bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient removal of Ni(II) by nanosecond pulsed discharge‐modified resins: Characterization, capacity, and kinetics
Authors: Wang Hong‐Li , Chen Chao‐Jun , Yuan Hao , Xu Qing‐Nan , Liang Jian‐Ping , Li Si‐Si , Wang Wen‐Chun , Yang De‐Zheng 
Published: August 2023

AbstractA porous functionalized resin was produced using nanosecond pulsed discharge for the purpose of removing Ni(II). The implications of discharge time, O2 concentration, and solution pH on adsorption capacity were explored. Studies indicated that nanosecond pulsed discharge‐modified resins in 5 min of discharge time and 8% of O2 concentration had a removal efficiency of 97.2%, and an increase of 36% compared to the raw. Furthermore, the residual Ni(II) concentration (0.3 mg/L) is well below the national permitted emission standard (0.5 mg/L). The rise in Ni(II) removal efficiency is associated with the increased specific surface area and oxygen‐containing functional groups. Chemisorption played an important role in the removal of Ni(II) by raw and modified resins.
Aeolus Rayleigh‐channel winds in cloudy conditions
Authors: Marseille Gert‐Jan , de Kloe Jos , Dabas Alain , Flament Thomas , Rennie Michael 
Published: August 2023

AbstractAeolus carried the first Doppler wind lidar to measure wind profiles from space. Aeolus was a European Space Agency explorer mission with the objective to retrieve winds data from the collected atmospheric return signal that is the result of Mie and Rayleigh scattering of laser emitted light by atmospheric molecules and particulates. During the course of the mission the quality of Aeolus winds measured in clear‐air conditions from Rayleigh‐channel‐collected data, so called Rayleigh‐clear winds, improved substantially. The same is true for winds measured in cloudy and aerosol‐rich atmospheric conditions from Mie‐channel‐collected data, the so‐called Mie‐cloudy winds. For the latter conditions, good quality winds can in principle also be obtained from Rayleigh‐channel‐collected data, the so‐called Rayleigh‐cloudy winds, if contamination of the purely molecular signal by Mie scattering is well addressed. We assess a linear and nonlinear correction for Mie contamination, the latter with the aid of numerical weather prediction model data for determing the correction parameters. We show that the nonlinear correction is able to provide unbiased Rayleigh‐cloudy winds. This makes Rayleigh‐cloudy winds suitable for use in numerical weather prediction, but also for direct comparison with other wind observations obtained in cloudy conditions, such as atmospheric motion wind vectors.
Prospects for developing an Hepatitis C virus E1E2‐based nanoparticle vaccine
Authors: Toth Eric A. , Andrianov Alexander K. , Fuerst Thomas R. 
Published: August 2023

AbstractGlobally, more than 58 million people are chronically infected with Hepatitis C virus (HCV) with 1.5 million new infections occurring each year. An effective vaccine for HCV is therefore a major unmet medical and public health need. Since HCV rapidly accumulates mutations, vaccines must elicit the production of broadly neutralising antibodies (bnAbs) in a reproducible fashion. Decades of research have generated a number of HCV vaccine candidates. Based on the available data and research through clinical development, a vaccine antigen based on the E1E2 glycoprotein complex appears to be the best choice, but robust induction of humoral and cellular responses leading to virus neutralisation has not yet been achieved. One issue that has arisen in developing an HCV vaccine (and many other vaccines as well) is the platform used for antigen delivery. The majority of viral vaccine trials have employed subunit vaccines. However, subunit vaccines often have limited immunogenicity, as seen for HCV, and thus multiple formats must be examined in order to elicit a robust anti‐HCV immune response. Nanoparticle vaccines are gaining prominence in the field due to their ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both arms of the immune system. This review discusses the potential for development of a nanoparticle‐based HCV E1E2 vaccine, with an emphasis on the potential benefits of such an approach along with the major challenges facing the incorporation of E1E2 into nanoparticulate delivery systems and how those challenges can be addressed.
Nonlinear control design for walking assistance training robotic systems
Authors: Liu Wei , Wang Shuoyu , Shi Peng 
Published: August 2023

