bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive Force/Position NN Control of Asymmetric Teleopertion System Based on Fuzzy CMAC
Authors: Chen Hua , Shi Baoyu 
Published: August 2024

Abstract

Metallurgical industrial robots play an increasingly indispensable role in modern industries. Today, with a significant increase in industrial automation, people continually demand higher performance from metallurgical industrial robots. The application of teleoperated system in the metallurgical field is becoming increasingly widespread. A teleoperation system consists of a master robot and a slave robot. Stability, tracking, and transparency are the performance requirements for teleoperation systems. Asymmetric teleoperation systems are more widely used than isomorphism teleoperation systems. This study chose an asymmetric teleoperation system in which the master robot is a novel form of 6-degree of freedom mechanism consists of series and parallel. The slave robot is Baxter robot. An Adaptive CAMC intelligent control algorithm was used for asymmetric teleoperation system force/position control. The experiment results showd that the Adaptive Fuzzy CAMC control algorithm outperforms Adaptive Fuzzy & PID control algorithm in terms of control performance for Asymmetric teleoperation systems. The experiment also demonstrates that the master hand satisfies the asymmetric teleoperation system..
An Efficient Cluster Based Spectrum Sharing System in Cognitive Wireless Environment
Authors: N Mahesh Kumar , SEGU RAMGOPAL , Harshitha B , N KEERTHY 
Published: August 2024

Abstract

With the expeditious progression of the Internet of Things (IoT), there is a growing need for robust connectivity solutions that can accommodate the increasing number of cell-edge users. These users often need help with the inferior signal quality compared to those at the cell centre. The User-Pairing NOMA (UPNOMA) system that integrates the Spatial Modulation (SM) with Non-Orthogonal Multiple Access (NOMA) named SM-UPNOMA enhanced the IoT connectivity and spectral efficiency in networks dominated by cell-edge users. However, the optimal time slots and bandwidth between the paired users are remarkable in obtaining these advantages. Hence, a novel Crayfish Optimized SM-UPNOMA (COSU) system that aims for optimal allocation of time and bandwidth. It offers two distinct variants: Time-sharing SM-UPNOMA (TSM-UPNOMA) and bandwidth-sharing SM-UPNOMA (BSM-UPNOMA). The designed method is meticulously crafted to optimize bit error rate (BER) performance and maximize the sum rate, effectively addressing the challenges of high spectral efficiency in dense cell-edge environments. The advantages of the proposed systems are manifold, including improved spectral efficiency and enhanced BER performance even under less-than-ideal successive interference cancellation (ISIC) conditions. By leveraging spatial modulation, the system effectively utilizes the spatial domain as an additional multiplexing dimension, enabling support for more users without significantly increasing complexity. This research focuses on wireless communication systems, specifically advanced signal processing techniques for Next-Generation Networks (NGNs). Through extensive simulations, this paper demonstrates the superior performance of SM-UPNOMA systems compared to conventional NOMA (C-NOMA), SM-aided conventional NOMA (SM-CNOMA), and SM-aided OMA (SM-OMA). Furthermore, the analytical verification of the BER performance for TSM-UPNOMA provides practical validation for the proposed model.
Intestinal microbiota shifts as a marker of thermal stress during extreme heat summer episodes in farmed gilthead sea bream (Sparus aurata)
Authors: Domingo-Bretón Ricardo , Cools Steven , Moroni Federico , Belenguer Álvaro , Calduch-Giner Josep Àlvar , Croes Evi , Holhorea Paul George , Naya-Català Fernando , Boon Hans , Pérez-Sánchez Jaume 
Published: August 2024

