Hany , Romanelli Fabio , El-Hadidy Mahmoud , ElGabry Mohamed N.
Published: December 2024

The available historical documents for the city of Alexandria indicate that it was damaged to varying degrees by several (historical and instrumentally recorded) earthquakes and by highly destructive tsunamis reported at some places along the Mediterranean coast. In this work, we applied the neo-deterministic seismic hazard analysis (NDSHA) approach to the Alexandria metropolitan area, estimating ground motion intensity parameters, e.g., peak ground displacement (PGD), peak ground velocity (PGV), peak ground acceleration (PGA), and spectral response, at selected rock sites. The results of this NDSHA zonation at a subregional/urban scale, which can be directly used as seismic input for engineering analysis, indicate a relatively high seismic hazard in the Alexandria region (e.g., 0.15 g), and they can provide an essential knowledge base for detailed and comprehensive seismic microzonation studies at an urban scale. Additionally, we established detailed tsunami hazard inundation maps for Alexandria Governorate based on empirical relations and considering various Manning’s Roughness Coefficients. Across all the considered scenarios, the average estimated time of arrival (ETA) of tsunami waves for Alexandria was 75–80 min. According to this study, the most affected sites in Alexandria are those belonging to the districts of Al Gomrok and Al Montazah. The west of the city, called Al Sahel Al Shamally, is less affected than the east, as it is protected by a carbonate ridge parallel to the coastline. Finally, we emphasize the direct applicability of our study to urban planning and risk management in Alexandria. Our study can contribute to identifying vulnerable areas, prioritizing mitigation measures, informing land-use planning and building codes, and enhancing multi-hazard risk analysis and early warning systems.
Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets
Authors: Wawer Michał , Chudziak Jarosław A. , Niewiadomska-Szynkiewicz Ewa
Published: December 2024

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability due to their black-box nature. This paper presents ElliottAgents, a multi-agent system that combines the Elliott wave principle with LLMs, showcasing the application of deep reinforcement learning (DRL) and natural language processing (NLP) in financial analysis. By integrating retrieval-augmented generation (RAG) and deep reinforcement learning (DRL), the system processes vast amounts of market data to identify Elliott wave patterns and generate actionable insights. The system employs a coordinated team of specialized agents, each responsible for specific aspects of analysis, from pattern recognition to investment strategy formulation. We tested ElliottAgents on both stock and cryptocurrency markets, evaluating its effectiveness in pattern identification and trend prediction across different time scales. Our experimental results demonstrate improvements in prediction accuracy when combining classical technical analysis with AI-driven approaches, particularly when enhanced by DRL-based backtesting process. This research contributes to the advancement of financial technology by introducing a scalable, interpretable framework that enhances market analysis capabilities, offering a promising new methodology for both practitioners and researchers.
Thermomechanical Behavior of Parallel Multi-U-Shaped Energy Piles Under the Summer Condition
Authors: Yang Junbing , Yuan Jianghuai , Zhao Liang , Chen Zhi , Wang Fusong
Published: December 2024

As a green and sustainable geothermal development technology, energy piles have received wide attention in the field of energy underground structure engineering. This work examines the variation rules of the pile-soil temperature, stress-strain, side friction resistance, and pile-top displacement of a full-scale parallel multi-U-type energy pile under the action of temperature loading using a combination of numerical modeling and field testing. As the number of parallel U-type heat exchanger tubes increases, the temperature of the energy pile body rises. However, after a certain number is reached, the effect of additional increases on the temperature rise of the pile body decreases. Additionally, there is a phenomenon known as thermal interference between the heat exchanger tubes, which should be kept at a suitable distance to minimize their mutual influence. The parallel 5U-type energy pile’s temperature is fairly evenly distributed along the depth direction, and the strain of the pile body demonstrates that the two ends of the pile body change in size, with the middle end having the largest restraining stress. When combined with the change in pile side resistance, this location is thought to produce the displacement’s zero point. The pile top displacement increases by 0.65 mm for every 5 °C increase in the water entry temperature.
Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
Authors: Luo Yutao , Sun Aining , Hong Jiawei
Published: December 2024

