bluuebunny/crossref_metadata_2025 huggingface dataset:
Remodeled connexin 43 hemichannels alter cardiac excitability and promote arrhythmias
Authors: Lillo Mauricio A. , Muñoz Manuel , Rhana Paula , Gaul-Muller Kelli , Quan Jonathan , Shirokova Natalia , Xie Lai-Hua , Santana Luis Fernando , Fraidenraich Diego , Contreras Jorge E.
Published: May 2023

Connexin-43 (Cx43) is the most abundant protein forming gap junction channels (GJCs) in cardiac ventricles. In multiple cardiac pathologies, including hypertrophy and heart failure, Cx43 is found remodeled at the lateral side of the intercalated discs of ventricular cardiomyocytes. Remodeling of Cx43 has been long linked to spontaneous ventricular arrhythmia, yet the mechanisms by which arrhythmias develop are still debated. Using a model of dystrophic cardiomyopathy, we previously showed that remodeled Cx43 function as aberrant hemichannels (non-forming GJCs) that alter cardiomyocyte excitability and, consequently, promote arrhythmias. Here, we aim to evaluate if opening of remodeled Cx43 can serve as a general mechanism to alter cardiac excitability independent of cellular dysfunction associated with a particular cardiomyopathy. To address this issue, we used a genetically modified Cx43 knock-in mouse (S3A) that promotes cardiac remodeling of Cx43 protein without apparent cardiac dysfunction. Importantly, when S3A mice were subjected to cardiac stress using the β-adrenergic agonist isoproterenol (Iso), they displayed acute and severe arrhythmias, which were not observed in WT mice. Pretreatment of S3A mice with the Cx43 hemichannel blocker, Gap19, prevented Iso-induced abnormal electrocardiographic behavior. At the cellular level, when compared with WT, Iso-treated S3A cardiomyocytes showed increased membrane permeability, greater plasma membrane depolarization, and Ca2+ overload, which likely caused prolonged action potentials, delayed after depolarizations, and triggered activity. All these cellular dysfunctions were also prevented by Cx43 hemichannel blockers. Our results support the notion that opening of remodeled Cx43 hemichannels, regardless of the type of cardiomyopathy, is sufficient to mediate cardiac-stress-induced arrhythmogenicity.
Characterization of the thermal conductivity and diffusivity of graphene nanoplatelets strips: a low-cost technique
Authors: Giovinco G , Sibilia S , Maffucci A
Published: May 2023

This paper proposes a new technique to characterize the thermal conductivity and diffusivity of thin strips made by graphene nanoplatelets (GNP). The evaluation of these parameters is essential for a reliable design of thermal and electrothermal applications of graphene and is usually performed by means of assessed but expensive techniques such as those based on Raman effects and laser flash. The technique proposed here is simpler and less demanding in terms of equipment, and combines the results of an experimental characterization of the strip heated by the Joule effect obtained with infrared camera, with those provided by an electro-thermal model. Specifically, the evaluation of the thermal conductivity and diffusivity is the result of the analysis of the transient behavior of the measured and simulated solutions. The methodology is here successfully validated by applying it to commercial graphene strips and benchmarking against the thermal parameters provided by the manufacturers. Then, a complete characterization is provided for commercial strips based on different formulations of GNP and binders such as polyurethane, epoxy resin, and boron nitride. For these materials, the values of thermal conductivity and diffusivity are found in the ranges (50–450) W m−1 K−1 and (0.5–3.5) × 10−4 m2 s−1, respectively.
A denoising model based on multi-agent reinforcement learning with data transformation for digital tomosynthesis
Authors: Nam Kibok , Lee Dahye , Lee Seungwan
Published: May 2023

Objective. Denoising models based on the supervised learning have been proposed for medical imaging. However, its clinical availability in digital tomosynthesis (DT) imaging is limited due to the necessity of a large amount of training data for providing acceptable image quality and the difficulty in minimizing a loss. Reinforcement learning (RL) can provide the optimal pollicy, which maximizes a reward, with a small amount of training data for implementing a task. In this study, we presented a denoising model based on the multi-agent RL for DT imaging in order to improve the performance of the machine learning-based denoising model. Approach. The proposed multi-agent RL network consisted of shared sub-network, value sub-network with a reward map convolution (RMC) technique and policy sub-network with a convolutional gated recurrent unit (convGRU). Each sub-network was designed for implementing feature extraction, reward calculation and action execution, respectively. The agents of the proposed network were assigned to each image pixel. The wavelet and Anscombe transformations were applied to DT images for delivering precise noise features during network training. The network training was implemented with the DT images obtained from the three-dimensional digital chest phantoms, which were constructed by using clinical CT images. The performance of the proposed denoising model was evaluated in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). Main results. Comparing the supervised learning, the proposed denoising model improved the SNRs of the output DT images by 20.64% while maintaining the similar SSIMs and PSNRs. In addition, the SNRs of the output DT images with the wavelet and Anscombe transformations were 25.88 and 42.95% higher than that for the supervised learning, respectively. Significance. The denoising model based on the multi-agent RL can provide high-quality DT images, and the proposed method enables the performance improvement of machine learning-based denoising models.
SQUID current sensors with an integrated thermally actuated input current limiter
Authors: Körber R , Krzysteczko P , Klemm M , Liu T , Storm J-H , Beyer J
Published: May 2023