Abstract

Climate change and its associated extreme events alter a number of physiological processes that also affect the homeostatic relationship of the host with their microbial communities. The aim of this study was to gain more insights on this issue, examining the effect of the record breaking-heat summer of 2022 on the gut microbiota of farmed gilthead sea bream (Sparus aurata), reared from May to August at the IATS research infrastructure (Spain´s Mediterranean coast). Fish were fed daily with four experimental diets, containing two different lipid levels (16% and 14%) with/without a commercial emulsifier (0.1%; Volamel Aqua, Nukamel). On August 9th, concurrently with the historical record of water temperature (30.49 ºC), fish were sampled for analysis of blood-stress markers and water/intestinal microbiota. Gut microbiota analysis clearly evidenced the increased abundance of bacteria of Spirochaetota phylum, mainly represented by the genus Brevinema. This microbiota shift was not driven by environmental colonization as this bacteria genus remained residual in water samples with the increase of temperature. Bayesian network and functional enrichment analyses suggested that the high abundance of Brevinema exploits and negatively enhances a condition of imbalance in intestinal homeostasis, which was almost completely reversed by the use of dietary emulsifiers in combination with low energized diets. This phenotype restoration occurred in concomitance with changes in circulating levels of cortisol and glucose. Altogether this highlights the potential use of Brevinema as a heat-stress biomarker, reinforcing the value of dietary intervention as a valuable solution to mitigate the negative impact of global warming on aquaculture production.
Thermal-Hydraulic Coupling Solution: Quantifying velocity and temperature fields in convective heat transfer from sparse data
Authors: Xu Ruina , Weng Qingyan , Cao Yuli , Lu Haowei , Jiang Pei-xue 
Published: August 2024

Abstract

Thermal-hydraulic coupling system, a physical process where flow and heat transfer interact mutually, is ubiquitous in nature and industrial applications. Although experimental measurements and solution of governing equations can be used to quantify physical fields such as velocity and temperature, acquiring dense and high-fidelity data is still challenging and costly. Here, we develop a physics-informed deep learning framework, the Thermal-Hydraulic Coupling Solution (THCS), that simultaneously exploits the information available from sparse data and governing equations (mass, momentum, and energy). THCS is composed of a physics-informed neural network, a thermal property mapping module, and a hydraulic parameterization modeling module. To demonstrate its capability, we choose the supercritical fluid turbulent convection, characterized by sharp changes in thermal properties when fluids cross the pseudocritical temperature. The ablation experiments demonstrate that THCS shows favorable generalizability and robustness, and the proposed multi-head structures improve both the convergence stability and prediction accuracy of models. Finally, we apply THCS to practical experiment scenarios, demonstrating its potential to directly quantify physical fields from sparse data.
Microscopic insights into Cu-N-C catalyst stability and leaching mechanisms through orbital interactions
Authors: Chen Yuanzheng , Zhang Xiaotao , Wang H.Y. , Tang Yongliang , Feng Yuan Ping , Chen Zhongfang , Lu Haiyan 
Published: August 2024

Abstract

Copper-nitrogen-codoped graphene (Cu-N-C) single-atom catalysts (SACs) feature an intriguing dynamic transformation between copper single atoms and clusters under electrochemical conditions, a behavior absent in the other metal-centered M-N-C SACs. Yet, the underlying cause of this distinctive phenomenon remains poorly understood. Herein, we delve into the modulation of electronic structure in M-N-C SACs by the solvent effects and electrochemical potentials, revealing the leaching mechanisms of copper atoms based on Crystal Field Theory (CFT) and hybrid-solvation constant potential method. We demonstrate that in M-N-C SACs, the orientation of d-orbitals nearest to the Fermi level determines the stability of M-N bonds. The d9 electronic configuration of copper imparts the Cu-N bond with the highest sensitivity to external voltage. Meanwhile, it is revealed that proton transfer (PT) on central copper atoms rearranges the energy levels of d-orbitals near the Fermi level, accelerating charge accumulation in the anti-bonding state in Cu-N bonds and ultimately inducing copper atoms leaching. These findings provide microscopic insights into the interaction between electronic orbitals and leaching behaviors in Cu-N-C SACs, advancing the mechanistic understanding of dynamic phenomena in electrochemical systems.
Integral force-based design approach for the seismic analysis and design of liquid storage tanks
Authors: Chatterji Soumitra , Butenweg Christoph , Klinkel Sven 
Published: August 2024

