bluuebunny/crossref_metadata_2025 huggingface dataset:
The emergence of supersonic flow on wind turbines
Authors: De Tavernier Delphine , Terzi Dominic von
Published: June 2022

The future generation of wind turbines will be characterised by longer and more flexible blades. These large wind turbines are facing higher Reynolds numbers, as a consequence of longer chord lengths and increased relative wind speeds. Higher tip speeds, however, also result in an increased Mach number. Although the maximum tip speed in steady design conditions may remain (well) below the critical value, the presence of turbulence, wind gusts, blade deflections, etc. in combination with the flow acceleration over the airfoil surface, may cause a significant increase in the velocity perceived over the blade surface. We have evaluated the operational conditions of the IEA 15MW reference turbine using OpenFAST in normal design and off-design conditions to demonstrate that, if unabated, near-future wind turbines will be at risk of suffering from local supersonic flow. The driving factor is identified to be inflow turbulence, however, the tip airfoil is also of major importance. Local supersonic flow conditions may lead to severe lifetime degradation.
Aeroelastic modelling of tail fins for small wind turbines
Authors: Hammam Mohamed M , Wood David H
Published: June 2022

Many small upwind turbines use a tail fin to align the rotor with the wind. Despite the importance of a well-designed fin for efficient operation and in generating ultimate and fatigue loads, the aeroelastic modelling of tail fins is not well developed. This work extends the previous linearized analyses by including nonlinear effects and the difference between the yaw angle and the angle of attack. The analysis is based on unsteady slender body theory, but includes the vortex lift generated at high angles of attack and the effects of vortex bursting. The model predicts with reasonable accuracy the yaw behaviour of delta-shaped tail fins (without a rotor) released at 45° in a wind tunnel, provided allowance is made for the viscous friction in the yaw bearings. The difference in the response frequency between the linear and nonlinear model increases at the higher yaw angle of 80° for which no wind tunnel measurements are available. As a first step towards simplifying the nonlinear model, the difference in yaw angle predictions from the linear model is estimated. The maximum difference is a function of initial yaw angle and is large for yaw angles of magnitude greater than 45°.
Design Guidelines for Deployable Wind Turbines for Defense and Disaster Response Missions
Authors: Naughton B , Houchens B , Summerville B , Jimenez T , Preus R , Reen D , Gentle J , Lang E
Published: June 2022

Access to on-site electrical energy is critical to ensuring a successful military or humanitarian response to conflicts and disasters. These missions typically rely on access to liquid fuel that could be vulnerable to disruption or attack during transport. Generating power on location with wind technology—whether at a contingency base or disaster response coordination point—can reduce this risk and enhance mission reach by diversifying energy sources. Common characteristics of these missions are short planning and execution time horizons and a global scope of potential locations. Compared to conventional wind turbine applications, defense and disaster response applications place a premium on rapid shipping and installation, short-duration operation (days to months), and quick teardown upon mission completion. These design drivers depart from features found in conventional distributed wind turbines, thus necessitating unique design guidance. The supporting information for this guidance comes from available relevant references, technical analyses, and input from industry and military stakeholders. This paper serves as a summary of the full report, Design Guidelines for Deployable Wind Turbines for Military Operational Energy Applications (Sandia report SAND2021-14581 R [1]), which presents the best currently available design guidance for deployable wind turbines to facilitate the effective development and acquisition of technology solutions to support mission success.
Effect of tip speed ratio on the aerodynamic noise of a small wind turbine: An optimization study
Authors: Pourrajabian Abolfazl , Dehghan Maziar , Rahgozar Saeed , Wood David
Published: June 2022

A dual-objective optimization study was carried out to illuminate how the tip speed ratio of a small wind turbine affects the aerodynamic noise as well as the blade geometry. A 0.75 kW three-bladed small horizontal axis wind turbine was selected as the case study. Two important goals were considered in the study: maximization of the output power and the minimization of the aerodynamic noise. The former was calculated by the well-known blade-element momentum theory while a validated semi-empirical model was adopted for the computation of the latter. A combination of these goals defined the objective function and the weighted-sum approach was employed to find the optimal values for the design variables of the optimization including the distributions of the chord and the twist angle along the blade together with the tip speed ratio. The extracted power was calculated at the rated wind speed of 10 m/s while the noise was computed at lower wind speeds of 5 and 7.5 m/s. The genetic algorithm technique was adopted for the muti-objective optimization. The results revealed the importance of the blade tip region in producing the emitted aerodynamic noise. Specifically, an increase in the chord and twist values near the tip results in the reduction in the emitted noise. Results also show that a good compromise between the two goals is achievable such that a noticeable reduction in the emitted aerodynamic noise is attainable in exchange for a very small drop in the power coefficient. The optimization results also indicated that the noise could be adequately reduced at the smaller value of the tip speed ratio without a large reduction in output power which is due to the decrease in the rotational speed of the turbine.
Large Eddy Simulation of HAWT and VAWT performances in the vicinity of a building
Authors: Tene Hedje P. , Zeoli S. , Vigny U. , Houtin-Mongrolle F. , Benard P. , Bricteux L.
Published: June 2022