Due to the challenge that the behavior of traffic participants in the driving environment is highly stochastic and uncertain, it is difficult for self-driving vehicles to make accurate decisions based only on the current environmental state. In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. Then, a reinforcement learning-based driving strategy method is constructed using some of the predictive features of the ego-vehicle and surrounding vehicles as predictive state inputs. Finally, based on the real dataset and CARLA simulation platform, the prediction ability of the prediction model and the effectiveness of the prediction-based decision-making model are verified. The simulation results prove that the prediction algorithm can achieve the minimum error compared with the baseline trajectory prediction algorithm, and effectively improves the accuracy and reliability of the autopilot decision-making in various dynamic scenarios.
Comparison of DC XLPE Insulation Under Two Manufacturing Processes: From Electrical Tree to Molecular Weight Distribution
Authors: Yan Zhimin , Qiao Bo , Yang Wei , Zhang Lei , Le Yanjie , Zheng Zhe
Published: December 2024

High-performance cross-linked polyethylene (XLPE) is currently employed in ultra-high-voltage direct current (UHVDC) cables, with the electrical tree being an important cause of DC cable breakdown. The comparison of XLPE samples under different manufacturing processes can provide a reference for the progress of cable production processes. This paper compares laboratory-prepared XLPE samples (DC-XLPE) with XLPE samples extracted from actual cables (Cable-XLPE) through electrical tree experiments, X-ray diffraction (XRD), and gel permeation chromatography (GPC). The experimental findings indicate that the breakdown time of DC-XLPE increased by nearly 50% compared to Cable-XLPE, with slower electrical tree growth and lower average discharge magnitude observed. Overall, DC-XLPE exhibited superior resistance to DC electrical tree and partial discharge. XRD and GPC analyses revealed minimal differences in crystallinity and grain size between the two types, with the primary distinction being DC-XLPE’s notably higher molecular weight and more concentrated molecular weight distribution. The differences in physicochemical properties may be attributed to more precise and uniform temperature control during the crosslinking process in laboratory settings, as well as a higher removal rate of crosslinking byproducts, ultimately leading to enhanced resistance to electrical tree and partial discharge in DC-XLPE.
Mathematical Modeling of Fractals via Proximal F-Iterated Function Systems
Authors: Zahid Muhammad , Ud Din Fahim , Younis Mudasir , Ahmad Haroon , Öztürk Mahpeyker
Published: December 2024

We propose a novel approach to fractals by leveraging the approximation of fixed points, emphasizing the deep connections between fractal theory and fixed-point theory. We include a condition of isomorphism, which not only generates traditional fractals but also introduces the concept of generating two fractals simultaneously, using the framework of the best proximity point: one as the original and the other as its best proximity counterpart. We present a notion of the Proximal F−Iterated Function System (F−PIFS), which is constructed using a finite set of F*−weak proximal contractions. This extends the classical notions of Iterated Function Systems (IFSs) and Proximal Iterated Function Systems (PIFSs), which are commonly used to create fractals. Our findings show that under specific conditions in a metric space, the F−PIFS has a unique best attractor. In order to illustrate our findings, we provide an example showing how these fractals are generated together. Furthermore, we intend to investigate the possible domains in which our findings may be used.
Correction: Vegh et al. North America’s Potential for an Environmentally Sustainable Nickel, Manganese, and Cobalt Battery Value Chain. Batteries 2024, 10, 377
Authors: Vegh Gary , Madikere Raghunatha Reddy Anil Kumar , Li Xia , Deng Sixu , Liu Tongchao , Amine Khalil , Zaghib Karim
Published: December 2024

Tongchao Liu was not included as an author in the original publication [...]
Enhancing the Aroma of Dealcoholized La Mancha Tempranillo Rosé Wines with Their Aromatic Distillates
Authors: Alises M. Osorio , Sánchez-Palomo E. , González Viñas M. A.
Published: December 2024

