bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Automotive Smoothness Driven by Wheel Hub Motors with Different Configuration under Dual Excitation
Authors: Lin Yumeng , Zhao Haijun , Wang Feng , Wang Xulong
Published: July 2024

In response to the smoothness issue of wheel hub motor driven vehicles, electromagnetic vertical excitation was added on the basis of road excitation, and the noise source of a three-phase 8-pole 48-slot permanent magnet motor was investigated in the frequency domain; A 1/4 vehicle 2-DOF vertical vibration system model with three suspension configurations under dual excitation was established, and the vehicle smoothness problem was analyzed from a time domain perspective. The results show that the overall trend of the response values of each evaluation indicator under dual coupling excitation is correspondingly higher than that under single excitation. Under dual excitation, the suspension configuration of absorber type exhibits the smallest maximum variation in the vertical acceleration response of the vehicle over time compared to the distributed and isolated suspension configurations.
Research on Parameter Identification of Induction Motor Based on Model Reference Adaptive System
Authors: Wu Qize , Wang Chenyu , Zhao Jiayi , Hu Jiaxing , An Xue , Huan Xingzhong , Yang Shuang
Published: July 2024

This paper proposes a novel online parameter identification method based on model-reference adaptive systems to achieve more precise control of induction motors. This method efficiently and accurately obtains the parameters of induction motors during operation, facilitating effective control. Experimental validation is conducted on a hardware-in-the-loop test platform, demonstrating that the proposed online parameter identification method for induction motors achieves accurate parameter estimation with the advantages of high identification speed and precision. Control experiments on induction motors using the identified parameters further validate the feasibility of the proposed method for online parameter identification.
Reliability Evaluation of Distribution Networks Considering Natural Gas Network Integration
Authors: Zhang Guozheng , Wang Yong , Wu Wei , Zhang Peiyue
Published: July 2024

With the transformation of energy structure, the distribution network in the form of multi-energy interconnection is gradually emphasized. The access of gas turbines to the distribution network puts high requirements on the reliability assessment of the distribution network counting and natural gas network access. Models of distribution network components and natural gas network equipment are established. Based on the traditional sequential MC reliability assessment, the calculation of reliability indexes for the distribution network with gas turbine access is accomplished by identifying the islanded areas under fault conditions and judging the gas turbine output state, taking into account the energy flow constraints. The results of the calculations show that the reliability of the distribution network with natural gas network access is improved, and the reliability of the load nodes near the gas turbine is improved significantly.
Real-time monitoring method for power system voltage based on optical voltage sensors
Authors: Zhou Lifu , Wu Guoxing , He Zhenhua , Zheng Qingmeng , Zhu Ruichao , Zhong Tianyong
Published: July 2024

A real-time monitoring method for power system voltage based on optical voltage sensors is proposed to obtain transient voltage signals and achieve real-time tracking of instantaneous changes in electrical parameters of the power system accurately and effectively. Analyzing the topology principle of power system transformers clarifies the installation location of optical voltage sensors. By combining the measurement principle of optical voltage sensors, the electrical, optical modulation, and optical-electrical conversion of voltage signals are achieved, and the voltage signal of the power system is output. Using the short-time Fourier transform method to perform temporal processing on the output signal and obtaining a voltage signal sliding window based on wavelet packet decomposition, we set the normal range threshold of the voltage signal in the sliding window, combined with information entropy theory to extract key features of the voltage signal and achieve real-time monitoring of the power system voltage. The experimental results show that the proposed method can effectively obtain transient voltage signals of the power system, with an average detection accuracy of 92.61%, and the output voltage can be adjusted promptly to reflect the instantaneous changes in the power system voltage.
Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters
Authors: Wu Hao , Zhang Yanchi , Shao Guangyi , Wei Qing , Wu Qian
Published: July 2024

In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.
A high-precision hazard ranging method for transmission lines based on laser point cloud
Authors: Liu Jing , Cai Fudong , Cao Songyuan , Xia Lingzhi , Kong Zhiqiang , Liu Wei , Zhang Mingxiang , Cheng Yang , Liu Xiaoqing , Wang Chaohui
Published: July 2024

