bluuebunny/crossref_metadata_2025 huggingface dataset:
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.
Capacity building for providers of cognitive rehabilitation in Queensland: a needs analysis survey
Authors: Fleming Jennifer , Prescott Sarah , Claridge Larissa , Doig Emmah , Copley Anna , Finch Emma , Kerr Christina , Henry Julie
Published: February 2024

Background Cognitive rehabilitation of people with traumatic brain injury is a complex and challenging area of practice. Practitioners working in cognitive rehabilitation require ongoing training to stay abreast of new research and best practice interventions. A needs analysis was conducted to inform the development of a capacity building program for cognitive rehabilitation providers. Methods A cross-sectional online survey of providers of cognitive rehabilitation services in Queensland collected data on demographic information, perceptions of knowledge, skills and confidence in cognitive rehabilitation, previously completed training, desired training opportunities and delivery methods, and barriers and facilitators to engaging in training. Results The 103 respondents included 67 occupational therapists, 17 speech pathologists, 12 psychologists and seven social workers with a broad range of practice experience. Participants perceived a need for further training, with executive function and functional cognition the most desired topics. The number of topics previously trained on was significantly correlated with levels of knowledge, skills and confidence (P < 0.01). Barriers to training were time and affordability, and facilitators included flexibility, workplace support, positive practitioner mindset and targeted content. Conclusion Cognitive rehabilitation providers in Queensland reported a need for further training, delivered flexibly, with a focus on managing complex cognitive impairments.
White Americans’ preference for Black people in advertising has increased in the past 66 years: A meta-analysis
Authors: Lenk Julia Diana , Hartmann Jochen , Sattler Henrik
Published: February 2024

This study investigates Black and White consumers’ preferences for Black versus White people in United States advertising contexts over 66 y, from 1956 until 2022, a time in which the United States has experienced significant ethno-racial diversification. Examining Black and White consumers’ reactions to visual advertising over more than half a century offers a unique and dynamic view of interracial preferences. Mass advertising reaches an audience of billions and can shape people’s attitudes and behavior, emphasizing the relevance of clarifying the influence of race in advertising, how it has evolved over time, and how it may contribute to mitigating discrimination based on racial perceptions. A meta-analysis of extant experiments into the relationship between the depicted endorser’s race (i.e., the model in a visual ad) and the reaction of Black and White viewers pertains to 332 effect sizes from 62 studies reported in 52 scientific papers, comprising 10,186 Black and White participants. Our results are anchored in a conceptual framework, including a comprehensive set of perceiver (viewer), target (endorser), social/societal context, and publication characteristics. Without accounting for temporal dynamics, the results indicate ingroup favoritism, such that White viewers prefer White models and Black viewers prefer Black models. But by controlling for the publication year, it is possible to observe a time-dependent trend: Historically, White consumers preferred endorsers of the same race, but this preference has significantly shifted toward Black endorsers in recent years. In contrast, the level of Black consumers’ reactions to endorsers of the same race remains largely unchanged over time.
Breaking the symmetry of cell contractility drives tubulogenesis via CXCL1 polarization
Authors: Kuo Cheng-Hsiang , Lee Gang-Hui , Wu Hua-Lin , Huang Jyun-Yuan , Tang Ming-Jer
Published: February 2024

The intricate interplay between biomechanical and biochemical pathways in modulating morphogenesis is an interesting research topic. How biomechanical force regulates epithelial cell tubulogenesis remains poorly understood. Here, we established a model of tubulogenesis by culturing renal proximal tubular epithelial cells on a collagen gel while manipulating contractile force. Epithelial cells were dynamically self-organized into tubule-like structures by augmentation of cell protrusions and cell–cell association. Reduction and asymmetric distribution of phosphorylated myosin light chain 2, the actomyosin contractility, in cells grown on soft matrix preceded tube connection. Notably, reducing matrix stiffness via sonication of collagen fibrils and inhibiting actomyosin contractility with blebbistatin promoted tubulogenesis, whereas inhibition of cytoskeleton polymerization suppressed it. CXC chemokine ligand 1 (CXCL1) expression was transcriptionally upregulated in cells undergoing tubulogenesis. Additionally, inhibiting actomyosin contractility facilitated CXCL1 polarization and cell protrusions preceding tube formation. Conversely, inhibiting the CXCL1–CXC receptor 1 pathway hindered cell protrusions and tubulogenesis. Mechanical property asymmetry with cell–collagen fibril interaction patterns at cell protrusions and along the tube structure supported the association of anisotropic contraction with tube formation. Furthermore, suppressing the mechanosensing machinery of integrin subunit beta 1 reduced CXCL1 expression, collagen remodeling, and impaired tubulogenesis. In summary, symmetry breaking of cell contractility on a soft collagen gel promotes CXCL1 polarization at cell protrusions which in turn facilitates cell–cell association and thus tubule connection.
On the different regimes of stochastic gradient descent
Authors: Sclocchi Antonio , Wyart Matthieu
Published: February 2024