The increasing demand for non-alcoholic beverages has led to the development of dealcoholized wines. However, current dealcoholization techniques often negatively impact wine aroma due to the loss of volatile compounds. This study investigates the impact of incorporating an aromatic distillate, collected during the spinning cone column (SCC) dealcoholization process, back into dealcoholized Tempranillo rosé wines. The aromatic distillate was added to dealcoholized wine in varying concentrations (0.5%, 1.0%, and 1.5% v/v). A total of 57 volatile compounds, including 25 varietal and 32 fermentative compounds, were identified and quantified using gas chromatography–mass spectrometry (GC-MS). The addition of the aromatic distillate significantly increased the concentration of several volatile compounds, notably C6 compounds, terpenes, benzene compounds, and esters. The odor activity values (OAVs) reveal that increasing distillate concentrations led to a higher number of compounds with OAVs greater than 1, indicating enhanced individual aroma contributions. The fruity and sweet aromatic series were predominant in all samples, with their total intensity increasing with higher distillate concentrations. However, the addition of 1.5% v/v of the aromatic distillate (AW3) resulted in an alcohol content exceeding the legal limit for dealcoholized wine, classifying it as a reduced-alcohol wine. The study concludes that adding 1% v/v of the aromatic distillate to dealcoholized Tempranillo rosé wine effectively enhances the aroma profile while remaining within regulatory limits for dealcoholized wine. This approach presents a viable method for producing high-quality, aromatic, dealcoholized wines that meet consumer demand for non-alcoholic beverages.
Relaxation Behavior of Cerclage Cables and Its Effect on Bone Clamping Force
Authors: Moffat Audrey , Kim Wonsuk , Rahman Tahsin , Podlewski Kayla , Silverton Craig , Argento Alan
Published: December 2024

Cerclage is an orthopedic surgical fixation technique using a cable wrapped, tensioned, and secured around a bone’s circumference. It is important to minimize the loss in cable tension that often occurs due to stress relaxation. The purpose of this work was to study the effect of tensioning protocols on the long-term loss of tension due to stress relaxation. The native mechanical properties and relaxation behavior of the cables were determined using traditional mechanical testing machines and methods. Four step-wise cable tensioning protocols were then trialed to compare the cable tension losses. A testing apparatus was developed to simultaneously measure cable tension and the resulting clamping force on a real bone. A five-parameter linear viscoelastic model was used to fit relaxation data to estimate the long-term relaxation of the cables beyond the time of the experiment. The four cables were found to have similar mechanical and viscoelastic behaviors. A two-step cable-tightening protocol was found to significantly reduce cable tension loss when compared to a one-step protocol for all cables. The benefit of the two-step protocol was reinforced by the relaxation results of the cable wrapped and tightened around a pig femoral bone. These results indicate that one retightening step should be conducted during the surgical placement of a cerclage cable to reduce the loss of cable tension resulting from relaxation.
Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests—A Systematic Review
Authors: Mușat Florentina , Păduraru Dan Nicolae , Bolocan Alexandra , Palcău Cosmin Alexandru , Copăceanu Andreea-Maria , Ion Daniel , Jinga Viorel , Andronic Octavian
Published: December 2024

Background. Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Results. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Conclusions. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources.
Predictive Value of N-Terminal Pro B-Type Natriuretic Peptide for Short-Term Outcome of Cardioversion in Patients with First-Diagnosed or Paroxysmal Atrial Fibrillation Presenting to the Emergency Department
Authors: Diakantonis Antonios , Verras Christos , Bezati Sofia , Bistola Vasiliki , Ventoulis Ioannis , Velliou Maria , Boultadakis Antonios , Ikonomidis Ignatios , Parissis John T. , Polyzogopoulou Effie
Published: December 2024