This work compares the performances of a Horizontal Axis Wind Turbine (HAWT) and a Vertical Axis Wind Turbine (VAWT) using Wall modeled Large Eddy Simulation (WMLES) coupled with an actuator line method. The wind turbines are located in the vicinity of a real size industrial building. Both wind turbines are sized to produce the same power at their respective optimum Tip Speed Ratio for a same incident wind speed. Two relevant incident wind directions (SW and SSW) are investigated, the influence of the building on the performance of the two wind turbines is also analysed. The results obtained show that the HAWT has a better overall performance compared to the VAWT. Overspeeds are observed for both directions analysed, due to the presence of the building which locally increases the flow velocity. However, these overspeeds remain low due to the low height of the building. The change of wind direction only slightly impacts the HAWT production, while the VAWT production remains insensitive. However, the presence of the building improves the global production of both wind turbines. Qualitatively, this change of wind direction induces a deviation in the wake of both turbines, which is greater for a SW direction.
Tensor Networks Meet Neural Networks
Authors: Xu Zenglin
Published: June 2022

As a simulation of the human cognitive system, deep neural networks have achieved great success in many machine learning tasks and are the main driving force of the current development of artificial intelligence. On the other hand, tensor networks as an approximation of quantum many-body systems in quantum physics are applied to quantum physics, statistical physics, quantum chemistry and machine learning. This talk will first give a brief introduction to neural networks and tensor networks, and then discuss the cross-field research between deep neural networks and tensor networks, such as network compression and knowledge fusion, including our recent work on tensor neural networks. Finally, this talk will also discuss the connection to quantum machine learning.
Research on Path Planning of Mobile Robot in Static Environment Based on Improved Ant Colony Algorithm
Authors: Sun Yisong , Bao Changchun , Li Le , Geng Zhenglin
Published: June 2022

In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.
Road Roughness Recognition Based on Lidar
Authors: Chen Kai
Published: June 2022

The ups and downs of the road will have a great impact on the driving performance of the vehicle. Identify the road surface information in front of the vehicle in advance, and transmit this information to the vehicle’s control system. The control system can carry out targeted control according to the road conditions. Since the information on the road surface of the vehicle is mastered, the driving performance of the vehicle can be greatly improved. Firstly, a vehicle road recognition system is constructed using lidar as an environmental perception sensor to scan the road information ahead of the vehicle. Secondly, use GPS and inertial navigation system to monitor the vehicle’s driving position and attitude information, perform motion compensation on the lidar point cloud data, and extract the road point cloud. Thirdly, the elevation information of the road in front of the vehicle is obtained through filtering and interpolation of the point cloud. Finally, the error analysis of this method is carried out. The results show that the road elevation recognition error is within an acceptable range.
RA-ViT:Patch-wise Radially-Accumulate Module for ViT in Hyperspectral Image Classification
Authors: Wu Yuyang , Weng Qian , Lin Jiawen , Jian Cairen
Published: June 2022

Hyperspectral Images(HSIs) are data containing abundant spatial and spectral information, which is collected by advanced remote sensors. HSI Classification is a pixel-wise classification task that has broad prospects in the era of science and technology. In recent years, the widely used convolutional neural networks (CNNs) have come to the leading place in HSI Classification. However, the lack of utilization of spatial information limits its further application. To solve this issue, we considered the recently proposed Vision Transformer(ViT), which is modularized structures that are entirely based on self-attention mechanism. Furthermore, we proposed a patch-wise radially-accumulate module for ViT(RA-ViT) in HSI Classification. We evaluated the proposed method on Indian Pines(IP) and Kennedy Space Center(KSC) datasets. The results of experiments demonstrate the effectiveness of RA-ViT with comparison to current advanced models.
Design and Achieve of Management System for Optimal Allocation of Municipal Resources
Authors: Fan Lin , Wang Wei , Liu Qian , Li Tong-han
Published: June 2022

In order to achieve the informational and intelligent management of the municipal resources, this article uses platforms like IntelJ IDEA development tools and MySQL database, with the use of spring framework and B/S architecture, develops a municipal resource optimization configuration management system. In this system, MyBaits finishes the management of the data. The grey models and exponential smoothing algorithms have done the prediction part of the demand for the municipal resources corresponding to the flow of people and vehicles. The municipal department can perceive the demand for resources through this forecasting function and then allocate the corresponding municipal resources in advance. The development of this system can simplify the work process of municipal departments when there is a demand to manage the resources. It is of great significance to improve the working efficiency and the resources utilization rate of the municipal departments.
Research on Key Technologies of Street View Video Privacy Information Processing
Authors: Zhu Qianqian , Liu Yingnan , Zhao Zihao
Published: June 2022