Abstract

Historical observations reveal that Liquid Storage Tanks (LST) have suffered significant earthquake-induced damages. The structural response of LST are sensitive to earthquakes due to dynamic fluid-tank interaction. Since designing with consideration of fluid-tank interaction in the time domain is complex, simplified calculation approaches have been developed to calculate base shear and overturning moments, but not pressure distributions. These approaches distinguish between flexible and rigid tanks, which is difficult to decide prior analysis. This paper presents an integral calculation concept that determines support reactions and pressure distributions independently of the tank's stiffness by applying static equivalent loads. The research focuses on the distinction between rigid and flexible design approaches, review existing codes, their limitations, and challenges associated with relative acceleration response spectra. It scrutinizes varying definitions of impulsive components and superposition principles. An integral force-based design approach is suggested that integrates rigid and flexible design principles into a unified method. The approach uses standardized pressure curves of individual hydrodynamic pressure components, linked with absolute and relative spectral accelerations and appropriate superposition methods. The integral formulation is validated through a previously conducted experimental and numerical research on above-ground steel tank. The validation and application of the integral approach forms the basis for the new generation of Eurocode prEN 1998-4, which is demonstrated for different tank geometries. The practical application is demonstrated on a squat and a slender tank using linear finite element (FE) model, and the results are compared with various approaches in the international standards and literature.
Análise Epidemiológica das Doenças Exantemáticas em Crianças de 0-9 Anos entre 2018 e 2022 no Brasil
Authors: Borges Tayná de Farias , Cavalcante Ingrid Mikaelly Cardoso , Martins Guilherme Henrique Pereira Franco , Fontes Mariana Spinola , Pinheiro Nássara Letícia Müller , Araújo Matheus Nassar Alves de , Souza Louise Fagundes Ribas , Machado Lara Cândida de Souza 
Published: August 2024

Objetivo: Determinar o perfil epidemiológico das doenças exantemáticas no Brasil no período de 2018 a 2022, além de analisar a provável ligação do aumento do número de casos com a cobertura vacinal não plena. Método: Trata-se um estudo observacional, com delineamento descritivo, transversal, retrospectivo, ecológico e com abordagem quantitativa, sendo realizadas análises em bancos de dados do Sistema Nacional de Agravos de Notificação (Sinan) e do Programa Nacional de Imunizações (PNI). Resultados: O total de casos confirmados foi 13.557, com predomínio da doença na macrorregião sudeste (55,53%) e no sexo masculino (51,3%). Em relação à faixa etária, constatou-se que, no período de 2018 a 2022, a maior prevalência ocorreu em <1 ano, com 52,29%, seguido de 37,50 % para a faixa de 1 a 4 anos. A cura ficou em torno de 90,4%, com apenas 31 casos de óbitos notificados. A cobertura vacinal total ficou abaixo de 90% em todas as regiões brasileiras. Conclusão: Verifica-se um aumento do número de casos de sarampo e rubéola, em consonância a cobertura vacinal apresenta uma diminuição em relação ao período analisado.
Biomechanical surface roughness analysis of ramie-low melt polyester nonwovens exposed to plasma
Authors: Paramahasti Markus , Galih Vidia Putra Valentinus , Yusuf Yusril 
Published: August 2024

This study aims to characterize ramie-low melt polyester nonwoven fabrics treated with low-temperature plasma and to analyze 3-D images based on object-depth mapping (ODM) using the MATLAB® R2022a software. We examined the low-temperature plasma treatment of nonwoven ramie fabrics using a plasma generator with 30 kV output power, six-minute treatment times, and a 4.5 cm distance between electrodes. The fabric’s chemical properties and surface topography were investigated using scanning electron microscopy (SEM) and infrared spectroscopy (FTIR). An analysis of the SEM images was performed using a statistical approach and image processing to determine the level of surface roughness. FTIR analysis revealed that fabrics exposed for six minutes differed from those that were not. Our findings indicated that plasma treatment caused the following: 1) Ramie fabrics to become more hydrophilic, as shown by their increased T% in the FTIR of hydrophilic functional groups such as hydroxyl (O-H), carboxyl (-COOH), and carbonyl (C=O); 2) A higher surface roughness was observed in nonwoven fabric during SEM testing and image processing; 3) Plasma treatment of fabrics resulted in a higher coefficient of variation (CV) than untreated fabrics; 4) Nonwoven fabric mass reduction. Based on this study, we found the relationship between plasma-treated ramie fabric and textiles in biomechanics. Plasma treatment reduced the mass of ramie fabric by 0.11 %, according to our findings. We found that the greater the mass reduction, the greater the surface roughness value. The novelty of this study is the use of 3-D images based on object-depth mapping (ODM) using the MATLAB® R2022a software in SEM to observe surface roughness to the physical properties of ramie fabric for the first time.
Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors
Authors: Kleandrova Valeria V. , Cordeiro M. Natália D. S. , Speck-Planche Alejandro 
Published: August 2024

