bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integration of Artificial Neural Network (ANN) in Set-Based Concurrent Engineering (SBCE) for Automotive Front Reinforcement Bumper Design
Authors: Shaharudin Muhammad Syafiq bin , Maulana Muhd Ikmal Isyraf bin Mohd , Najib Abdillah Sani bin Mohd , Kadir Aini Zuhra binti Abdul , Bakar Saiful Anuar bin Abu , Saufi Mohd Syahril Ramadhan bin Mohd 
Published: January 2025

Abstract
This research evaluates the effectiveness of the SBCE application, specifically targeting the design and optimization of the front reinforcement bumper of a car. SBCE is an enabler of lean product development process. The front reinforcement bumper is a critical component in automotive safety and performance, and its optimization requires evaluating numerous design alternatives to ensure the best possible solution. The primary goal of this integrated strategy is to enhance the efficiency of product creation, improve product performance, eliminate unnecessary waste, and reduce costs. SBCE, a collaborative methodology, facilitates this by systematically assessing multiple designs before selecting the most optimal solution. Conventional product development methodologies often lead to extended and costly iterations of modifications and inadequate final products. In contrast, SBCE promotes the development of diverse design alternatives systematically. However, manual decision-making and communication present considerable obstacles due to inherent biases and dependence on human implicit knowledge. By utilizing data analysis and pattern discovery, decision-making can be enhanced through integrating SBCE with machine learning (ML) that significantly improves the analysis of complex designs where optimal design solution generated automatically, particularly for the front reinforcement bumper of a car. This methodology results in enhanced design optimization based on customer requirement by reduce 22% part weight with increasing 56% for safety factor of the front reinforcement bumper.
Engine Performance Analysis Using Rice Bran Biodiesel Fuel Blended
Authors: Hisham A.N. , Noor M.M. , Lu H.K. , Beg M.S. , Abdullah A.R. , Norhafana M. , Harikrishnan S. 
Published: January 2025

Abstract
This paper explores the engine performance characteristics of rice bran biodiesel fuel and its potential as a sustainable alternative to traditional diesel fuel. The study investigates the composition and properties of blended rice bran biodiesel, conducts experimental tests on a single-cylinder engine, and optimizes the biodiesel blend for improved engine performance. The study also analysed the characteristics, performance experiments, and optimization of rice bran biodiesel blends. The experimental work has been carried out to assess rice bran biodiesel blends at different ratios, particularly for B10, B20, B30, and B40. According to the study’s results, optimizing the combustion processes for each fuel blend is essential to lower emissions, improve engine performance, and reduce combustion emissions. As the world explores more sustainable and renewable energy sources, rice bran biodiesel promises a more efficient and environmentally friendly future, particularly in regions with abundant rice bran resources.
Extreme Learning Machine Optimization based on Hippopotamus Optimization Algorithm for Gear Fault Diagnosis
Authors: Amirulaminnur R. , Firdaus Isham M. , Harith M. K. , Saufi M. S. R. , Saad W. A. A. , Hasan M. D. A. , Talib M. H. A. 
Published: January 2025

Abstract
It is crucial to ensure the dependability, reliability, and sustainability of machines to optimize industrial productivity and efficiency. Any malfunction or breakdown of machine components or mechanical equipment may result in unexpected downtime and financial losses. This study presents a maintenance strategy for mechanical equipment, primarily focusing on a gear failure diagnosis approach using an extreme learning machine optimization based on the hippopotamus optimization algorithm (HO). The proposed method was evaluated using sets of gear vibration signals obtained from an online database, that included both healthy and malfunctioning data. The HO approach was employed to determine an optimal parameter for the ELM method, specifically the number of neurones, input weight, and bias range values. The findings indicate that the proposed approach enhances the classification efficacy of ELM by 12% compared to traditional ELM. The proposed strategy can be applied in any relevant industry to improve the sustainability and dependability of its plants.
Lumped parameter model for 2D Dynamics Vibration Absorber
Authors: Saad Fazidah , Hamid Muhammad Najib Abdul , Yusuf Zainal Nazri Mohd , Mazlan Ahmad Zhafran Ahmad 
Published: January 2025