To ensure the safe operation of the line, it is necessary to discover the hidden danger in time and accurately. This paper proposes a high-precision hidden danger-ranging method for transmission lines based on the laser point cloud. This method uses LiDAR to form a data acquisition system, which is installed in the fixed position of the transmission line channel power tower to obtain the point cloud data of the channel field at regular intervals, according to a certain rule. Two ends of the suspension points and some of the conductor point cloud data are used to fit the whole conductor in three-dimensional space and carry out the precise hidden danger ranging through the fitted conductor combined with the point cloud data and ranging. By comparing the original conductor point cloud data with the fitted conductor data to verify the reliability of the conductor fitting, and verifying the feasibility of the conductor arc prolapse measurement method based on the measured arc prolapse of the conductor and the distance to the ground, it can provide more timely and effective early warning information for the safety of the transmission line channel.
Particle size distribution of two-phase medium measured by ultrasonic wave
Authors: An Yuxiang , Hou Huaishu , Li Shengtao
Published: July 2024

Measurement of ultrasonic attenuation spectra of particle suspensions using a variable acoustic range approach is introduced. This study is based on the improved ECAH model. In this paper, an experimental test was performed on an emulsion with a concentration of 15%. Then, the NNLS algorithm is used for inversion calculations. Compared with laser particle size meter results, the measurement results are in good agreement.
A Transient-Enhanced Capacitorless LDO Design
Authors: Liang Jinhui , Zhou Qiang , Chen Haichuan
Published: July 2024

This paper introduces a capacitor-less low drop-out voltage regulator (LDO) designed for fast transient response. Traditional capacitor-less LDOs often suffer from suboptimal transient responses, leading to unstable outputs during load transitions. To address this challenge, the circuit employs voltage rise and fall suppression circuits to mitigate voltage spikes. It utilizes slew rate enhancement techniques to improve the transient response characteristics of the LDO. Miller compensation is applied to achieve phase margin compensation with a smaller capacitor area. Simulation outcomes indicate that within the voltage range of 2.2 V to 3.3 V, the circuit provides a stable output voltage of 1.8 V while accommodating a peak load current of 100 mA. When the load current undergoes a step change between 1 mA and 100 mA, the transient enhancement circuit reduces the undershoot of the output voltage by 58 mV, a decrease of 39%, with a response time of 1.13 μs. The overshoot amplitude is reduced by 212 mV, a decrease of 70.6%, with a response time of 1.21 μs.
Modeling and prediction of degradation induced by light on monocrystalline silicon solar cell
Authors: Li Hailing , Li Wenhao , He Mengyuan , Zhao Wanying
Published: July 2024

In this work, the qualitative and quantitative study of degradation extent caused by LeTID on monocrystalline silicon solar cells under different treatment temperatures and irradiance intensities is carried out. A mathematical model of the relationship between the degradation temperature and irradiance intensity is established. The concept of equivalent temperature and equivalent irradiance intensity is introduced to establish the relationship between the constant temperature and irradiance in the laboratory and the changing temperature and irradiance outdoors, and the outdoor degradation prediction of LeTID of monocrystalline silicon cells is realized. Using this model can help the PV industry to predict the outdoor long-term performance of PERC solar cells.
Comparative analysis of two types of mechanical grippers for gripping flexible packaging materials
Authors: Zhang Zhiyuan , Zhu Chufeng , Ren Hao , Yuan Kanghui
Published: July 2024

To study the different structure types of flexible packaging material gripping mechanical grippers and their application to the working environment, two types of cylinder-type soft packaging material gripping mechanical grippers are designed in this paper. According to the structural characteristics of the mechanical gripper, mathematical analysis is carried out to determine the coordinate relationship of each joint of the mechanical gripper. The plot function in MATLAB is used to plan and simulate the path curves of the two types of mechanical grippers and to determine the working environment of the two types of mechanical grippers applied respectively; the movement of the mechanical gripper is analyzed by using the fifth-degree polynomial, and MATLAB Robotics toolbox is used for simulation, which results in the displacement, velocity, and speed of the end of the two mechanical grippers. The fifth-degree polynomial is used to analyze the motion of the mechanical gripper, and the MATLAB Robotics toolbox is adopted for simulation, as well as the displacement, velocity, and acceleration curves of the end of the two mechanical grippers. The aim is to understand the motion characteristics of the two kinds of mechanical grippers, master the two kinds of mechanical grippers’ working state, and provide a reference for subsequent research on flexible packaging material gripping mechanical grippers.
Preparation of printable copper submicron particle paste
Authors: Wu Zijian , Xie Xiaosheng , Pan Daocheng , Shi Xinan
Published: July 2024

