bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-enzymatic electrochemical sensor based on ZnO nanoparticles/porous graphene for the detection of hypoxanthine in pork meat
Authors: Le N. T. H. , Viet N. X. , Anh N. V. , Bach T. N. , Thu P. T. , Ngoc N. T. , Manh D. H. , Ky V. H. , Lam V. D. , Kodelov V. , Von Gratowski S. , Binh N. H. , Anh T. X. 
Published: February 2024

In this study, we developed a pioneering non-enzymatic electrochemical sensor utilizing a flexible porous graphene electrode modified with ZnO nanoparticles (ZnO/fPGE sensor) to assess hypoxanthine (HXA) content in pork at post-mortem time. The ZnO/fPGE sensor was synthesized via hydrothermal method and direct laser writing with a CO2 laser on a polyimide film at ambient conditions. Its characterization was analyzed by Raman, Fourier-transform infrared spectroscopy, field-emission scanning microscopy, energy-dispersive x-ray spectroscopy, and cyclic voltammetric techniques. Linear response, the limit of detection, and sensitivity to the HXA were enhanced with the values of the range from 1.5 to 150, 0.14 µM, and 6.6 µA μM−1 cm−2, respectively. Effective resistance to common physiological interferences (such as uric acid, ascorbic acid, dopamine, glucose, and xanthine) was indicated, and additionally, the determination of HXA concentration in real samples with good selectivity is attributed to the synergistic effects between ZnO nanoparticles and fPGE. Therefore, the ZnO/fPGE has provided a favorable electrical environment for developing high-performance electrochemical biosensors to determine hypoxanthine in pork meat.
An initial investigation into the use of machine learning methods for prediction of carcass component yields in F2 broiler chickens
Authors: Bani Saadat Hossein , Vaez Torshizi Rasoul , Manafiazar Ghader , Masoudi Ali Akbar , Ehsani Alireza , Shahinfar Saleh 
Published: February 2024

Context As evaluation of carcass components is costly and time consuming, models for prediction of broiler carcass components are useful. Aims The aim was to investigate the feasibility of machine learning methods in the prediction of carcass components from measurements on live birds during the rearing period. Methods Three machine learning methods, including regression tree, random forest and gradient-boosting trees, were applied to predict carcass yields, and benchmarked against classical linear regression. Two scenarios were defined for prediction. In the first scenario, carcass yields were predicted by live bodyweight, shank length and shank diameter features, recorded at 2, 3 and 4 weeks of age. In the second scenario, predictor features recorded at 5, 6 and 7 weeks of age were used. The two scenarios were reanalysed by including effective single-nucleotide polymorphisms associated with bodyweight, shank length and shank diameter as new predictor features. Key results The correlation coefficient between predicted and observed values for predicting weight of carcass traits ranged from 0.50 for wing to 0.59 for thigh in the first scenario, and from 0.63 for wing to 0.74 for carcass in the second scenario. These predictions for the percentage of carcass components ranged from 0.30 for wing to 0.39 for carcass and breast in the first scenario, and from 0.34 for thigh to 0.43 for carcass in the second scenario when random forest was used. Conclusions Predictive accuracy in the first scenario was lower than in the second scenario for all prediction methods. Including single-nucleotide polymorphisms as predictor features in either scenario did not increase the accuracy of the prediction. Implications In general, random forest had the best performance among machine learning methods, and classical linear regression in two scenarios, suggesting that it may be considered as an alternative to conventional linear models for prediction of carcass traits in broiler chickens.
Electrocaloric effect with enhanced temperature stability in an interfacial polarization controlled ferroelectric laminated composite
Authors: Roy Pranab Kumar , Murugavel P 
Published: February 2024

Abstract
Research on zero-emission lead-free ferroelectric materials is fascinating in the field of solid-state refrigeration due to the observed large electrocaloric phenomenon. However, one of the challenges involved is realizing a large adiabatic temperature change (