Background:
Cancers are complex multi-genetic diseases that should be tackled in
multi-target drug discovery scenarios. Computational methods are of great importance to accelerate
the discovery of multi-target anticancer agents. Here, we employed a ligand-based approach
by combining a perturbation-theory machine learning model derived from an ensemble of
multilayer perceptron networks (PTML-EL-MLP) with the Fragment-Based Topological Design
(FBTD) approach to rationally design and predict triple-target inhibitors against the cancerrelated
proteins named Tropomyosin Receptor Kinase A (TRKA), poly[ADP-ribose] polymerase
1 (PARP-1), and Insulin-like Growth Factor 1 Receptor (IGF1R).

Methods:
We extracted the chemical and biological data from ChEMBL. We applied the Box-
Jenkins approach to generate multi-label topological indices and subsequently created the
PTML-EL-MLP model.

Results:
Our PTML-EL-MLP model exhibited an accuracy of around 80%. The application
FBTD permitted the physicochemical and structural interpretation of the PTML-EL-MLP model,
thus enabling a) the chemistry-driven analysis of different molecular fragments with a positive
influence on the multi-target activity and b) the use of those favorable fragments as building
blocks to virtually design four new drug-like molecules. The designed molecules were predicted
as triple-target inhibitors against the aforementioned cancer-related proteins.

Conclusion:
Our study envisages the capabilities of combining PTML modeling with FBTD for
the generation of new chemical diversity for multi-target drug discovery in oncology research
and beyond.
Exploring the Feasibility and Initial Impact of an mHealth-Based Disease Management Program for Chronic Ischemic Heart Disease: Formative Study
Authors: Miki Takahiro , Yamada Junya , Ishida Shinpei , Sakui Daisuke , Kanai Masashi , Hagiwara Yuta 
Published: August 2024

Background
Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, requiring innovative management strategies. Traditional disease management programs often struggle to maintain patient engagement and ensure long-term adherence to lifestyle modifications and treatment plans. Mobile health (mHealth) technologies have emerged as a promising approach to address these challenges by providing continuous, personalized support and monitoring. However, the reported use and effectiveness of mHealth in the management of chronic diseases, such as IHD, have not been fully explored.

Objective
The primary aim of this study was to evaluate the feasibility and initial impact of an mHealth-based disease management program on coronary risk factors, specifically focusing on low-density lipoprotein cholesterol (LDL-C) levels, in individuals with chronic IHD. This formative study assessed changes in LDL-C and other metabolic health indicators over a 6-month period to determine the initial impact of the program on promoting cardiovascular health and lifestyle modification.

Methods
This study was conducted using data from 266 individuals enrolled in an mHealth-based disease management program between December 2018 and October 2022. Eligibility was based on a documented history of IHD, with participants undergoing a comprehensive cardiac risk assessment before enrollment. The program included biweekly telephone sessions, health tracking via a smartphone app, and regular progress reports to physicians. The study measured change in LDL-C levels as the primary outcome, with secondary outcomes including body weight, triglyceride levels, and other metabolic health indicators. Statistical analysis used paired 2-tailed t tests and stratified analyses to assess the impact of the program.

Results
Participants experienced a significant reduction in LDL-C, with LDL-C levels decreasing from a mean of 98.82 (SD 40.92) mg/dL to 86.62 (SD 39.86) mg/dL (P<.001). The intervention was particularly effective in individuals with high baseline LDL-C levels. Additional improvements were seen in body weight and triglyceride levels, suggesting a broader impact on metabolic health. Program adherence and engagement metrics suggested high participant satisfaction and compliance.