Thanks to the rise of flexible printed electronics, printable conductive pastes have received more and more attention, especially copper paste that can be prepared on a large scale at low cost. We first prepared submicron copper particles by reducing copper sulfate solution with glucose and hydrazine as the reducing agents and then modified the surface of copper submicron particles with formate and ethylene glycol for antioxidant treatment. The surface-modified copper particles exhibit better corrosion and oxidation resistance. Next, the copper submicron particles were dispersed in propylene glycol to make copper slurry. The copper conductive pattern with a low resistivity of 6.09×10−4 Ω·cm can be fabricated by printing copper paste with a dispensing machine, followed by a sintering process in an argon atmosphere at 200°C for 10 min. As prepared copper conductive patterns have excellent mechanical flexibility on polyimide (PI) substrate, which will be conducive to the large-scale application of copper paste on flexible substrates.
Slope estimation based on multi-source information fusion
Authors: Zhu Yifei , Chen Luran , Yin Dejun
Published: July 2024

This study presents a novel methodology for slope estimation, addressing limitations in current unidirectional estimation research. A comprehensive vehicle kinematics and dynamics model is developed using Inertial Measurement Unit (IMU) data, enabling a thorough assessment of vehicle slope. The proposal advocates employing the Interactive Multiple Model Unscented Kalman Filter algorithm to enhance algorithmic stability and prevent divergence from singular model errors. This approach ensures precise computation of dynamic slopes. The algorithm’s efficacy is demonstrated through collaborative simulations on the CarSim/Simulink platform. Introducing innovative concepts and methods for slope estimation, this research has significant practical and theoretical implications.
Influence of Pre-strain on the Microstructure and Mechanical Properties of GH4169 Superalloy
Authors: Liu Wentao , Shi Songyi , Xie Mingzhao , Liu Yaojie , Qin Hailong , Lv Yuting
Published: July 2024

In this study, GH4169 superalloy was subjected to pre-straining after aging, with pre-strain deformations ranging from 0.2% to 10%. Subsequently, the tensile performance of the alloy was tested at various deformation levels. The microstructure of several pre-tensile deformed alloys was also examined. The results show that when the pre-strain deformation increases, the strength of the alloy is improved, the elongation is greatly reduced, and the orientation of equiaxed crystals increases. The pre-strain of GH4169 superalloy increases LAGB and dislocation density while decreasing HAGB and annealing twins.
Effect of hot isostatic pressing on microstructure and mechanical properties of TZM alloy formed by powder metallurgy
Authors: Su Pengsheng , Zhou Leyu , Ren Dongchao , Yang Hao , Zhang Linping , Zhai Yuewen
Published: July 2024

TZM alloy billets were fabricated using powder metallurgy (PM) and powder metallurgy + hot isostatic pressing (PM+HIP) techniques, and the influence of these two processes on the microstructure and mechanical properties of TZM alloy billets was compared. The experimental findings indicate that an enhanced hot isostatic pressing process effectively reduces the internal porosity of TZM alloy, leading to increased tensile strength and elongation at room temperature. However, there is no significant improvement in hardness and tensile strength at 600℃.
Study on Preparation Technology of Novel Fluorosilicone Rubber Composite Insulator
Authors: Xu Mingshen , Yu Shumin , Xu Ruixin , Zhang Jingzhong , Wang Shijie , Wang Wentao , Zheng Suhang , Geng Jianghai
Published: July 2024

Currently, commonly used external insulation materials for power products lack operational stability compared to high-temperature vulcanized silicone rubber. The excellent hydrophobicity of PTFE provides a direction for further enhancing high-temperature vulcanized silicone rubber. By utilizing fluorinated occupation directional design technology and improving the dispersion degree of the dispersed phase, we have successfully prepared high-temperature vulcanized fluorine silicone rubber. After testing its flammability and other aspects, the prepared material achieved HC1-level hydrophobicity, surpassing conventional silicone rubber insulators in terms of self-cleaning performance.
Effect of Compound Curing System on Properties of NBR/CM Blends
Authors: Han Xiao , Zhang Zhaoyang , Zhang Ling , Cheng Wei
Published: July 2024