Δ
T

) over a wide temperature span. Here, a laminated ferroelectric structure with layers, having successive phase transitions covering room temperature, is proposed to overcome this problem. BaTi1−x
Sn
x
O3 with x = 0.08, 0.12, and 0.15 compositions exhibiting the required ferroelectric Curie temperatures was chosen for the component layers. The designed interfacial polarization engineered laminated structure with 0.15/0.08/0.12 compositional layers yielded a value of adiabatic temperature change ΔT
max ⩾ 0.6 °C by an indirect method using Maxwell’s thermodynamic equations over a wide temperature span (30 °C–85 °C). The observed results are correlated to the interrelated cumulative effect of reduced interfacial polarization, homogeneous electric field distribution, field-induced phase transitions, and enhanced domain switching, which are the manifestation of the designed laminated geometry.
Difficulties in producing CT caused by the current ratio regulation in IEC standard and suggestions of modification
Authors: Gan Mengying , You Hongsen , Duan Dapeng , Chi Yuan , Zhao Cheng , Yuan Jiansheng 
Published: February 2024

Abstract
The integer current ratio requirements by standard IEC 61869-2 and users result in the numbers of current transformer (CT) winding turns being decimal, such as 5.94, 9.9, and 14.85. This makes the manufacturing of CTs complex, causing socioeconomic waste. It is pointed out in this paper that if the integer current ratio requirements can be changed to decimals, the number of CT winding turns may be made an integer, which makes it easy to produce low-error CTs. The error refers to the difference between the actual current ratio and the required current ratio for CTs. Additionally, if this change is made, iron core materials do not need to have high permeability. Furthermore, it is better to allow the manufacturers to mark freely the current ratio within a certain range on CT rating plates. The measure of changing the current ratio requirements or changing the way of marking the ratio on rating plates can significantly reduce the manufacturing difficulties and costs of CTs.
Construction of two-qubit gates using
B
Gate
Authors: Karthick Selvan M , Balakrishnan S 
Published: February 2024

Abstract
In the circuit model of quantum computation, an entangling two-qubit gate and a set of single-qubit gates are used as universal gate set or basis gates for doing quantum computation. CNOT and

iSWAP

gates, the perfect entanglers that can create maximally entangled two-qubit states in one application, are broadly used entangling two-qubit basis gates in quantum computers. In this paper, we analyze the potentiality of

B

gate, an unexplored non-perfect entangler of the form,

exp

i
π
(

σ

ˆ

x

⨂

σ

ˆ

x

)

8

+

i
π
(

σ

ˆ

y

⨂

σ

ˆ

y

)

16

, as an entangling two-qubit basis gate in quantum computers by studying its ability to generate other two-qubit gates. We derive a necessary condition for a two-qubit gate to be generated by n applications of

B

gate. Using this condition, we show that the gates that can be generated by two and three applications of

B

gate are contained in the 50% and 92.97% of the volume of Weyl chamber of two-qubit gates, respectively. We prove that two applications of

B

gate can generate both

SWAP

and

SWAP

†

which is not possible for CNOT and

iSWAP

gates; further, we conjecture that three applications of

B

gate can generate all perfect entanglers. Finally, we discuss about the construction of a three independent parameter universal two-qubit quantum circuit using four

B

gates that can generate all two-qubit gates. In the end, we mention about the schemes to implement

B

gate in ion-trap quantum computers.
A machine learning approach for optimizing and accurate prediction of performance parameters for stacked nanosheet transistor
Authors: Kumar Naveen , Rajakumari V , Padhy Ram Prasad , Routray S , Pradhan K P 
Published: February 2024

Abstract
In this article, the possibilities of accurate prediction of wide range of parameters and optimizing the same through machine learning (ML) approach have been demonstrated for the multi stacked nanosheet transistor (NSFET). The machine is trained by the generated data of the tedious calibrated technology computer aided (TCAD) simulations. An innovative strategy is employed that combines ML with device simulations. Numerous devices are simulated with different geometric parameters like height, width, length and equivalent oxide thickness of the channel. The input, output, and CV characteristics are extrapolated from the simulation which is predicted by ML models. The DC, Analog and RF parameters are derived with a domain expertise approach. The device parameters are anticipated with actual values by the ML approach. Random forest regression, linear regression, polynomial regression and decision tree regression are employed for the prediction of the performance parameters. Random forest regression models provide significant R2 score with minimal error percentage. It indicates that TCAD-augmented ML can be considered an alternative to device simulation due to its reduction in computational cost. This work can also be treated as a benchmark for accurate prediction of the NSFET.
Compact antenna for higher order harmonic reduction using efficient defected ground structure
Authors: Roy Subhadra Deb , Roy Bappadittya , Batabyal Surajit , Bhattacherjee Anup Kumar 
Published: February 2024