The development of superconducting quantum interference device (SQUID) current sensors with a thermally actuated input current limiter (TCL) integrated into the input circuit of the sensor is presented. The TCL is based on an unshunted Josephson Junction (JJ) series array, around which meanders a galvanically isolated, but tightly, thermally coupled, resistive heater element. By applying a current to the heater, the JJ critical currents can be reduced or completely suppressed, while the other on-chip SQUID circuit elements remain unaffected. The functional parameters of the TCL are determined by direct transport measurements and by static and dynamic flux coupling tests via the input circuit. In liquid helium, a heater power, which reliably suppresses the critical current of the JJ array to zero, of ∼1.5 mW was needed, with the TCL normal state resistance being at 500 Ω. In this configuration, typical TCL switching times of approximately 20 µs were observed in the direct transport measurements. The TCL can be used to temporarily or even permanently disable malfunctioning channels in multichannel SQUID magnetometer systems, when feedback into their otherwise superconducting input circuits is not possible. In doing so, significant signal distortions in neighbouring channels from screening currents in these input circuits are avoided. Furthermore, by completely suppressing and restoring the TCL critical current, dc offset currents in the superconducting input circuits can be prevented. This is relevant, for instance, in SQUID-based spin precession experiments on hyperpolarized noble gases, in which even dc currents of a few µA in the input circuit can lead to significant magnetic field distortions in the adjacent sample region contributing to the transverse spin-spin relaxation rate

1

/

T

2

.
Effect of photovoltaic panel electric field on the wind speed required for dust removal from the panels
Authors: Li Xingcai , Wang Juan , Liu Yinge , Ma Xin
Published: May 2023

Methods to remove dust deposits by high-speed airflow have significant potential applications, with optimal design of flow velocity being the core technology. In this paper, we discuss the wind speed required for particle removal from photovoltaic (PV) panels by compressed air by analyzing the force exerted on the dust deposited on inclined photovoltaic panels, which also included different electrification mechanisms of dust while it is in contact with the PV panel. The results show that the effect of the particle charging mechanism in the electric field generated by the PV panel is greatly smaller than the effect of the Van der Waals force and gravity, but the effect of the particle charged by the contact electrification mechanism in the electrostatic field is very pronounced. The wind speed required for dust removal from the PV panel increases linearly with the PV panel electric field, so we suggest that the nighttime, when the PV electric field is relatively small, would be more appropriate time for dust removal. The above results are of great scientific importance for accurately grasping the dust distribution law and for achieving scientific removal of dust on PV panels.
Saturation thickness of stacked SiO2 in atomic-layer-deposited Al2O3 gate on 4H-SiC
Authors: Shao Zewei , Xu Hongyi , Wang Hengyu , Ren Na , Sheng Kuang
Published: May 2023

High-k materials as an alternative dielectric layer for SiC power devices have the potential to reduce interfacial state defects and improve MOS channel conduction capability. Besides, under identical conditions of gate oxide thickness and gate voltage, the high-k dielectric enables a greater charge accumulation in the channel region, resulting in a larger number of free electrons available for conduction. However, the lower energy band gap of high-k materials leads to significant leakage currents at the interface with SiC, which greatly affects device reliability. By inserting a layer of SiO2 between the high-k material and SiC, the interfacial barrier can be effectively widened and hence the leakage current will be reduced. In this study, the optimal thickness of the intercalated SiO2 was determined by investigating and analyzing the gate dielectric breakdown voltage and interfacial defects of a dielectric stack composed of atomic-layer-deposited Al2O3 layer and thermally nitride SiO2. Current–voltage and high-frequency capacitance–voltage measurements were performed on metal–oxide–semiconductor test structures with 35 nm thick Al2O3 stacked on 1 nm, 2 nm, 3 nm, 6 nm, or 9 nm thick nitride SiO2. Measurement results indicated that the current conducted through the oxides was affected by the thickness of the nitride oxide and the applied electric field. Finally, a saturation thickness of stacked SiO2 that contributed to dielectric breakdown and interfacial band offsets was identified. The findings in this paper provide a guideline for the SiC gate dielectric stack design with the breakdown strength and the interfacial state defects considered.
Study of surface morphology in GaAs by hydrogen and helium implantation at elevated temperature
Authors: Huang Rui , Wang Zhiyong , Li Hui , Wang Qing , Guo Yecai
Published: May 2023

