dataesr/scientific-paragraphs-categorization huggingface dataset:
More specific identification of measures were also sufficient for inclusion (e. g. feed substitution, genetic improvements to reduce enteric fermentation methane emissions). Malley et al (2022)  describe in further detail the process of analysing each NDC, and present the results from each individual NDC analysis, including the mitigation measures identified within each NDC, and the specific text extracted from each NDC that justifies the identification of each mitigation measure. Secondly, to assess the extent to which the implementation of mitigation measures explicitly included in NDCs could contribute to achieving the GMP, national and sectoral methane emission estimates from the Emissions Data for Global Atmospheric Research v7. 0 (EDGAR) emission inventory database were used  (Crippa et al 2021).
Background: Heating and cooling in buildings represents a significant amount of the energy demand in the EU, but the market penetration of renewable solutions is still marginal. The SunHorizon project aims at proving the viability and benefits of innovative coupling between heat pumps and various advanced solar panels. Methods: This study focuses on the optimal operation strategies of a technological package composed of hybrid photovoltaic thermal (PVT) panels, a gas driven heat pump and a hot water storage tank. In this work, a model predictive control is developed, based on mixed integer linear programming (MILP) optimization. This model uses innovative elements compared to traditional model predictive control (MPC), with environmental indicators for the electricity grid accounting for imports, co-simulation with TRNSYS using the transmission control protocol (TCP) and modelling of inter-seasonal storage for long and short-term decisions.
This glycol loop can be used to increase temperature in the heat pump's evaporator side compared to external temperature, therefore increasing the seasonal efficiency of the heat pump. A smart heater will also allow the storage of electricity produced by DualSun panels in the water tank. The overall layout of the demo site is presented in Figure  2. Finally, it is possible in Latvia to benefit from a net metering billing mechanism. Grid net metering is a mechanism that allows the user to feed the PV electricity production into the grid and buy the equivalent amount later for a discounted price. It is a kind of electricity storage on the grid. The sizing of this innovative technological package was performed by Chèze et al. 4  2. 2 MPC implementation MPC is based on MILP optimization. In this type of control, the considered system is modelled as a MILP problem, taking various time series as inputs and calculating the optimized trajectories of a set of control variables in order to minimize an objective function.
During the successive optimizations, the controller is given feedback from a TRNSYS digital twin, in order to update the initial state of the MILP problem with actual behavior of the controlled system. The detailed structure of MPC is presented in Figure  3. The input time series for the MILP model are weather data, heating and electricity dem ands and electricity related data such as variable price and CO2 intensity. Demands are detailed in Section 2. 3. 1 and electricity indicators in Section 2. 3. 2. The MILP model of the considered energy system on which relies the optimization part of the MPC is detailed in Section 2. 4. The optimizer sends to the TRNSYS model optimized control variables, the smart heater power, and gets as feedback from the simulation the actual level of the Ratiotherm storage.
Co-simulation with TRNSYS As part of the MPC methodology, in order to account for the non-linear phenomenon that cannot be modelled through MILP, feedbacks from actual system or simulation model are needed at each timestep to update the state of the system. As real building site is not operational, co-simulation is implemented with the simulation model in TRNSYS. Co-simulation is implemented on PEGASE, a platform developed in CEA  8  that provides a framework for the design and deployment of advanced control strategies Co-simulation is usually done through FMU. Even if an open source project for an FMU tool for TRNSYS is available online, compatibility issues between 32 bits TRNSYS model and 64 bits optimization software made the use of standard FMU impossible. An alternative method was developed in this paper, using a local TCP (Transmission Control Protocol) server and sockets.
Identifying MEA degradation behavior To precisely investigate the degradation behavior inside the MEA, a customized synchrotron cell (Fig. 1 ) was used for performing COE experiments. An industrially relevant current density of 200 mA cm - 2 was used to operate the cell for 3 h with the anolyte of 0. 1 M CsOH, meanwhile operando WAXS was measured. A detailed introduction of the MEA con guration and operating conditions are given in the experimental procedures (Methods). During COE measurement, a high transmission and focused synchrotron X-ray beam (5 µm × 20 µm, vertical × horizontal) periodically scanned the MEA from the IrO 2 anode through the membrane and all the way to the cathode ow eld. Each scan takes 210 s to acquire 100 WAXS patterns across the MEA region. Therefore, each operando experiment produces substantial amounts of WAXS data in terms of both time and space, allowing for the tracking of real-time behavior of the entire scanned MEA.
