bluuebunny/crossref_metadata_2025 huggingface dataset:
Shallow Univariate ReLU Networks as Splines: Initialization, Loss Surface, Hessian, and Gradient Flow Dynamics
Authors: Sahs Justin , Pyle Ryan , Damaraju Aneel , Caro Josue Ortega , Tavaslioglu Onur , Lu Andy , Anselmi Fabio , Patel Ankit B.
Published: May 2022

Understanding the learning dynamics and inductive bias of neural networks (NNs) is hindered by the opacity of the relationship between NN parameters and the function represented. Partially, this is due to symmetries inherent within the NN parameterization, allowing multiple different parameter settings to result in an identical output function, resulting in both an unclear relationship and redundant degrees of freedom. The NN parameterization is invariant under two symmetries: permutation of the neurons and a continuous family of transformations of the scale of weight and bias parameters. We propose taking a quotient with respect to the second symmetry group and reparametrizing ReLU NNs as continuous piecewise linear splines. Using this spline lens, we study learning dynamics in shallow univariate ReLU NNs, finding unexpected insights and explanations for several perplexing phenomena. We develop a surprisingly simple and transparent view of the structure of the loss surface, including its critical and fixed points, Hessian, and Hessian spectrum. We also show that standard weight initializations yield very flat initial functions, and that this flatness, together with overparametrization and the initial weight scale, is responsible for the strength and type of implicit regularization, consistent with previous work. Our implicit regularization results are complementary to recent work, showing that initialization scale critically controls implicit regularization via a kernel-based argument. Overall, removing the weight scale symmetry enables us to prove these results more simply and enables us to prove new results and gain new insights while offering a far more transparent and intuitive picture. Looking forward, our quotiented spline-based approach will extend naturally to the multivariate and deep settings, and alongside the kernel-based view, we believe it will play a foundational role in efforts to understand neural networks. Videos of learning dynamics using a spline-based visualization are available athttp://shorturl.at/tFWZ2.
Plant Health Status Affects the Functional Diversity of the Rhizosphere Microbiome Associated With Solanum lycopersicum
Authors: Adedayo Afeez Adesina , Fadiji Ayomide Emmanuel , Babalola Olubukola Oluranti
Published: May 2022

The microorganisms inhabiting soil perform unique functions in the growth and development of plants. However, little is known about how plant health status affects their potential functions. We examined the functional diversity of the microbiome inhabiting the rhizosphere of powdery mildew diseased and healthy tomato plants alongside the bulk soils in South Africa's Northwest Province employing a shotgun metagenomics approach. We envisaged that the functional categories would be abundant in the healthy rhizosphere (HR) of the tomato plant. We collected soil from the rhizosphere of healthy, powdery mildew diseased tomato plants (DR), and bulk soil (BR). After that, their DNA was extracted. The extracted DNA was subjected to shotgun metagenomic sequencing. Our result using the SEED subsystem revealed that a total of fifteen (15) functional categories dominated the healthy rhizosphere, seven (7) functional categories dominated the diseased rhizosphere. At the same time, six (6) functions dominated the bulk soil. Alpha (α) diversity assessment did not reveal a significant difference (p > 0.05) in all the soil samples, but a considerable difference was observed for beta (β) diversity (P = 0.01). The functional categories obtained in this research were highly abundant in HR. Therefore, this study shows that the functions groups of the rhizosphere microbiomes were more abundant in HR samples as compared to others. The high prevalence of functions groups associated with rhizobiomes in the tomato rhizosphere indicates the need for more research to establish the functional genes associated with these rhizosphere microbiomes.
Post-mortem Computed Tomographic Angiography in Equine Distal Forelimbs: A Feasibility Study
Authors: Blaettler Chantal , Kaessmeyer Sabine , Grabherr Silke , Koch Christoph , Schweizer Daniela , Van der Vekens Elke
Published: May 2022

In-depth understanding of pathophysiological processes occurring in the vasculature of the equine distal limb is of great importance to improve both diagnostic and therapeutic approaches to diseases. To gain further insights, a model allowing high-resolution 3D-visualization of the vasculature is necessary. This pilot study evaluated the feasibility of restoring vascular perfusion in frozen-thawed distal equine cadaver limbs without prior preparation using computer tomographic imaging (CT). Five frozen-thawed, radiographically normal forelimbs were perfused with a lipophilic contrast agent through the median artery and radial vein in three phases (arterial, venous, and arterial-venous combined (AVC) dynamic). For comparison, one additional limb was perfused with a hydrosoluble contrast agent. The CT-studies (16-slice MDCT, 140 kV, 200 mA, 2 mm slice thickness, 1 mm increment, pitch 0.688) were evaluated at 11 specified regions for visualization of the vasculature and presence of artifacts or anatomic variations. The protocol used in this study proved to be feasible and provided good visualization (93.1%) of vasculature with low rates of artifacts. During the different phases, vascular visualization was similar, but while filling defects decreased in the later phases, extravasation worsened in the 2 limbs where it was observed. Subjectively, the best quality of angiographic images was achieved during the AVC dynamic phase. Perfusion with hydrosoluble contrast resulted in significantly lower vascular visualization (74.0%) and higher artifact rates. This study shows that reperfusion of frozen-thawed equine distal limbs with a lipophilic contrast agent allows for high-quality 3D-visualization of the vasculature and may serve as a model for in situ vascular evaluation in the future.
Effect of a Phytogenic Feed Additive in Preventing Calves' Diarrhea
Authors: Turini Luca , Mantino Alberto , Tozzi Beatrice , Bonelli Francesca , Silvi Alina , Mele Marcello , Sgorbini Micaela , Meucci Valentina , Minieri Sara
Published: May 2022