Background: Atrial fibrillation (AF) is a common arrhythmia in the emergency department (ED). We investigated the role of N-terminal pro b-type natriuretic peptide (NT-proBNP) in predicting both the outcome of AF cardioversion and the risk of AF recurrence or persistence on the 8th (D8) and 30th (D30) day post-cardioversion. Methods: This prospective, observational study evaluated patients with recent-onset AF, managed by either pharmacological (PC) or electrical cardioversion (EC) in the ED. Patients were treated either immediately or electively after 3 weeks of anticoagulation. NT-proBNP assessments were performed prior to cardioversion. Results: Of the 148 patients enrolled, 56% had paroxysmal AF, 85% underwent immediate cardioversion and 72% received EC. Successful cardioversion to sinus rhythm (SR) was achieved in 85% of patients. Patients with successful cardioversion and those who remained free from AF on D8 had lower NT-proBNP levels compared to patients with failed cardioversion or with AF recurrence or persistence on D8 [day of cardioversion, D0: SR vs. non-SR, 387 (127–1095) pg/mL vs. 1262 (595–2295), p = 0.004; D8: SR vs. non-SR, 370 (127–1095) vs. 1366 (718–2295), p = 0.002]. In multivariate analysis, higher logNT-proBNP was associated with higher risk of cardioversion failure [OR, 95%CI: 4.80 (1.58–14.55), p = 0.006] and AF recurrence or persistence on D8 [OR, 95%CI: 3.65 (1.06–12.59), p = 0.041]. ROC analysis confirmed the predictive ability of NT-proBNP for both outcomes (D0: AUC 0.735, p < 0.001; D8: AUC 0.761, p < 0.001). A cut-off value of NT-proBNP > 580 pg/mL was able to predict failure of AF conversion and occurrence of recurrent/persistent AF at D8. Conclusions: NT-proBNP is a promising biomarker for identifying patients presenting to the ED with recent-onset AF who run a greater risk of cardioversion failure and post-discharge AF recurrence/persistence in the immediate and short term.
Administration of Adipose-Derived Stem Cells After the Onset of the Disease Does Not Lower the Levels of Inflammatory Cytokines IL1 and IL6 in a Rat Model of Necrotizing Enterocolitis
Authors: Wolski Marek , Ciesielski Tomasz , Buczma Kasper , Fus Łukasz , Girstun Agnieszka , Trzcińska-Danielewicz Joanna , Cudnoch-Jędrzejewska Agnieszka
Published: December 2024

Background/Objectives: Research on the roles of stem cells in necrotizing enterocolitis (NEC) has primarily focused on the effects of bone marrow- and amniotic fluid-derived stem cells in mitigating the clinical manifestations of the disease. However, the potential of adipose tissue-derived stem cells (ADSCs) remains unexplored in this context. The aim of this study was to evaluate the therapeutic potential of ADSC administration during the active inflammatory phase of NEC, with a specific focus on reducing the levels of the inflammatory cytokines IL-1 and IL-6. Methods: A self-modified hypoxia–hypothermia–formula feeding rat NEC model was employed. A total of 117 rat pups were divided into two groups: a treatment group (NEC-ADSC, n = 55) and a control group (NEC-PLCB (placebo), n = 62). In the NEC-ADSC group, ADSCs were administered intraperitoneally 24 h into the NEC protocol. After 72 h, bowel and fluid samples were collected for analysis. Results: The analysis revealed no significant effect on NEC histopathology (p = 0.347) or on the levels of IL-1 and IL-6 (p = 0.119 and p = 0.414, respectively). Conclusions: The administration of adipose tissue-derived stem cells after the onset of necrotizing enterocolitis does not reduce the levels of inflammatory cytokines IL-1 and IL-6, nor does it influence the histopathological outcomes of the disease in the rat model. Further research is needed to explore the potential therapeutic role of adipose tissue-derived stem cells in the treatment of necrotizing enterocolitis.
The Prognostic Value of Preoperative Total Cholesterol in Surgically Treated Oral Cavity Cancer
Authors: Tsai Yao-Te , Tsai Ming-Hsien , Kudva Adarsh , Vito Andrea De , Lai Chia-Hsuan , Liao Chun-Ta , Kang Chung-Jan , Tsai Yuan-Hsiung , Hsu Cheng-Ming , Huang Ethan I. , Chang Geng-He , Tsai Ming-Shao , Fang Ku-Hao
Published: December 2024

Background: With growing evidence linking lipid profile changes to tumor development and cancer prognosis, we investigated the prognostic significance of preoperative serum total cholesterol (TC) levels in patients with oral cavity squamous cell carcinoma (OSCC) undergoing surgical treatment. Methods: We conducted a retrospective observational study involving 310 patients with primary OSCC who received surgery at our hospital from January 2009 to December 2018. Receiver operating characteristic curve analysis was performed to determine the optimal preoperative TC cutoff value, with the Youden Index employed as the optimization criterion to maximize the sum of sensitivity and specificity. Variables with p < 0.1 in the univariable analysis were included in the multivariable Cox regression model, and stepwise selection was used to identify the optimal subset of prognostic factors for overall survival (OS) and disease-free survival (DFS). Results: An optimal TC cutoff of 157 mg/dL was established. Patients with TC < 157 mg/dL exhibited significantly lower 5-year rates of OS and DFS (p < 0.001 and p = 0.006, respectively). Multivariable analysis confirmed that TC < 157 mg/dL represented an independent prognostic factor for reduced OS and DFS rates. Subgroup analyses reinforced the consistent prognostic significance of TC. We also constructed a nomogram (concordance index: 0.74) to provide personalized OS predictions, enhancing the clinical utility of TC. Conclusions: Preoperative TC appears to be a significant prognostic factor for OS and DFS after OSCC surgery. Routine TC assessment facilitates the development of nomograms for personalized survival predictions, supports clinicians in tailoring treatment strategies, and guides nutritional or metabolic interventions to enhance patient outcomes. Further multicenter prospective studies are needed to validate our findings.
Metabolomic Profiling of Oral Potentially Malignant Disorders and Its Clinical Values
Authors: Mohd Faizal Nur Fatinazwa , Vincent-Chong Vui King , Ramanathan Anand , Paterson Ian C. , Karen-Ng Lee Peng , Zaini Zuraiza Mohamad
Published: December 2024

