bluuebunny/crossref_metadata_2025 huggingface dataset:
Changes in Antioxidant Defence System in Durum Wheat under Hyperosmotic Stress: A Concise Overview
Authors: Laus Maura Nicoletta , De Santis Michele Andrea , Flagella Zina , Soccio Mario
Published: December 2021

Durum wheat is one of the most commonly cultivated species in the world and represents a key commodity for many areas worldwide, as its grain is used for production of many foods, such as pasta, bread, couscous, and bourghul. Durum wheat grain has a relevant role in the human diet, providing carbohydrates, proteins, lipids, fibres, vitamins, and minerals, as well as highly valued bioactive compounds contributing to a healthy diet. Durum wheat is largely cultivated in the Mediterranean basin, where it is mainly grown under rain-fed conditions, thus currently undergoing drought stress, as well as soil salinity, which can hamper yield potential and influence the qualitative characteristics of grain. When plants suffer drought and/or salinity stress, a condition known as hyperosmotic stress is established at cellular level. This leads to the accumulation of ROS thus generating in turn an oxidative stress condition, which can ultimately result in the impairment of cellular integrity and functionality. To counteract oxidative damage due to excessive ROS production under stress, plants have evolved a complex array of both enzymatic and non-enzymatic antioxidant mechanisms, working jointly and synergically for maintenance of ROS homeostasis. Enhancement of antioxidant defence system has been demonstrated as an adaptive mechanism associated to an increased tolerance to hyperosmotic stress. In the light of these considerations, this review provides a concise overview on recent advancements regarding the role of the ascorbate-glutathione cycle and the main antioxidant enzymes (superoxide dismutase, catalase, and peroxidases) in durum wheat response to drought and salt stresses that are expected to become more and more frequent due to the ongoing climate changes.
Molecular Dynamics Investigation of the Thermo-Mechanical Properties of the Moisture Invaded and Cross-Linked Epoxy System
Authors: Sheng Can , Wu Gai , Sun Xiang , Liu Sheng
Published: December 2021

In spite of a high market share of plastic IC packaging, there are still reliability issues, especially for the effects of moisture. The mechanism between moisture and epoxy polymer is still obscure. A multi-step cross-linking approach was used to mimic the cross-linking process between the DGEBA resin and JEFFAMINE®-D230 agent. Based on the molecular dynamics method, the thermo-mechanical properties and microstructure of epoxy polymer were analyzed. In this paper, the degree of cross-linking ranged from 0% to 85.4% and the moisture concentration ranged from 0 wt.% to 12 wt.%. The hydrogen bonds were investigated in the moisture invaded epoxy polymer. Although most of the hydrogen bonds were related to water molecules, the hydrogen bonds between the inside of epoxy polymer were reduced only a little as the concentration of moisture increased. The diffusion coefficient of the water molecules was found to increase with the increase of moisture concentration. When the moisture concentration was larger than 12 wt.% or smaller than 1.6 wt.%, the diffusion coefficient was less affected by the epoxy polymer. In addition, the free volume and the thermal conductivity of the epoxy polymer were considered. It was found that the moisture could increase the thermal conductivity from 0.24 to 0.31 W/m/K, identifying a coupling relationship between moisture and thermal properties. Finally, the mechanical properties of epoxy polymer were analyzed by uniaxial tensile simulation. The COMPASS and DREIDING force fields were used during the uniaxial tensile simulation. A better result was achieved from the DREIDING force field compared with the experiment. The degree of cross-linking was positively correlated with mechanical properties. For the system with the largest degree of cross-linking of 85.4%, the Young’s modulus was 2.134 ± 0.522 GPa and the yield strength was 0.081 ± 0.01 GPa. There were both plasticizing and anti-plasticizing effects when the water molecules entered the epoxy polymer. Both the Young’s moduli and yield strength varied in a large range from 1.38 to 2.344 GPa and from 0.062 to 0.128 GPa, respectively.
Natural Polymers Used in Edible Food Packaging—History, Function and Application Trends as a Sustainable Alternative to Synthetic Plastic
Authors: Teixeira-Costa Barbara E. , Andrade Cristina T.
Published: December 2021

In this review, a historical perspective, functional and application trends of natural polymers used to the development of edible food packaging were presented and discussed. Polysaccharides and proteins, i.e., alginate; carrageenan; chitosan; starch; pea protein, were considered. These natural polymers are important materials obtained from renewable plant, algae and animal sources, as well as from agroindustrial residues. Historically, some of them have been widely used by ancient populations for food packaging until these were replaced by petroleum-based plastic materials after World War II. Nowadays, biobased materials for food packaging have attracted attention. Their use was boosted especially because of the environmental pollution caused by inappropriate disposal of plastic packaging. Biobased materials are welcome to the design of food packaging because they possess many advantages, such as biodegradability, biocompatibility and low toxicity. Depending on the formulation, certain biopolymer-based packaging may present good barrier properties, antimicrobial and antioxidant activities Thus, polysaccharides and proteins can be combined to form diverse composite films with improved mechanical and biological behaviors, making them suitable for packaging of different food products.
Degradation of Selected Antidepressants Sertraline and Citalopram in Ultrapure Water and Surface Water Using Gamma Radiation
Authors: Bojanowska-Czajka Anna , Pyszynska Marta , Majkowska-Pilip Agnieszka , Wawrowicz Kamil
Published: December 2021