In this work, the surface morphology and internal defect evolution process of GaAs substrates implanted with light ions of different fluence combinations are studied. The influence of H and He ions implantation on the atomic mechanism of the blister phenomenon observed after annealing is investigated. Raman spectroscopy is used to measure the surface stress change of different samples before and after implantation and annealing. Optical microscopy and atomic force microscopy are used to characterize the morphology changes of the GaAs surface under different annealing conditions. The evolution of bubbles and defects in GaAs crystals is revealed by transmission electron microscopy. Through this study, it is hoped that ion implantation fluence, surface exfoliation efficiency and exfoliation cost can be optimized. At the same time, it also lays a foundation for the heterointegration of GaAs film on Si.
PMotion: an advanced markerless pose estimation approach based on novel deep learning framework used to reveal neurobehavior
Authors: Lv Xiaodong , Liu Haijie , Chen Luyao , Dai Chuankai , Wei Penghu , Hao Junwei , Zhao Guoguang
Published: May 2023

Objective. The evaluation of animals’ motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation methods are unreliable, unpractical, and inaccurate. Approach. Data augmentation (random scaling, random standard deviation Gaussian blur, random contrast, and random uniform color quantization) is adopted to augment image dataset. For the key points recognition, we present a novel efficient convolutional deep learning framework (PMotion), which combines modified ConvNext using multi-kernel feature fusion and self-defined stacked Hourglass block with SiLU activation function. Main results. PMotion is useful to predict the key points of dynamics of unmarked animal body joints in real time with high spatial precision. Gait quantification (step length, step height, and joint angle) was performed for the study of lateral lower limb movements with rats on a treadmill. Significance. The performance accuracy of PMotion on rat joint dataset was improved by 1.98, 1.46, and 0.55 pixels compared with deepposekit, deeplabcut, and stacked hourglass, respectively. This approach also may be applied for neurobehavioral studies of freely moving animals’ behavior in challenging environments (e.g. Drosophila melanogaster and openfield-Pranav) with a high accuracy.
A method for density fluctuation measurements using pulse reflectometry
Authors: Krutkin O. , Brunner S. , Coda S.
Published: May 2023

An analytical 1D model based on the WKB approximation is used to study the properties of a short pulse reflectometry diagnostic in a magnetic fusion device. Expressions linking the pulse delay with the parameters of the turbulence near the cut-off layer are derived for both ordinary and extraordinary polarizations of the probing beam. These results are used to develop a method for measuring the turbulence amplitude and the radial correlation length. The analytical conclusions and the proposed method are validated using full-wave numerical modelling. The latter is also used to study the limitations of the method and potential experimental effects not included in the reduced model.
Steel surface defect detection based on Improved YOLOX Algorithm
Authors: Li Guohao , Mokhtarzadeh Amir Ali
Published: May 2023

As one of the very common industrial products, strip steel plays a very important role in various fields. Steel production has received extensive attention. In industrial production, scratches are unavoidable due to numerous forces beyond human control. Cracks and other flaws appear, and their presence has a direct impact on the product’s quality. Therefore, it is of great significance to develop efficient and accurate strip surface defect detection. In this study, the most widely used metal material strip steel is selected as the research object, and its surface defect detection problem is studied accordingly. The activation function and loss function are improved on the basis of the YOLOX model. Resolve the problem of overexposure and content distortion of the image caused by the high reflecting characteristics of the strip, which degrades the image quality, and the embedding of the sub-attention mechanism to enhance the regional characteristics of hidden small targets. The experimental results show that the mAP of the improved model for strip surfaces defect detection is 80.7%, which is 4.1% higher than that of the original YOLOX algorithm, and the improved model can reduce the false detection and missed detection rate of defects.
A deep neural network based feature fusion panoramic segmentation method
Authors: Fei Hongyan , Mokhtarzadeh Amir Ali , Zhou Jiayu , Geng Junzuo
Published: May 2023

Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality.
Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning
Authors: Gao Han , Mokhtarzadeh Amir Ali , Li Shaofan , Fei Hongyan , Geng Junzuo , Wang Deye
Published: May 2023

In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images.
A Small Sample Photovoltaic Hot Spot Identification Method Based on Deep Transfer Learning
Authors: Zhou Jincheng , Luo Xiao
Published: May 2023