Abstract
The Dynamic Vibration Absorber (DVA) is a common and simple solution which can be easily tailored to eliminate resonant vibration of a vibrating system. A DVA is equipped by a spring/mass system that is installed to vibrating system which is usually called as the primary system to lessen the resonant response of it. The DVA is designed to have sufficient weight to transform the resonance frequency of the single degree of freedom primary system into two degree of freedom with two natural frequencies. This happened by properly tuning the DVA mass and stiffness to the system so that the initial resonant peak of the primary system will vastly reduce. Practically, it is absurd to design a DVA without using numerical solutions and intricate calculations to dampen a system. Therefore, the objective of this research is to produce a simple and resolute correlations DVA parameters simulation by using MATLAB Simulink to be used in designing and analysing the DVA system effectiveness. In this research, the mass ratio of 0.25 is considered during the initial design step of the DVA as this is the optimum mass ratio of the primary and DVA. Subsequently, the parameters of DVA system are developed in 2D lumped mass model using MATLAB Simulink for simplicity and the output is obtained in frequency and amplitudes. The results show the effects of vibration reduction vary depending on whether the frequency of the DVA is the same as the primary system’s natural frequency or not. It is proved that when the DVA is operated at the system’s resonance (122.96rad/s) with the highest amplitude, the vibration reduction is great, at 99.99%. When the DVA is applied at frequencies lower or higher than the primary system’s natural frequency, the vibration reduction is acceptable between 64.29% (at 128 rad/s) and 78.56% (at 110 rad/s). These data indicate that the DVA is most effective at the same frequency as the primary system, but when used at other frequencies, it is still capable of reducing the vibration. This has established that all parameters of the system can be employed in the constructed MATLAB Simulink lumped model of the DVA to optimize the DVA design with the aim to reduce the primary system’s resonant frequency.
Cogen2Co: Oil Palm Biomass Cogeneration Designer Software
Authors: Hanipah Ummi Najwa Abu , Zailan Roziah binti , Azizi Aniq Akmal Anaqi , Shiun Lim Jeng , Jamaluddin Khairulnadzmi , Mansor Fatin Zafirah 
Published: January 2025

Abstract
This study was conducted to develop a software, Cogen2Co capable of designing and planning the biomass cogeneration project. The system is leading to sustainable energy and environmentally beneficial solutions for energy use through simultaneous heat and electricity generation from oil palm biomass (OPB). However, these systems can be difficult to design without designated tools and prone to error. The proposed Cogen2Co software streamlines the design process by integrates all technical, economic and environmental aspects of a cogeneration design into one platform that coded in Visual Basic for Applications (VBA). A case study was conducted to validate the software, and then a cost-benefit analysis for OPB cogeneration system was performed. The results showed that while the initial investment and operating costs of the OPB cogeneration system was higher compared to a baseline system, it is still more financially viable. In fact, it found that profit fell 25% lower for return on investment (ROI) than the baseline. The OPB cogeneration system was also apparent in lower greenhouse gas emissions, resulting in a more efficient alternative and greener as well.
The Influences of the Fluid Initial Temperature and Fluid Flow Rate to the Boiling Heat Transfer: A Short Review
Authors: Shaidan Faiz , Basrawi Firdaus , Yudin Ahmmad Shukrie Md , Azman Nurul Aini Mohd 
Published: January 2025