The aims of the present study were to evaluate the preventive and the therapeutic effect of Stodi®as phytogenic feed additive rich in phenolic substances on the calf diarrhea, during the first 24 days of life. A total of 40 calves were included and randomly divided into Group C (control group) and Group T (treated group) with placebo or treatment administration started from the third day of life (T0). Calves belonged to group C received 2 L of warm water, while the calves assigned to group T received 2L of warm water plus 30 g of Stodi®. Solutions administration was maintained until day 21 (T21) that was the end of the experimental period. Calves were weighed at T0 and T21 to assess the average daily gain (ADG). Physical examination and fecal score evaluation were performed daily. The duration of a diarrheic episode, the age of the first diarrhea outbreak (TDE) and the frequency of diarrheic episodes were recorded. Complete blood count, methemoglobin and liver enzymes were evaluated at T0 and at T21 in all the calves by spectrophotometer and clinical chemistry analysis, respectively. Data were analyzed using a mixed model. A Chi-square and a Mann-Whitney test were also performed. No difference was found for ADG between the groups. The difference of mean age at TDE was not statistically significant between C and T group. The number of calves with diarrhea in the C group tended to be higher than that of T group (p= 0.13). Calves in group C spent more days with clinical sign of diarrhea compared to group T (p= 0.016). Complete blood count, methemoglobin and liver enzymes were within the reference ranges. The feed additive Stodi®seemed to be effective in shortening neonatal diarrhea episodes in calves thanks to the administration of 30 g per day of product. The fixed dosage of Stodi®used in our study did not show a preventive effect to reduce the incidence of calf diarrhea.
Psychosocial Factors Associated With Adherence to COVID-19 Preventive Measures in Low-Middle- Income Countries, December 2020 to February 2021
Authors: Pengpid Supa , Peltzer Karl , Sathirapanya Chutarat , Thitichai Phanthanee , Faria de Moura Villela Edlaine , Rodrigues Zanuzzi Tamara , de Andrade Bandeira Felipe , Bono Suzanna A. , Siau Ching Sin , Chen Won Sun , Hasan M Tasdik , Sessou Philippe , Ditekemena John D. , Hosseinipour Mina C. , Dolo Housseini , Wanyenze Rhoda K. , Nelson Siewe Fodjo Joseph , Colebunders Robert
Published: May 2022

Objectives: To investigate psychosocial factors associated with adherence to COVID-19 preventive measures in low- and middle-income countries (LMICs).

Methods: This online cross-sectional survey included 10,183 adults (median age 45 years) from nine LMICs. Participants were asked about adhering to four COVID-19 preventive measures (physical distancing, wearing a face mask, hand, and cough hygiene); a composite adherence score was calculated, ranging from 0–4 positive responses. Psychosocial measures included worry, anxiety, depression, social and demographic, and COVID-19 related factors.

Results: Factors associated with adherence to more preventive measures included being a participant from Malaysia or Bangladesh, older age, higher education, belonging to the healthcare sector (either as or worker), having health personnel as a trusted source of COVID-19 information/advice, possessing correct COVID-19 knowledge, worry or fear about being (re)infected with COVID-19, and screening negative for general anxiety symptoms.

Conclusion: Moderate to high adherence to COVID-19 preventive measures was found, with significant variations across countries. Psychosocial factors (worry, anxiety, knowledge, education, age, and country) seemed determinant in predicting the number of measures to which participants adhered.
Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network
Authors: Sun Guodong , Yang Xiong , Xiong Chenyun , Hu Ye , Liu Moyun
Published: May 2022

The traditional rolling bearing diagnosis algorithms have problems such as insufficient information on time-frequency images and poor feature extraction ability of the diagnosis model. These problems limit the improvement of diagnosis performance. In this article, the input of the time-frequency image and intelligent diagnosis algorithms are optimized. Firstly, the characteristics of two advanced time-frequency analysis algorithms are deeply analyzed, i.e., multisynchrosqueezing transform (MSST) and time-reassigned multisynchrosqueezing transform (TMSST). Then, we propose time-frequency compression fusion (TFCF) and a residual time-frequency mixed attention network (RTFANet). Among them, TFCF superposes and splices two time-frequency images to form dual-channel images, which can fully play the characteristics of multi-channel feature fusion of the convolutional kernel in the convolutional neural network. RTFANet assigns attention weight to the channels, time and frequency of time-frequency images, making the model pay attention to crucial time-frequency information. Meanwhile, the residual connection is introduced in the process of attention weight distribution to reduce the information loss of feature mapping. Experimental results show that the method converges after seven epochs, with a fast convergence rate and a recognition rate of 99.86%. Compared with other methods, the proposed method has better robustness and precision.
Novel Security Models for IoT–Fog–Cloud Architectures in a Real-World Environment
Authors: Aleisa Mohammed A. , Abuhussein Abdullah , Alsubaei Faisal S. , Sheldon Frederick T.
Published: May 2022