Abstract
To suppress higher order harmonic using Inverted ‘U’ shapes defected ground structure (DGS) with two symmetric inverted ‘L’ shape stubs is presented here. At first, a study was made on the resonant frequencies of traditional line feed microstrip antenna. An Inset feed with inverted slots is introduced to eliminate all the higher-order modes up to the third harmonics in the WiMAX Band. To get wide-area stopband characteristics, dimensions and position of DGS and stubs are optimized. The proposed DGS area along with the radiated patch is more compact and stable rather than in a few reported articles. This microstrip antenna is designed and perfectly matches the WiMAX frequency band (3.3–3.7 GHz) application while suppressing other frequencies. The proposed inverted ‘U’ shapes DGS are designed with an equivalent circuit. Finally, the proposed Stub with DGS Antenna (SDA) is fabricated and measured. The proposed structure is verified with measurements and it has satisfactory gain, radiation pattern and reflection coefficient at 3.5 GHz.
Delignification and characterization of Tule (Typha Dominguensis) for its potential use in the production of green concrete.
Authors: Romero R G , Onofre E , Espíndola A C 
Published: February 2024

Abstract
In the search to mitigate the ecological footprint originated by the construction industry, the elaboration of green concrete is an alternative. However, it is necessary to study the material and determine if it requires a treatment prior to its incorporation into the concrete mixture. Natural fibers of Typha Dominguensis were extracted and analyzed in winter and summer seasons. A process of de-lignification with H2O2 was carried out to observe transformations in the morphological, chemical and structural properties. It was found that the physicochemical properties vary according to the extraction season even when the extraction site is the same. As an example, the percentage of ash obtained doubled in winter season. By means of an optical microscope an increase in roughness was observed without showing damage to the fibers, as for the chemical and structural properties a considerable decrease in the signals attributed to non-cellulosic material is appreciated. Additionally, the crystallinity is favoured by 13.58%, the humidity and hydrophilicity of the fiber remain similar considering the extraction season, although after de-lignification this percentage tends to decrease. Finally, the research has shown that it is necessary to previously perform a de-lignification process to favour the interaction of Tule as a material that can be incorporated into a new green concrete.
Optoelectronic system for measuring the sugar content in citrus fruits
Authors: Ruiz-Colunga C E , Filoteo-Razo J D , Barron-Zambrano J H , Martinez-Angulo J R , Elizondo-Leal J C , Diaz-Manriquez A , Saldivar-Alonso V P , Delgado-Martínez R , Gonzalez-Gutierrez I , Hernandez-Garcia J C 
Published: February 2024

Abstract
The color of oranges originates from carotenoids, which are natural pigments ranging from yellow to red color and is indicative of the sugar level and degree of ripeness. During the degreening process, the fruit quality can deteriorate, which is manifested as insufficient ripeness and sugar (sucrose) content. Therefore, in this study, we have designed a low-cost, easy-to-use optoelectronic system to measure the sugar content in orange and lemon juice. The system consists of a U-shaped plastic optical fiber (POF) and a visible multispectral sensor. The device identifies variation in the absorption of the evanescent field of the POF when it comes in contact with orange juice. The response of the multispectral sensor in the wavelength range of 450–650 nm is digitally captured, improving the efficiency and accuracy of the measurements. To validate the multispectral sensor measurements, the sugar contents are compared with those obtained using a visible spectrum analyzer and refractometer.
Empirical Model for electrical power demand in Mexico
Authors: Caraveo-Cacep Gibran E , Martínez-López Andrea G , Tinoco Julio C 
Published: February 2024