A small-sample photovoltaic hot spot identification method based on deep transfer learning has been proposed as a solution to the problem that traditional deep learning models require a substantial amount of training data, whereas the number of hot spot effect samples is relatively low and difficult to collect. This issue can be resolved by the development of a solution that employs deep transfer learning. The Inception-v 4 model serves as the basis for building a deep transfer learning model. After finishing the training procedure using a small-sample hot spot dataset of negative sample multi-classification, a network model for hot spot detection is built. Experiments reveal that even when the number of accessible samples is restricted, the model trained using the deep transfer learning technique has great identification accuracy, a low false detection rate, and strong generalization capability.
Pore extraction method of rock thin section based on Attention U-Net
Authors: Li Mingjiang , Zhang Pan , Hai Tao
Published: May 2023

This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue improving the network by optimizing the parameters, with the number of network layers and the total number of parameters remaining unaltered. This requirement must be satisfied before moving on to the next stage. The capability of generalization enhances the impact of segmentation on particles as well as their accuracy. Experiments show that this method is significantly better than the traditional method for segmenting rock flakes with manual operation and has better results in the segmentation and extraction of fine particles compared with the mainstream convolutional neural network.
Indoor location recognition device and method based on indoor robot
Authors: Wang Zhiqiang , Mokhtarzadeh Amir Ali
Published: May 2023

This paper describes an indoor location recognition device and method based on an indoor robot. The device includes a computer Raspberry Pi, a lidar, a CMOS camera, an ultrasonic sensor, a first microcontroller, and a servo motor; the lidar is connected to the computer Raspberry Pi. Pie, the lidar is driven by a servo motor, the ultrasonic sensor is connected to the computer Raspberry Pi through the first microcontroller, and the computer Raspberry Pi controls the lidar to scan the location to measure the width and length of the current location, and controls the ultrasonic sensor to the location. Perform up and down scanning to measure the distance between the ultrasonic sensor and the ground and the ceiling respectively, and obtain the height of the room; the CMOS camera is connected to the computer Raspberry Pi, and the computer Raspberry Pi sends an instruction to control the CMOS camera to take pictures of objects and places and use them Opencv and other related technologies achieve the purpose of identification. The device and method can continuously identify indoor locations under various complex conditions, and at the same time improve the accuracy and reliability of location recognition.
Quantifying the contribution of major carbon producers to increases in vapor pressure deficit and burned area in western US and southwestern Canadian forests
Authors: Dahl Kristina A , Abatzoglou John T , Phillips Carly A , Ortiz-Partida J Pablo , Licker Rachel , Merner L Delta , Ekwurzel Brenda
Published: May 2023

Increases in burned forest area across the western United States and southwestern Canada over the last several decades have been partially driven by a rise in vapor pressure deficit (VPD), a measure of the atmosphere’s drying power that is significantly influenced by human-caused climate change. Previous research has quantified the contribution of carbon emissions traced back to a set of 88 major fossil fuel producers and cement manufacturers to historical global mean temperature rise. In this study, we extend that research into the domain of forest fires. We use a global energy balance carbon-cycle model, a suite of climate models, and a burned area (BA) model to determine the contribution of emissions traced to the major carbon producers to the long-term increase in VPD during 1901–2021 and to cumulative forest fire area during 1986–2021 in the western US and southwestern Canada. Based on climate model data, we find that emissions traced to these carbon producers contributed 48% (interquartile range (IQR) 38%–63%) of the long-term rise in VPD between 1901 and 2021. BA modeling indicates that these emissions also contributed 37% (IQR 26%–47%) of the cumulative area burned by forest fires between 1986 and 2021 in the western US and southwestern Canada. The increase in VPD in this region is linked to both increased fire activity and the region’s current and prolonged megadrought. As loss and damage from these hazards mounts, this research can inform public and legal dialogues regarding the responsibility carbon producers bear for addressing past, present, and future climate risks associated with fires and drought in the western US and southwestern Canada.
Impact of volcanic eruptions on extratropical atmospheric circulations: review, revisit and future directions
Authors: Paik Seungmok , Min Seung-Ki , Son Seok-Woo , Lim Eun-Pa , McGregor Shayne , An Soon-Il , Kug Jong-Seong , Yeh Sang-Wook
Published: May 2023