With the rise of the Internet of Things (IoT), there is a demand for computation at network edges because of the limited processing capacity of IoT devices. Fog computing is a middle layer that has appeared to address the latency issues between the Internet of things (IoT) and the cloud. Fog computing is becoming more important as companies face increasing challenges in collecting and sending data from IoT devices to the cloud. However, this has led to new security and privacy issues as a result of the large number of sensors in IoT environments as well as the massive amount of data that must be analyzed in real time. To overcome the security challenges between the IoT layer and fog layer and, thus, meet the security requirements, this paper proposes a fine-grained data access control model based on the attribute-based encryption of the IoT–Fog–Cloud architecture to limit the access to sensor data and meet the authorization requirements. In addition, this paper proposes a blockchain-based certificate model for the IoT–Fog–Cloud architecture to authenticate IoT devices to fog devices and meet the authentication requirements. We evaluated the performance of the two proposed security models to determine their efficiency in real-life experiments of the IoT–Fog–Cloud architecture. The results demonstrate that the performance of the IoT–Fog–Cloud architecture with and without the blockchain-based certificate model was the same when using one, two, or three IoT devices. However, the performance of the IoT–Fog–Cloud architecture without the access control model was slightly better than that of the architecture with the model when using one, two, or three IoT devices.
Development of PA6/GF Long-Fiber-Reinforced Thermoplastic Composites Using Pultrusion and Direct Extrusion Manufacturing Processes
Authors: Kim Sung-Eun , Ahn Jun-Geol , Ahn Seungjae , Park Do-Hyung , Choi Da-Hee , Lee Jae-Chul , Yang Hyun-Ik , Kim Ki-Young
Published: May 2022

The mechanical properties of polyamide 6 glass fiber (PA6/GF) long-fiber-reinforced thermoplastic (LFT) composites were characterized by studying the process conditions in terms of manufacturing methods (direct extrusion and pultrusion) and material characteristics (void content and fiber volume fraction). The LFT composites prepared through the pultrusion process have higher mechanical properties than those prepared via the direct extrusion process. The PA6/GF composite prepared via pultrusion had the tensile and flexural strengths of 233 MPa and 338 MPa, respectively. The impact strength measured using the Izod method was 296 J/m, which is 64% higher than that of the composite fabricated via the direct process. The optical microscope images showed that the glass fiber length of the pultruded composites is longer than the fiber length of the direct composites, leading to higher mechanical properties of the LFT composites prepared through the pultrusion process. Moreover, the interfacial shear strength between the resin and the fiber, measured via single fiber pullout tests, can account for the higher fiber reinforcing efficiency. If the void content of a composite is sufficiently small to not be detrimental to the composites, the mechanical properties are observed to be proportional to the fiber volume fraction of the composites.
A Two-Objective ILP Model of OP-MATSP for the Multi-Robot Task Assignment in an Intelligent Warehouse
Authors: Gao Jianqi , Li Yanjie , Xu Yunhong , Lv Shaohua
Published: May 2022

Multi-robot task assignment is one of the main processes in an intelligent warehouse. This paper models multi-robot task assignment in an intelligent warehouse as an open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). A two-objective integer linear programming (ILP) model for solving OP-MDTSP is proposed. The theoretical bound on the computational time complexity of this model is O(n!). We can solve the small multi-robot task assignment problem by solving the two-objective ILP model using the Gurobi solver. The multi-chromosome coding-based genetic algorithm has a smaller search space, so we use it to solve large-scale problems. The experiment results reveal that the two-objective ILP model is very good at solving small-scale problems. For large-scale problems, both EGA and NSGA3 genetic algorithms can efficiently obtain suboptimal solutions. It demonstrates that this paper’s multi-robot work assignment methods are helpful in an intelligent warehouse.
A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
Authors: Bai Yang , Tan Zheng , Lv Qunbo , He Peidong , Huang Min
Published: May 2022