Abstract
In recent decades, the average global temperature has risen rapidly, due to the modern society activities. Hence, the United Nations has suggested that an increase of 1.5°C above the reference temperature corresponding to the beginning of industrial revolution era must be avoided. One proposal to mitigate this global warming is the migration towards green energy generation systems, however, suitable predictions of electricity demand are required to properly manage the implementation and operation of hybrid energy generation systems with high penetration of renewable energies. In this paper, a simple mathematical model based on sinusoidal functions which consider the normal time periods of the society like daily, weekly, monthly, and annual is proposed for the electricity demand in Mexico. The model parameters were obtained from the historical data from 2017 to 2021, furthermore, the model was used to estimate the electricity demand for the year 2022 as a demonstration of its usefulness, obtaining a mean absolute error of 0.9% in the whole year period. The proposed model is simple and can be used to reproduce and estimate the electricity demand of the national interconnected system.
Physicochemical characterization of urban dust in Southern Tamaulipas using X-ray fluorescence
Authors: Guadalupe Lima-Castillo Mayte , Alarcón-Ruiz Erika , Socorro Ovando-Rocha Mireya del , Caballero-Briones Felipe 
Published: February 2024

Abstract
Urban dust is composed mostly of particles emitted by anthropogenic processes such as industry, vehicular, and maritime transport, so it may contain heavy metals and PM10 and PM2.5 particles, which affect human health, so its characterization should be taken into account in an environmental monitoring plan. Regarding UD, there are studies about heavy metal contamination conducted in Mexico City (CdMx) and the suburbs of the State of Mexico, but to date, there are none for the Tampico, Ciudad Madero, and Altamira. The manuscript describe the elemental distribution found in urban dust and the possible relationship of the detected elements with emission sources according to the meteorological parameters of the area. The study presents the elemental composition of dust particles from 17 samples collected in the January-April 2023 period, at determined points in the Tampico-Madero-Altamira metropolitan area (TMA). The samples were collected in polyethylene bags and analyzed by X-ray fluorescence in air at an energy of 50 kV. There were 21 elements identified in the TMA. Through the Mexican regulation Pollutant Release and Transfer Register (RETC), it was possible to identify and relate some of the detected elements to some elements reported by local companies such as nickel, lead, and chromium. The most frequently detected metals were Ca, Mn, Zr, Sr, Pb, and V. These results indicate that more extensive work is needed to quantify elements such as Ni, Cr, Pb, and V due to the health problems they may cause. Funded by CONAHCYT 2022-321595.
Crack detection in metallic materials using a mobile robot
Authors: Montes de Oca‑Mora Natanael J. , Woo‑Garcia Rosa M. , Herrera‑May Agustín L. , Martinez-Castilo Jaime , Júarez-Aguirre Raúl , Restrepo Johans , Lopez‑Huerta Francisco 
Published: February 2024

Abstract
Cracks and flaws are typical failure modes in mechanical structures such as oil and gas pipeline networks, buildings, aircraft fuselages, and spacecraft. Early detection of these failures is critical for safety, timely replacement of damaged or corroded parts, accident prevention, and resource and cost savings. This requires low-cost, easy-to-implement, non-destructive, and reliable testing. For this purpose, we developed an embedded system in a Zumo 32U4 mobile robot for the magnetic detection of cracks in metal plates. Two AVR microcontrollers were used. One controls the magnetic sensing, signal processing, and data acquisition, and the other controls the robot’s displacement. A graphical user interface (GUI) was also implemented. Low-cost commercial magnetoresistive sensors (Sparkfun MAG3110) were used to monitor the variations of the 3D magnetic field around the cracks. Our detection method is based on the magnetic memory method, where local magnetic distortions are expected around a crack in a metallic material. Such distortions are due to the breaking of the symmetry of the crystal in the region where cracks appear. Experimental tests were carried out on a steel plate with machine-induced cracks of depth and width variations. The magnetic field changes associated with these cracks were analyzed and compared. This crack detection method could be used for structural health monitoring of mechanical infrastructure.
Evolution of structural features in GO/CdS multilayer films for advanced optoelectronic devices
Authors: Tolentino-Hernandez R. V. , Ovando-Rocha M. S. , Ruiz-Perez F. , Espinosa-Faller F. J. , Caballero-Briones F. 
Published: February 2024