As image and video data are recorded, stored or applied worldwide, data privacy protection is receiving increasing attention. Before data application, it is necessary to hide the information that can be personally identified in the video, for example, the human face that can be directly located to the personal identity, the license plate number that can be indirectly located to the personal identity and other data, while retaining the appropriate degree of data availability. Taking the actually collected European road street view video as an example, and taking the face and license plate as the privacy information, this paper proposes a method to automatically detect the face and license plate area in the street view video based on the YOLO5s model, and hide the detected object area with the Gaussian blur in the image processing technology. The experiment shows that the method is scientific, efficient and feasible.
Image Recognition With Haar Wavelet and Pseudoinverse Learning Algorithm Based Autoencoders
Authors: Feng Qingshuang , Yin Qian , Guo Ping
Published: June 2022

In this work, a recognition model with low computation and high training efficiency is proposed for images recognition. The proposed model consists of two modules: 1) haar wavelet transformation extracts features and compresses images; 2) a representation learning module using autoencoder. Because of its powerful function, Haar wavelet transformation has wide use in the feature extraction of images. The mother wavelet and scale wavelet can extract features from different scales. The representation learning module is trained with a non-gradient descent algorithm based on autoencoder structure. Two benchmark image datasets, MNIST and Fashion-MNIST, have been used to validate the proposed model. Experimental results show that our proposed model achieves its expected effect. Compared with filters, CNN, and pseudoinverse learning autoencoders, our model takes less training time, at the same time it acquires comparative recognition accuracy.
Unsupervised Change Detection in Hyperspectral Images using Principal Components Space Data Clustering
Authors: Li Yinhe , Ren Jinchang , Yan Yijun , Liu Qiaoyuan , Petrovski Andrei , McCall John
Published: June 2022

Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy is a big problem, which increases the computational complexity and brings negative factor to detection performance. To address this problem, the principal component analysis (PCA) has been widely used for dimension reduction. It has the capability of projecting the original multi-dimensional hyperspectral data into new eigenvector space which allows it to extract light but representative information. The difference image of the PCA components is obtained by subtracting the two dimensionality-reduced images, on which the change detection is considered as a binary classification problem. The first several principal components of each pixel are taken as a feature vector for data classification using k-means clustering with k=2, where the two classes are changed pixels and unchanged pixels, respectively. The centroids of two clusters are determined by iteratively finding the minimum Euclidean distance between pixel’s eigenvectors. Experiments on two publicly available datasets have been carried out and evaluated by overall accuracy. The results have validated the efficacy and efficiency of the proposed approach.
The Application of Particle Swarm Optimization in Neural Networks
Authors: Gao Lizhen , Lin Yanfen
Published: June 2022

The birth of the neural network model has provided a technological platform for the development of various industries. Among them, the branch network scientific research management model represented by the scientific research field is particularly profitable. China’s traditional research management system has been unable to meet the requirements of the present era and the management system reform is imperative. Therefore, based on Particle Swarm Optimization (PSO), this dissertation studies the neural network model based on the prerequisite that the branch network research management system retains the advantages of the traditional model. It tracks the optimal value of current search through cross and mutation. The optimal solution value is finally determined by solving the relationship between particle weight and inertia weight. In the final test, we establish the feasibility of the program.
Research on Continuous Static Coverage of Multiple Heterogeneous Warning Vehicles Considering No-fly Areas
Authors: Duan Xiaowen , Bo Li
Published: June 2022

Through the analysis of the types of tasks and flight routes faced by the warning vehicle, this paper proposes the problem of continuous static coverage of the multi-heterogeneous warning vehicle (referred to as WV) considering No-fly areas to be solved. By analysing things like detection area, no-fly area, WV patrol strategy form, patrol strategy planning mechanism and detection capability of WV, based on the philosophy of model stratification and time segmentation, a multi-layer multi-stage patrol strategy planning method (referred to as MLMPSPM) for determining the patrol strategies of multiple heterogeneous warning vehicles (WVs) during multi-patrol cycles considering no-fly areas is proposed, including the process of determining the planning stage index. In detail, MLMPSPM mainly introduces the patrol strategy planning method of
EF
and
ER
. Whether
EF
or
ER
includes input section, planning section, stage warning effect of planning stage, and patrol strategy objective planning, etc.
EU
planning processes is the extension of
EF
and
ER
. In the experimental results section, the relevant experimental parameters are designed in detail, the experimental results show that MLMPSPM can not only determine the patrol strategies of all WVs throughout the alert process considering time-varying no-fly areas, but also achieve a large total warning effect.
Conformity Assessment of Informative Labels in Car Engine Compartment with Deep Learning Models
Authors: Ferreira R , Barroso J , Filipe V
Published: June 2022