Aiming at the motion blur restoration of large-scale dual-channel space-variant images, this paper proposes a dual-channel image deblurring method based on the idea of block aggregation, by studying imaging principles and existing algorithms. The study first analyzed the model of dual-channel space-variant imaging, reconstructed the kernel estimation process using the side prior information from the correlation of the two-channel images, and then used a clustering algorithm to classify kernels and restore the images. In the kernel estimation process, the study proposed two kinds of regularization terms. One is based on image correlation, and the other is based on the information from another channel input. In the image restoration process, the mean-shift clustering algorithm was used to calculate the block image kernel weights and reconstruct the final restored image according to the weights. As the experimental section shows, the restoration effect of this algorithm was better than that of the other compared algorithms.
The Facial Reconstruction of a Mesolithic Dog, Muge, Portugal
Authors: Moraes Cicero , Pereira Hugo Matos , Requicha João Filipe , Alves Lara , Alexandre-Pires Graça , de Jesus Sandra , Guimarães Silvia , Ginja Catarina , Detry Cleia , Ramalho Miguel , Pires Ana Elisabete
Published: May 2022

This paper presents the facial reconstruction of a Mesolithic dog whose skeleton was recovered from the Muge shell middens (Portugal) in the 19th century. We used the anatomical deformation approach based on a collection of computer tomography images as an attempt to reconstruct the Muge dog’s head appearance. We faced a few challenges due to the level of bone displacement and the absence of some cranium anatomical parts, as well as accurate information on soft tissue thickness for modern dogs. This multidisciplinary study combined anatomical, veterinary, zooarchaeological, artistic and graphic aspects to allow for the facial reconstruction of the Muge dog. Albeit an approximation, it confers a recognition to this prehistoric finding.
Study on the Structural Properties of an Ecospatial Network in Inner Mongolia and Its Relationship with NPP
Authors: Wang Xiaoci , Wang Ruirui , Yu Qiang , Liu Hongjun , Liu Wei , Ma Jun , Niu Teng , Yang Linzhe
Published: May 2022

In the context of strengthening the construction of ecological civilization and accelerating the “carbon peak” in China, the regional ecological pattern and its connection with carbon sink capacity have become an urgent topic. Given that Inner Mongolia is a large carbon emission province and the conflict between economic development and ecological protection is particularly prominent, we took Inner Mongolia as an example to extract its ecospatial network, then calculated the integrity index, topological indices, and recovery robustness of the network and evaluated integrity and other properties of the ecospatial network structure by combining them with the ecological background. In addition, we analyzed the relationship between the topological indices and net primary productivity (NPP). The results showed that the network was scale-free and heterogeneous, with low integrity, connectivity and stability, which were the focus of future optimization. The nodes with important functions were mainly distributed in the farm-forest ecotone, grasslands, and the agro-pastoral ecotone; under the simulation attack, the node recovery robustness was stronger than the corridor recovery robustness, and NPP was negatively and significantly correlated with the woodland nodes and grassland nodes. In terms of ecological restoration, the unused land in the west is a key area, and it is necessary to add new ecological nodes and corridors. In terms of enhancing carbon sequestration capacity, under the premise of ensuring network connectivity, the appropriate and rational merging of ecological nodes and corridors within woodlands and grasslands is a particularly effective means. This study provides a reference for evaluating and optimizing the ecological pattern of areas with prominent ecological problems and improving the carbon sink of ecosystems in terms of their ecospatial network structure.
Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
Authors: Yu Wen , Wang Fenggang , Lin Jianguo , Li Dong
Published: May 2022

An internal wave is a typical dynamic process. As an internal wave, an internal solitary wave usually occurs between two layers of fluids with different densities. Compared with general internal waves, internal solitary waves have large amplitudes, fast propagation speeds, short-wave periods, and often have tremendous energy. The propagation causes strong convergence and divergence of seawater and generates a sudden strong current. Due to its various characteristics, the propagation of internal solitary waves can cause serious harm to offshore engineering structures. Therefore, studying the effects of internal solitary waves on risers is vital in preventing environmental pollution caused by riser damage. Although the research on internal solitary waves has achieved very fruitful results, the research on structures is mostly focused on a single condition, and the occurrence of internal solitary wave, as a complex ocean phenomenon, is often accompanied by many situations. Therefore, this paper constructs a numerical simulation of the interaction between two columns of internal solitary waves and risers. This study explores the force and flow field changes of the riser under the condition of multiple internal solitary waves using the Star-CCM+ software in the simulation. The improved K-epsilon turbulence model was adopted to close the three-dimensional incompressible Navier–Stokes equation, and the solitary wave solution of the eKdV equation was used as the initial and boundary conditions. The interaction between single and double internal solitary waves and a riser was calculated, compared, and analyzed using numerical analysis. The experiment results indicate that the conditions of two internal solitary waves differ from those of a single internal solitary wave. After colliding at the riser, the waves gradually merge into a single wave, and the flow field reaches its minimum velocity. Under the two-wave condition, the horizontal force on the riser as a whole is less than the single-wave condition. As the amplitude difference between the two internal solitary waves gradually decreases, the horizontal opposing force received by the riser first increases and then decreases, while the horizontal positive force gradually decreases.
Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
Authors: Gao Ying , Chen Junjie , Zheng Liang
Published: May 2022

