bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and feasibility analysis of magnetorheological flexible joint for upper limb rehabilitation
Authors: Li Ganglin , Yang Jiabin , Zhang Guang , Ouyang Qing , Hu Hongsheng 
Published: February 2024

Abstract
Traditional upper limb rehabilitation robots have several disadvantageous. For example, they can only conduct rehabilitation training along predetermined trajectories, their safety systems are unreliable, and they lack the ability to adjust or train the affected limb based on the expected torque of the human body. To overcome these limitations, this study proposes a flexible safety system for joint rehabilitation utilising magnetorheological (MR) fluid. MR damper inverters offer significant advantages, including high torque, rapid response, controllable flexibility, and safety assurance. The range of motion trajectories can be adjusted using a four-lever hinge mechanism. The necessary driving force is provided by the motor actuator, and the MR damper imparts flexibility and variable damping characteristics to the output torque. The system uses a force/position impedance safety-control method, and using an internal position closed-loop controller, the MR upper limb rehabilitation flexible joint guides the affected limb to a safe position. A simulation is performed to verify the accuracy of the system’s motion torque and position. Extensive research has been conducted on the safe rehabilitation outcomes of the upper limb rehabilitation system under three working conditions (step, incremental, and equation) involving the interaction moment of the affected limb. Simulation and experimental results demonstrate that the MR damper effectively controls the upper limb rehabilitation system to achieve the desired results, even when subjected to incremental and abrupt interaction forces from the patient. The tracking accuracy error remains within the range of 3%–7% for a certain period, confirming the safety and feasibility of the MR-based upper limb rehabilitation robot design.
Finslerian wormhole solution in the framework of modified gravity
Authors: Malligawad Manjunath , Narasimhamurthy S K , Nekouee Z , Mallikarjun Y Kumbar 
Published: February 2024

Abstract
This article investigates the properties of a wormhole model in a specific gravity theory, namely f(Ric, T) = Ric + 2λ
T. The wormhole solution is analyzed using an exponential shape function. The study examines various parameters, such as density, radial pressure, transverse pressure, equation-of-state parameters, and energy conditions, within the framework of deformed gravity. The research emphasizes the influence of the parameter λ on energy condition violations and the equilibrium state of the Finslerian wormhole solution. These effects are attributed to anisotropic and hydrostatic forces present in modified gravity. The study demonstrates that the gravity model effectively captures the characteristics of wormholes within the Finslerian space-time. Additionally, the identified features of the wormhole are utilized to visualize its structure by creating a three-dimensional representation of the embedded surface. In summary, this research contributes to understanding wormholes in modified gravity theories, highlighting the importance of the parameter λ in determining their behavior and properties.
A simple probabilistic neural network for machine understanding
Authors: Xie Rongrong , Marsili Matteo 
Published: February 2024

Abstract
We discuss the concept of probabilistic neural networks with a fixed internal representation being models for machine understanding. Here, ‘understanding’ is interpretted as the ability to map data to an already existing representation which encodes an a priori organisation of the feature space. We derive the internal representation by requiring that it satisfies the principles of maximal relevance and of maximal ignorance about how different features are combined. We show that, when hidden units are binary variables, these two principles identify a unique model—the hierarchical feature model—which is fully solvable and provides a natural interpretation in terms of features. We argue that learning machines with this architecture possess a number of interesting properties, such as the continuity of the representation with respect to changes in parameters and data, the possibility of controlling the level of compression and the ability to support functions that go beyond generalisation. We explore the behaviour of the model with extensive numerical experiments and argue that models in which the internal representation is fixed reproduce a learning modality which is qualitatively different from that of traditional models, such as restricted Boltzmann machines.
Effect of v-PP/r-PET composites filament on specific density, structural and mechanical properties
Authors: Pérez-Herbert C A , González-Hernández A , Morales-Cepeda A B , Hernández-Castillo Y , Barragán-Ramírez R , Bautista-García R , Aguilar-Rodríguez M D , Gamboa-Soto F 
Published: February 2024