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch sizeB, and the step size or learning rateη. For smallBand largeη, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the “temperature”T≡η/B. Yet this description is observed to break down for sufficiently large batchesB≥B∗, or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in theB-ηplane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch sizeB∗separating regimes (i) and (ii) scale with the sizePof the training set, with an exponent that characterizes the hardness of the classification problem.
Effective green treatment of sewage sludge from Fenton reactions: Utilizing MoS
2
for sustainable resource recovery
Authors: Liang Zhiyan , Yan Qingyun , Ou Huase , Li Dawei , Zhang Yayun , Zhang Jinlong , Zeng Lixi , Xing Mingyang
Published: February 2024

Effectively managing sewage sludge from Fenton reactions in an eco-friendly way is vital for Fenton technology’s viability in pollution treatment. This study focuses on sewage sludge across various treatment stages, including generation, concentration, dehydration, and landfill, and employs chemical composite MoS
2
to facilitate green resource utilization of all types of sludge. MoS
2
, with exposed Mo
4+
and low-coordination sulfur, enhances iron cycling and creates an acidic microenvironment on the sludge surface. The MoS
2
-modified iron sludge exhibits outstanding (>95%) phenol and pollutant degradation in hydrogen peroxide and peroxymonosulfate-based Fenton systems, unlike unmodified sludge. This modified sludge maintains excellent Fenton activity in various water conditions and with multiple anions, allowing extended phenol degradation for over 14 d. Notably, the generated chemical oxygen demand (COD) in sludge modification process can be efficiently eliminated through the Fenton reaction, ensuring effluent COD compliance and enabling eco-friendly sewage sludge resource utilization.
MXene-mediated reconfiguration induces robust nickel–iron catalysts for industrial-grade water oxidation
Authors: Yu Qian , Chen Yuzhen , Liu Jiao , Li Cheng , Hu Jingguo , Xu Xiaoyong
Published: February 2024

Nickel–iron oxy/hydroxides (NiFeO
x
H
y
) emerge as an attractive type of electrocatalysts for alkaline water oxidation reaction (WOR), but which encounter a huge challenge in stability, especially at industrial-grade large current density due to uncontrollable Fe leakage. Here, we tailor the Fe coordination by a MXene-mediated reconfiguration strategy for the resultant NiFeO
x
H
y
catalyst to alleviate Fe leakage and thus reinforce the WOR stability. The introduction of ultrafine MXene with surface dangling bonds in the electrochemical reconfiguration over Ni-Fe Prussian blue analogue induces the covalent hybridization of NiFeO
x
H
y
/MXene, which not only accelerates WOR kinetics but also improves Fe oxidation resistance against segregation. As a result, the NiFeO
x
H
y
coupled with MXene exhibits an extraordinary durability at ampere-level current density over 1,000 h for alkaline WOR with an ultralow overpotential of only 307 mV. This work provides a broad avenue and mechanistic insights for the development of nickel–iron catalysts toward industrial applications.
Electrocaloric effect with enhanced temperature stability in an interfacial polarization controlled ferroelectric laminated composite
Authors: Roy Pranab Kumar , Murugavel P
Published: February 2024

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Large-scale distributed photovoltaic cluster partition method based on SLM algorithm
Authors: Wang Ziyuan , Gao Qingzhong
Published: February 2024