The effects of conventional vulcanization (CV), semi-efficient vulcanization (SEV), efficient vulcanization (EV), and equilibrium cure (EC) on the properties of nitrile rubber (NBR)/ chlorinated polyethylene (CM) blends were studied. The results showed that the SEV system had a faster vulcanization rate, larger torque, and larger vulcanization temperature coefficient, and the EC system had reversion resistance and a longer vulcanizing period. The modulus of the SEV system, the tensile strength of the CV system, and the elongation at the break of the EC system were high. The SEV system had good seawater and oil resistance; SEV > EV > CV >EC appeared in G’ and G”. The tanδ of the SEV system was larger than that of the EC system, but the tanδ of CV and EV system was close.
Research on the key technologies of the whole process of digital intelligent detection system of silicate binding material
Authors: Xu Tongxin , Li Pengju , Liu Lu , Hu Penghui
Published: July 2024

In construction engineering, silicate binding material detection is the key work to ensure the safety of building structures. Although the current detection instruments have made great progress compared with the traditional detection methods, there are still problems such as low efficiency, heavy workload, and error-prone data due to manual operation. Based on the practical problems faced by silicate binding material quality detection, this paper studies the key technologies of the whole process digital intelligent detection system of silicate binding material, including multi-machine cooperative automatic dumping and scraping technology, multi-strategy robot scheduling algorithm and full-link traceability technology, and shows the application effect through the LIMS system function platform. The research effectively improves the working efficiency of silicate binding material detection, realizes real-time monitoring of data and unique coding of the whole process, and ensures the timeliness, authenticity, accuracy, and traceability of detection data.
Prediction of Longitudinal Resonance Elastic Modulus of Wood-plastic Composites Based on GM (1, N) Neural Network
Authors: Qin Nan , Bing Shi
Published: July 2024

Based on the grey neural network combined prediction model, the prediction model of the mechanical properties of WPC composites was established by taking the orthogonal design test results of the main processing factors of WPC composites as data samples. Taking the main processing factors of WPC as input, grey GM (1, N) prediction of these variables was carried out, and then BP neural network prediction was carried out to predict the mechanical process index of WPC. The results show that the established prediction model can predict the mechanical properties and technological indexes of WPC composites well.
Implementation of energy reduced 90Sr/90Y radiation fields, or: Propagation of beta radiation, a case study
Authors: Behrens R.
Published: July 2024

Reference beta-particle radiation fields are well described
in the standard series ISO 6980 issued by the International
Organization for Standardization (ISO). In its 2022/2023 edition,
two new radiation fields are defined consisting of a radioactive
 90Sr/90Y source and a 3 mm or 4 mm plastic absorber
located 4 cm in front of the source — with the reference plane of
the radiation field being located 20 cm from the source. In this
work, the detailed method of how to implement and use these new
radiation fields using a Beta Secondary Standard, BSS 2, is
described. Furthermore, the influence of the position and thickness
of the plastic absorber on the radiation field, i.e., on its
spectral and angular distribution as well as on the dose rate, was
investigated. It turned out that a change of position by one
centimeter or a thickness change of a tenth of a millimeter result
in significant changes of the dose rate (∼ 7 % to
 ∼ 10 %). Finally, the dependence of the angular and spectral
distribution on the position and thickness of the absorber was
investigated in detail.
The forest loss within small reservoirs surpasses that of large reservoirs across the tropics
Authors: Yang Feng , Qin Yingzuo , Xu Rongrong , Deng Lei , Zeng Zhenzhong
Published: July 2024

