bluuebunny/crossref_metadata_2025_split huggingface dataset:
Session 22. Oral Presentation for: Empowering process safety hazard awareness and technical learning in energy operations
Authors: Ledrut Laurence 
Published: June 2024

Presented on Wednesday 22 May: Session 22 The Process Safety Management discipline is still misunderstood and deemed complex despite the efforts and progress that have been made over the past decades in the energy industry. This is important as the energy industry turns towards the energy transition and needs to remember the fundamentals of managing process safety risks in emerging technologies. At Origin Energy, leadership, risk management and learning are tightly connected when it comes to ensuring good process safety. In this context, Origin Energy’s Integrated Gas Business Unit has implemented Process Safety Management initiatives over the last 5 years grouped under three Process Safety Management strategic pillars: empowered process safety leadership; open-minded learning culture and best practice process hazard management. The aim of these initiatives was to develop a heightened process safety culture across the business unit. The approach used was to demystify process safety concepts to generate engagement and ownership at all levels. This was supplemented with implementing a companywide Engineering and Technical Community of Practice to enable sharing of technical learning across boundaries and building strong process safety risk awareness foundations supported by a Process Hazard Analysis Revalidation program. To access the Oral Presentation click the link on the right. To read the full paper click here
Towards accessible robot-assisted physical play for children with physical disabilities
Authors: Mahdi Hamza , Jouaiti Melanie , Saleh Shahed , Dautenhahn Kerstin 
Published: June 2024

Abstract
MyJay is an open-source robot designed to facilitate play between children with and without physical disabilities.
The robot acts as a proxy for children with upper limb challenges, allowing them to participate in physical games with their
peers. Our design was inspired by the FIRST Robotics Competition, which involves teleoperating robots to manipulate objects.
Taking a user-centred perspective, we consulted therapists and conducted remote interviews with children with disabilities and
their guardians at various stages of the design process. We then conducted an in-person feasibility study with 18 typically
developing children in a school setting. The study involved children teleoperating the robot to pick up and throw balls into a
designated goal, and the interaction was evaluated using the user experience questionnaire and the Robotic Social Attributes
Scale. The results of the study show great potential for MyJay to act as a play mediator in various scenarios, and the response
from the children was positive. The ultimate aim of our research agenda is to pave the way towards creating more inclusive play
environments through robot-mediated interactions, breaking barriers posed by physical limitations.
Long-Cycle Lithium Batteries with LiNi0.8Co0.1Mn0.1O2 Cathodes above 4.5V Enabled by Uniform Coating of Nanosized Garnet Electrolytes
Authors: Wang 王 Jianqun 建群 , Zhao 赵 Ning 宁 , Guo 郭 Xiangxin 向欣 
Published: June 2024

Abstract
The pursuit of high-energy cathode materials has been focused on raising the charging cutoff voltage of nickel (Ni)-rich layered oxide cathode such as LiNi0.8Co0.1Mn0.1O2 (NCM811). However, the NCM811 suffers from rapid capacity fading upon cycling at cutoff voltage higher than 4.5 V, owing to their structural degradation and labile surface reactivity. Surface-coating with solid electrolytes has been recognized as an effective method to mitigate the performance failure of NCM811 at high voltage. Herein, the nano-sized Li6.4La3Ta0.6Zr1.4O12 (LLZTO) is uniformly coated on the surface of single-crystal NCM811 particles, accompanied with the long-range Ta5+ diffusion into the transition metal layer of NCM811 lattice. It is revealed that the LLZTO coating can not only inhibit the surface reactions of NCM811 with liquid electrolytes but also play an important role in suppressing the bulk microcracking within the NCM811 particles. The incorporation of Ta5+ ion expands the lattice spacing and thereby improves the homogeneity of the Li+ diffusion in the single-crystal NCM811, which alleviates the mechanical strain and intragranular cracks caused by nonuniform phases-transformation at high charging voltage. The synergy of surface protection and structural stabilization realized by LLZTO coating enables the NCM811-based lithium batteries to achieve a remarkable electrochemical performance. Typically, LLZTO coated NCM811 delivers a high reversible specific capacity of 202.1 mAh⋅g−1 with an excellent capacity retention as high as 70% over 1000 cycles upon charging to 4.5 V at 1 C.
Stochastic electromechanical bidomain model
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Authors: Bendahmane M , Karlsen K H , Mroué F 
Published: June 2024