Industry 4.0 has been changing and improving the manufacturing processes. To embrace these changes, factories must keep up to date with all the new emerging technologies. In the automotive industry, the growing demand for customization and constant car model changes leads to an inevitable grow of complexity of the final product quality inspection process. In the project INDTECH 4.0, smart technologies are being explored in an automotive factory assembly line to automate the vehicle quality control, which still relies on human inspection based on paper conformity checklists. This paper proposes an automated inspection process based on computer vision to assist operators in the conformity assessment of informative labels affixed inside the engine compartment of the car. Two of the most recent object detection algorithms: YOLOv5 and YOLOX are evaluated for the identification of labels in the images. Our results show high mean average precision on both algorithms (98%), which overall, tells us that both algorithms showed good performances and have potential to be implemented in the shop floor to support the vehicle quality control.
A Compact Object Detection Architecture with Transformer Enhancing
Authors: Cui Liyuan , Zhong Guoqiang , Liu Xiang , Xu Hongwei
Published: June 2022

With the advancements in rising computer vision processing, Transformer has attracted increasing interesting in this field. However, it is limited because of its unprecedented storage, heavy reliance on data size and intolerable computational power consumption. While lightweight network is in other extreme, pursuing the compact architectures accompanied by performance loss. In this paper, we enhance an architecture as the backbone of object detection networks through combining right-size Transformer, i.e. Vision Transformer module. Specifically, based on GhostNet, a well-known lightweight neural network structure moreover, embed this Vision Transformer module at the end of GhostNet, and use the input data with slicing design to reduce the computational burden of the neural networks. Vision Transformer is taken to enhance the architecture as the backbone of object detection networks, and the well-known YOLOv5 as the baseline. We conduct multi-metric comparison experiments on two medium-scale object detection datasets with large, medium and small scale networks. Results show that without relying on ultra-large dataset and pre-trained models, the proposed Transformer module enhanced architecture achieves comparable or even higher mAP metrics with only half of the model size and floating-point computation of the baseline.
Rotate-Yolov5 for Aerial Images
Authors: Chen H , Liu F X , Huang X L
Published: June 2022

In Recent years, great progress has made in object detection. However, since the orientation of object in aerial image is random, the regular horizontal object detection method is not suitable for aerial images. In this paper, we present a Rotate-Yolov5 network based on Yolov5. We use an Adaptive Rotating Anchor Generation Module (ARAGM) to generate anchors with object orientation information. Then the orientation information is used for Rotate-Deformable Convolution Module (R-DCM) to extract features. Finally, we use a decouple detection head as Oriented Object Detection Module (OODM) to yield classification and regression results. Moreover, Rotate-Smooth L1 is used to optimize the loss function. We evaluate the proposed Rotate-Yolov5 on DOTA datasets and the mAP reached 75.4, which demonstrate the superiority of its effectiveness.
Research on License Plate Location Algorithm Based on YOLOv5
Authors: Zhu Qianqian , Liu Yingnan , Zhao Zihao , Ma Xin
Published: June 2022

License plate location is a key part of license plate recognition, and its positioning accuracy seriously determines the final result of license plate recognition. Firstly, the traditional license plate location methods are compared and analyzed. Secondly, in order to solve the localization problem in low resolution and multi-vehicle environment, a license plate method based on YOLOv5s was proposed by using deep learning image recognition technology, and data enhancement was introduced to improve YOLOv5s. Finally, the YOLOv5 algorithm is applied to real vehicle images under complex environmental conditions for experimental verification, and the results show that the accuracy is 99.12%. The experimental results show that the license plate location model based on YOLOv5S can effectively solve the influence of illumination, image quality and other factors on license plate location, improve the efficiency and accuracy compared with the traditional license plate location method, the algorithm has good robustness and fast calculation speed.
Face Shape Classification Based on Bilinear Network with Attention Mechanism
Authors: Duan Jiawei , Su Xueping , Ren Jie , Xie Longsheng
Published: June 2022

Face shape is an important information in face recognition, personalized recommendation and other applications. For example, rough face shape filtering before face recognition can effectively improve the recognition accuracy and speed. At the same time, an effective face shape classification can be used to construct a recommendation system for hairstyles and glasses. Therefore, this paper proposes a new face shape classification algorithm. Firstly, the M-RetinaFace network is proposed to align face image. Secondly, by combing the attention mechanism with the EfficientNet bilinear network, the AB-CNN network is proposed to extract feature. Finally, a bilinear pooling layer is used to classify face shape. Experiments on public data sets show that, compared with existing algorithms, the algorithm proposed in this paper is get state-of-the-art results and significantly improves the accuracy of face shape classification.
Brain Tumor Image Segmentation Based on Grouped Convolution
Authors: Wu Mingliang , Ye Hai-Li , Wu Yun , Li Jianmin
Published: June 2022