In this study, the seismic response and damage behavior of an isolated bridge under near-fault ground motion are studied and compared with the non-isolated bridge. To consider the local damage of the bridge pier and its evolution process, the nonlinear behavior of the bridge pier in the analysis is simulated by the fiber beam-column element model combined with the damage constitutive model of steel and concrete materials. The mechanical behavior of the isolation bearing is simulated by the three-dimensional isolation bearing model that can consider the instability and failure of the bearing. It is found that the isolation bearings do not work, and even amplify the seismic response of the bridge (pier displacement and pier shear) under the near-fault ground motion. This amplification also leads to more severe damage to the piers of the isolated bridge. In the case of the analyzed concrete bridge type and near-fault earthquake data, it can be concluded that the used isolating lead rubber bearings should not be recommended. In general, it can be stated that the selection of the types of bridge bearings in near-fault areas should always be analyzed in detail by designers in order to avoid unintentional amplifying of the bridge’s seismic response.
Deep Reinforcement Learning-Based Relay Selection Algorithm in Free-Space Optical Cooperative Communications
Authors: Gao Shi-Jie , Li Ya-Tian , Geng Tian-Wen
Published: May 2022

Relay-aided free-space optical (FSO) communication systems have the ability of mitigating the adverse effects of link disruption by dividing a long link into several short links. In order to solve the relay selection (RS) problem in a decode and forward (DF) relay-aided FSO system, we model the relay selection scheme as a Markov decision process (MDP). Based on a dueling deep Q-network (DQN), the DQN-RS algorithm is proposed, which aims at maximizing the average capacity. Different from relevant works, the switching loss between relay nodes is considered. Thanks to the advantage of maximizing cumulative rewards by deep reinforcement learning (DRL), our simulation results demonstrate that the proposed DQN-RS algorithm outperforms the traditional greedy method.
Nonsynchronous Rotor Blade Vibrations in Last Stage of 380 MW LP Steam Turbine at Various Condenser Pressures
Authors: Rzadkowski Romuald , Troka Pawel , Manerowski Jerzy , Kubitz Leszek , Kowalski Miroslaw
Published: May 2022

This paper presents an analysis of nonsynchronous rotor blade vibrations in the last stage of an LP steam turbine at various condenser pressures. The nonlinear least squares Levenberg–Marquardt method is used in a tip-timing analysis to determine nonsynchronous multimode rotor blade vibrations, which is a novelty. This is done with two sensors in the casing and a once-per-revolution sensor. The accuracy of the nonlinear least squares Levenberg–Marquardt multimode method is compared with the one-mode linear method. The algorithm is verified by comparing it with one-mode tip-timing methods for synchronous and nonsynchronous vibrations. The analysis shows that the rotor blades vibrate simultaneously with two modes in non-nominal conditions, which is also a novelty. The rotor frequencies are unchanged, although the blade vibration amplitudes vary, depending on the pressure in the condenser. Flutter does not appear in the last stage for the various condenser pressures and powers that were tested.
Co-Training Method Based on Semi-Decoupling Features for MOOC Learner Behavior Prediction
Authors: Wang Huanhuan , Xu Libo , Huang Zhenrui , Wang Jiagong
Published: May 2022

Facing the problem of massive unlabeled data and limited labeled samples, semi-supervised learning is favored, especially co-training. Standard co-training requires sufficiently redundant and conditionally independent dual views; however, in fact, few dual views exist that satisfy this condition. To solve this problem, we propose a co-training method based on semi-decoupling features, that is, semi-decoupling features based on a known single view and then constructing independent and redundant dual views: (1) take a small number of important features as shared features of the dual views according to the importance of the features; (2) separate the remaining features one by one or in small batches according to the correlation between the features to make “divergent” features of the dual views; (3) combine the shared features and the “divergent” features to construct dual views. In this paper, the experimental dataset was from the edX dataset jointly released by Harvard University and MIT; the evaluation metrics adopted F1, Precision, and Recall. The analysis methods included three experiments: multiple models, iterations, and hyperparameters. The experimental results show that the effect of this model on MOOC learner behavior prediction was better than the other models, and the best prediction result was obtained in iteration 2. These all verify the effectiveness and superiority of this algorithm and provide a scientific and feasible reference for the development of the future education industry.
Closing the Gap: Horizontal Transfer of Mariner Transposons between Rhus Gall Aphids and Other Insects
Authors: Ahmad Aftab , Su Xu , Harris AJ , Ren Zhumei
Published: May 2022