The vast potential of hydropower remains crucial in addressing the escalating need for clean energy, particularly in Tropical Moist Forests (TMFs) regions. Yet, the widespread construction of reservoirs within TMFs has resulted in the inundation of forested areas, exacerbating the fragmentation of forest landscapes and contributing additional loss of carbon stored in these ecosystems. Despite this, the scale and degree of forest loss within reservoirs due to inundation from reservoir construction remain poorly quantified and mapped across tropical regions. Here, we leverage long-term TMFs data spanning from 1990 to 2021 to investigate forest loss within reservoirs across tropical regions. We reveal that forest losses within reservoirs total 3521 km2, constituting a relatively small fraction of total forest loss. Nonetheless, the spatial distribution of forest loss within reservoirs varies significantly across the tropics, with patchy distributions observed in the American and African TMFs, and striped patterns evident in the Asian TMFs. Contrary to common assumptions, we show that small reservoirs exhibit higher levels of forest loss compared to large reservoirs, particularly pronounced in the African TMFs region. Furthermore, our projections indicate that the exclusive construction of small reservoirs contributes to approximately 27% of Africa’s total forest area lost. We underscore the importance of policymakers carefully evaluating the trade-offs associated with the construction of large versus small reservoirs in TMF regions, to minimize the adverse impacts of hydropower development on forest ecosystems.
Asymptotic matching the self-consistent expansion to approximate the modified Bessel functions of the second kind
Authors: Steinbock Chanania , Katzav Eytan
Published: July 2024

The self-consistent expansion (SCE) is a powerful technique for obtaining perturbative solutions to problems in statistical physics but it suffers from a subtle problem—too much freedom! The SCE can be used to generate an enormous number of approximations but distinguishing the superb approximations from the deficient ones can only be achieved after the fact by comparison to experimental or numerical results. Here, we propose a method of using the SCE to a priori obtain uniform approximations, namely asymptotic matching. If the asymptotic behaviour of a problem can be identified, then the approximations generated by the SCE can be tuned to asymptotically match the desired behaviour and this can be used to obtain uniform approximations over the entire domain of consideration, without needing to resort to empirical comparisons. We demonstrate this method by applying it to the task of obtaining uniform approximations of the modified Bessel functions of the second kind,

K

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(
x
)

.
Hot deformation behavior and processing workability of a powder forged Fe-2.1Cu-0.5C alloy
Authors: Wang Sui , Chen Zhijun , Yang Xinke , Zhao Tian , Du Quanqing , Fu Anqing
Published: July 2024

Hot deformation behavior of as-sintered Fe-2.1Cu-0.5C alloy was evaluated through the construction of constitutive equations and a hot processing map. The hot deformation behavior was investigated at different temperatures and strain rates and microstructure observations were subsequently conducted. Flow stress for the initiation of dynamic recrystallization mechanism was determined. Tensile and fatigue strength tests were conducted at room temperature. It was observed that the microstructure of the alloy forged at lower temperature exhibited smaller grain size and its grain size increased with higher forging temperatures. Mechanical testing results revealed that an increase in the forging temperature led to a lower tensile strength and a higher fatigue strength. This change can be attributed to the larger grain sizes and the reduction of microcracks. A forging temperature at 1050 °C was recommended to achieve optimal mechanical properties for powder forged Fe-2.1Cu-0.5C products. These results might give some hints for optimizing process conditions and improving mechanical properties of powder metallurgy alloys by the use of constitutive equations and thermal processing maps.
Switching from nuclear science to diplomacy
Authors: Skipper Katherine
Published: July 2024

Craig Jantzen is the first secretary and regional manager for the UK’s Science Innovation Network in the Nordic and Baltic regions. As a former nuclear scientist, he talks to Katherine Skipper about his varied career, how he learned to make mistakes, and why he thinks scientists can make great diplomats.
Effects of bubble-induced strain on the magnetic properties of van der Waals ferromagnet CrBr3
Authors: Grebenchuk Sergey , Grzeszczyk Magdalena , Chen Zhaolong , Novoselov Kostya S , Koperski Maciej
Published: July 2024

Two-dimensional materials provide with ability to control their properties with a number of methods. One of such methods is using strain and compression. In this work, we investigated the influence of locally induced strain through bubbles in thin ferromagnetic CrBr3 using low-temperature magnetic force microscopy. As a result, domain pinning and higher coercive and saturation fields were observed in the bubble. In addition, nontrivial spin arrangements are allowed to take place in a non-homogeneously strained area, leading to different responses to the external magnetic field in comparison to a non-strained region. Finally, Raman spectroscopy and magneto-photoluminescence spectroscopy were performed to show alternation of the magnetic properties of the sample under mechanical deformation.
Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism
Authors: Wang Jianxing , Wang Yue , Li Jian
Published: July 2024

Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.
Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies
Authors: S Arun Prakash , K Gokul Kumar
Published: July 2024