The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. The model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. The model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy.
An Automatic Detection Method for COVID-19 in CT Images
Authors: Li Yarong , Jiang Yizhang , Gu Yi , Qian Pengjiang
Published: June 2022

There are many new cases of new coronary pneumonia (named COVID-19) every day around the world. In such a severe situation, effective detection of COVID-19 has become extremely important. Studies have shown that chest CT images can be used for COVID-19 detection because they can show bilateral changes in the lungs of people infected with COVID-19. It is not difficult for experienced radiologists to make preliminary judgments based on CT images. However, with the emergence of a large number of suspected cases, the explosive demand has overwhelmed doctors. Therefore, the automatic diagnosis of COVID-19 CT images is of great significance to realize early diagnosis, early isolation, and early treatment. In this paper, we study an automatic identification method that uses transfer learning technology, which transfers the pre-trained VGG16 network for feature extraction, and combines VAE augmentation data to reduce over-fitting, and finally uses integrated technology to achieve better detection results. Our experiments have shown that the accuracy of our method for identifying whether a patient’s CT image is positive or negative for COVID-19 is 91%, the precision is 88%, the recall rate is 94%, and the F1-score is 91%. Compared with other the state of art methods, the method proposed in this article can provide more efficient classification and identification of COVID-19.
Development of agarose–gelatin bioinks for extrusion-based bioprinting and cell encapsulation
Authors: Dravid Anusha , McCaughey-Chapman Amy , Raos Brad , O’Carroll Simon J , Connor Bronwen , Svirskis Darren
Published: June 2022

Three-dimensional bioprinting continues to advance as an attractive biofabrication technique to employ cell-laden hydrogel scaffolds in the creation of precise, user-defined constructs that can recapitulate the native tissue environment. Development and characterisation of new bioinks to expand the existing library helps to open avenues that can support a diversity of tissue engineering purposes and fulfil requirements in terms of both printability and supporting cell attachment. In this paper, we report the development and characterisation of agarose–gelatin (AG–Gel) hydrogel blends as a bioink for extrusion-based bioprinting. Four different AG–Gel hydrogel blend formulations with varying gelatin concentration were systematically characterised to evaluate suitability as a potential bioink for extrusion-based bioprinting. Additionally, autoclave and filter sterilisation methods were compared to evaluate their effect on bioink properties. Finally, the ability of the AG–Gel bioink to support cell viability and culture after printing was evaluated using SH-SY5Y cells encapsulated in bioprinted droplets of the AG–Gel. All bioink formulations demonstrate rheological, mechanical and swelling properties suitable for bioprinting and cell encapsulation. Autoclave sterilisation significantly affected the rheological properties of the AG–Gel bioinks compared to filter sterilisation. SH-SY5Y cells printed and differentiated into neuronal-like cells using the developed AG–Gel bioinks demonstrated high viability (>90%) after 23 d in culture. This study demonstrates the properties of AG–Gel as a printable and biocompatible material applicable for use as a bioink.
Quantum geodesics on quantum Minkowski spacetime
Authors: Liu Chengcheng , Majid Shahn
Published: June 2022

We apply a recent formalism of quantum geodesics to the well-known quantum Minkowski spacetime [x

i
, t] = ıλ

p

x

i
with its flat quantum metric as a model of quantum gravity effects, with λ

p
the Planck scale. As examples, quantum geodesic flow of a plane wave gets an order λ

p
frequency dependent correction to the classical geodesic velocity. A quantum geodesic flow with classical velocity v of a Gaussian with width

2
β

initially centred at the origin changes its shape but its centre of mass moves with

⟨

x

⟩

⟨

t

⟩

=
v

(

1
+

3

λ

p

2

2
β

+
O

(

λ

p

3

)

)

, an order

λ

p

2

correction. This implies, at least within perturbation theory, that a ‘point particle’ cannot be modelled as an infinitely sharp Gaussian due to quantum gravity corrections. For contrast, we also look at quantum geodesics on the noncommutative torus with a 2D curved weak quantum Levi-Civita connection.
Parallel bandit architecture based on laser chaos for reinforcement learning
Authors: Urushibara Takashi , Chauvet Nicolas , Kochi Satoshi , Sunada Satoshi , Kanno Kazutaka , Uchida Atsushi , Horisaki Ryoichi , Naruse Makoto
Published: June 2022

Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a massive number of states, unlike previously demonstrated bandit problems where the number of states is only one. Q-learning is a well-known approach in reinforcement learning that can deal with many states. The architecture of Q-learning, however, does not fit well photonic implementations due to its separation of update rule and the action selection. In this study, we organize a new architecture for multi-state reinforcement learning as a parallel array of bandit problems in order to benefit from photonic decision-makers, which we call parallel bandit architecture for reinforcement learning or PBRL in short. Taking a cart-pole balancing problem as an instance, we demonstrate that PBRL adapts to the environment in fewer time steps than Q-learning. Furthermore, PBRL yields faster adaptation when operated with a chaotic laser time series than the case with uniformly distributed pseudorandom numbers where the autocorrelation inherent in the laser chaos provides a positive effect. We also find that the variety of states that the system undergoes during the learning phase exhibits completely different properties between PBRL and Q-learning. The insights obtained through the present study are also beneficial for existing computing platforms, not just photonic realizations, in accelerating performances by the PBRL algorithms and correlated random sequences.
1D quasicrystals and topological markers
Authors: Sykes Joseph , Barnett Ryan
Published: June 2022

Local topological markers are effective tools for determining the topological properties of both homogeneous and inhomogeneous systems. The Chern marker is an established topological marker that has previously been shown to effectively reveal the topological properties of 2D systems. In an earlier work, the present authors have developed a marker that can be applied to 1D time-dependent systems which can be used to explore their topological properties, like charge pumping under the presence of disorder. In this paper, we show how to alter the 1D marker so that it can be applied to quasiperiodic and aperiodic systems. We then verify its effectiveness against different quasicrystal Hamiltonians, some which have been addressed in previous studies using existing methods, and others which possess topological structures that have been largely unexplored. We also demonstrate that the altered 1D marker can be productively applied to systems that are fully aperiodic.
Carbon dioxide adsorption/desorption performance of single- and blended-amines-impregnated MCM-41 mesoporous silica in post-combustion carbon capture
Authors: Muchan Pailin , Saiwan Chintana , Nithitanakul Manit
Published: June 2022

High-surface-area, hexagonal-structured mesoporous silica, MCM-41, was synthesized and wet impregnated with three different amines of 2-(ethylamino) ethanol (EAE), ethylenediamine (EDA), and tetraethylenepentamine (TEPA) for use as solid adsorbents in post-combustion CO2 capture application. The CO2 adsorption test was performed at 25°C and atmospheric pressure using 15/85 vol% of CO2/N2 at a 20-mL/minute flow rate. Desorption was carried out at 100°C under 20 mL/minute of N2 flow. The results show that the capacity and rate of CO2 adsorption obtained from all the amine-modified adsorbents were significantly increased with increasing amine loading due to carbamate formation. Desorption efficiency and heat duty for regeneration were also affected by the amount of amine loading. The more stable the carbamate produced, the higher the energy was required. They exhibited the highest adsorption–desorption performance at 60 wt% amines used for impregnation. Blended EAE/TEPA at different weight ratios at a total concentration at 60 wt% amines was impregnated on MCM-41 adsorbent. Sorbent impregnated with 50%/10% of EAE/TEPA showed the best performance of 4.25 mmolCO2/g at a high adsorption rate, a low heat duty of 12 kJ/mmolCO2 and with 9.4% reduction of regeneration efficiency after five repeated adsorption–desorption cycles.
Innovative design for a portable hybrid system of hydroponic and microalgae to produce food and biofuels
Authors: Alkhalidi Ammar , Jarah Hala , Khawaja Mohamad K
Published: June 2022

This study investigates an innovative hybrid system that combines hydroponics and microalgae in a compact portable shipping container. This container is divided into two parts; one contains the microalgae system and the other contains the hydroponic system. This combined system works by dividing the 24 hours into 12 hours of light and 12 hours of dark for each part. Both parts are connected using light-impermeable pipes that pass carbon dioxide from the dark side to the lit side and oxygen from the lit side to the dark side. In this paper, the authors developed a validated mathematical model for hydroponic and microalgae to evaluate the system’s performance. Results found by the model show the optimum parameters for the split photobioreactor and hydroponic system. The first investigated parameter is the sparger diameter for split the photobioreactor and the second is the number of plants that give the better and optimum result. The optimum modelling design for the combined hydroponic and microalgae system was using 100 plants for lettuce and three photobioreactors with a 0.009-m diameter for sparger to a photobioreactor system with an area of 15.6 m2.
Gut microbiome and autoimmune disorders
Authors: Shaheen Walaa Abdelaty , Quraishi Mohammed Nabil , Iqbal Tariq H
Published: June 2022

Autoimmune diseases have long been known to share a common pathogenesis involving a dysregulated immune system with a failure to recognize self from non-self-antigens. This immune dysregulation is now increasingly understood to be induced by environmental triggers in genetically predisposed individuals. Although several external environmental triggers have been defined in different autoimmune diseases, much attention is being paid to the role of the internal micro-environment occupied by the microbiome, which was once termed “the forgotten organ.” In this regard, the gut microbiome, serving as an intermediary between some of those external environmental effectors and the immune system, helps programming of the immune system to be tolerant to innocent external and self-antigens. However, in the presence of perturbed gut microbiota (dysbiosis), the immune system could be erroneously directed in favor of pro-inflammatory pathways to instigate different autoimmune processes.