Abstract
Polypropylene (PP) and its derivatives are widely used to produce molded plastics due to their lightweight and impact resistance. Polyethylene terephthalate (PET) is considered one of the essential engineering polymers for its applications, widely used to manufacture containers for liquids such as water, oil, etc. The compatibility of PP/PET increases the impermeability, thermic and mechanical properties. The present study shows the blend binary of recycling PET as refill into a v-PP matrix extruded in filament analyzing the specific density and XRD. The result XRD pattern exhibited the presence of miscibility of monoclinic α-form phase v-PP/r-PET peaks predominantly attributed to the crystallinity of baseline material. Likewise, crystallinity, and amorphous peaks are attributed to the semi-crystalline phase of r-PET with a triclinic structure. In the impact strength test, all specimens tested showed partial failure on the surface, majorly presenting cracks, and fractures.
Analysis of Mexican Amber and Natural Resins Using Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy
Authors: Vazquez-Bautista G , Zapata Torres M , Chalé Lara F 
Published: February 2024

Abstract
Amber is a semi-precious gemstone, it is a natural resin formed as a fossil and consists of labdanoid diterpenes. This study involves a compositional analysis of Mexican amber using FTIR Spectroscopy and Raman Spectroscopy, comparing it with resins mixed with sulphur, copal, and rosin, to determine the main spectral features of Mexican amber and identify differences from other fake amber resins. Samples of yellow, red, and green amber were utilised. The equipment used included an ATR FTIR spectrometer and a Raman spectrometer with a 785 nm laser source. In the FTIR analysis, the results reveal that the colour variations in Mexican amber do not reflect structural differences. The ester group is identified in amber through the C=O carbonyl bond at 1727 cm-1, confirmed by a band at 1243 cm-1 and a set of bands between 1200-1060 cm-1 corresponding to C-O-H and O-C-C bonds, respectively. The range between 1000-600 cm-1 is identified as the most crucial for distinguishing authentic amber and determining whether the resin is young or fossilised. In Raman, the main peaks to identify amber are at 1646 cm-1 and 1450 cm-1 with which the ratio of intensities was quantified to determine the maturity of the fossil resin.
Study of cerium adsorption in graphene oxide decorated with ZnO by X Ray Fluorescence
Authors: Hernández Castillo Yarazett , Nava Diguero Pedro , González Hernández Andrés , Caballero Briones Felipe 
Published: February 2024

Abstract
With the goal of recover strategic materials contained in spent electronic and electric devices; this work focuses on the recovery of Ce from Ce synthetic solutions, by using graphene oxides-based (GO) adsorbent materials. Graphene oxide is synthesized by the Hummers’ method and decorated with zinc oxide (ZnO-GO). After that, adsorption experiments at pH 2 and 6 were carried out using Ce(NO3)3 aqueous solutions with concentrations of 100-1000 mg/L. Compounds of GO and ZnO-GO with and without Ce were analysed by Raman where changes of GO structure were observed; by FTIR spectroscopy, after decoration, the reduction of COOH, C=O, C-OH and C-O groups were observed and after the adsorption the decrease of COOH and C-O groups as well as the presence of NO3 and CH2CH3 groups were detected. TEM micrographs show particles adsorbed in GO sheets, and XRF analysis helped to quantify cerium from solutions after adsorption. The ZnO-GO, obtained at pH 6, has higher Ce adsorption (3837.5 mgce/mgads) and highest percentage of Ce removal (43% of cerium); It concludes that ZnO improve the Ce adsorption, however, the adsorption mechanisms of Ce onto the graphene network are unknown, and Zn-Ce ion exchange was found depending on the pH.
First-principles simulations of magnetic properties of Co- and Ni-doped ZnSe
Authors: Jafarova Vusala N , Abdurahmanova Ulkar S 
Published: February 2024