Employing tailor-made alloys with uneven thickness achieves light weighting, a critical issue for reducing emissions, leading to lower aircraft pollutants and fuel costs. The research utilizes advanced machine learning techniques such as Gaussian process regression (GPR), artificial neural networks (ANN) linear regression (LR), and support vector machines (SVM) to predict the ultimate tensile strength of underwater friction stir welding of AA6082-T6 and A2219-T83 tailor-made joints. The models have been evaluated with an assortment of kernel functions, including the polynomial kernel (PK), the radial basis function (RBF), and the Pearson VII universal kernel (PUK). To acquire experimental data, we used a Central Composite Design (CCD) technique, incorporating various factors in the process encompassing tool tilt angle (TA), rotating speed (RS), and welding speed (WS). The SVM radial basis function model (SRBP) had a maximum correlation coefficient of 0.9995 and a minimum root mean square error value (RMSE) of 0.5433 in the training set and 0.6271 in the test set. The ANN model predicted the UTS with an error margin of 0.21%, while the SRBP model showed a 0.52% error, and the LR model exhibited a significantly higher error of 7.73%. A peak tensile strength of 252.98 MPa was recorded in the S20 specimen, accounting for 85.61% of the base metal’s (AA6082 T6) strength. A reduced acute tearing ridge indicates petite, shallow dimples due to the inherent cooling. Through the analysis of metrics and residuals, high accuracy rates were observed when employing the ANN and SRBP models to predict mechanical traits.
A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions
Authors: Agdam Mohammed , Assalaou Khalid , Aitiaz Elmostafa , Hmamou Dris Ben , El aidi Idrissi Yassine , Lidaighbi Souad , Driss Saadaoui , Elyaqouti Mustapha
Published: July 2024

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).
Ultrafast jet classification at the HL-LHC
Authors: Odagiu Patrick , Que Zhiqiang , Duarte Javier , Haller Johannes , Kasieczka Gregor , Lobanov Artur , Loncar Vladimir , Luk Wayne , Ngadiuba Jennifer , Pierini Maurizio , Rincke Philipp , Seksaria Arpita , Summers Sioni , Sznajder Andre , Tapper Alexander , Årrestad Thea K
Published: July 2024

Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that

O

(
100
)

ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
Authors: Stark Wojciech G , van der Oord Cas , Batatia Ilyes , Zhang Yaolong , Jiang Bin , Csányi Gábor , Maurer Reinhard J
Published: July 2024

Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases.
Retrieving past quantum features with deep hybrid classical-quantum reservoir computing
Authors: Nokkala Johannes , Giorgi Gian Luca , Zambrini Roberta
Published: July 2024

Machine learning techniques have achieved impressive results in recent years and the possibility of harnessing the power of quantum physics opens new promising avenues to speed up classical learning methods. Rather than viewing classical and quantum approaches as exclusive alternatives, their integration into hybrid designs has gathered increasing interest, as seen in variational quantum algorithms, quantum circuit learning, and kernel methods. Here we introduce deep hybrid classical-quantum reservoir computing for temporal processing of quantum states where information about, for instance, the entanglement or the purity of past input states can be extracted via a single-step measurement. We find that the hybrid setup cascading two reservoirs not only inherits the strengths of both of its constituents but is even more than just the sum of its parts, outperforming comparable non-hybrid alternatives. The quantum layer is within reach of state-of-the-art multimode quantum optical platforms while the classical layer can be implemented in silico.
CResU-Net: a method for landslide mapping using deep learning
Authors: Pham Thang M , Do Nam , Pham Ha T T , Bui Hanh T , Do Thang T , Hoang Manh V
Published: July 2024

Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.
Impact of long-term exposure to ambient particulate matter and nitrogen dioxide on chronic obstructive pulmonary disease: results from the Women’s Health Initiative cohort
Authors: Nori-Sarma Amruta , Eliot Melissa N , Whitsel Eric A , Saquib Nazmus , Bhatti Parveen , Mu Lina , Kaufman Joel D , Sears Clara G , Wellenius Gregory A , Kulick Erin R
Published: July 2024