Understanding the impacts of volcanic eruptions on the atmospheric circulations and surface climate in the extratropics is important for inter-annual to decadal climate prediction. Previous studies on the Northern Hemisphere climate responses to volcanic eruptions have shown that volcanic eruptions likely induce northern Eurasian warming through the intensified Arctic polar vortex in the stratosphere and the positive phase of Arctic Oscillation/North Atlantic Oscillation in the troposphere. However, large uncertainties remain and the detailed physical processes have yet to be determined. The circulation responses in the Southern Hemisphere also remain controversial with large differences between the observed and model-simulated results. In this paper, we review previous studies on the extratropical circulation and surface climate responses to volcanic eruptions and update our understanding by examining the latest observational datasets and climate model simulations. We also propose new insights into the crucial role of the latitude of volcanic eruptions in determining the extratropical circulation changes, which has received less attention. Finally, we discuss uncertainty factors that may have important implications to the extratropical circulation responses to volcanic eruptions and suggest future directions to resolve those issues through systematic model experiments.
Achieving net-zero emissions in agriculture: a review
Authors: Rosa Lorenzo , Gabrielli Paolo
Published: May 2023

Agriculture accounts for 12% of global annual greenhouse gas (GHG) emissions (7.1 Gt CO2 equivalent), primarily through non-CO2 emissions, namely methane (54%), nitrous oxide (28%), and carbon dioxide (18%). Thus, agriculture contributes significantly to climate change and is significantly impacted by its consequences. Here, we present a review of technologies and innovations for reducing GHG emissions in agriculture. These include decarbonizing on-farm energy use, adopting nitrogen fertilizers management technologies, alternative rice cultivation methods, and feeding and breeding technologies for reducing enteric methane. Combined, all these measures can reduce agricultural GHG emissions by up to 45%. However, residual emissions of 3.8 Gt CO2 equivalent per year will require offsets from carbon dioxide removal technologies to make agriculture net-zero. Bioenergy with carbon capture and storage and enhanced rock weathering are particularly promising techniques, as they can be implemented within agriculture and result in permanent carbon sequestration. While net-zero technologies are technically available, they come with a price premium over the status quo and have limited adoption. Further research and development are needed to make such technologies more affordable and scalable and understand their synergies and wider socio-environmental impacts. With support and incentives, agriculture can transition from a significant emitter to a carbon sink. This study may serve as a blueprint to identify areas where further research and investments are needed to support and accelerate a transition to net-zero emissions agriculture.
Numerical Investigation the Effects of Cone Diameters on the Flow Pattern and Separation Efficiency in a Cyclone Separator
Authors: Ajarostaghi Seyed Soheil Mousavi , Mousavi Seyed Sina , Bhojaraju Chandrasekhar
Published: May 2023

In this work, the impact of cone diameters on the flow field and separation efficiency in a cyclone separator is examined numerically employing Reynolds Stress Model for eight various geometries of cyclone separators. The motion of solid particle in the flow field is modelled utilizing the Eulerian-Lagrangian method. Three-dimensional simulation of the air flow with solid particles in the cyclone is carried out by a commercial computational fluid dynamics software, ANSYS Fluent 18.2. The outcomes depict that by augmenting the small cone diameter, the maximum tangential velocity, static pressure, and collection efficiency decrease, however, cut-off diameter rises. Moreover, the axial velocity has steady trend throughout the cyclone. In addition, according to the obtained results, increasing the large cone diameter causes an increase in the tangential velocity and static pressure and improves the collection efficiency. While, the higher large cone diameter, the lower cut-off diameter. Growing the cyclone’s cone small diameter leads to a decrease in the collection efficiency. At a constant particle diameter, four microns, as the cone small diameter ratio augments by 150%, collection efficiency declines by about 10.42%. Augmentation the cyclone’s cone small diameter causes an increase in the cutoff diameter. By increasing the cone small diameter by about 150%, the cut-off diameter value increases by about 75%. Furthermore, the collection efficiency rises by growing the cyclone’s cone large diameter. At a constant particle diameter, four microns, as the cone large diameter augments by 50%, the collection efficiency increases by about 18.75%. Also, in all considered cone large diameter, the collection efficiency increases as the particle diameter rises. The cut-off diameter decreases by growing the cyclone’s cone large diameter. By augmenting the cone large diameter by about 50%, the cut-off diameter value declines by about 57.69%.
The Slope Instability Potential in the Balaroa area based on the Finite Element Method using Shear Strength Reduction
Authors: Ramadhani S , Martini  , Ihzan M , Basnur
Published: May 2023