AbstractIn this paper, the control problem for a novel walking assistance training robot is considered where the main objective is utilizing the robot to control the tilt angle and the position of a user. First, the novel walking assistance training robot together with a user is modeled as a nonlinear dynamical system in state space form with control input where the control input includes two elements standing for pulling forces on two straps which attach to the chest and back, respectively, of the user. Then, based on the nonlinear dynamical system and some results proposed in this paper, a feedback control system is developed, and a nonlinear state feedback control algorithm is designed where the control input includes two parts, namely, a dynamical part and a constant part. By doing this, it is ensured that the feedback control system is stable, and achieves the main objective under some limits for the control input. A simulation example is given to illustrate the effectiveness and performance of the proposed nonlinear state feedback control algorithm.
Automatically drawing vegetation classification maps using digital time‐lapse cameras in alpine ecosystems
Authors: Okamoto Ryotaro , Ide Reiko , Oguma Hiroyuki 
Published: August 2023

AbstractAlpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.
Tuning Elastic Properties of Metallic Nanoparticles by Shape Controlling: From Atomistic to Continuous Models
Authors: Erbì Matteo , Amara Hakim , Gatti Riccardo 
Published: August 2023

AbstractUnderstanding and mastering the mechanical properties of metallic nanoparticles is crucial for their use in a wide range of applications. In this context, atomic‐scale (molecular dynamics) and continuous (finite elements) calculations is used to investigate in details gold nanoparticles under deformation. By combining these two approaches, it is shown that the elastic properties of such nano‐objects are driven by their size but, above all, by their shape. This outcome is achieved by introducing a descriptor in the analysis of the results enabling to distinguish among the different nanoparticle shapes studied in the present work. In addition, other transition‐metal nanoparticles are considered (copper and platinum) using the aforementioned approach. The same strong dependence of the elastic properties with the shape is revealed, thus highlighting the universal character of the achievements.
A Skin‐Inspired Multifunctional Conductive Hydrogel with High Stretchable, Adhesive, Healable, and Decomposable Properties for Highly Sensitive Dual‐Sensing of Temperature and Strain
Authors: Ge Si Jia , Liu Shi Nian , Gu Zhong Ze , Xu Hua 
Published: August 2023

AbstractDeveloping smart hydrogels with excellent physicochemical properties and multi‐sensing capabilities for various simulation of human skin's functions still remains a great challenge. Here, based on simple and convenient one‐step covalent cross‐linking method enhanced by dynamic RS‐Ag interactions, a skin‐inspired multifunctional conductive hydrogel with desirable physicochemical properties (including high stretchability, self‐adhesion, self‐healing, decomposition and removability) is developed for highly sensitive dual‐sensing of temperature and strain. Benefiting from the synergistic action of multiple hydrogen bonds, RS‐Ag bonds and S‐S bonds, the gel exhibited a novel thermosensitive mechanism. The prepared hydrogels exhibited extremely high mechanical properties (maximum tensile strength of 0.35 MPa, elongation at break nearly 1800%, compressive stress over 4.43 MPa), excellent self‐healing (96.82% (stress), 88.45% (temperature), 73.89% (mechanical property)), decomposition (the molecular weight after decomposition is below 700) and self‐adhesion (enhanced contact with the material interface). In addition, this conductive hydrogel could also simultaneously achieve highly sensitive temperature‐sensing (TCR: 10.89) and stress‐sensing (GF: 1.469). As a proof‐to‐concept, the hydrogel displayed superior capability for simulation of human skin to perception of touch, pressure and ambient temperature simultaneously, indicating promising applications in the fields of wearable devices, personal health care, and human‐machine interfaces.
The ‘Mousetrap’: Challenges of the Fluctuating Demand on the Electricity Grid in the UK
Authors: Milev George , Al-Habaibeh Amin 
Published: August 2023

AbstractCurrently, in the UK the power demand fluctuates throughout the day, especially during wintertime. The shape of the demand curve resembles the profile of a ‘mouse’. Around the tail area the energy consumption increases gradually, and that represents the demand during early morning hours which eventually flattens until the early evening. The head of the ‘mouse’ represents the early evening hours and the load on the grid peaks sharply. These sudden surges in power demand could potentially damage the grid leading to possible blackouts. In addition, this makes it difficult to depend on renewable energy such as wind turbines and solar panels without the use of energy storage. To examine the link between people’s behaviour and the grid, Covid-19 lockdown patterns were explored as it was a time of change in behaviour. Even during COVID-19 restrictions, which had an impact on people’s lives, the shape of the power demand curve did not significantly change; it followed the same shape of a ‘mouse’ compared to pre and post Covid periods, but during the pandemic the energy demand levels were slightly lower. The results suggests that in order to improve the stability of the demand, and hence remove this ‘mousetrap’, alternatives such as using renewable energy combined with electric and thermal energy storage systems can be integrated into the grid, which eventually can lead to flatten the demand curve.
Towards the Tees Valley Energy Transition—Residential Decarbonisation and Skills Analysis
Authors: van Schaik Paul , Cotton Matthew , Dawood Huda , Dawood Nashwan , Imani Elena , Knowles Michael , Leighton Charlotte , Lorrimer Susan , Mountain Andrea , Segovia Edgar , Stubbs Rosemary , Vall Natasha 
Published: August 2023