Abstract
We analyse a system of nonlinear stochastic partial differential equations (SPDEs) of mixed elliptic-parabolic type that models the propagation of electric signals and their effect on the deformation of cardiac tissue. The system governs the dynamics of ionic quantities, intra and extra-cellular potentials, and linearised elasticity equations. We introduce a framework called the active strain decomposition, which factors the material gradient of deformation into an active (electrophysiology-dependent) part and an elastic (passive) part, to capture the coupling between muscle contraction, biochemical reactions, and electric activity. Under the assumption of linearised elastic behaviour and a truncation of the nonlinear diffusivities, we propose a stochastic electromechanical bidomain model, and establish the existence of weak solutions for this model. To prove existence through the convergence of approximate solutions, we employ a stochastic compactness method in tandem with an auxiliary non-degenerate system and the Faedo–Galerkin method. We utilise a stochastic adaptation of de Rham’s theorem to deduce the weak convergence of the pressure approximations.
A sound-generating polarity detector for visually impaired students
Authors: Celikkanli Nuray Onder , Karadag Mustafa 
Published: June 2024

Abstract
This study aims to develop a polarity detector that generates sounds in two different patterns for positive and negative polarity, which enables visually impaired students to detect the polarity of electrically charged objects in static electricity experiments. The traditional electroscopes used in physics laboratories are not practical enough to determine the polarity of electrically charged objects. For visually impaired students to perform static electricity experiments on their own, there is a need for an electroscope that informs them about the polarity of electrically charged objects, for example, by generating sounds or vibrations in different patterns. With this motivation, we developed a sound-generating polarity detector with a small budget for two polarities. Visually impaired students and others can easily use such a digital educational tool in the physics laboratories. Physics teachers can use this laboratory tool as an educational material that reduces their workload, and it would be beneficial to bring it into the existing literature.
Efficient ECCD non-inductive plasma current start-up, ramp-up, and sustainment for an ST fusion reactor
Authors: Ono M. , Berkery J.W. , Bertelli N. , Shiraiwa S. , Delgado-Aparicio L. , Menard J.E. , Sánchez-Villar Á. , Shah K. , Shevchenko V. , Idei H. , Hanada K. 
Published: June 2024

Abstract
The elimination of the need for an Ohmic heating solenoid may be the most impactful design driver for the realization of economical compact fusion tokamak reactor systems. However, this would require fully non-inductive start-up and current ramp-up from zero plasma current and low electron temperature of sub-keV to the full plasma current of ∼10–15 MA at 20–30 keV electron temperature. To address this challenge, an efficient solenoid-free start-up and ramp-up scenario utilizing a low-field-side-launched extraordinary mode at the fundamental electron cyclotron harmonic frequency (X–I) is proposed, which has more than two orders of magnitude higher electron cyclotron current drive (ECCD) efficiency than the conventional ECCD for the sub-keV start-up regime. A time dependent model was developed to simulate the start-up scenarios. For the Spherical Tokamak Advanced Reactor (STAR) (Menard et al 2023 Next-Step Low-Aspect-Ratio Tokamak Design Studies (IAEA)), it was found that to fully non-inductively ramp-up to 15 MA, it would take about 25 MW of EC power at 170 GHz. Because of the relatively large plasma volume of STAR, radiation losses must be considered. It is important to make sure that high Z impurities are kept sufficiently low during the early current start-up phase where the temperature is sub-keV range. Since the initial current ramp up takes place at a factor of ten lower density compared to the sustained regimes, it is important to transition into a higher bootstrap fraction discharge at lower density to minimize the ECCD power requirement during the densification. For the sustainment phase an array of eight gyrotron launchers with a total of about 60 MW of fundamental O-mode was found to be sufficient to provide the required axis-peaked external current drive. High efficiencies between 19–57 kA MW−1 were found with optimal aiming, and these were resilient to small changes in aiming angles and density and temperature profiles.
Properties of ethanol-based foamed asphalt binders using the molecular dynamics (MD) method
Authors: Yao Hui , Da Costa Santana Feriana , Wang Yiran 
Published: June 2024