The traditional distribution network with centralized control faces the problems of communication delay, large amount of calculation and too many control devices. This research offers a distributed power cluster division technique based on Smart Local Moving (SLM) to overcome the problem of challenging regulation of distributed power supply in modern distribution networks. First of all, on the basis of the modularity division standard, introduce the comprehensive cluster division index of intra-group reactive power and active matching degree index, so as to ensure the strength of the cluster structure on the basis of making it have a certain degree of voltage regulation capability. Secondly, the cluster division scheme of distributed power supply is formed based on SLM algorithm. Lastly, the feasibility and efficacy of the suggested method are validated using IEEE33 cluster partitioning.
Optimal dispatching of wind turbine-photovoltaic-pumped storage new energies and thermal generators combined distribution network
Authors: Zhao Duoxian , Ma Ming , Yuan Chen , Lv Qingquan
Published: February 2024

This paper mainly investigates the optimal dispatching problem of the combined distribution network consisting of wind turbine generators (WTGs), photovoltaic generators (PVGs), pumped storage generators (PSGs) and thermal generators (TGs). According to the load demand in the distribution network and the output conditions of different renewable energy generators, PSGs and TGs, a joint optimal dispatching model based on the operation cost of the distribution network is set up. Based on the power flow model of second-order cone relaxation (SOCR) technology, the nonlinear constraints on voltage and current in the distribution network are linearized, so as to solve the cost optimization scheduling problem more efficiently. Finally, an improved IEEE33-node distribution network is used as the basic grid model for simulation analysis, so as to verify the effectiveness of the proposed model.
Event-driven based simulation method for security and stability control strategies of power system
Authors: Zhou Bo , Sun Xinwei , Wang Yongkang , Xu Yunyang , Huang Shaowei
Published: February 2024

Since the security and stability control strategies (SSCSs) play a pivotal role in maintaining system stability, devising the strategies accurately and reliably is of great significance. Currently, SSCSs are typically formulated according to the ‘offline simulation and online matching’ approach. However, existing commercial simulation software is difficult to simulate the dynamic triggering mechanisms and intricate control logic of SSCSs. To simulate the set of SSCSs systematically and comprehensively, this paper has proposed an event-driven approach for simulations. Firstly, the SSCS is modeled as an event-driven model containing three elements: event, event listening, and event processing. Then, on the basis of the event triggering mechanism presented in this paper, the complex control logics of SSCSs, such as the prioritization of tripping generators, are implemented. Through event-driven models of SSCSs and subsequent utilization of the event-triggered mechanism, independent and dynamic responses of SSCSs can be realized, thus avoiding interference in the simulation of multiple SSCSs. Finally, the effectiveness of the proposed method is verified based on the IEEE 39-bus system.
Eight-channel signal synchronous acquisition system based on PCI and LabVIEW
Authors: Wang Xuan , Chen Guochu
Published: February 2024

In response to the multi-channel acquisition of the power grid simulator data collection system, this article builds a 8-channel signal synchronization acquisition system based on PCI-1706U and LabView, which introduces the characteristics of PCI-1706U hardware and LabView software DAQ system. A LabView DAQ program that uses the PCI-1706U to collect 8-way signal from the signal generator, complete the experiments of multi-channel synchronous acquisition and delayed start to collection, verify the feasibility and stability of the system, and analyzed the collected signals. Finally, the main work and innovation points of the thesis are summarized, and suggestions and outlook for future improvement are given. This study provides an effective implementation method based on PCI-1706U and LabView multi-channel signal synchronous acquisition system and delay acquisition function, with certain application value and promotion space.
Study on the conductivity of proppant in shale reservoir
Authors: Wen Xiaoyong , Huang Penggang , Zhang Yu , Chen Jingyang , Dou Liangbin
Published: February 2024

At present, in the process of developing shale reservoirs by hydraulic fracturing, proppants play a critical role in the overall fracturing effect. Therefore, accurately evaluating the conductivity of different proppants determines the development effect of shale reservoirs. In this paper, proppant screening is first carried out, and the conductivity of four different proppants under laying concentration and closing pressure is studied, including 10-20 mesh quartz sand proppants with non-standard particle size, and the influence of different factors on the embedding degree is analyzed. It is concluded that the bearing capacity of ceramsite proppant is usually 2-3 times stronger than that of quartz sand. The larger the particle size of quartz sand proppant is and the higher the concentration of sand is, the stronger the conductivity is. When the closed pressure of large-size quartz sand is less than 30 MPa, the laying concentration of 14 kg/m2 can obtain higher conductivity, and when it is greater than 30 MPa, 12 kg/m2 is more suitable for field application. The ceramsite proppant has been on the rise with the increase of closing pressure and sand concentration. The embedding depth gradually increases with the increase of closing pressure and the decrease of sand concentration. The embedding depth of shale is greater than that of sandstone, and the flow conductivity of proppant is usually lower than that of sandstone. According to the experimental results, large-size quartz sand has strong conductivity, but it is not easy to migrate and has poor bearing capacity. Therefore, large-size quartz sand proppant can be used in the low-pressure part near the well, supplemented by small-size quartz sand (40-70 mesh) to obtain better fracturing results. This study provides some technical guidance for shale reservoir development and shale fracturing dessert identification.
A simple zero-voltage switching technique for a single-phase inverter based on bi-directional inductor current
Authors: Wu Hongxue , Zhao Lei
Published: February 2024