Rationale: Although COPD prevalence and exacerbations have been linked to ambient pollutants, evidence on the impact of ambient pollutants on COPD incidence is relatively sparse. Objectives: To evaluate the associations of long-term ambient particulate matter (PM2.5; PM10), nitrogen dioxide (NO2), and incident self-reported COPD in the Women’s Health Initiative (WHI), a large prospective cohort study of post-menopausal women across the United States. Methods: We estimated annual average residential pollutant concentrations using validated spatiotemporal models and monitored data. We estimated pollutant-COPD associations as hazard ratios (HRs) and 95% confidence intervals (CI) per inter-quartile range (IQR) increase in pollutant using time-varying Cox proportional hazards models adjusted for potential confounders including sociodemographic characteristics, lifestyle and health factors, and WHI Clinical Center at baseline. Finally, we assessed the joint impact of exposure to multiple pollutants using quantile-based G-computation for survival outcomes. Measurements and Main Results: During the median follow-up time of 11.1 years, the study participants experienced 3532 cases of COPD. HRs ranged from 1.20 (95% CI:1.15, 1.26) per IQR increase in PM2.5, to 1.19 (95% CI:1.13, 1.26) per IQR increase in NO2, to 1.10 (95% CI:1.06, 1.15) per IQR increase in PM10. In our multi-pollutant model, a quartile increase in PM2.5 and NO2 was associated with a HR of 1.16 (95% CI:1.11, 1.20). Conclusions: In this national cohort of post-menopausal women, the long-term residential concentrations of ambient particulate matter (PM2.5 and PM10), and NO2 were associated with a higher risk of incident COPD.
Solar irrigation potential in Sub-Saharan Africa: a crop-specific techno-economic analysis
Authors: Wamalwa Fhazhil , Maqelepo Lefu , Williams Nathan , Falchetta Giacomo
Published: July 2024

In this study, we introduce an integrated modeling framework that combines a hydrologic model, a biophysical crop model, and a techno-economic model to assess solar irrigation potential in Sub-Saharan Africa (SSA) based on seven commonly grown food crops-maize, wheat, sorghum, potato, cassava, tomato, and onion. The study involves determining the irrigation requirements, location-specific capital investment costs, crop-specific profitability, and the cropland area under various cost scenarios (low and high) and soil fertility (low, moderate, near-optimal, and optimal) scenarios. Our research reveals considerable potential for solar irrigation, with profitability and viable cropland areas that vary according to crop type, irrigation system cost scenarios, and soil fertility levels. Our assessment shows that approximately 9.34 million ha of SSA’s current rainfed cropland are hydrologically and economically feasible for solar irrigation. Specifically, maize and onion display the lowest and highest viability, spanning 1–4 million ha and 29–33 million ha, respectively, under optimal soil fertility conditions. In terms of profitability, maize and onion rank as the least and most economically viable crops for solar irrigation, yielding average annual returns of $50-$125/ha and $933-$1450/ha, respectively, under optimal soil fertility conditions. The lower and upper bounds of profitability and cropland range correspond to high-cost and low-cost scenarios, respectively. Furthermore, our study reveals distinct regional differences in the economic feasibility of solar irrigation. Eastern Africa is more economically favorable for maize, sorghum, tomato, and cassava. Central Africa stands out for onion cultivation, whereas West and Southern Africa are more profitable for potato and wheat, respectively. To realize the irrigation benefits highlighted, an energy input of 940-2,168 kWh/ha/yr is necessary, varying by crop and geographic sub-region of the SSA sub-continent. Our model and its results highlights the importance of selecting the right crops, applying fertilizers at the appropriate rates, and considering regional factors to maximize the benefits of solar irrigation in SSA. These insights are crucial for strategic planning and investment in the region’s agricultural sector.
Death on the permafrost: revisiting the 1918-1920 influenza pandemic in Alaska using death certificates
Authors: Sattenspiel Lisa , Mamelund Svenn-Erik , Dahal Sushma , Wissler Amanda , Chowell Gerardo , Tinker-Fortel Emma
Published: July 2024