Balaroa area is an alternative road connecting Palu City with Sigi and Donggala Regencies which experienced landslides due to the September 2018 earthquake. The landslide formed a steep slope and was prone to landslide. This study is to determine slope stability and design of optimal and safe slope geometry against landslides. Input and data analysis based on the Finite Element Method using Shear Strength Reduction with the help of RS2 software. The analysis resulted safety factor (SF) value on the existing slope without an earthquake load at locations 1 and 2 is in a stable condition with SF>1, after being given an earthquake load of 0.35g, the SF<1, while location 2 remained safe under the earthquake load with SF> 1. The total value of displacement on existing slope without earthquake load at location 1 was 0,006-0,040m and location 2 was 0,01-0,1m, after being given an earthquake load, it decreased 0,0045-0,030m at location 1 and increased 0,006-0,15m at location 2. This illustrates the existence of an earthquake greatly affects SF and total displacement. Safe slope geometry design for location 1 is slope height must be 10,2m with angles of 46° for level 1and 32° for level 2, with SF value 1.25.
Effect of soybean variety and pest management on the sustainable production
Authors: Aryati V , Ulina E S
Published: May 2023

Pests are one of the limiting factors in increasing soybean production. Pest control carried out by farmers using pesticides is feared to threaten the sustainability of soybean production in Indonesia. This study aimed to identify the effect of different pest management systems and various soybean varieties on pest population dynamics, abundance and diversity of natural enemies, plant damage, and soybean production. A split plot design with three replications was applied, where the main plot was pest management systems i.e. (1) control (which received neither chemicals nor integrated pest management), (2) chemical control, and (3) integrated pest management, and the sub-plot was soybean varieties i.e. (1) Anjasmoro, (2) Demas 1, and (3) Dering 1. Results indicated that variety affects pest diversity and abundance, however, it affected leaf damage, pod damage, and production. The pest management systems influenced the diversity and abundance of pests and natural enemies, plant damage, and production. Interaction between variety and pest management system affected plant damage. We conclude that the use of resistant varieties combined with integrated pest management could reduce plant damage, maintain the natural enemy population, and optimize soybean production.
Analysis of Volatile Metabolites from Papaya Seeds as Potential Organic Insecticides against Myzus persicae Sulz
Authors: Indratmi D , Haryanto C T , Rachmawan MD , Zakia A
Published: May 2023

Myzus persicae is a toxic pest that acts as a limiting factor in increasing chili production in Indonesia. Symptoms of leaf curl are always found in every chili growing season along with the presence of these pests. In the field M.persicae controlled with chemical insecticides that trigger pest resistance. The aim of the study was to determine the effectiveness of papaya seed extract in controlling M. persicae , its effect on chili yields, and the content of volatile toxic compounds in papaya seeds which have potential as organic insecticides. Efficacy test in the form of 7 treatments, 5 replicates, namely application of papaya seed 10, 30, 50, 70, and 90 g/l. Analyzed data using analysis of variance with the F test and Duncan Multiple Range Test to determine the best treatment. Analysis of volatile metabolites was performed using Gas Chromatography-Mass Spectrometry to obtain specific metabolites. Papaya seed extract contains several volatile metabolites that act as organic insecticides, namely Heptasiloxane, 1,1,3,3, 5,5,7,7,9,9,11,11,13,13 tetradecamethyl compounds. The efficacy test in the field showed that papaya seed extract with a concentration of 50-70 g/l was able to suppress Myzus persicae attacks to the low attack category level, and increase chili production up to 2 times (100%). The use of papaya seed waste as an organic insecticide is in accordance with the concept of sustainable agriculture because it is environmentally friendly and improves food safety.
Above ground biomass after 39 years logging in Kintap Research Forest, South Kalimantan
Authors: Qirom Muhammad Abdul , Halwany Wawan , Lestari Fajar , Rachmanadi Dony
Published: May 2023

Land degradation causes the fragmentation of forest areas. The fragmentation can be in the form of a decrease in species diversity and carbon storage in an area including logged-over areas. This study aims to obtain stand abundance, basal area, and potential of above ground biomass that can be used in estimating natural recovery in logged-over areas. Stand measurements were carried out in 3 different areas with the distance of the plot from the road as a differentiating factor. The measurement parameters include tree species, diameter, and tree height with the smallest diameter being 10 cm. Making plots at each location covering an area of 1 ha with an observation sub-plot measuring 20 m x 20 m (400 m2). Estimation of carbon storage used the allometric model compiled by Brown, Ketterings, and Basuki. The results showed that the biomass potential reached 190 tones/hectares – 290 tones/hectares. This potential is mostly contributed by trees with a diameter of more than 20 cm. Trees with a diameter of 20 cm accounted for more than 50% of the total biomass. This condition indicates that silvicultural technique in the form of maintaining the remaining stands after felling are very necessary for accelerating the growth of small-diameter trees so that natural succession can occur more quickly.
Study of the surface roughness and optimization of machining parameters during laser-assisted fast tool servo machining of glass-ceramic
Authors: Fan Mingxu , Zhou Xiaoqin , Chen Shunfa , Jiang Shan , Song Jinzhou
Published: May 2023