Abstract
We purposed to analyze the theoretical ab initio study of ferromagnetism and to estimate Curie temperature of Co2+ and Ni2+ 3d transition metal (TM) ions doped Zn1-x
TM
x
Se compounds for 12.5 % and 6.25 % concentrations of impurity TM(Co, Ni) atoms. The calculations are carried out using the norm-conserving Fritz-Haber-Institute ionic pseudopotentials based on density functional theory within the local spin density approximation and Hubbard U (U(Znd)=4.5 eV; U(Sep)=3.8 eV) corrections. The analysis of the total and partial electron density of states shows the half-metallic ferromagnetic behavior with a half-metallic bandgap. The theoretically obtained total magnetic moments of TM(Co, Ni) doped Zn1-x
TM
x
Se systems are equal to 3 μB and 4 μB, correspondingly.
Deposition of CsFAPbI3 thin films by single source flash evaporation
Authors: Hernández M , Pacio M , Juárez H , Serrano L E , Pacio A 
Published: February 2024

Abstract
Formamidinium-cesium lead iodide (CsFAPbI3) is a promising perovskite material for photovoltaic applications with a suitable bandgap of 1.45 eV and excellent optoelectronic properties. In this work, CsFAPbI3 perovskite thin films were deposited by single-source flash evaporation on glass substrates using presynthesized crystalline powders as the source material in which the source challenges of simultaneously controlling the evaporation of organic and inorganic sources are avoided. The structural properties of the powders were evaluated by X-ray diffraction, thermal properties by TGA analysis and optical properties by UV-Vis absorption. We find that the formation of mixed phases is inevitable in flash evaporation of thin films. This undesirable phase could reduce the optical bandgap and the thermal stability which can affect the performance of the thin films. To obtain the cubic phase, a post-annealing process should be employed. We carried out structural, morphological, and optical characterizations to determine the phase purity in the films. These preliminary results suggest that flash evaporation deposition parameters can be optimized to understand the formamidinium evaporation and condensation dynamics for improve the properties of CsFAPbI3 perovskite thin films.
Obtaining and characterization of MAPbSnBr3 perovskite crystals trough AVC method
Authors: Pacio Castillo A. , Medina Velazquez D. Y. , Pacio Castillo M. , Reyes Miranda J. , Hernández M , Serrano De la Rosa L. E. , Antonio Hernández C. , Cortez Santiago A. 
Published: February 2024

Abstract
The obtaining tin-based perovskite from solution is challenging due to the rapid and uncontrollable crystallization of tin compared to lead-based perovskite. Another challenge is to reduce the use of lead to avoid poisoning. In this sense, Sn-based perovskite compounds such as CsSnBr3 have attracted great attention as an effective replacement, but current photovoltaic devices still problems of low efficiency and instability chemistry. The band gap of ASnX3 compounds is between 1.2 and 1.4 eV, being the ideal range for maximum theoretical efficiency in a solar cell. In contrast, APbX3 compounds have band gaps in the range of 1.45 to 1.6 eV. We synthesize MAPbSnBr3 perovskite crystals through the AVC method. The structural properties of the crystals were obtained by X-ray diffraction, Raman spectroscopy, these shown the presence of Sn and Pb in the crystals obtained, and the optical properties by UV-Vis absorption and photoluminescence spectroscopy obtaining the variation of the band gap in the perovskite.
The results obtained from the MAPbSnBr3 perovskite (such as the band gap, absorption spectrum and crystal lattice) meet the necessary requirements for its possible application in photovoltaic devices. It is shown that it is possible to obtain MAPbSnBr3 crystals with a lower amount of Pb and how the bandgap is affected by varying these elements, for their possible application in solar cells.
Study on the effect of pressure in humid air dehumidificationournal
Authors: Wang Xuefei , Wang Han 
Published: February 2024