The majority of soft-switching inverters often use auxiliary semiconductor switches that run for extremely brief periods to provide ZVS conditions for the main switches, potentially raising the inverter system’s cost. This work presented a simple zero-voltage switching (ZVS) approach by employing a bi-directional inductor current for the single-phase inverter, which reduced the cost of auxiliary switches and simplified the soft-switching inverter design. All switches can achieve ZVS operation without additional resonant components. The conventional PWM control strategies are applied in the proposed technique, and thus the problems existing in variable switching frequency converters are removed. This article describes the soft-switching approach and detailed circuit functioning. Different key parameters are optimized to improve efficiency. To confirm the suggested ZVS method, a hardware inverter prototype is created, built, and tested. To validate the inverter’s characteristics and validity, experimental results are presented.
Big data analysis method based on time series temperature field data of substation transformer
Authors: Zhang Yubo , Lu Yufeng , Zhang Wei , Liu Xu
Published: February 2024

Transformers are one of the main electrical equipment in the power system, and their operating status directly determines the safety, stability, and reliability of the power supply in the power system. The temperature or temperature rise of a transformer is the most important technical parameter that describes the operating status (accidents, faults, and abnormal operation) of the transformer. The following mainly introduces that the monitoring area is gridded by modeling the three-dimensional reality of the transformer, and then the collected infrared temperature data is accurately mapped into the monitoring grid in three dimensions to form the patrol time sequence temperature big data. Through trend analysis, temperature difference and temperature rise analysis, temperature field gradient analysis and other models, the equipment temperature hidden danger detection focusing on prevention is realized.
A wind power ramp event detection method based on eigenvalue correction and trend integration
Authors: Wang Jin , Wu Huajian , Gao Bing , Ma Ruijie , Chen Lei
Published: February 2024

Wind power ramp events have the characteristic of small probability and big hazard, so it is significant to improve the recognition rate and accuracy of ramp events for the safe and stable operation of the power grid. In order to improve the efficiency of ramp event detection, a wind power ramp event detection method based on eigenvalue evaluation correction and trend integration is proposed by combining the feature information of ramp events. The original wind power data is extracted with extreme value feature points and corrected with eigenvalue evaluation to achieve the trend feature extraction effect. In order to avoid being affected by small power jitter events, a trend labeling method is used to integrate the correction sequences. The actual wind power data of a wind farm in Xinjiang is used as an example for ramp event detection. The results of the case show that compared with the original swinging door algorithm, the proposed method has both better trend extraction effect and can avoid the influence of power small jitter events, and can more accurately and more completely identify ramp events.
Permanent magnet synchronous motor stator flux trajectory tracking control based on switching time correction
Authors: Xiao Yeran , Shao Junbo , Wang Dingquan , Qiu Changqing , Ji Kai
Published: February 2024

In this paper, aiming at the problems that the existing stator flux trajectory tracking control method often needs to design complex fundamental wave component observers and the switching time correction based on steady-state solution lacks accurate theoretical calculation equations, a permanent magnet synchronous motor stator flux trajectory tracking control method using optimal PWM (Pulse Width Modulation) is proposed. The outer loop is the speed regulator, whose output is combined with the flux linkage of the permanent magnet of the motor as the reference vector of the stator flux linkage. And the motor speed with the rotor position is combined to calculate the amplitude and phase angle of the reference stator voltage vector, which is used to optimize PWM pulse mode and to determine switching action. Meanwhile, the switching time of optimal PWM is corrected in real time through the stator flux deviation to eliminate the deviation between the stator flux reference value and the actual value. This method does not need to estimate the fundamental component of voltage, current or flux linkage. And unlike the traditional vector method, there is no current inner loop, which can significantly improve the dynamic response speed while simplifying the control structure. Simulation and experiments also fully verify the effectiveness of the proposed control method.
Low voltage ride through control strategy of permanent magnet wind turbine based on improved virtual synchronous generator control
Authors: Jiang Zhixian , Zheng Wenna , Mei Jiale , Liu Xiaoling
Published: February 2024