AbstractGuided by the theory of planned behaviour, we conducted an interview study to identify beliefs that potentially influence social-housing residents’ future behaviour with domestic low-carbon technology in retrofit housing. The study included (group interview n = 6, individual interviews n = 14). Behavioural beliefs, normative beliefs and control beliefs were identified from participants’ answers using the theory of planned behaviour and thematic analysis. For successful implementation, it will be important to ensure that the predominant benefits (positive beliefs) emerging from this study are implemented (e.g., lower heating bills) and the barriers (negative beliefs) are avoided or alleviated (e.g., upfront costs). The results will be quantified in a follow-up, survey study.
Exploring Windows Opening Behaviour of Occupants of Residential Buildings Using Artificial Intelligence
Authors: Salim Sherna , Al-Habaibeh Amin 
Published: August 2023

AbstractThe residential sector contributes significantly to the overall energy consumption in the UK. Occupant’s behaviour is a major factor that sometimes could be overlooked when considering energy efficiency of buildings. Although building simulations play a major role in the design of energy efficient buildings, significant discrepancies have been seen between the actual and predicted energy values in simulation models. This is because simulation does not consider occupant behaviour in many cases or finds it difficult to present such behaviour. This study attempts to investigate the reason behind occupant’s behaviour of windows opening; to analyse the collected data, to understand the occurrences that lead to an occupant opening window in a residential building. In this paper, data were collected from a selected house in Nottingham. Data were experimentally collected in peak winter of 2020, from end of Nov/20 to March/21 during Covid-19 pandemic. Sensors were fitted to measure radiator temperature, room temperature, room humidity and near-window temperature both upstairs and downstairs. Outdoor ambient temperature for the same period was also collected. Stochastic models were implemented with datasets with window opening behaviour as response. The input parameters to the model were the radiator temperature, room temperature, outside ambient temperature, and time. The predicated output was the window status (open/close). The prognostic precision of the suggested model was verified by testing the models with the rest of the collected data. The predicted values were compared with the actual measured values. The results show a strong prediction with success rate of 76.2%. The findings from this investigation have been found to be able to identify the factors that contribute to the ‘performance gap’ in energy efficiency of a building.
Development of an Affordable MGT-CHP for Domestic Applications
Authors: Hosseini Seyedvahid , Madani Seyed Hossein , Hatami Sara , Izadi Ali , Sarkandi Mohamad Ali , Norouzi Ali , Chizari Mahmoud 
Published: August 2023

AbstractThe micro gas turbine (MGT) is considered one of the main solutions for the future power generation system to provide secure and stable energy. Thanks to its multi-fuel capability and high values of power-to-weight ratio, it is a suitable candidate for many applications such as Combined Heat and Power (CHP) systems, range extenders, and auxiliary power units. Among these applications, the micro-CHP system benefits from both the electricity and exhaust heat of the MGT for household or industrial process applications. The MGT could be integrated with a heat exchanger to introduce a CHP boiler to the domestic boiler market. To reduce the cost and size of the package and to compete with a traditional boiler the simple Brayton cycle without the recuperator is considered and all of the useful energy in the exhaust gas is transferred to the heat exchanger to provide hot water. To further reduce the cost of the system to compete in the market, off-the-shelf components were adopted in this project. In this article, the development process of this product is presented including conceptual design based on the type and size of the market. It follows with an evaluation of off-the-shelf compressor and turbine modulus from the automotive turbochargers to match the operating conditions. Here, the MGT is designed in a way that can be adapted to the boilers with minimum components change. A high-speed alternator was powered with a tie grid drive/inverter to enable a bi-directional connection of the power unit to the network. A comparison between the product definition and experimental results of a demonstrator prototype is presented which reveals gaps between design and prototype outcomes. Analysis shows that 23% of the power degradation can be recovered by enhancing the cooling. Potential development and improvement scenarios are addressed for future development.
Oil and Gas Supply Chain: Analysing Stakeholder Sustainability Risk Perception
Authors: Egila Ashem , Kamal Muhammad Mustafa , Tjahjono Benny 
Published: August 2023