Conclusions
The results of this study suggest that the mHealth-based disease management program is feasible and has an initial positive impact on reducing LDL-C levels and improving metabolic health in individuals with chronic IHD. However, the study design does not allow for a definitive conclusion regarding whether mHealth-based disease management programs are more effective than traditional face-to-face care. Future studies are needed to further validate these findings and to examine the comparative effectiveness of these interventions in more detail.
A Novel Cognitive Behavioral Therapy–Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study
Authors: Held Philip , Pridgen Sarah A , Chen Yaozhong , Akhtar Zuhaib , Amin Darpan , Pohorence Sean 
Published: August 2024

Background
Digital mental health tools, designed to augment traditional mental health treatments, are becoming increasingly important due to a wide range of barriers to accessing mental health care, including a growing shortage of clinicians. Most existing tools use rule-based algorithms, often leading to interactions that feel unnatural compared with human therapists. Large language models (LLMs) offer a solution for the development of more natural, engaging digital tools. In this paper, we detail the development of Socrates 2.0, which was designed to engage users in Socratic dialogue surrounding unrealistic or unhelpful beliefs, a core technique in cognitive behavioral therapies. The multiagent LLM-based tool features an artificial intelligence (AI) therapist, Socrates, which receives automated feedback from an AI supervisor and an AI rater. The combination of multiple agents appeared to help address common LLM issues such as looping, and it improved the overall dialogue experience. Initial user feedback from individuals with lived experiences of mental health problems as well as cognitive behavioral therapists has been positive. Moreover, tests in approximately 500 scenarios showed that Socrates 2.0 engaged in harmful responses in under 1% of cases, with the AI supervisor promptly correcting the dialogue each time. However, formal feasibility studies with potential end users are needed.

Objective
This mixed methods study examines the feasibility of Socrates 2.0.

Methods
On the basis of the initial data, we devised a formal feasibility study of Socrates 2.0 to gather qualitative and quantitative data about users’ and clinicians’ experience of interacting with the tool. Using a mixed method approach, the goal is to gather feasibility and acceptability data from 100 users and 50 clinicians to inform the eventual implementation of generative AI tools, such as Socrates 2.0, in mental health treatment. We designed this study to better understand how users and clinicians interact with the tool, including the frequency, length, and time of interactions, users’ satisfaction with the tool overall, quality of each dialogue and individual responses, as well as ways in which the tool should be improved before it is used in efficacy trials. Descriptive and inferential analyses will be performed on data from validated usability measures. Thematic analysis will be performed on the qualitative data.

Results
Recruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited.

Conclusions
The development of Socrates 2.0 and the outlined feasibility study are important first steps in applying generative AI to mental health treatment delivery and lay the foundation for formal feasibility studies.

International Registered Report Identifier (IRRID)
DERR1-10.2196/58195
PENGEMBANGAN MEDIA PEMBELAJARAN MATEMATIKA BERBASIS VISUAL BASIC 6.0 PADA MATERI MATRIKS KELAS XI
Authors: Mursalim Ummi Kalsum , Sulasteri Sri , Rasyid Muhammad Rusydi , Nur Fitriani , Latuconsina Nur Khalisah 
Published: August 2024

Penelitian pengembangan ini bertujuan untuk mengetahui prosedur pengembangan serta kualitas media pembelajaran matematika berbasis Visual Basic 6.0 pada materi matriks ditinjau dari tingkat kevalidan, kepraktisan, keefektifan. Research and Development (RD) adalah jenis penelitian yang digunakan pada penelitian ini dengan prosedur pengembangan ADDIE. Siswa kelas XI di MA Al-Hidayah kota Makassar menjadi subjek penelitian. Produk yang dihasilkan adalah media pembelajaran matematika berbasis Visual Basic 6.0 pada materi matriks mencakup operasi matriks terkhusus ordo dua dan tiga yang didesain dengan menarik yang dapat dioperasikan dengan komputer secara offline. Lima tahapan prosedur pengembangan ADDIE antara lain: tahap Analysis, Design, Development, Implementation, dan Evaluation. Selanjutnya, hasil dari kriteria kevalidan didasarkan pada hasil validasi oleh tim validator adalah 3,66 (sangat valid). Kriteria kepraktisan didasarkan pada hasil kuesioner tanggapan guru 97,5% (positif) dan lembar observasi kemampuan guru mengelola pembelajaran 3,89 (tinggi), kemudian dari kedua hasil analisis tersebut media memenuhi kriteria praktis. Kriteria keefektifan didasarkan pada hasil kuesioner tanggapan peserta didik 90,7 (sangat positif), pengamatan aktivitas peserta didik 89% (sangat baik), dan tes hasil belajar diperoleh persentase ketuntasan 90%, sehingga dari hasil pengukuran. diperoleh media memenuhi kriteria efektif. Dengan demikian, dapat disimpulkan bahwa media pembelajaran materi matriks layak untuk digunakan dalam pembelajaran