Horizontal transfer of transposons (HTT) is an essential source of genomic evolution in eukaryotes. The HTT dynamics are well characterized in eukaryotes, including insects; however, there is a considerable gap in knowledge about HTT regarding many eukaryotes’ species. In this study, we analyzed the events of the HTT between Rhus gall aphids (Hemiptera) and other insects. We analyzed the Mariner-like transposable elements (MLEs) belonging to Rhus gall aphids for the possible HT events. The MLEs have a patchy distribution and high similarity over the entire element length with insect MLEs from different orders. We selected representative sequences from the Rhus gall MLEs and identified five events of HT between MLEs of Rhus gall aphids and other insects from five different orders. We also found multiple HTT events among the MLEs of insects from the five orders, demonstrating that these Mariner elements have been involved in recurrent HT between Rhus gall aphids and other insects. Our current study closed the knowledge gap surrounding HTT and reported the events between Rhus gall aphids and other insects for the first time. We believe that this study about HTT events will help us understand the evolution and spread of transposable elements in the genomes of Rhus gall aphids.
Matrix Metalloproteinases in Health and Disease in the Times of COVID-19
Authors: Fernandez-Patron Carlos , Hardy Eugenio
Published: May 2022

Much has been written about matrix metalloproteinases (MMPs) in health and disease conditions, but their roles in the setting of COVID-19 and associated illnesses remain understudied [...]
Lipid Metabolic Alterations in the ALS–FTD Spectrum of Disorders
Authors: Godoy-Corchuelo Juan Miguel , Fernández-Beltrán Luis C. , Ali Zeinab , Gil-Moreno María J. , López-Carbonero Juan I. , Guerrero-Sola Antonio , Larrad-Sainz Angélica , Matias-Guiu Jorge , Matias-Guiu Jordi A. , Cunningham Thomas J. , Corrochano Silvia
Published: May 2022

There is an increasing interest in the study of the relation between alterations in systemic lipid metabolism and neurodegenerative disorders, in particular in Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD). In ALS these alterations are well described and evident not only with the progression of the disease but also years before diagnosis. Still, there are some discrepancies in findings relating to the causal nature of lipid metabolic alterations, partly due to the great clinical heterogeneity in ALS. ALS presentation is within a disorder spectrum with Frontotemporal Dementia (FTD), and many patients present mixed forms of ALS and FTD, thus increasing the variability. Lipid metabolic and other systemic metabolic alterations have not been well studied in FTD, or in ALS–FTD mixed forms, as has been in pure ALS. With the recent development in lipidomics and the integration with other -omics platforms, there is now emerging data that not only facilitates the identification of biomarkers but also enables understanding of the underlying pathological mechanisms. Here, we reviewed the recent literature to compile lipid metabolic alterations in ALS, FTD, and intermediate mixed forms, with a view to appraising key commonalities or differences within the spectrum.
The Effects of Exercise-Based Interventions on Fluid Overload Symptoms in Patients with Heart Failure: A Systematic Review and Meta-Analysis
Authors: Fu Mei Rosemary , Li Yuan , Conway Catherine , Masone Alessandra , Fang Jinbo , Lee Christopher
Published: May 2022

Patients with heart failure are subjected to a substantial burden related to fluid overload symptoms. Exercise can help the lymphatic system function more effectively to prevent fluid build-up in tissues and interstitium, thus potentially mitigating the symptoms due to fluid overload. The objective of this systematic review was to examine the effects of exercise-based interventions on fluid overload symptoms among patients with heart failure. MEDLINE, Embase, Cochrane Library, and CINAHL databases were systematically searched for relevant studies published from inception to August 2021. We included randomized controlled trials that compared exercise-based interventions of different modalities and usual medical care for adult patients with heart failure and reported the effects of interventions on any symptoms related to fluid overload. A random-effects meta-analysis was used to estimate the effectiveness, and a subgroup analysis and univariate meta-regression analysis were used to explore heterogeneity. Seventeen studies covering 1086 participants were included. We found robust evidence indicating the positive effect of exercises in dyspnea relief (SMD = −0.48; 95%CI [−0.76, −0.19]; p = 0.001); the intervention length also influenced the treatment effect (β = 0.033; 95%CI [0.003, 0.063]; p = 0.04). Initial evidence from existing limited research showed that exercise-based intervention had positive effect to alleviate edema, yet more studies are needed to verify the effect. In contrast, the exercise-based interventions did not improve fatigue compared with usual care (SMD = −0.27; 95%CI [−0.61, 0.06]; p = 0.11). Findings regarding the effects of exercises on bodily pain, gastro-intestinal symptoms, and peripheral circulatory symptoms were inconclusive due to limited available studies. In conclusion, exercise-based interventions can be considered as an effective nonpharmacological therapy for patients with heart failure to promote lymph flow and manage fluid overload symptoms. Exercise-based interventions seem to have very limited effect on fatigue. More research should investigate the mechanism of fatigue related to heart failure. Future studies with high methodological quality and comprehensive assessment of symptoms and objective measure of fluid overload are warranted.
Study on the Effect of Vegetation Coverage on Urban Cooling and Energy Conservation: A Case Study of a Typical Hilly City, Chenzhou, China
Authors: Luo Xi , Wang Jingwei , Li Jiayu
Published: May 2022