Abstract
The molecular structure models of asphalt binder, ethanol additive, and ethanol-based foamed asphalt were constructed through the Molecular Dynamics (MD) method. The standard ethanol-based foamed asphalt model was employed to describe the modifier with its different compositions, including 10%, 20%, and 30% ethanol. The simulation calculations were done for the ethanol-based foamed asphalt molecular models under the NPT and NVT ensembles. The density, glass transition temperature, and radial distribution function of ethanol-based foamed asphalt molecular models were obtained to verify the rationalization of asphalt models and analyze the variation of density parameters with temperature and ethanol content for ethanol-based foamed asphalt molecular models. The results show that the simulated densities of the asphalt binder and three ethanol-based foamed asphalt molecular models remained constant with the increase of simulation steps. The simulated density values of basic and 10%-ethanol-based foamed asphalt molecular models are close to 0.9 g cm−3. The simulated densities of 20%-ethanol-based and 30%-ethanol-based foamed asphalt molecular models were 0.8 g cm−3 and 0.75 g cm−3. Meanwhile, the simulated density values of both asphalt binder and all ethanol-based foamed asphalt decreased with the increase in temperature and ethanol additive dosage. The glass transition temperatures of basic asphalt binder, 10%-ethanol-based, 20%-ethanol-based, and 30%-ethanol-based foamed asphalt occurred in the range of 275–295 K, 330–350 K, 330–350 K, and 320–340 K, respectively. In contrast, the glass transition temperature of ethanol-based foamed asphalt increased with the increase of ethanol additive dosage, indicating that adding ethanol additive significantly improved the high-temperature resistance of matrix asphalt. In the radial distribution function diagrams of all samples, the first strong peak appeared at 0.85–1.3 Å, and the second strong peak appeared at 1.95–2.35 Å. Moreover, both peaks increased with the increase of ethanol additive dosage, suggesting that the contact between ethanol molecules and asphalt molecules was closer with the rise of ethanol additive dosage.
The impact of bio-oil on the structure, rheology, and adhesion properties of lignin-modified asphalt
Authors: Wu Qun , Hou Guangzhen 
Published: June 2024

Abstract
This research investigates the efficacy of bio-oil as a sustainable modifier for lignin-modified asphalt (LMA), aiming to enhance its performance characteristics. Utilizing Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), the study analyzes the chemical and microstructural changes induced by bio-oil in LMA. Rheological properties were evaluated using Dynamic Shear Rheometry (DSR), revealing that the addition of 5%–10% bio-oil to LMA significantly reduced stiffness and brittleness, improving ductility and fatigue resistance. For instance, LMAs with 10% bio-oil demonstrated a fatigue life at 2.5% strain comparable to unmodified asphalt. Additionally, bio-oil inclusion increased adhesive strength between asphalt and aggregates, enhancing moisture resistance. Low-temperature properties assessed by dynamic mechanical analysis (DMA) showed improved flexibility and thermal crack resistance with bio-oil addition. These findings underscore the potential of bio-oil in developing high-performance, sustainable asphalt binders, contributing to the advancement of eco-friendly road construction materials.
Broadband dielectric spectroscopy of Nb-doped 0.7BiFeO3–0.3BaTiO3 ceramics
Authors: Haronin V , Yang Z , Grigalaitis R , Calisir I , Banys J , Hall D A 
Published: June 2024

Abstract
Bismuth ferrite-barium titanate (BF-BT) solid solutions are lead-free ferroelectrics that show great promise as the basis for high temperature piezoelectric transducers. This article investigates the dielectric properties of such materials using broadband dielectric spectroscopy. The study focuses on the re-entrant relaxor ferroelectric behaviour of Nb-doped BF-BT ceramics, exploring a wide frequency range from approximately 20 Hz to 30 GHz and temperature from 200 to 500 K. The results reveal the presence of thermally induced transitions between ordered and disordered states. Quantitative analysis of the dielectric dispersion is accomplished using the Cole-Cole model, modified to account for the contribution from conduction losses at low frequencies/high temperatures. This analysis revealed that the freezing temperature of the polar nanoregions is around 158 K, with an activation energy of 0.194 eV. The findings contribute to understanding the dielectric relaxation mechanisms and thermal evolution of functional properties in BiFeO3-BaTiO3 ceramics.
Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation
Authors: Xie Minzhu , Kuang Yabin , Song Mengyun , Bao Ergude 
Published: June 2024