Abstract
Recently, Low Temperature Vacuum Drying with induced Nucleation Boiling (LTVD-NB) has been studied for water reduction methods. However, low dewatering temperature led to longer drying process. Hence, to improve the LTVD-NB dewatering performance, the study on Boiling Heat Transfer (BHT) such as parameters, conditions and phenomena that may affect the heat transfer in boiling process should be understood further. Currently, some of parameters such as dewatering temperature, surface roughness, and initial water content effects in LTVD-NB have been discovered. However, the effect of the fluid initial temperature and hot water flow rate to the BHT in LTVD-NB is still unclarified. Hence, the objective of this paper is to review and examine the influences of fluid initial temperature and the effect of fluid flow rate to the BHT. In general, the result shows that the higher fluid initial temperature and flow rate significantly enhanced the BHT. As a result, the BHT in LTVD-NB system can be improved which will significantly improve the dewatering process of honey in the future.
Interlayer charge transfer in graphene–2D polyimide heterostructures
Authors: Falorsi Francesca , Zhao Shuangjie , Liu Kejun , Eckel Christian , Pöhls Jonas F , Bennecke Wiebke , Reutzel Marcel , Mathias Stefan , Watanabe Kenji , Taniguchi Takashi , Wang Zhiyong , Polozij Miroslav , Feng Xinliang , Heine Thomas , Weitz R Thomas 
Published: January 2025

Abstract
The vertical integration of multiple two-dimensional (2D) materials in heterostructures, held together by van der Waals forces, has opened unprecedented possibilities for modifying the (opto-)electronic properties of nanodevices. This not only allows for the exploration of new physical phenomena but also greatly broadens the application horizon of existing monolayer devices. Graphene, with its remarkable opto-electronic properties, is an ideal candidate for such applications. The other potential candidates are 2D polymers, crystalline polymeric materials with customizable structures and electronic properties, as they can be synthesized in all mathematically possible Bravais lattices. In this study, we investigated the optoelectronic properties of a heterostructure created by pristine graphene and a rectangular 2D polyimide (2DPI) film. This imprints a new superlattice on graphene in conjunction with a direct influence on its electronic properties. Theoretical and experimental analyses reveal that interlayer charge exchange between the 2D polymer and graphene induces hole doping in the graphene layer. We have also observed that the properties of the heterostructure are dependent on the substrate used in experiments, likely due to the porous character of the 2DPI allowing direct interaction of graphene with the support. Furthermore, we demonstrate a direct correlation between the thickness of the 2DPI layer and the extent of hole doping in graphene. These findings highlight the unique ability to tailor functionalities in 2D polymers-based heterostructures, opening avenues for the development of optoelectronic devices with precisely engineered properties and stimulating further exploration of the diverse phenomena accessible through tailored designs of the 2D polymers.
Road Layer Detection and Volume Calculation Using UAV Technologies and Artificial Intelligence
Authors: Iqbal Muhammad Hammad , Iqbal Muhammad Junaid , Saleem Tahir 
Published: January 2025

Abstract
Traditional methods for monitoring road construction often rely on manual measurements and visual inspections, which are time-consuming and prone to errors. This study introduces a novel approach that uses unmanned aerial vehicles (UAVs) and the You Only Look Once (YOLO) model (YOLOv11-seg) to significantly improve the accuracy and efficiency of road layer detection and volume calculation. By utilizing high-resolution images taken by the DJI Phantom 4 Pro drone, the model successfully identifies and segments crucial road layers, such as subgrade, asphalt, and aggregate base. The YOLOv11-seg model demonstrates remarkable classification accuracy, achieving 95% for both asphalt and aggregate base and 83% for subgrade. Additionally, the model reaches a maximum precision of 1.0 at the highest confidence level. Its F1 score is 0.92 at an optimal confidence threshold of 0.779, and it maintains a recall of 0.96 at lower confidence levels. For volumetric analysis, this study evaluates four software tools: Agisoft Metashape, CloudCompare, Civil 3D, and WebODM. It identifies WebODM as the most accurate, achieving volume accuracies of 99.85% for subgrade and asphalt and 99.9% for aggregate base. This application of drone imagery and object detection technology simplifies monitoring and significantly reduces labor and errors associated with traditional road construction monitoring methods.
Transference-Focused Psychotherapy
Authors: Blüml Victor 
Published: January 2025