Urban vegetation coverage is a core index in urban planning, which has been confirmed to be an effective indicator for the urban thermal environment. Through the urban thermal environment, this study aims to further quantify the impact of vegetation coverage on urban energy consumption. Chenzhou, a typical hilly city, was selected as the study object for its diversified vegetation coverages. Remote sensing technology and correlation and regression models were employed in this study. Firstly, the data of land surface temperature and vegetation coverage were calculated with remote sensing technology, followed by data analysis with the correlation and regression models. Then, employing the “λ-T” model, a statistical model corresponding to urban temperature and energy, this study clarified the impact of temperature on urban energy consumption. Finally, through urban temperature, this study analyzed the impact of urban greening coverage on urban energy consumption. This study shows that when the temperature ranges from 22 °C to 28.9 °C, every 10% of additional vegetation coverage will reduce the air conditioning energy demands by 5.5%, and when the temperature is between 28.9 °C and 37 °C, every 10% of additional vegetation coverage will reduce the mean air conditioning energy demands by 2.4%.
Windswept Deformity a Disease or a Symptom? A Systematic Review on the Aetiologies and Hypotheses of Simultaneous Genu Valgum and Varum in Children
Authors: Jansen Niels J. , Dockx Romy B. M. , Witlox Adhiambo M. , Straetemans Saartje , Staal Heleen M.
Published: May 2022

Objective: The objective of this study is to create an overview of the possible aetiologies of windswept deformity and to emphasize the points of attention when presented with a case. Methods: A systematic search according to the PRISMA statement was conducted using PubMed, African Journals Online, Cochrane, Embase, Google Scholar, and Web of Science. Articles investigating the aetiology of windswept deformity at the knee in children, and articles with windswept deformity as an ancillary finding were included. The bibliographic search was limited to English-language articles only. The level of evidence and methodological appraisal were assessed. Results: Forty-five articles discussing the aetiology of windswept deformity were included. A variety of aetiologies can be brought forward. These can be divided into the following groups: ‘Rickets and other metabolic disorders’, ‘skeletal dysplasias and other genetic disorders’, ‘trauma’ and ‘descriptive articles without specific underlying disorder’. With rickets being the largest group. Interestingly, in the group without a specific underlying disorder, all patients were from African descent, being otherwise healthy and presented with windswept deformity between two and three years of age. Conclusion: We have presented an overview that may help identify the underlying disorder in children with windswept deformity. A step-by-step guide for clinicians who see a child with windswept deformity is provided. Even though, according to the Oxford level of evidence, most articles have a low level of evidence.
Kingella kingae Spinal Infections in Children
Authors: Samara Eleftheria , Lutz Nicolas , Zambelli Pierre-Yves
Published: May 2022

Nowadays, Kingella kingae is considered an important cause of primary spinal infections in children aged between 6 and 48 months. The presentation of the disease is often characterized by mild clinical features and a moderate biological inflammatory response, requiring a high index of suspicion. Performing magnetic resonance imaging (MRI) and obtaining an oropharyngeal specimen and subjecting it to a K. kingae-specific nucleic acid amplification test are recommended for its diagnosis. Most patients respond promptly to conservative treatment after administration of antibiotic therapy, which is prolonged for up to 3 months according to the individual clinical and biological response. Invasive surgical procedures are not required except for children who do not improve with antibiotic treatment, develop signs of cord compression, or if the presence of atypical microorganisms is suspected. Kingella kingae spinal infections usually run an indolent and benign clinical course, living no permanent sequelae.
DriverMVT: In-Cabin Dataset for Driver Monitoring including Video and Vehicle Telemetry Information
Authors: Othman Walaa , Kashevnik Alexey , Ali Ammar , Shilov Nikolay
Published: May 2022

Developing a driver monitoring system that can assess the driver’s state is a prerequisite and a key to improving the road safety. With the success of deep learning, such systems can achieve a high accuracy if corresponding high-quality datasets are available. In this paper, we introduce DriverMVT (Driver Monitoring dataset with Videos and Telemetry). The dataset contains information about the driver head pose, heart rate, and driver behaviour inside the cabin like drowsiness and unfastened belt. This dataset can be used to train and evaluate deep learning models to estimate the driver’s health state, mental state, concentration level, and his/her activity in the cabin. Developing such systems that can alert the driver in case of drowsiness or distraction can reduce the number of accidents and increase the safety on the road. The dataset contains 1506 videos for 9 different drivers (7 males and 2 females) with total number of frames equal 5119k and total time over 36 h. In addition, evaluated the dataset with multi-task temporal shift convolutional attention network (MTTS-CAN) algorithm. The algorithm mean average error on our dataset is 16.375 heartbeats per minute.
Federated Learning with Dynamic Model Exchange
Authors: Hilberger Hannes , Hanke Sten , Bödenler Markus
Published: May 2022