Abstract

Motivation
The identification of cancer subtypes plays a crucial role in cancer research and treatment. With the rapid development of high-throughput sequencing technologies, there has been an exponential accumulation of cancer multi-omics data. Integrating multi-omics data has emerged as a cost-effective and efficient strategy for cancer subtyping. While current methods primarily rely on genomics data, protein expression data offers a closer representation of phenotype. Therefore, integrating protein expression data holds promise for enhancing subtyping accuracy. However, the scarcity of protein expression data compared to genomics data presents a challenge in its direct incorporation into existing methods. Moreover, striking a balance between omics-specific learning and cross-omics learning remains a prevalent challenge in current multi-omics integration methods.

Results
We introduce Subtype-MGTP, a novel cancer subtyping framework based on the translation of Multiple Genomics To Proteomics. Subtype-MGTP comprises two modules: a translation module, which leverages available protein data to translate multi-type genomics data into predicted protein expression data, and an improved deep subspace clustering module, which integrates contrastive learning to cluster the predicted protein data, yielding refined subtyping results. Extensive experiments conducted on benchmark datasets demonstrate that Subtype-MGTP outperforms nine state-of-the-art cancer subtyping methods. The interpretability of clustering results is further supported by the clinical and survival analysis. Subtype-MGTP also exhibits strong robustness against varying rates of missing protein data and demonstrates distinct advantages in integrating multi-omics data with imbalanced multi-omics data.

Availability and implementation
The code and results are available at https://github.com/kybinn/Subtype-MGTP.
Why there is Now Non-Western International Relations Theory
Authors: Xiao Ren 
Published: June 2024

Abstract
Over the past two decades, Chinese international relations scholars have embarked on a journey of innovative thinking in world politics. The endeavour has borne theoretical fruits that both complement existing paradigms and potentially constitute a distinct “Chinese school” of international relations. Contrary to common misconceptions, this emerging school seeks not to challenge Western theoretical hegemony outright, but rather to reflect the organic evolution of Chinese intellectual contributions, independent of direct government influence. Notably, such theories as gongsheng/symbiosis have, in diverging from Western IR frameworks, matured autonomously. The emergence of a “Chinese school” thus heralds a significant stride towards a more inclusive Global IR programme.
Application of artificial intelligence to chronic kidney disease mineral bone disorder
Authors: Lederer Eleanor D , Sobh Mahmoud M , Brier Michael E , Gaweda Adam E 
Published: June 2024

ABSTRACT
The global derangement of mineral metabolism that accompanies chronic kidney disease (CKD-MBD) is a major driver of the accelerated mortality for individuals with kidney disease. Advances in the delivery of dialysis, in the composition of phosphate binders, and in the therapies directed towards secondary hyperparathyroidism have failed to improve the cardiovascular event profile in this population. Many obstacles have prevented progress in this field including the incomplete understanding of pathophysiology, the lack of clinical targets for early stages of chronic kidney disease, and the remarkably wide diversity in clinical manifestations. We describe in this review a novel approach to CKD-MBD combining mathematical modelling of biologic processes with machine learning artificial intelligence techniques as a tool for the generation of new hypotheses and for the development of innovative therapeutic approaches to this syndrome. Clinicians need alternative targets of therapy, tools for risk profile assessment, and new therapies to address complications early in the course of disease and to personalize therapy to each individual. The complexity of CKD-MBD suggests that incorporating artificial intelligence techniques into the diagnostic, therapeutic, and research armamentarium could accelerate the achievement of these goals.
Advancing PAM-less genome editing in soybean using CRISPR-SpRY
Authors: Chen Xiao , Zhong Zhaohui , Tang Xu , Yang Suxin , Zhang Yaohua , Wang Shoudong , Liu Yiqian , Zhang Ye , Zheng Xuelian , Zhang Yong , Feng Xianzhong 
Published: June 2024