Abstract
In this work graphene oxide/cadmium sulphide [GO/CdS]n multilayer films were obtained onto FTO/SLG substrates by cyclic electrophoretic deposition (EPD) and successive ion layer adsorption and reaction (SILAR) methods, with 3, 5, 7 and 10 cycles. The optical characterization for 3 cycles sample exhibits an optical band gap of ∼2.9 eV due the nanocrystalline nature of CdS, from 5 to 10 cycles a blue shift is observed in the band gap around ∼2.15 eV by the electronic disorder induced by graphene oxide intercalation and the mixture of hexagonal/cubic phases of CdS. Raman spectra shows the longitudinal optical modes LO, 2LO and 3LO, the LO peak position shows a blue shift in comparison to the bulk CdS. The multilayer films of 3 and 5 cycles shows a wide range of photoluminescence emission from 350 to 700 eV, for samples of 7 and 10 cycles a quenching and red shift is observed attributed to the CdS crystals growth with a peak emission at 615 eV from bulk CdS, which make the materials suitable for photovoltaic solar cells and UV-Vis diodes for optoelectronic devices.
Short-term gas load prediction based on GRA-IPSO-LSTM
Authors: Li Yue 
Published: February 2024

Abstract
With the improvement of natural gas production, supply and marketing mechanism, the effective prediction of natural gas short-term load is of great significance for rational allocation of resources and solving urban energy security. Grey relation analysis (GRA) was used to determine the main controlling factors affecting the short-term load of natural gas. The inertia weight and acceleration factor of the particle swarm optimization (PSO) algorithm were improved by applying a nonlinear change strategy. The hyperparameters suitable for the LSTM model were found by using an optimization algorithm, and a combination model of GRA-IPSO-LSTM was formed. Other models are compared to verify the accuracy and reliability of the model. The results show that, according to the size of the grey correlation degree, the factors that have little influence on the daily load can be deleted step by step to reduce the complexity of the subsequent prediction model. The iteration speed, convergence accuracy, and optimization quality of the IPSO algorithm are improved, which reduces the limitation of the artificial selection of LSTM model hyperparameters. The MAPE, RMSE, and R of the GRA-IPSO-LSTM combined model are 5461.02 m3, 0.49%, and 0.99932, which significantly improve the prediction accuracy, proving that the combined model in this paper can be used for accurate prediction of short-term natural gas load.
Neural network-based modeling of solid oxide fuel cells for marine applications
Authors: Zhong Jinpeng , Xiao Hua , Chen Aiguo , Lai Shini , Lu Jiaxin , Nie Yuhong , Yin Huawei 
Published: February 2024

Abstract
As the solid oxide fuel cell (SOFC) experimental test is still quite cost-effective and time-consuming, there is a growing need for developing effective simulation tools to reduce the time and cost of the marine SOFC performance test and optimization. The present paper is aimed to study the modeling and simulation of marine solid oxide fuel cells by artificial intelligence method. A neural network based on a particle swarm optimization algorithm is used to establish a marine solid oxide fuel cell model for voltage/current characteristic analysis. The model is also compared with BP neural network and Hopfield neural network methods. The simulation results compared with experimental data show that the effectivity of the particle swarm optimization neural network algorithm is best, which can accurately predict the voltage/current characteristic curves of a SOFC under different fuel flow-air volume ratios. The model study can provide support for SOFC performance characteristics analyses and has significant potential in SOFC optimization applications.
Dynamic optimal control strategy for distributed electric heating system under electricity pricing lever
Authors: Zhou Feng , Li Yunhui , Zhu Jie , Hao Ting , Heng Hu , Yang Peng 
Published: February 2024

Abstract
To address wind curtailment, a method is proposed to improve wind power consumption by controlling user demand response. Considering residents’ comfort demand for heating in winter, a distributed electric heating system is established and an optimal control model is established to enhance wind power consumption capacity. By considering the influence of electricity price and comparing the optimal model under basic tariffs, time-of-use and real-time tariffs, a dynamic optimal control method for distributed electric heating systems driven by electricity price leverage is proposed. The simulation results show that the method can effectively guide electric heating users to actively participate in load regulation, significantly improve wind curtailment and ensure economic and stable power grid operation. Using different electricity prices as leverage can systematically incorporate a large number of distributed electric heating users into the controllable load of wind power consumption, providing a new method for large-scale wind power consumption.