Abstract
This chapter (“Transference-Focused Psychotherapy”) (TFP) provides an object relations perspective on the new ICD-11 personality disorder (PD) classification, addressing diagnostic and conceptual considerations as well as sketching out clinical implications. TFP is an evidence-based psychodynamic treatment for PDs developed by Otto Kernberg and coworkers. It is argued that there exist many points of convergence between an object relations–based approach to assessing and treating personality pathology with TFP and the new ICD-11 PD classification. The chapter reviews commonalities such as the focus on the severity of personality pathology and the importance given to disturbances in self- and interpersonal functioning for the diagnosis of PDs, while more critically evaluating the complementing trait-based model of maladaptive personality traits. The chapter concludes with a clinical example of the diagnostic process in TFP, including the use of the Structured Interview for Personality Organization (STIPO), and delineates some of the clinical implications.
Mentalization-Based Treatment
Authors: Simonsen Sebastian , Kongerslev Mickey T , Bateman Anthony 
Published: January 2025

Abstract
Mentalization-based therapy is an evidence-based psychotherapy originally developed for borderline personality disorder. The mentalizing model is founded on attachment theory and suggests that a key mechanism behind personality disorder is disruption of the ability to make sense of oneself and other people. We explain key constructs in the mentalizing model and review the evidence base for mentalization-based treatment for personality disorder. We explain how mentalization-based therapy may be considered a transdiagnostic approach yet may also be utilized in more specialized ways with persons who have specific trait constellations in the ICD-11. The clinician is guided on how to approach treatment for (a) moderate or severe personality disorder with prominent Negative Affectivity and/or Disinhibition, (b) moderate or severe personality disorder with prominent Dissociality and/or Negative Affectivity and/or Disinhibition, (c) mild–moderate personality disorder with some Dissocial traits and (d) mild–severe personality disorder with prominent Detachment and Negative Affectivity and/or Anankastia.
Compass
Authors: Sauer-Zavala Shannon , Fruhbauerova Martina , Stumpp Nicole E , Croom Hannah 
Published: January 2025

Abstract
As the International Classification of Diseases 11th revision (ICD-11) mental health diagnostic system shifts away from a categorical classification of personality disorders in favor of a dimensional, severity and trait-based approach, it is important to adapt psychological treatment in line with this evolution. COMPASS, which loosely stands for Cognitive Behavioral Modules for Personality System, is a novel psychological intervention with discrete treatment components to address trait domains of Neuroticism/Negative Affectivity, (low) Conscientiousness/Disinhibition, and (low) Agreeableness/Dissociality. This chapter describes the rationale, development, and utility of the Compass model, along with its application for ICD-11 personality disorders and related traits, and co-occurring forms of psychopathology.
Cross-Cultural Application
Authors: Hualparuca-Olivera Luis , Matsumoto Chihiro 
Published: January 2025

Abstract
The ICD-11 Classification of Personality Disorders and Related Traits, published by the World Health Organization (WHO), is to be employed across all cultures. This chapter deals with the ICD-11 personality disorder diagnosis from the perspective of culture, race, ethnicity, individualist/collectivist philosophy, historical/generational trauma, immigration, acculturation, religion, and gender. Efforts of researchers to design and validate culturally sensitive ICD-11 personality disorder measures are also overviewed. Four particular measures were specifically developed under cross-cultural conditions and by linguistically diverse experts. Finally, culturally relevant research directions are proposed to better capture all the ethnic edges that the implementation of the ICD-11 PD model implies.
Comparison of Different Hemostatic Net Applications in a Rat Dorsal Skin Flap Model
Authors: İsmail Bilge Kaan , Fındıkçıoğlu Kemal , Şibar Serhat , Elmas Çiğdem 
Published: January 2025

Abstract

Background
Although several articles have discussed the use of hemostatic nets to close dead spaces, no in vivo experimental studies have examined simultaneously the histology and tissue perfusion of these techniques.