Abstract
In this paper, there are problems such as large energy consumption and complex system in the field of condensation dehumidification. It is found that increasing pressure can strengthen the effect of condensation dehumidification, simplify the system and reduce energy consumption. So this paper simulates the condensation and dehumidification process of humid air in horizontal pipe under positive pressure. The results show that the dehumidification capacity increases by 25%∼30% when the pressure increases by 100kPa on average. The increase of pressure has little effect on the heat transfer coefficient, but the increase of humidity increases the heat transfer coefficient more obvious. When the humidity of wet air increases by 20%, the heat transfer coefficient increases by 18% ∼ 33%. The dehumidification capacity and heat transfer coefficient increase with the increase of pipe diameter and fluid flow rate.
Effects of different heating modes on room temperature and heat consumption of existing residential buildings in rural areas
Authors: Feng Shuaibin , Qi Yudong , Gao Guoxu 
Published: February 2024

Abstract
Affected by economic conditions, living habits and other factors, residents in rural areas have a high sense of self-regulation when heating. Rural residents often choose the corresponding heating mode according to the specific situation. This paper takes an existing building in the countryside of Zhangjiakou as the research object. The room temperature change and heat consumption under different heating modes are simulated and analyzed. The results show that the temperature of each room is higher than that of the natural room temperature under different heating modes. Even if the room is not heated during the day, the room temperature can be maintained at more than 10 °C. When the heating temperature is set to 18°C in the active state and 14°C in the sleeping state, the heat consumption can be reduced by up to 32.05% in each heating mode. The heat consumption of each mode is comparatively low, and the room temperature in each time period is similarly lower, when the heating temperature of all states is set to 14 °C.
Multi-scenario research on the low-carbon transition in energy-deficient cities-view from the power supply side
Authors: Zhu Zhifang , Wang Wenxiu , Lin Zihan , Dong Hong , Gao Yanna , Hu Liujun , Lu Juntong , Hao Weitao 
Published: February 2024

Abstract
Cities are the places where power supply demand is highly concentrated. How to ensure power supply security while realizing low-carbon transition is the key challenge faced by the construction of new power systems. In large cities with a shortage of local power generation resources, this problem is particularly prominent. The study takes Guangzhou city as a case study area, which has the typical characteristics of “serious energy shortage and extremely high-power load density” in China. This study explores the differences in power supply capacity, structure, carbon emissions, and power supply costs under the six different scenarios set up from the power supply side for future power supply structures. The optimal power supply modes have been matched for cities with different development characteristics and needs. The change in power supply structure and the realization path under the optimal power supply modes are discussed. The results show that the power supply capacity of Guangzhou is constantly improving under different scenarios, with the highest self-sufficiency rates of power production capacity and power production quantity reaching 80% and 50%, respectively, by 2030. Despite this, Guangzhou still does not have the basic conditions for building a new power supply system through the development of local high-proportion renewable energy only due to the limitation of resource endowment. The combination with increasing the ratio of renewable energy in outsourcing power is an inevitable choice for low-carbon transition for the city. This study provides a new idea on how to build a new power supply system for an energy shortage city. It can be used as a reference for safe power supply and low-carbon transition for the power supply sectors of other cities.
Microgrid energy management strategy using deep learning neural network
Authors: Li Jiageng , Wei Bin , Han Xiaoqing , Qiao Sen 
Published: February 2024

Abstract
Under the background of a large number of uncertain new energy connected to microgrids, traditional model driven methods could be hard to address the speed problem of microgrid optimization. Therefore, we come up with a data-model hybrid driven microgrid scheduling strategy. This strategy includes three stages: day-ahead dispatching, preparing for day-in dispatching and day-in dispatching. In the day-ahead dispatching stage, basing on the day-ahead forecast data of renewable energy, the mixed integer linear programming (MILP) is used to solve the day-ahead dispatching plan. In the preparing for intraday scheduling stage, a neural network is introduced as the day scheduling model, new energy power and load power regard as inputs to the neural network, controllable units in the microgrid as outputs to train the neural network. During the intraday scheduling phase, neural networks are used to optimize the scheduling of controllable units in microgrids based on ultra short term predictions and pre day predictions. Finally, the effectiveness of the raised method was verified through numerical analysis.
Study of hybrid energy storage system with energy management for electric vehicle applications
Authors: Shi Zhiqiang , Lu Renxiang 
Published: February 2024