An accumulating body of evidence, including both experimental and human studies (observational and interventional), points to the role of the gut microbiome in different autoimmune diseases. Such evidence could provide a rationale for gut microbiome manipulation with therapeutic and even preventative intent in patients with established or predisposed to autoimmune diseases, respectively.

Perturbations of the gut microbiome have been delineated in some immune mediated diseases, IBD in particular. However, such patterns of disturbance (microbiome signatures) and related pathogenetic roles of the gut microbiome are context dependent and cannot be generalized in the same exact way to other autoimmune disorders, and the contribution of the gut microbiome to different disease phenotypes has to be precisely defined.

In this review, we revise the evidence for a role of the gut microbiome in various autoimmune diseases and possible mechanisms mediating such a role.
First Case of Human Brucella canis Infection in the Netherlands
Authors: Kolwijck Eva , Lutgens Suzanne P M , Visser Vanessa X N , van Apeldoorn Marjan J , Graham Heather , Koets Ad P , Schrauwen Michelle M W P , Reubsaet Frans A G , Broens Els M , Kortbeek Laetitia M
Published: June 2022

A patient was diagnosed with Brucella canis following exposure to infected dogs in her breeding facility. Transboundary spread of B. canis through (illegal) import of infected dogs to non-endemic countries in Europe suggest that B. canis infection should be considered in European patients with occupational exposure to dogs.
Efficacy of Calcineurin Inhibitors for Induction of Remission in Intestinal Behçet’s Disease
Authors: Kawaguchi Takaaki , Fukata Masayuki , Omori Teppei , Kiyohara Hiroki , Sugimoto Shinya , Nanki Kosaku , Sujino Tomohisa , Mikami Yohei , Kanai Takanori
Published: June 2022

Background

The efficacy of calcineurin inhibitors (CNIs) for induction of remission in intestinal Behçet’s disease (intestinal BD) has not been explored.

Methods

A multicenter retrospective case series study of patients with active intestinal BD treated with CNIs (cyclosporin and tacrolimus) was conducted.

Results

Of 16 patients, 12 (75%) showed a clinical response and 5 (31.3%) achieved clinical remission after 2 weeks of CNI treatment. Similar efficacy of CNIs was observed even in 7 patients refractory to antitumor necrosis factor-alpha therapies. Endoscopic improvement was observed in 11 of 12 patients.

Conclusions

CNIs may be promising treatment options for refractory intestinal BD.
Molecular genetics in 4408 cardiomyopathy probands and 3008 relatives in Norway: 17 years of genetic testing in a national laboratory
Authors: Stava Tonje Talsnes , Leren Trond P , Bogsrud Martin Prøven
Published: June 2022

Aims

To describe results from genetic testing for cardiomyopathies in a national laboratory for genetic testing in Norway since 2003.

Methods and results

Retrospective data collection from the laboratory information management system at Unit for Cardiac and Cardiovascular Genetics, Oslo University hospital. Data from 4408 probands and 3008 relatives were available. Three probands had two variants, nine had incidental findings of variants not related to their cardiomyopathy diagnosis. Of the remaining 4396 probands, 65.1% were males, age at genetic testing was 50.9 (±18.1) years and 6.1% were under the age of 18. A likely pathogenic or pathogenic variant (216 different variants including 67 novel) was detected in 574 probands, corresponding to a hit-rate of genetic testing of 13.1% in total, 11.9% in hypertrophic, 14.1% in dilated, and 14.9% in arrhythmogenic right ventricular cardiomyopathy. Of the 3008 relatives, 47.6 % were males, age at genetic testing was 39.3 (±20.5) years, 17.9% were under the age of 18, and 43.2% were positive for the variant found in their family. Probands and relatives combined, 1/2809 persons in Norway were found to be heterozygous for a cardiomyopathy variant. Next Generation Sequencing provided more findings in dilated cardiomyopathy, especially in TTN accounting for 44.2% of all variants. Otherwise, the majority of variants were found in the classical sarcomeric and desmosomal genes.