The 1918-1920 influenza pandemic devastated Alaska’s Indigenous populations. We report on quantitative analyses of pandemic deaths due to pneumonia and influenza (P&I) using information from Alaska death certificates dating between 1915 and 1921 (n = 7147). Goals include a reassessment of pandemic death numbers, analysis of P&I deaths beyond 1919, estimates of excess mortality patterns overall and by age using intercensal population estimates based on Alaska’s demographic history, and comparisons between Alaska Native (AN) and non-AN residents. Results indicate that ANs experienced 83% of all P&I deaths and 87% of all-cause excess deaths during the pandemic. Alaska Native mortality was 8.1 times higher than non-AN mortality. Analyses also uncovered previously unknown mortality peaks in 1920. Both subpopulations showed characteristically high mortality of young adults, possibly due to imprinting with the 1889-1890 pandemic virus, but their age-specific mortality patterns were different: non-AN mortality declined after age 25-29 and stayed relatively low for the elderly, while AN mortality increased after age 25-29, peaked at age 40-44, and remained high up to age 64. This suggests a relative lack of exposure to H1-type viruses pre-1889 among AN persons. In contrast, non-AN persons, often temporary residents, may have gained immunity before moving to Alaska.
1161 Fostering International Surgical Trainee Collaboration: The UK-Iceland Trainee Collaborative
Authors: Cairns G , Shekleton F , Mihailidis T , Lewis H , Schrire T , Conroy S , Pringle H , Barlow C , Mutimer J
Published: July 2024

The Foundation

In 2019, the UK Iceland Trainee Collaborative emerged in response to Iceland’s adoption of the Intercollegiate Surgical Curriculum Programme (ISCP), aligning training portfolios with the UK’s practices. A collaboration between core surgical trainees, this new initiative aimed to champion the transition of Icelandic trainees to a new portfolio system, as well as foster a new relationship to encourage combined research and projects between the two nations.

The Progression

Beyond portfolio alignment, the collaboration expanded into a multifaceted educational platform. Monthly seminars and an annual conference, initially delivered online, provided a forum for knowledge exchange, featuring research showcases, consultant talks, and insights into portfolio optimization and program developments.

The Impact

The collaborative facilitated seamless integration of ISCP into Icelandic trainees’ competency progression, mirroring UK practices. The annual conference, delivered in person in its 3rd edition in Reykjavik, Iceland, in October 2023, received overwhelmingly positive feedback, with high delegate satisfaction rates, highlighting the leadership skills honed by core surgical trainees leading this initiative.

The Future

The collaborative now aims to intensify research partnerships between UK and Icelandic trainees, deepening knowledge exchange and advancing collective understanding. The trajectory for 2024 involves strengthening bonds and knowledge sharing among trainees from both nations through collaborative research initiatives. We hope the annual conference will continue to grow and will alternate between the two nations, strengthening the relationship.
273 Outcomes Reported in Elective Colorectal Cancer Surgery Research for Older Patients: A Scoping Review
Authors: Xavier H , Bhattacharya Y , Poobalan A , Brazzelli M , Ramsay G
Published: July 2024

Aim

This scoping review aimed to identify outcome measures for assessing the impact of elective colorectal cancer surgery on older patients, given their higher surgical risk and mortality rates. Conventional surgery-specific outcomes, like 5-year mortality rates, may not be appropriate, necessitating a focus on postoperative quality of life. The challenge lies in determining optimal outcomes to assess the effects of colorectal cancer resections in the older population.

Method

A comprehensive literature search of major electronic databases from inception to March 2023 was conducted. Studies exploring frail or elderly individuals with colorectal cancer undergoing elective surgery and reporting postoperative outcomes were included. Outcomes were classified as surgery-specific versus person-centred and summarised using a narrative synthesis. The type and rate of surgery-specific outcomes were tabulated.

Results

Of 1366 identified citations, 16 studies focused on person-centred outcomes and 66 exclusively on surgery-specific outcomes. Nine of the 16 ‘person-centred outcome’ studies reported discharge destination, with home discharge being predominant. Postoperative delirium ranged from 8.2% to 18.1% in six studies. Four studies explored geriatric syndromes and three analysed activities of daily living. Three studies reported a significant improvement in quality of life. The 66 ‘surgery-specific outcome’ studies assessed mortality (N=61); length of stay (N=40); postoperative complications (N=47); readmission (N=18); reoperation (N=16); and survival (N=42).

Conclusions

Person-centred outcomes are underreported, despite their importance in guiding patient management. Older patients require adequate information about their recovery after surgery to manage their lives better. Future research must address this gap to improve care for older patients undergoing elective colorectal cancer surgery.