From the intensity and background changes of characteristic signals in the WAXS mapping, the change in electrolyte content and the formation of new crystalline phases can be identi ed  35. Moreover, the gas products during the COE and liquid products after COE were measured by in-line gas chromatography (GC) and ex-situ high-performance liquid chromatography (HPLC), respectively. Detailed discussions of operando X-ray measurements and data analyses are given in Methods and Supporting Information (Note 1). Sputtered Cu on a commercial GDE (i. e. , 39BB) was used as a baseline cathode. Its morphology and surface chemical state are given in Fig. S2. The cross-section SEM image and corresponding EDS mapping of a typical MEA con guration are shown in Fig. S3. The MEA consisting of an AEM with a compressed thickness of ~ 35 µm, is sandwiched by the Cu cathode and a commercial IrO 2 anode.
A full description of synchrotron cell is provided in our previous work  35. All wide-angle X-ray scattering (WAXS) measurements were carried out at the ID31 beamline of the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. The high-energy monochromatized beam (68keV) was focused on the cell (5×20 µm 2 v×h) in grazing incidence mode (beam parallel to the MEA layers). The scattered signal was collected using a 2D Dectris Pilatus CdTe 2M detector positioned 780 mm behind the cell. A standard CeO 2 powder (NIST) was used for the calibration of the sample-detector distance and detector tilt. Calibration and data integration to the 1D pattern were performed using the pyFAI software package  49. Electrochemical cell assembly measurements A commercial CO 2 electrolyzer purchased from Dioxide Materials was used for the long-term stability test (Fig.
Structural and surface composition studies of surface modified Co 3 O 4 nanostructures The morphology of as ready nanomaterials was studied finished scanning electron microscopy (SEM) by JEOL JSM-6480A, operating voltage of 20 kV, with (EDS) energy dispersive spectroscopy was functional to determine sample composition. For finding the purity and crystalline phase of surface modified Co 3 O 4 nanostructures, powder X-ray diffraction (XRD) was used (Philips PANanlytical) with CuKa radiation (l ¼ 1. 5418 A) was used as X-rays source, using 45 kV and current45 mA, and analysis was carried out with High score plus software  [60, 66]. X-ray photoelectron spectroscopy (XPS) was used for quantitative information about the chemical state and surface chemical composition of surface modified Co 3 O 4 nanostructures using a model "the scienta ESCA 200" working through monochromatic x-ray source Al (k-alpha) of photons at 1486 eV under ultra-high vacuum using a pressure of 10 À10 mbar.
The charge transfer resistance was measured by EIS with experimental conditions of 100 kHz to 0. 1 Hz (as sweeping frequency range), sinusoidal potential of 5 mV and 1. 4 V vs reversible hydrogen electrode (RHE) as a (OER Over-Potential). The raw EIS experimental data was further simulated by Zview software in order to identify the right circuit to calculate accurate charge transfer resistance. The durability experiments very essential to confirm the performance evaluation of electrocatalyst for long term applications, therefore we did this measurement at conditions of current density of 20 mA/ cm À2 for 40 h. The active surface area of electrocatalyst is very important for understanding the actual number of active sites involved in electrochemical reaction, hence cyclic voltammetry (CV) was adapted as method for this measurement at different scan rates.
Acknowledgment We would like to thank the platform "Microscopies, Microprobes and Metallography (3 M)" ( Institut Jean Lamour, IJL, Nancy, France ) for TEM and SEM facilities. A. I. M. acknowledges  RTI2018-099668-BC22 ,  RyC-2015-17870  and  UMA18-FEDERJA-126  projects (Spain). The authors also express their gratitude to the support of Princess Nourah bint Abdulrahman University Researchers Supporting Project number ( PNURSP2023R58 ),  Princess Nourah  bint  Abdulrahman   University, Riyadh, Saudi Arabia.