Conclusion

Genetic testing provided a genetic basis of the cardiomyopathy in 13.1% of probands, and subsequent family testing identified almost three times as many variant-positive relatives which could be offered preventive follow-up.
Robinia pseudoacacia decline and fine root dynamics in a plantation chronosequence in the Yellow River Delta, China
Authors: Guo Longmei , Pang Yuanxiang , Cao Banghua , Fan Zhaofei , Mao Peili , Li Zexiu , Liu Wei , Li Pengfei
Published: June 2022

Fine roots (<2 mm in diameter) play a significant role in tree growth and stand productivity. However, knowledge of fine root dynamics in saline alkali soil remains limited. Using sequential soil core and ingrowth core methods, we assessed the dynamics of aboveground biomass (AGB) and production (AGP), fine root biomass (FRB), production (FRP) and turnover (FRT), and soil properties (water content [SWC], salt content [SSC], pH and nutrients) in 2-, 18-, 32- and 42-year-old black locust (Robinia pseudoacacia L.) stands in the Yellow River Delta, China. Corresponding to the unimodal growth pattern in AGP, the AGB of black locust rapidly increased until age 32 and then slowed down. In contrast, across all depths, FRB and FRP took a unimodal pattern with a decrease in growth around age 32, and FRT continually decreased with stand age. SWC and soil nutrients increased with stand age, whereas SSC and pH decreased with stand age until age 32 and then increased. Based on the correlation analysis, we may presume that in addition to the age effect, the decline of black locust in AGP is most likely caused by SWC, SSC, and pH stresses through the dysfunction in nutrient absorption by fine roots.
Modifying mosquitoes to suppress disease transmission: Is the long wait over?
Authors: Powell Jeffrey R
Published: June 2022

For more than 50 years it has been a dream of medical entomologists and public health workers to control diseases like malaria and dengue fever by modifying, through genetics and other methods, the arthropods that transmit them to humans. A brief synopsis of the history of these efforts as applied to mosquitoes is presented; none proved to be effective in reducing disease prevalence. Only in the last few years have novel approaches been developed or proposed that indicate the long wait may be over. Three recent developments are particularly promising: CRISPR-Cas9 driven genetic modification, shifting naturally occurring allele frequencies, and microbe-based modifications. The last is the furthest along in implementation. Dengue fever incidence has been reduced between 40% and 96% in 4 different regions of the world where Wolbachia-infected Aedes aegypti have been established in the field. It is not yet clear how sustainable such control programs will prove to be, but there is good reason for optimism. In light of this, the time is ripe for reinvigorated research on vectors, especially genetics. Vector-borne diseases primarily affect under-developed countries and thus have not received the attention they deserve from wealthier countries with well-developed and funded biomedical research establishments.
Gene expression profiles in sporadic ALS fibroblasts define disease subtypes and the metabolic effects of the investigational drug EH301
Authors: Fels Jasmine A , Casalena Gabriella , Konrad Csaba , Holmes Holly E , Dellinger Ryan W , Manfredi Giovanni
Published: June 2022

Metabolic alterations shared between the nervous system and skin fibroblasts have emerged in amyotrophic lateral sclerosis (ALS). Recently, we found that a subgroup of sporadic ALS (sALS) fibroblasts (sALS1) is characterized by metabolic profiles distinct from other sALS cases (sALS2) and controls, suggesting that metabolic therapies could be effective in sALS. The metabolic modulators nicotinamide riboside and pterostilbene (EH301) are under clinical development for the treatment of ALS. Here, we studied the transcriptome and metabolome of sALS cells to understand the molecular bases of sALS metabotypes and the impact of EH301. Metabolomics and transcriptomics were investigated at baseline and after EH301 treatment. Moreover, weighted gene coexpression network analysis (WGCNA) was used to investigate the association of the metabolic and clinical features. We found that the sALS1 transcriptome is distinct from sALS2 and that EH301 modifies gene expression differently in sALS1, sALS2 and the controls. Furthermore, EH301 had strong protective effects against metabolic stress, an effect linked to the antiinflammatory and antioxidant pathways. WGCNA revealed that the ALS functional rating scale and metabotypes are associated with gene modules enriched for the cell cycle, immunity, autophagy and metabolic genes, which are modified by EH301. The meta-analysis of publicly available transcriptomic data from induced motor neurons by Answer ALS confirmed the functional associations of genes correlated with disease traits. A subset of genes differentially expressed in sALS fibroblasts was used in a machine learning model to predict disease progression. In conclusion, multiomic analyses highlighted the differential metabolic and transcriptomic profiles in patient-derived fibroblast sALS, which translate into differential responses to the investigational drug EH301.
Extracorporeal membrane oxygenation support in carinal resection for recurrent chondrosarcoma after previous distal tracheal resection
Authors: Voltolini Luca , Salvicchi Alberto , Cianchi Giovanni , Bongiolatti Stefano
Published: June 2022

Carinal re-resection for tumour recurrence is rarely performed due to increased difficulty in airway reconstruction. We reported a successful case of carinal resection and reconstruction for recurrent chondrosarcoma after previous distal tracheal resection. Due to the technical complexity of the reconstruction and the poor respiratory reserve of the patient, the veno-venous extracorporeal membrane oxygenation support was used.