Oral potentially malignant disorders (OPMD) are a group of lesions carrying the risk of developing into cancer. The gold standard to predict which lesions are more likely to undergo malignant transformation is the presence of dysplasia histologically. However, not all dysplastic lesions progress, and non-dysplastic lesions may also undergo malignant transformation. Oral carcinogenesis is a complex molecular process that involves somatic alterations and the deregulation of transcriptions, protein expression, and metabolite levels. Metabolomics, which is the scientific study of metabolites, has emerged as a promising high-throughput approach to investigate the metabolic changes of small molecules in biological pathways. In this review, we summarize the data relating to the metabolomic profiling of OPMDs, which will help elucidate the complex process of oral carcinogenesis. Furthermore, we identify that among all metabolites, citrate, pyruvate, and glutamate may serve as potential biomarkers for oral leukoplakia (OLK). Notably, metformin and gluconate have been shown to target glutamate and citrate, respectively, in cancer cells. Based on these findings, we propose that targeting these metabolites in patients with OPMD could be a promising therapeutic strategy to mitigate OPMD progression and potentially reduce the risk of malignant transformation. We also discuss the limitations and future directions of metabolomics in OPMD. Understanding these important metabolites is crucial for early detection and monitoring of oral cancer progression.
An Octopus-Inspired Soft Pneumatic Robotic Arm
Authors: Papadakis Emmanouil , Tsakiris Dimitris P. , Sfakiotakis Michael
Published: December 2024

This paper addresses the design, development, control, and experimental evaluation of a soft robot arm whose actuation is inspired by the muscular structure of the octopus arm, one of the most agile biological manipulators. The robot arm is made of soft silicone and thus possesses enhanced compliance, which is beneficial in a variety of applications where the arm may come into contact with delicate features of its environment. The arm is composed of three elongated segments arranged in series, each one of which contains several pneumatically actuated chambers embedded in its silicone body, which may induce various types of deformations of the segment. By combining the segment deformations, and by imitating the antagonistic muscle group functionality of the octopus, the robot arm can bend in various directions, increase or decrease its length, as well as twist around its central axis. This is one of the few octopus-inspired soft robotic arms where twisting is replicated in its motion characteristics, thus greatly expanding the arm’s potential applications. We present the design process and the development steps of the soft arm, where the molding of two-part silicone of low hardness in 3d-printed molds is employed. In addition, we present the control methodology and the experimental evaluation of both a standalone segment and the entire three-segment arm. This experimental evaluation involves model-free closed-loop control schemes, exploiting visual feedback from a pair of external cameras in order to reconstruct in real time the shape of the soft arm and the pose of its tip.
Nanopipettes as a Potential Diagnostic Tool for Selective Nanopore Detection of Biomolecules
Authors: Kuanaeva Regina M. , Vaneev Alexander N. , Gorelkin Petr V. , Erofeev Alexander S.
Published: December 2024

Nanopipettes, as a class of solid-state nanopores, have evolved into universal tools in biomedicine for the detection of biomarkers and different biological analytes. Nanopipette-based methods combine high sensitivity, selectivity, single-molecule resolution, and multifunctionality. The features have significantly expanded interest in their applications for the biomolecular detection, imaging, and molecular diagnostics of real samples. Moreover, the ease of manufacturing nanopipettes, coupled with their compatibility with fluorescence and electrochemical methods, makes them ideal for portable point-of-care diagnostic devices. This review summarized the latest progress in nanopipette-based nanopore technology for the detection of biomarkers, DNA, RNA, proteins, and peptides, in particular β-amyloid or α-synuclein, emphasizing the impact of technology on molecular diagnostics. By addressing key challenges in single-molecule detection and expanding applications in diverse biological areas, nanopipettes are poised to play a transformative role in the future of personalized medicine.
Sensing Technologies for Outdoor/Indoor Farming
Authors: Wang Luwei , Xiao Mengyao , Guo Xinge , Yang Yanqin , Zhang Zixuan , Lee Chengkuo
Published: December 2024