(See Fig. 2 ). Based on regression, the model presented in this paper aims to determine the best variation of the room temperature set point as a function of an outdoor temperature and an energy consumption reference for the building. So the model outputs a dataset relating energy consumption to each room temperature set-point, for different external temperatures. An algorithm is used to choose the best set-point according to two criteria: thermal comfort and PV energy selfconsumption. So, in short, the active learning surrogate acts in real time to reduce the difference between PV production and energy consumption. In the end, the objective of this temperature set-point selection algorithm is to maximise the self-consumed PV energy. The main proposition of self-consumption concept is to make the energy produced and consumed locally, which means, among other advantages, reducing transmission and distribution costs, emitting less greenhouse gases and reducing final energy costs of the building.
Building model: OpenModelica To build the initial simulation model, there is a need for a software that can integrate electrical and physical behaviours. For doing this, the chosen one was the open-source modelling language Modelica. The Modelica Language is a non-proprietary, object-oriented language used to model complex physical systems containing, among others, mechanical, electrical, electronic, hydraulic, thermal, control, or processoriented sub-components. Projects can be designed using code or helped by a block-diagram-based front end. Both methods are equivalent, and they can be extended with Python, through an Application Programming Interface (API) called OMPython. OMPython API is an open source Python based interactive session handler for Modelica scripting. It provides the modeller with components for leveraging a complete Open Modelica (OM) modelling, compilation, and simulation environment.
Initial problem Before starting with a complex model of a building, a proof of concept with a simple model was developed to verify that the integration between Modelica and Python can provide an appropriate performance for the regression process. Also, this small model was used to analyse which regressor could be the most valuable for the problem of controlling the temperature of a building. Bearing this in mind, a simple model of a thermo-resistor connected to a voltage source was built using OM. The equivalent resistance is given by two parts: a fixed and characteristic resistance value and a variable value that depends on the external temperature, so the final resistance is given by the Eq. 1: R eq = R(1 + α(T heatport -T ref )) (1) Where R is the intrinsic resistance, T ref is the reference characteristic temperature, T heatport is the external temperature, and α is the temperature coefficient of resistance, given in K -1 , which basically means that the greater the difference between external and reference temperature, the higher the equivalent resistance is.
3. Briefly, a few simulations are done in OM (red stars in Fig. 4a ), then, with these simulations, a first prediction is made, and its uncertainty is evaluated (blue curve in Fig. 4a ). The worst point, i. e. , highest standard deviation point is chosen and a new simulation is added to the training vector, which is used to perform the new prediction. When this prediction is good enough, the model is created, and it can be used instead the old one. Standardisation of datasets is a common requirement for many machine learning estimators. They might behave badly if individual features do not more or less look like standard normally distributed data, i. e. Gaussian with zero mean and unit variance. The regression is performed in two steps. The initial one, where a set of initial input and output data are given to the surrogate to estimate the pattern between input and output.
WT studies of Cu EXAFS con rmed that one intensity maximum at 5. 3 Å -1 in CuNi SAs/UiO-66(Hf) was attributed to the Cu-O bond (Fig. 3c ). Similarly, only one intensity maximum at 5. 6 Å -1 and a peak of Ni at 1. 60 Å were acquired (Fig. 3d  and 3f ). As a result, only Cu-O or Ni-O bonds were found by XANFS data analysis in CuNi SAs/UiO-66(Hf), and there is no other bonding form for these two metals. Thus, these observations con rmed that there are no direct interactions or bonding between the Ni and Cu atoms in the bimetallic single-atom samples. Catalytic activities driven by  60  Co γ-ray The catalytic CO 2 reduction activity was rst evaluated in CO 2 -saturated aqueous solutions under  60  Co γray irradiation (Fig. 4a ). The gaseous and liquid products evolution increases linearly with absorbed dose, so the radiolytic yield expressed as G-values is measured to qualify the e ciency.