Glass-ceramic is a typical hard and brittle material that is difficult to machine. In order to improve the surface quality of laser-assisted fast tool servo machining optical free-form surface of glass-ceramic, the effects of spindle speed, feed speed, piezoelectric frequency and laser power on the surface roughness were investigated. Firstly, the Taguchi method (TM) was used to establish the orthogonal experiment, and the contribution rate of each machining parameter to the surface roughness was obtained through variance and signal-to-noise ratio (S/N) analysis. The order of the influence degree of each parameter on the surface roughness is as follows: laser power > spindle speed > feed speed > piezoelectric frequency. The optimal machining parameter combinations obtained for the TM experiment are as follows: spindle speed 50 rpm, feed speed 0.01 mm rev−1, piezoelectric frequency 8 Hz, laser power 75 W. The range of surface roughness reduction obtained by comparing laser-assisted machining (LAM) with pure fast tool servo (FTS) machining is 38.75%∼58.77%. The Box-Behnken Design (BBD) in response surface methodology (RSM) was used to design experiments and a regression model for surface roughness was established through RSM. The deviation between the surface roughness predicted by the regression equation and the experimental value is less than ±6%. The influence law of various machining parameters on surface roughness was studied through three-dimensional response surface. RSM optimized the minimum surface roughness with a desirability of 99.43%. The optimal combination of machining parameters optimized through RSM is as follows: spindle speed 53.71 rpm, feed speed 0.02 mm rev−1, piezoelectric frequency 6.73 Hz, laser power 72 W. This paper is the first to combine LAM with FTS for machining optical free-form surface of glass-ceramic. This study provides a reference for laser-assisted fast tool servo machining and the research methods of surface quality.
Proximity-enhanced valley Zeeman splitting at the WS2/graphene interface
Authors: Faria Junior Paulo E , Naimer Thomas , McCreary Kathleen M , Jonker Berend T , Finley Jonathan J , Crooker Scott A , Fabian Jaroslav , Stier Andreas V
Published: May 2023

The valley Zeeman physics of excitons in monolayer transition metal dichalcogenides provides valuable insight into the spin and orbital degrees of freedom inherent to these materials. Being atomically-thin materials, these degrees of freedom can be influenced by the presence of adjacent layers, due to proximity interactions that arise from wave function overlap across the 2D interface. Here, we report 60 T magnetoreflection spectroscopy of the A- and B- excitons in monolayer WS2, systematically encapsulated in monolayer graphene. While the observed variations of the valley Zeeman effect for the A- exciton are qualitatively in accord with expectations from the bandgap reduction and modification of the exciton binding energy due to the graphene-induced dielectric screening, the valley Zeeman effect for the B- exciton behaves markedly different. We investigate prototypical WS2/graphene stacks employing first-principles calculations and find that the lower conduction band of WS2 at the

K

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K

′

valleys (the

C
B

−

band) is strongly influenced by the graphene layer on the orbital level. Specifically, our detailed microscopic analysis reveals that the conduction band at the Q point of WS2 mediates the coupling between

C
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and graphene due to resonant energy conditions and strong coupling to the Dirac cone. This leads to variations in the valley Zeeman physics of the B- exciton, consistent with the experimental observations. Our results therefore expand the consequences of proximity effects in multilayer semiconductor stacks, showing that wave function hybridization can be a multi-step energetically resonant process, with different bands mediating the interlayer interactions. Such effects can be further exploited to resonantly engineer the spin-valley degrees of freedom in van der Waals and moiré heterostructures.
Impact of high volume E.M.D. residue on the corrosion resistance and mechanical properties of construction materials in sulfate environment
Authors: Chousidis Nikolaos , Polymenis Stylianos , Batis George
Published: May 2023

This study examines the potential use of Electrolytic Manganese Dioxide (EMD) residue as a replacement of cement (20%wt by cement weight) in construction materials to provide anticorrosion protection on reinforcing steel and improve the strength of cementitious materials under sulfate attack. To assess the corrosion parameters, the constructed building materials were immersed in a 5%wt sulfate salt (Na2SO4), while concrete samples incorporating 20%wt EMD were prepared and subjected to mechanical, porosity and thermal tests. Moreover, SEM images were obtained in order to examine the microstructure of concrete and the extent of damage caused by sulfate ions. The results demonstrate that the inclusion of EMD caused a notable rise in the corrosion of steel bars within cement mortars, as well as a decrease in the mechanical strength of the building materials. Overall, the experimental outcomes of the study suggest that the incorporation of high volume (20%wt) EMD residue leads to the degradation of all measured properties.
A laser-produced plasma source based on thin-film Gd targets for next-generation extreme ultraviolet lithography
Authors: CHEN Xiao , LI Yao , HOU Jianbo , ZHANG Zhe , LU Xianyang , YAN Yu , HE Liang , XU Yongbing
Published: May 2023