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges.
Clinical Questions and Psychological Change: How Can Artificial Intelligence Support Mental Health Practitioners?
Authors: Orrù Luisa , Cuccarini Marco , Moro Christian , Turchi Gian Piero
Published: December 2024

Despite their diverse assumptions, clinical psychology approaches share the goal of mental health promotion. The literature highlights their usefulness, but also some issues related to their effectiveness, such as their difficulties in monitoring psychological change. The elective strategy for activating and managing psychological change is the clinical question. But how do different types of questions foster psychological change? This work tries to answer this issue by studying therapist–patient interactions with a ML model for text analysis. The goal was to investigate how psychological change occurs thanks to different types of questions, and to see if the ML model recognized this difference in analyzing patients’ answers to therapists’ clinical questions. The experimental dataset of 14,567 texts was divided based on two different question purposes, splitting answers in two categories: those elicited by questions asking patients to start describing their clinical situation, or those from asking them to detail how they evaluate their situation and mental health condition. The hypothesis that these categories are distinguishable by the model was confirmed by the results, which corroborate the different valences of the questions. These results foreshadow the possibility to train ML and AI models to suggest clinical questions to therapists based on patients’ answers, allowing the increase of clinicians’ knowledge, techniques, and skills.
Health Index-Based Maintenance of Prestressed Concrete Bridges Considering Building Information Modeling Application
Authors: Jeon Chi-Ho , Kwon Tae Ho , Kim Jaehwan , Jung Kyu-San , Park Ki-Tae
Published: December 2024

Bridge maintenance faces challenges regarding data management and decision-making efficiency, primarily owing to the manual processing of extensive inspection data and the absence of integrated digital solutions. This study addresses these challenges by proposing a health index (HI)-based bridge evaluation framework for prestressed concrete bridges, based on building information modeling (BIM) for inspection data integration and long short-term memory (LSTM) models for accurate deterioration prediction. The proposed framework categorizes and analyzes bridge deterioration data from inspection reports and develops a predictive LSTM model that allows quantitative bridge evaluation based on accumulated historical data. The results demonstrate that this approach enhances the efficiency and accuracy of bridge condition evaluation while enabling long-term prediction of deterioration trends. In a case study of a bridge, the bridge-level HI value decreased by 17% over 16 years, while the condition grade remained unchanged. The findings suggest that integrating BIM with HI-based bridge evaluation can support sustainable bridge maintenance strategies.
The Study of the Flexible Capacity of the Cross-Section of UHPC–Brick Masonry Composite Beams
Authors: Wang Ruochen , Zhu Yanzhu , Zhu Zhiyu , Wang Kun
Published: December 2024

This paper proposes a method for modifying brick masonry walls using ultra-high-performance concrete (UHPC) to effectively reduce the beam section width in composite members. First, a detailed finite element model of UHPC–brick masonry composite beams was established using the finite element software ABAQUS 2022 and validated through comparison with the existing literature. Then, the effects of factors such as the tensile reinforcement ratio, UHPC layer thickness, and UHPC strength grade on the load–midspan deflection curve of composite members were analyzed, along with a study of the bending performance under vertical loads. Based on the results of the finite element analysis, a calculation formula for the positive section bending capacity of UHPC–brick masonry composite beams was proposed. The study found that longitudinal tensile reinforcement yielded first, followed by the crushing of the compressive region of UHPC and brick masonry, resulting in overall bending failure. With the increase in the tensile reinforcement ratio and UHPC layer thickness, both the yield load and peak load significantly increased, while the UHPC layer thickness had a notable impact on the cracking load of the composite member. When the UHPC strength grade ranged from 150 MPa to 180 MPa, the bearing capacity of the composite member changed little. The proposed calculation method for bearing capacity correlated well with the finite element results, providing a theoretical basis for the design and analysis of UHPC–brick masonry composite beams.
A Simple Green Method for the Determination of Hydrogen Peroxide and Fe(III)/Fe(II) Species Based on Monitoring the Decolorization Process of Polymethine Dye Using an Optical Immersion Probe
Authors: Skok Arina , Bazel Yaroslav , Fizer Maksym
Published: December 2024