Acknowledgements We thank  Harry Gray ,  Roald Hoffmann , and  Peter Wolczanski  for valuable discussions. We acknowledge the  NSF  ( CHE-1954515  to K. M. L. , CHE-1753025 to C. D. M. , CHE-2153730 to D. A. V. , and  CHE-2155160  to L. M. M. ), the  NIH  ( R15 GM141650  to J. S. ), the  Welch Foundation  ( AA-1846  to C. D. M. ), and the  Agence Nationale de la Recherche  ( ANR-JCJC-20-CE07-0023-Ni4Rf  to N. N. ) for support. XAS data were obtained at SSRL, which is supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences  under Contract No. DE-AC02-76SF00515. The SSRL  Structural Molecular Biology Program  is supported by the  Department of Energy's Office of Biological and Environmental Research , and by  NIH/NIGMS  (including  P30GM133894 ). The work at SSRL was also supported by the  U.
5 Å or a JEOL JEM 1400 operating at 120 kV with a point resolution of 2. 0 Å, and on a JEOL JEM 2100F equipped with a field emission gun (FEG) operating at 200 kV with a point resolution of 2. 3 Å and high-angle annular dark-field imaging scanning transmission electron microscopy (HAADF-STEM) on a JEOL JEM-ARM200F Cold FEG operating at 200 kV with a point resolution of >1. 9 Å, respectively. The TEM samples were prepared by diluting a few drops of each RuNPs/IL mixture in acetonitrile and casting a drop of the solution on a carbon-coated copper grid. Size distributions and mean sizes of the NPs were determined by measurement of at least 200 individual NPs on a given grid using the software ImageJ. Thermogravimetric analyses (TGA) were carried out on a Mettler Toledo TGA/DSC 3+ instrument under a N 2 flow.
Samples were placed into an alumina crucible and then heated from 25 to 800 • C at a heating rate of 10 • C/min. DSC analyses were conducted from -120 to 40 • C with a heating rate of 10 • C/min after depositing the samples in a sealed aluminum pan and using a Netzsch DSC 3500 Sirius instrument. X-ray photoelectron spectroscopy (XPS) analyses were performed using a Thermo Scientific system at room temperature using AlKα radiation (1484. 6 eV) and a spot size of 400 µm. A flood gun was used to reduce sample charging effects and the obtained spectra were further corrected by setting the C 1s binding energy at 284. 8 eV. Data processing was performed using the Avantage 4. 87 software. Electrospray ionization mass spectrometry (ESI-MS) analyses were performed using a DSQ II Mass Spectrometer (Thermo Fisher Scientific) equipped with electron impact (EI) and chemical ionization (INN NH3) sources analyses up to 1000 mass at low resolution under argon.
Solubility Measurements The solubility of styrene (S) and ethylbenzene (EB) in the different ILs was determined through the following method: A mixture of IL (0. 5 mL) and S or EB (2 mL) was stirred at room temperature for 24 h in a vial and then allowed to settle for 0. 5 h. The excess of S or EB present on the top of the mixture was removed via cannula filtration. Then, pentane (5 mL) was added and the mixture vigorously stirred to extract the dissolved S or EB from the IL to the pentane. The pentane phase was analyzed by GC using octane (0. 03 mmol) as internal standard. This extraction procedure was repeated twice, and the quantitative GC data allowed to determine the concentration of S and EB dissolved in each IL (see Supporting Information Table  S3 ).
This study is based in India to motivate the managers of EV manufacturing industries to focus on important criteria for EV selection to boost market efficiency and profitability. The data was collected from industry experts and the results revealed that charging time, driving range and price are the most important criteria for an EV purchase in India. All the criteria are classified into cause and effect groups. The sensitivity analysis was performed to check the robustness of the model. To summarize, we believe the methodology presented in this work shows conclusively that OR methods are successful in practice and make a strong contribution in the OR field. This work contributes by employing a decision-support tool to provide real preferences of customers for EV adoption. Because many manufacturers are investing heavily in electric vehicles, this study would also aid manufacturers by studying probable customer preferences.