Aiming at the shortcomings of traditional low voltage control strategies for permanent magnet wind turbines in supporting microgrid stability and the inability of traditional virtual synchronous generator (VSG) control to ride through voltage drops, this paper proposes an improved control low voltage ride through (LVRT) strategy based on traditional low voltage control and VSG control methods. This strategy switches to reactive priority mode during the LVRT period, enabling VSG control to have LVRT function and meet the LVRT requirements standards. The simulation results demonstrate the effectiveness of the strategy.
Test and performance of the LiTE-DTU ASIC for the HL-LHC upgrade of the CMS ECAL barrel
Authors: Cossio Fabio ,   the CMS collaboration
Published: February 2024

A data conversion and compression ASIC, named LiTE-DTU, has been developed for the upgrade of the CMS electromagnetic calorimeter (ECAL) for the High-Luminosity phase of LHC. The ASIC integrates two 12-bit 160 MS/s ADCs, a data processing unit for gain selection and data compression, and a 1.28 Gb/s serializer.
The ASIC has been extensively tested in laboratory and in beam tests showing excellent yield and performance. The radiation tolerance has been verified with dedicated test campaigns for both total ionizing dose and single event effects. Results from these tests, showing the design readiness for mass production, will be presented.
Utilizing sustainable hemp biomass as an eco-friendly for potentially toxic elements removal from water
Authors: Üstün-Odabaşı Sevde
Published: February 2024

Potentially toxic elements in water is one of the important environmental problems. In this study, it was aimed to produce an environmentally friendly and cost-effective biosorbent using sustainable hemp biomass. The chemical composition of hemp biosorbents was characterized by FTIR, SEM, and XRD, and the results showed that the biosorbent could be a good alternative. A batch system was used to investigate the effects of initial concentration, pH, contact time, and temperature on the removal performance of Cu (II) and Zn (II) ions. The statistical analysis was performed, and the amount of adsorbed substance, kinetic values, and experiment results were evaluated for suitability. Kinetic data determined the best fit to pseudo-second-order kinetics for Cu (II) and Zn (II) ions. Adsorption determined the Langmuir model for Cu (II) ions and the Freundlich model for Zn (II) ions. The maximum adsorption capacity in the batch system was determined as 25.59 mg/g for Cu (II) and 12.97 mg/g for Zn (II) ions. The obtained thermodynamic data confirmed the endothermic nature of the adsorption. In desorption studies, after three cycles, the adsorption efficiency decreased from 83.3% to 52.8% for Cu (II) and from 82.1% to 49.7% for Zn (II). Study results showed that hemp biosorbent may be an alternative adsorbent that can be used to remove PTEs from wastewater.
Thermoelectric properties of the bismuth oxychalcogenides Bi2SO2, Bi2SeO2 and Bi2TeO2
Authors: Flitcroft J M , Althubiani A , Skelton J M
Published: February 2024

We present a detailed theoretical study of the thermoelectric properties of the bismuth oxychalcogenides Bi2ChO2 (Ch = S, Se, Te). The electrical transport is modelled using semi-classical Boltzmann transport theory with electronic structures from hybrid density-functional theory, including an approximate model for the electron lifetimes. The lattice thermal conductivity is calculated using first-principles phonon calculations with an explicit treatment of anharmonicity, yielding microscopic insight into how partial replacement of the chalcogen in the bismuth chalcogenides impacts the phonon transport. We find very good agreement between the predicted transport properties and a favourable cancellation of errors that allows for near-quantitative predictions of the thermoelectric figure of merit ZT. Our calculations suggest recent experiments on n-doped Bi2SeO2 have achieved close to the largest ZT possible in bulk materials, whereas the largest reported ZT for Bi2TeO2 could be improved sixfold by optimising the carrier concentration. We also predict that much larger ZT > 2.5, competitive with the benchmark thermoelectric SnSe, could be obtained for Bi2SO2 and Bi2SeO2 with heavy p-type doping. This study demonstrates the predictive power of this modelling approach for studying thermoelectrics and highlights several avenues for improving the performance of the Bi2ChO2.