We have found that the dye 1,3,3-trimethyl-2-((1′E,3′E,5′E)-5’-(1″,3″,3″-trimethylindol-(2′E)-ylidene)-penta-1″,3″-dien-1″-yl)-3H-indol-1-ium (DTMI-5) can be successfully used for the simple green determination of H2O2 and Fe(III)/Fe(II) species. The dye is characterized by a successful combination of spectral, protolytic, and redox properties, and the process of its decolorization in the Fenton reaction is monitored using an optical immersion probe. Theoretical calculations of the reactive sites in the DTMI-5 molecule under free radical attack reveal that the methine groups of the penta-1′,3′-dien-1′-yl linker serve as the primary reactive centers in Fe3+ or Fenton-type oxidation conditions. Density functional theory (DFT) calculations indicate that the redox potentials of the examined structures range from 0.34 to 1.65 eV. The experimentally observed broad peak at 340–360 nm, which appears after the interaction of DTMI-5 with the Fenton reagent, is attributed to the formation of aldehyde-type oxidation products, whose theoretical CIS(D) absorption maxima were 358.1 and 337.4 nm. The influence of various factors on the course of the reaction was experimentally investigated. The most important analytical characteristics of the methods, such as linearity intervals of calibration graphs, precision, LOD and LOQ values, selectivity coefficients, etc., were determined. The developed methods were applied to model and real samples (water, oxidation emulsion, and fertilizer).
Association Between Vitamin D Levels and Developmental Status in Short-Stature Children
Authors: Dewi Mia Milanti , Imron Akhmad , Risan Nelly Amalia , Purnami Grace Mediana , Judistiani Raden Tina Dewi , Setiabudiawan Budi
Published: December 2024

Background: The first two years of life are crucial for child growth and development, and short stature is a common issue influenced by nutritional deficiencies. This study aimed to determine the relationship between vitamin D levels and short stature in children under 2 years of age in Bandung. Methods: Conducted as part of the ALG Grant research entitled ‘The Role of Vitamin D in Reducing Maternal and Infant Mortality Rates Across Bandung Regency’, this case-control study included 221 children, of whom 46.1% were below average height. Results: Nutritional status played a significant role, with short stature being associated with underweight (21.6%), severely underweight (29.4%), wasted (12.7%), and severely wasted (10.8%) conditions. However, the analysis revealed no significant difference in vitamin D levels between children with short stature and those with normal stature (median 28.8 vs. 28.8, p = 0.555). Logistic regression showed that short stature increased the risk of developmental deviations by 5.46 times compared to normal stature. Conclusions: While short stature remains a concern in Bandung, vitamin D levels do not appear to influence the incidence of short stature or developmental deviations in this population.
Music Therapy as a Tool to Unveil Musical Potential or Hidden Savant in Children with Autism: A Case Study
Authors: Hatahet Mais , Sárváry Andrea , Sárváry Attila
Published: December 2024

Background/Objectives: Savant syndrome is a perplexing condition characterized by the exceptional abilities or talents of individuals with disabilities or low IQ. Methods: This study shows an individual case of a child with autism, detailing how music therapy may facilitate the discovery of musical abilities and how it can improve different areas of development, e.g., motor function, communication, social skills, and emotional expression. Results: The 17-year-old male (KH), diagnosed at the age of three with autism spectrum disorder (ASD) and having moderate abilities, exhibited no interest in music until the initiation of a music therapy program at the age of 11 years. KH consistently has repeated movements which impede his advancement in various tasks. This observation prompted the occupational therapist to purposefully incorporate this movement into piano training and recommend the initiation of music therapy sessions. He subsequently began utilizing the piano and demonstrated an extraordinary aptitude for musical note interpretation and creation. Conclusions: Occupational therapy may contribute to revealing hidden savant syndrome in children with ASD. Prolonged musical training has multiple impacts on motor functioning and multi-sensory perception, and it may also elicit favorable behavioral modifications in children with autism.
Biochemical Mechanisms and Rehabilitation Strategies in Osteoporosis-Related Pain: A Systematic Review
Authors: Iaconisi Giorgia Natalia , Mancini Rachele , Ricci Vincenzo , Donati Danilo , Sconza Cristiano , Marvulli Riccardo , Ranieri Maurizio , Megna Marisa , Varrassi Giustino , Della Tommasa Simone , Bernetti Andrea , Capobianco Loredana , Farì Giacomo
Published: December 2024