Objectives
The aim of this study was to compare variations of the hemostatic net technique commonly used in current practice.

Methods
Two different hemostatic net suturing techniques and 2 suture removal times were tested, with a control group for comparison. In a modified McFarlane flap model, hemostatic net sutures were placed in either a vertical or horizontal pattern. Suture removal times were set at 60 hours and 7 days. Perfusion in the proximal, middle, and distal parts of the flap was assessed by SPY-assisted immunofluorescence angiography (Novadaq, Kalamazoo, MI) at 0 minutes, 60 hours, and 7 days after the first surgery. The rat dorsal flap was photographed in a standardized manner 1 week postsurgery. Flap survival areas were calculated as a percentage using ImageJ software (US National Institute of Health, Bethesda, MD). On day 20, all rats were killed and sent for histological examination.

Results
There was no statistically significant difference in macroscopic flap survival between the groups (P &gt; .05). Group 5 (control) was statistically different with lower neovascularization scores than the other groups in all 3 segments (P &lt; .01).

Conclusions
Hemostatic nets may improve neovascularization at the flap base but do not significantly affect overall flap survival.
BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells
Authors: Cao Lan , Zhang Wenhao , Yang Fan , Chen Shengquan , Huang Xiaobing , Zeng Feng , Wang Ying 
Published: January 2025

Abstract
Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference. We demonstrated that the TF activity inferred by BIOTIC can serve as a characterization of cell identity, and outperforms baseline methods for the tasks of cell typing, cell development tracking, and batch effect correction. Additionally, BIOTIC trained on multi-omics data can flexibly be applied to the scenario where merely single-cell transcriptome sequencing is available, to infer TF activity and annotate the cell type by mapping the query cell into the reference TF activity space, as an emerging application of cell atlases. The structure of BIOTIC has been determined to be adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis. BIOTIC introduces a pioneering biological-mechanism-driven framework to infer TF activity and elucidate cell identity states at gene regulatory level, paving the way for a deeper understanding of the complex interplay between TFs and gene expression in living systems.
mTOR signaling mediates energy metabolic equilibrium in bovine and mouse oocytes during the ovulatory phase
Authors: Ran Zaohong , Liu Ruiyan , Shi Hongru , Wang Xiaodong , Wu Zian , Zhou Shanshan , Liao Jianning , Hu Lichang , Hu Yongtao , Zhou Jintao , He Changjiu , Li Xiang 
Published: January 2025

Abstract
The mammalian target of rapamycin (mTOR) signaling pathway is activated by luteinizing hormone in preovulatory follicle. However, its impact on ovulation remains inadequately explored. Utilizing in vivo studies and in vitro fertilization, we demonstrated that the negative effect of inhibition of mTOR signaling by rapamycin on oocyte quality during the ovulatory phase, with a notable decrease in the total cell count of blastocysts, a reduction in gastrula size, and fetal degeneration on the 16th day of gestation while not affecting ovulated oocyte count or granulosa cell luteinization. Mechanistically, our study elucidated that in the ovulatory phase, mTOR signaling inhibition enhances lipid consumption, mitochondrial membrane potential of oocytes, and ATP generation. As a result, embryos derived from these oocytes exhibit higher levels of reactive oxygen species, insufficient energy supply, and lower developmental potency. Furthermore, the impact of mTOR signaling on oocytes remains consistent across various species, and its inhibition has been demonstrated to enhance energy metabolism during the in vitro maturation process of bovine oocytes. These findings demonstrate the critical role of mTOR signaling during the ovulatory phase in balancing oocyte energy metabolism, enriching our understanding of the role of mTOR on ovulation regulation.