We have studied laser-produced plasma based on mass-limited thin-film Gd targets for beyond the current extreme ultraviolet (EUV) light source of 13.5 nm wavelength based on tin. The influences of the laser intensity on the emission spectra centered around 6.7 nm from thin-film Gd targets were first investigated. It is found that the conversion efficiency of the produced plasma is saturated when the laser intensity goes beyond 2 × 1011 W cm−2. We have systematically compared the emission spectra of the laser-produced plasma with the changes in the thicknesses of the thin-film Gd targets. It is proved that a minimum-mass target with a thickness of 400 nm is sufficient to provide the maximum conversion efficiency, which also implies that this thickness is the ablation depth for the targets. These findings should be helpful in the exploration of next-generation EUV sources, as the thin-film Gd targets will reduce the debris during the plasma generation process compared with the bulk targets.
Non-linear correlation analysis in financial markets using hierarchical clustering
Authors: Salgado-Hernández J E , Vyas Manan
Published: May 2023

Distance correlation coefficient (DCC) can be used to identify new associations and correlations between multiple variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets of variables that provide equivalent information, is zero only when variables are independent, and is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient (PCC). Hence, DCC provides more information than the PCC. We analyze numerous pairs of stocks in S&P500 database with the distance correlation coefficient and provide an overview of stochastic evolution of financial market states based on these correlation measures obtained using agglomerative clustering.
Structure and stability of the Indian power transmission network
Authors: Tamhane Vedang , Ambika G
Published: May 2023

We present the study on the Indian power transmission network using the framework of a complex network and quantify its structural properties. For this, we build the network structure underlying the Indian power grid, using two of its most prevalent power lines. We construct an equivalent model of an exponential network and study its structural changes with changes in two parameters related to redundancy and dead-ends. Then we analyze its stability against cascading failures by varying these two parameters using the link failure model. This helps to gain insight into the relation of network topology to its stability, and indicates how the optimum choice of these parameters can result in a power grid structure with minimum failed links. We apply the same model to study the robustness of the Indian power grid against such failures. In this case, we find that when a link connected to a generator fails, it results in a cascade that spreads in the grid until it is split into two separate stable clusters of generators and consumers, with over one-third of its nodes nonfunctional.
Tackling multimodal device distributions in inverse photonic design using invertible neural networks
Authors: Frising Michel , Bravo-Abad Jorge , Prins Ferry
Published: May 2023

We show how conditional generative neural networks can be used to efficiently find nanophotonic devices with desired properties, also known as inverse photonic design. Machine learning has emerged as a promising approach to overcome limitations imposed by the dimensionality and topology of the parameter space. Importantly, traditional optimization routines assume an invertible mapping between the design parameters and response. However, different designs may have comparable or even identical performance confusing the optimization algorithm when performing inverse design. Our generative modeling approach provides the full distribution of possible solutions to the inverse design problem, including multiple solutions. We compare a commonly used conditional variational autoencoder (cVAE) and a conditional invertible neural network (cINN) on a proof-of-principle nanophotonic problem, consisting in tailoring the transmission spectrum trough a metallic film milled by subwavelength indentations. We show how cINNs have superior flexibility compared to cVAEs when dealing with multimodal device distributions.
Review of welding fume emission factor development
Authors: Quecke Emily , Quemerais Bernadette , Hashisho Zaher
Published: May 2023

The fumes created from welding activities present a unique occupational hazard. Due to the complex processes which govern fume formation, the characterization of welding fumes is difficult. Emission factors (EFs) are one method to characterize fume formation from different processes and scenarios. This paper reviews the development of EFs and similar metrics both historic research which contributed to the US EPAs AP-42 summary of welding emission factors released in 1995, and more recent research initiatives. Through a critical analysis of what research has been done in this area and the strength of the emission factors developed, this paper proposes a set of recommendations for future research. Research on emission factors for gas metal arc welding (GMAW) is the most complete amongst the different types of electric arc welding. Despite it being generally known that flux core arc welding (FCAW) creates significant fume emissions compared to some of the other processes few studies have looked at FCAW since the AP-42. Shielded metal arc welding is also under-researched particularly in terms of metal-specific emission factors. The influence of different welding activity parameters such as welding location, speed or current is well defined for GMAW but requires more attention for other welding processes. Further effort towards compiling and comparing available emission factor data of quality, evaluating the available data statistically and organizing this data in a practically useful way is required. The availability of reliable emission factors will allow the development or improvement of exposure modelling tools that would be very useful for exposure assessment when monitoring is not practical.s