Literature review Several efforts have been made in the literature to identify crucial barriers towards electric vehicles adoption. However, these barriers are too generic and having too many factors are not useful in practice. In this section, we describe the process of literature search to identify potential barriers for EV adoption in India. A systematic literature review (SLR) has been employed to identify the body of literature on EV adoption across the globe. Initially, most widely accepted databases like Scopus, Web of Science and Pro-Quest are used to give a high level of rigour and identify relevant literature on the field of study. To identify the relevant literature and adoption barriers of electric vehicles, keywords including "electric vehicle", "sustainable transportation", "barriers", and "adoption" are used in the search field (refer Table  1 ).
Phase 1: Delphi method The Delphi method is a structured, iterative process that includes anonymous assessments and systematic improvement to obtain a collective view from experts from many fields  (Linstone & Turoff, 1975). The Delphi Method was established to eliminate the negative effects of expert influence caused in face-to-face discussions. The Delphi Method is used to emphasise expert viewpoints that are similar and to uncover areas of consensus on specific themes. Many of the previous researchers  (Kalantari & Khoshalhan, 2018; Emovon et al. , 2018; Hashemi et al. , 2022; ; Tripathy et al. , 2022)  have used Delphi method. The list of criterions was discussed with the experts in the first round of Delphi. The data was gathered during March-April 2022. The criterions were validated with the support of domain experts.
This work employed a non-probabilistic sampling method for expert selection. All experts have good knowledge about EV adoption. All experts were contacted by email and telephone to participate in this study. Agarwal et al. (2021) ; and Nimawat and Gidwani (  2021 ) conducted empirical research using the DEMATEL method using five experts for data collection that were treated as adequate expert's numbers. The expert profiles are summarized in Appendix B. The authors developed a questionnaire for a direct relationship matrix. The data was collected from all eleven experts each indicator's relative relevance matrix, which we used to evaluate each criterion. The relationship between identified barriers has been assessed using an integer scale ranging from 0 to 4 as shown in Table  2. Based on  Tzeng et al.
Results and discussions This work aims to examine the causal relationship between various criteria for EV adoption. To identify causal relationships, the DEMATEL method was employed, and the data were collected from experts to form a direct relationship matrix. According to degree of significance (D + R) values as shown in Fig. 3  Remarkably, three criterions "Battery Capacity (C1)" (1. 5379), "Transmission Type (C10)" (1. 4561), "Electric Motor Type (C7)" (1. 0956) were listed in the cause group category based on D-R values. These results are also aligned with the past academic literature by  (Digalwar & Giridhar, (2015; Egbue & Long, (2012; Egnér & Trosvik, (2018; Sonar & Kulkarni, (2021). Battery capacity has the highest D-R value (1. 5379). Biswas and Das (2019) also revealed that battery capacity is a crucial criterion in the selection of electric vehicles.
Conclusions Most buyers are still having trouble deciding which of the available electric vehicles is the best option based on available selection criteria. The purpose of this work is to prioritize important criteria for EV adoption and develop the causal relationship between them. A total of ten important criteria have been identified from the past academic literature and are validated via Delphi method. The DEMATEL approach has been employed to develop a causal relationship. The data was gathered from eleven experts. Prioritization has been done using D + R values. Criteria with higher D + R value ranked 1 and so on. Results revealed that charging time, driving range and price are the most important criteria for an EV purchase. All the criteria are classified into cause and effect groups.
Egnér and Trosvik (2018)  The impact of municipal policy instruments aimed to promote the adoption of electric vehicles is investigated in this study. Swedish municipalities data from 2010 to 2016 was used for newly registered battery vehicle. The findings also imply that government procurement of battery electric vehicles has the potential to be a useful policy tool. Vidhi and Shrivastava (2018)  This article looks at the many stages of an electric vehicle's (EV) life cycle, their impact on environmental emissions, and policy recommendations for various socio-economic groups in the Indian market. Das et al. (2019)  This study identifies the finest electric car model in the Asian market, allowing an EV buyer's needs to be met. The fuzzy analytic hierarchy technique was utilized to calculate the weight of the criterion, while mixed data evaluation was employed for performance evaluation and ranking.