AbstractRecent developments on a worldwide scale have highlighted the continued importance of the oil and gas supply chain, despite the growing demand for a more diverse energy portfolio, especially in many developing countries. This study investigates the sustainability risks present within the oil and gas supply chain by analysing the subjective risk perceptions of internal stakeholders. In the context of this study, sustainability risk factors are identified across the three pillars of sustainability (environmental, social, and economic). A quantitative research survey design of oil and gas industry stakeholders was undertaken, specifically identifying top management, middle management, supervisors, and operations to assess any variation in sustainability-related risk perception. Failure mode and effect analysis (FMEA) and partial least squares structural equation modelling (PLS-SEM) were used to determine the relative importance of the selected risks. The results indicate that stakeholder risk perception significantly impacted sustainability risk management within the oil and gas sectors. It also shows that socioeconomic risk variables were considered more severe and frequent than environmental risk factors as the most significant for internal stakeholders in the oil and gas industries within the study context.
Computational Analysis of Hydro-powered Bunyip Pump
Authors: Beard Scott Daniel , Al Qubeissi Mansour , Khanal Bidur 
Published: August 2023

AbstractWith the emerging energy demand, water shortage in rural areas, electric supply challenges, and urgent needs for net zero technologies, there has been a recent response with alternative Hydro-Powered Pumping (HPP) technology, known as the Bunyip. The recently developed system continues to build commercial success, designed to overcome several limitations associated with the previous technology of Hydraulic Ram Pump (HRP) system, such as capacity, height and water leakage issues. This paper is aimed at providing in-depth investigation into the HPP system and possible further hydraulic enhancement, using CFD parametric analysis. This could provide an insight into the fundamental flow mechanics, operational efficiency, standard capacity, and relative delivery. The investigation comprises an initial manufacture data appraisal of performance for three HPP devices. We paired our analysis with the meticulous application and numerical modelling to gather the parametric dataset, and validate against physical testing data. One key finding was that for a delivery head of 50 m, a 6 L/s supply at 4 m of head, resulted in an efficiency of 12% with respect to the water delivered relative to the volume ‘wasted’ through the discharge. Thus, in order to facilitate some of the distinguishing features for the Bunyip, the efficiency is sacrificed to a value lower than that comparable to an HRP.
S-confining gauge theories and supersymmetry enhancements
Authors: Bajeot Stephane , Benvenuti Sergio , Sacchi Matteo 
Published: August 2023

Abstract
We propose new classes of 4d$$ \mathcal{N} $$
N
= 1 S-confining gauge theories, with a simple gauge group, rank-two matter and cubic superpotentials. The gauge group can be symplectic, orthogonal or special unitary. In some cases we derive the dualities via the deconfinement technique that uses iteratively known, more fundamental, dualities. In the symplectic case we discuss the 3d reduction to a confining unitary gauge theory with monopole superpotential. This 3d S-confinement provides an understanding of a recently proposed 4d$$ \mathcal{N} $$
N
= 1 theory that flows to the conformal manifold of $$ \mathcal{N} $$
N
= 4 SYM with SU(2n + 1) gauge group. The 3d perspective allows us to generalize this construction to another similar flow with supersymmetry enhancement: a 4d$$ \mathcal{N} $$
N
= 1 theory that flows to the conformal manifold of a 4d$$ \mathcal{N} $$
N
= 2 necklace theory with SU(2n + 1)3 gauge group.
Improving Graph Neural Networks by combining active learning with self-training
Authors: Katsimpras Georgios , Paliouras Georgios 
Published: August 2023

AbstractIn this paper, we propose a novel framework, called STAL, which makes use of unlabeled graph data, through a combination of Active Learning and Self-Training, in order to improve node labeling by Graph Neural Networks (GNNs). GNNs have been shown to perform well on many tasks, when sufficient labeled data are available. Such data, however, is often scarce, leading to the need for methods that leverage unlabeled data that are abundant. Active Learning and Self-training are two common approaches towards this goal and we investigate here their combination, in the context of GNN training. Specifically, we propose a new framework that first uses active learning to select highly uncertain unlabeled nodes to be labeled and be included in the training set. In each iteration of active labeling, the proposed method expands also the label set through self-training. In particular, highly certain pseudo-labels are obtained and added automatically to the training set. This process is repeated, leading to good classifiers, with a limited amount of labeled data. Our experimental results on various datasets confirm the efficiency of the proposed approach.