Abstract
Although CRISPR-Cas9 technology has been rapidly applied in soybean genetic improvement, it is difficult to achieve the targeted editing of the specific loci in the soybean complex genome due to the limitations of the classical protospacer adjacent motif (PAM). Here, we developed a PAM-less genome editing system mediated by SpRY in soybean. By performing targeted editing of representative agronomic trait targets in soybean and evaluating the results, we demonstrate that the SpRY protein can achieve efficient targeted mutagenesis at relaxed PAM sites in soybean. Furthermore, the SpRY-based cytosine base editor SpRY-hA3A and the adenine base editor SpRY-ABE8e both can accurately induce C-to-T and A-to-G conversion in soybean, respectively. Thus, our data illustrate that the SpRY toolbox can edit the soybean genomic sequence in a PAM-free manner, breaking restrictive PAM barriers in the soybean genome editing technology system. More importantly, our research enriches soybean genome editing tools, which has important practical application value for precise editing and molecular design in soybean breeding.
An assessment of greenhouse gases emission from diesel engine by adding carbon nanotube to biodiesel fuel using machine learning technique
Authors: Beirami Ali Asghar Moslemi , Maghsoudlou Ebrahim , Nasrabadi Mohammadali , Sergeevna Klunko Natalia , Abdullaev Sherzod , Ibrahim Wubshet 
Published: June 2024

Abstract
Due to the depletion of fossil fuel reserves, the significant pollution produced during their combustion and the increasing costs, biodiesel sources have gained recognition as an attractive alternative energy source. The integration of carbon nanotubes (CNTs) as a catalyst with biofuels such as biodiesel and bioethanol has the potential to optimize engine performance and reduce emissions when used in conjunction with diesel fuel. An emissions and performance prediction model for diesel engines is introduced in this research, utilizing biodiesel and CNTs in conjunction with machine learning. Due to its proficiency in forecasting systems with limited data, the emotional artificial neural network (EANN) model of machine learning was implemented. As an innovative approach, this study considers the following variables: fuel calorific value, fuel speed, engine density, viscosity, fuel consumption, exhaust gas temperature, oil temperature, oxygen output from exhaust gas, humidity, ambient temperature and ambient air pressure. The model was informed of every effective technical and functional environment parameter. This study additionally assessed the pollution and engine performance forecasts generated by the EANN model. Adding 5% biodiesel to gasoline fuel decreased carbon monoxide emissions while increasing torque and braking power, according to the findings. The fuel’s specific consumption increased. These findings were consistent with previous investigations. Moreover, as the concentration of CNTs in the fuel mixture increased, NOx, NO, CO2 and CO emissions decreased. The addition of 120 ppm of CNT to biodiesel–diesel fuel decreased emissions of CO, NO, NO2 and NO by 12.90%, 14.53%, 18.80% and 47.68%, respectively. The performance of the EANN model was found to be optimal when trained with the rectified linear unit activation function, as demonstrated by the evaluation results using various neurons.
Investigating the effect of effective parameters on the optimization of zero liquid discharge system
Authors: Mohammadi Sharare , Ahmadi Mohammad Hossein , Ehsani Ramin 
Published: June 2024

Abstract
Due to the critical water situation in the world and the need for freshwater, the use of desalination systems is very common. One of the main problems of using such devices is the production of a high-concentration brine stream, which causes environmental problems. Therefore, the need to use systems that reduce the level of salt water to zero (zero liquid discharge [ZLD]) is felt more than ever. The biggest challenge for the widespread implementation of thermal ZLD systems is its high energy consumption and, as a result, high cost. Therefore, we seek to find effective parameters to reduce the heat transfer surface and energy consumption to make it economic. In this research, it is possible to mention the inlet and outlet salinity, Overall heat transfer coefficients, evaporator temperature and cooling water temperature among the parameters investigated in the evaporator and the parameters investigated in the crystallizer include the operating temperature of the crystallizer and steam temperature in the heat exchanger. The results showed that the most optimal state (the lowest total heat transfer surface and energy consumption) of the ZLD system in the overall heat transfer coefficient in the evaporator is equal to 4 kW/m2 °C. The total heat transfer surface is equal to 2258.78 m2 and energy consumption is 2111.5 kW.