Background/Objectives: Osteoporosis causes a bone mass reduction and often determines acute and chronic pain. Understanding the biochemical and neurophysiological mechanisms behind this pain is crucial for developing new, effective rehabilitative and therapeutic approaches. This systematic review synthesizes recent advances in muscle–bone interactions and molecular pathways related to osteoporosis-associated pain. Methods: We carried out a systematic review including studies published from 2018 to 2024 using PubMed, Scopus, clinicaltrials.gov and Cochrane Library. The Cochrane Collaboration tool was used to assess bias risk. The review adhered to PRISMA guidelines and is registered with PROSPERO (CRD42024574456); Results: Thirteen studies were included. It emerged that osteoporosis causes progressive bone loss due to disruptions in biochemical processes and muscle–bone interactions. This condition is also closely associated with the development of pain, both acute and chronic. Key findings include the role of the miR-92a-3p/PTEN/AKT pathway and the impact of muscle–bone disconnection on bone health. Mechanotransduction is critical for bone maintenance. Effective pain management and rehabilitation strategies include physical therapy and physical exercise, yoga, Pilates, and cognitive behavioral therapy (CBT); they all improve pain relief and functional outcomes by enhancing muscle strength, flexibility, and balance. Pharmacological options such as NSAIDs, opioids, and new agents like SHR-1222, along with surgical interventions like percutaneous vertebroplasty, offer additional pain reduction, especially when included in individualized rehabilitation projects; Conclusions: This review highlights advancements in understanding osteoporotic pain mechanisms and identifies promising treatments. Integrating targeted therapies and rehabilitation strategies can enhance patients’ pain relief.
Additive Manufacturing Gyroid Structures Used as Crash Energy Management
Authors: Rostro-González Horacio , Reyes-Pozo Guillermo , Puigoriol-Forcada Josep Maria , López-Valdés Francisco-José , Sundarram Sriharsha Srinivas , Garcia-Granada Andres-Amador
Published: December 2024

Gyroid-like structures are promising in terms of energy absorption levels. Due to additive manufacturing, they can now be manufactured and verified for different functions. In this article, it has been proven that a Gyroid manufactured by FDM using PLA with 0.2 relative density must be oriented so that compression takes place along the build direction to obtain higher levels of force and energy. The Gyroid can be scaled, allowing the use of a single compression curve with almost constant forces up to 50% compression. The model to predict properties as a function of relative density fits well with a power-law for n = 2.2. The ability of the Gyroid to absorb energy per kilogram is about seven times lower than that of a solid PLA cube, but it can be used to obtain desired levels of deceleration. It is possible to use a simple constant deceleration model to define the Gyroid size, mass, and velocity of the object to be impacted. The use of this approach allows the tailored combination of Gyroid sizes to meet multi-objective impact targets. The simulation of impacts with a finite element model of only 125 solid elements is possible with errors below 10%. By combining different Gyroid sizes, two different safety regulations can be met. Modeling the Gyroid by meshing the real geometry allows for the local maximum force magnified at high strain rates, but it is not able to correctly predict densification.
Multiband Superconductivity, Polarons, Jahn-Teller Polarons, Heterogeneity, and High-Temperature Superconductivity
Authors: Bussmann-Holder Annette , Keller Hugo
Published: December 2024

Early on, oxides were ruled out from superconductivity, since they are typically large-band-gap insulators. Nevertheless, a rather small number of them were found to be superconducting, with transition temperatures up to 14 K and a remarkably low carrier density. This was the starting point of K. Alex Müller (KAM) becoming interested in superconductivity in oxides. Step by step, he advanced the research on oxides and finally discovered, together with J. Georg Bednorz, high-temperature superconductivity (HTSC) in the perovskite-type compound Ba-La-Cu-O. Even though he was inspired by specific and clear ideas in his search, he added new impact in the understanding of HTSC for many years after receipt of the Nobel prize for this discovery.