Large amounts of data are needed to train accurate robust machine learning models, but the acquisition of these data is complicated due to strict regulations. While many business sectors often have unused data silos, researchers face the problem of not being able to obtain a large amount of real-world data. This is especially true in the healthcare sector, since transferring these data is often associated with bureaucratic overhead because of, for example, increased security requirements and privacy laws. Federated Learning should circumvent this problem and allow training to take place directly on the data owner’s side without sending them to a central location such as a server. Currently, there exist several frameworks for this purpose such as TensorFlow Federated, Flower, or PySyft/PyGrid. These frameworks define models for both the server and client since the coordination of the training is performed by a server. Here, we present a practical method that contains a dynamic exchange of the model, so that the model is not statically stored in source code. During this process, the model architecture and training configuration are defined by the researchers and sent to the server, which passes the settings to the clients. In addition, the model is transformed by the data owner to incorporate Differential Privacy. To trace a comparison between central learning and the impact of Differential Privacy, performance and security evaluation experiments were conducted. It was found that Federated Learning can achieve results on par with centralised learning and that the use of Differential Privacy can improve the robustness of the model against Membership Inference Attacks in an honest-but-curious setting.
Toward Smart Communication Components: Recent Advances in Human and AI Speaker Interaction
Authors: Kim Hyejoo , Hwang Sewoong , Kim Jonghyuk , Lee Zoonky
Published: May 2022

This study aims to investigate how humans and artificial intelligence (AI) speakers interact and to examine the interactions based on three types of communication failures: system, semantic, and effectiveness. We divided service failures using AI speaker user data provided by the top telecommunication service providers in South Korea and investigated the means to increase the continuity of product use for each type. We proved the occurrence of failure due to system error (H1) and negative results on sustainable use of the AI speaker due to not understanding the meaning (H2). It was observed that the number of users increases as the effectiveness failure rate increases. For single-person households constituted by persons in their 30s and 70s or older, the continued use of AI speakers was significant. We found that it alleviated loneliness and that human-machine interaction using AI speaker could reach a high level through a high degree of meaning transfer. We also expect AI speakers to play a positive role in single-person households, especially in cases of the elderly, which has become a tough challenge in the recent times.
Integrated Electro-Thermal Model for Li-Ion Battery Packs
Authors: Barcellona Simone , Colnago Silvia , Montrasio Paolo , Piegari Luigi
Published: May 2022

Lithium-ion battery is considered one of the most attractive energy storage systems for electric vehicles. However, one of its main drawbacks is the sensitivity to temperature. In a battery pack composed of lithium-ion batteries, during the charge/discharge operations, the temperature gradually increases, especially in the batteries positioned in the central part of the battery pack. This leads the central batteries to age faster and exposes them to the risk of a thermal runaway. In order to mitigate these problems, thermal management systems are needed. However, for the implementation of the control, it is important to know the temperature distribution inside the whole pack. In this paper, an integrated electro-thermal model capable of estimating the thermal behavior of each battery cell, composing the battery pack, only knowing the total current and ambient temperature, is proposed and analyzed. The proposed model was tuned and validated by means of experimental results. The circuital approach used in this model gives good results with a low degree of complexity.
Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers
Authors: Kalishettyhalli Mahadevaiah Mamathamba , Perez Eduardo , Lisker Marco , Schubert Markus Andreas , Perez-Bosch Quesada Emilio , Wenger Christian , Mai Andreas
Published: May 2022

The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.
Microwave Soil Heating Promotes Strawberry Runner Production and Progeny Performance
Authors: Brodie Graham Ian , McFarlane Dylan John , Khan Muhammed Jamal , Phung Valerie Buu Giao , Mattner Scott William
Published: May 2022

Strawberry runners (transplants) in many regions of the world are produced in soils treated with chemical fumigants to control pathogens and weeds and meet phytosanitary requirements. Many fumigants, however, are under threat of withdrawal because of concerns over their impact on the environment (e.g., methyl bromide). The current study considered the use of microwaves for heat disinfestation of soil for field-grown runners for the first time. Results from two field experiments showed that microwave treatment reduced the survival of buried inoculum of the strawberry pathogens Fusarium oxysporum (by up to 93%) and Sclerotium rolfsii (by up to 100%) compared with untreated soil. Furthermore, the treatment reduced the subsequent growth of these pathogens in the laboratory by up to 82% and 100%, respectively. Microwave treatment also reduced the natural DNA concentration of Pythium spp. (clades I & F) in soil by up to 94% compared with untreated soil. The effect of microwave against soilborne pathogens reduced as soil depth increased. Microwave treatment reduced the emergence of weeds in field soils by up to 65% and increased runner yields by 10–37%. The effect of microwave treatment on runner yield was greater when all soil was treated, rather than when strips of soil around the mother plants were treated. Results from complimentary pot experiments showed that early strawberry growth in the glasshouse was equivalent in soils treated with microwave or the fumigant methyl bromide/chloropicrin. Furthermore, the early performance of runners sourced from field soils treated with microwave or methyl bromide/chloropicrin was equivalent. Results from the pot experiments also showed that steam treatment required 10 times more energy per mass of soil to disinfest than microwave. The limitations of microwave in the current experiments are discussed, but the capacity for the technology to disinfest field soils in an energy-efficient manner demonstrates its potential for further development as an alternative to soil disinfestation with chemical fumigants.