Abstract
This paper conducts an in-depth study on the on-board energy storage system for electric vehicles. We analyze the advantages and disadvantages of domestic and foreign energy storage systems including battery, flywheel, superconductor and supercapacitor. We propose a hybrid energy storage system composed of battery and supercapacitor as the on-board power supply system. It adopts a two-phase staggered parallel bidirectional DC-DC converter as the main control circuit of the storage system. And we adopt the power outer loop and current inner loop control strategy. Then the rate limiter prevents the sudden change of battery power and realizes that the instantaneous power is provided by the supercapacitor individually. The purpose is to extend the battery life and achieve the purpose of reasonable and efficient utilization of energy. Finally, the simulation verification is carried out by Matlab/Simulink. And the results show that the energy management strategy is more efficient for both power and energy management, which meets the design expectation and achieves better experimental results.
Research on energy purchase combination strategies for multiple energy commercial buildings considering multistage market uncertainties
Authors: Xu Haowen , Tang Jun , Huang Qifeng , Zhuang Zhong , Duan Meimei , Sheng Ju 
Published: February 2024

Abstract
With the gradual development of the power markets, various types of uncertainty are gradually increasing. The overlapping effects of various uncertainties have a significant impact on the operational economy of multiple energy commercial buildings (MECB) relying on various energy sources. MECB can purchase electricity from multiple stages of the market, such as spot from medium to long term and the market electricity price risk varies at different stages. Therefore, this paper investigates the energy purchasing strategies of MECB in multistage markets, taking into account various uncertainties. Considering the actual operational characteristics of MECB, this paper proposes a MECB low-carbon economic optimization model and establishes an energy purchase strategy model for MECB under various risk scenarios based on random optimization theory. Research has found that in scenarios with higher risks, MECB will participate more conservatively in the market. By simultaneously participating in the medium to long-term market and spot market, MECB can effectively avoid the cost risks brought about by various uncertainties.
Long and short-term power supply and demand forecasting based on time series analysis under high proportion clean energy integration
Authors: Ma Luyu , Yang Jianhua , Peng Shuxi , Jiang Yanni 
Published: February 2024

Abstract
This paper focuses on the prediction of power supply and demand in the electric power system under a high proportion of clean energy integration using time series analysis. Firstly, the impact of clean energy integration on power supply and demand is analyzed, taking into account factors such as the volatility and seasonality of renewable energy sources. A case study is conducted in Region A, which comprises four areas in China, to forecast the power supply and demand in the electric power system. In terms of power demand, we employ two commonly used methods in time series analysis, namely SARIMAX and factor decomposition, to establish a comprehensive forecasting model. These methods are applied from both short-term and long-term perspectives to analyze the monthly maximum electricity demand of users, aiming to accurately predict power demand under a high proportion of clean energy integration. As for power supply, we utilize the least squares method to regressively fit the installed capacity of energy sources and analyze future trends. Through the predictions of both demand and supply, the stable operation of the power system is ensured.
Research on economic dispatching strategy of electric vehicles participating in frequency regulation ancillary service
Authors: Wu Zhouyang 
Published: February 2024

Abstract
Electric vehicles (EVs) are demand-side resources capable of rapid power adjustments. When clustered as aggregators in frequency regulation (FR) services, EVs have the advantages of low deployment cost and fast response speed. This passage studies description of uncertain scenarios when aggregators participate in FR services. With a focus on the optimization of charging and frequency reserve capacity for aggregators participating in FR, a conversion method from FR signal sequence to FR demand is proposed based on historical data of FR signals. The intent is to address the uncertainty of both system FR and load electricity consumption. This passage provides an uncertain characterization of FR demand, which performs as a key indicator of proposed economic dispatching strategy. The result shows how different levels of risk appetite affects the real-time responsiveness, thus mentoring the strategic decision of how much reserve capacity should be provided for FR.