Gamma radiation was applied to degradation selected antidepressants in ultrapure water and surface water. Additionally, the influence of typical radical scavengers like carbonate, nitrate and humic acid was determined. The cytotoxicity towards liver cells HepG2 and colon cells Caco2 were measured during the radiation process. It was found that radiation technology, specifically ionizing radiation, can achieve satisfactory degradation efficiency with both SER and CIT. It was shown that the process of decomposition of the tested antidepressants with the highest efficiency occurs in the reaction with the hydroxyl radical.
Application of Computational Fluid Dynamics (CFD) in the Deposition Process and Printability Assessment of 3D Printing Using Rice Paste
Authors: Oyinloye Timilehin , Yoon Won
Published: December 2021

Computational fluid dynamics (CFD) was utilized to investigate the deposition process and printability of rice paste. The rheological and preliminary printing studies showed that paste formed from rice to water ratio (100:80) is suitable for 3D printing (3DP). Controlling the ambient temperature at 47±5 °C also contributed to improving the printed sample’s structural stability. The viscoelastic simulation indicated that the nozzle diameter influenced the flow properties of the printed material. As the nozzle diameter decreased (1.2 mm to 0.8 mm), the die swell ratio increased (13.7 to 15.15%). The rise in the swell ratio was a result of the increasing pressure gradient at the nozzle exit (5.48×106 Pa to 1.53×107 Pa). The additive simulation showed that the nozzle diameter affected both the residual stress and overall deformation of the sample. CFD analysis, therefore, demonstrates a significant advantage in optimizing the operating conditions for printing rice paste.
Validation of De Novo Peptide Sequences with Bottom-Up Tag Convolution
Authors: Vyatkina Kira
Published: December 2021

De novo sequencing is indispensable for the analysis of proteins from organisms with unknown genomes, novel splice variants, and antibodies. However, despite a variety of methods developed to this end, distinguishing between the correct interpretation of a mass spectrum and a number of incorrect alternatives often remains a challenge. Tag convolution is computed for a set of peptide sequence tags of a fixed length k generated from the input tandem mass spectra and can be viewed as a generalization of the well-known spectral convolution. We demonstrate its utility for validating de novo peptide sequences by using a set of those generated by the algorithm PepNovo+ from high-resolution bottom-up data sets for carbonic anhydrase 2 and the Fab region of alemtuzumab and indicate its further potential applications.
Pan Arabism 2.0? The Struggle for a New Paradigm in the Middle East
Authors: Boms Nir Tuvia , Aboubakr Hussein
Published: December 2021

The Abraham Accords, signed in September 2020 have helped shed a light on a new discourse emerging from the Gulf that seeks to challenge some of the old dogmas that have dominated the region in the last few decades. A decade of turmoil that followed what was once dubbed as the “Arab Spring” finds a divided region, full of ethnic and religious conflict, ungoverned territories, and the growing reality of failed states. An “axis of resistance”, led by radical elements from both the Shi’a and the Sunni world, is perceived as a growing challenge to a group of actors led by a number of Gulf countries who identify radicalization as an existential threat. Facing the “axis of resistance”, a new “axis of renaissance” is coming of age with an alternative vision that seeks to change the face of the Middle East. In parallel to the rapid decline of the traditional Arab capitals, the Gulf is emerging as a more significant voice in the region due to its economic, political, and media influence. This article seeks to capture and explain the rise of this new Gulf-led axis and the early formulation of a new agenda of a more tolerant Middle East through a radical reshuffling of the order of priorities in the region.
An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image
Authors: Yao Hongtai , Wang Xianpei , Zhao Le , Tian Meng , Jian Zini , Gong Li , Li Bowen
Published: December 2021

The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be reconstructed into the original image; the number of categories and region granularity for these partition images are set. Then, the MRF model is built on the partition images and the original image, their segmentations are alternately updated. The update path adopts a circular path, and the correlation assumption is adopted to establish the connection between the label fields of partition images and the original image. Finally, the relationship between each label field is constantly updated, and the final segmentation result is output after the segmentation has converged. Experiments on texture images and different remote sensing image datasets show that the proposed OMRF-PGAU algorithm has a better segmentation performance than other selected state-of-the-art MRF-based methods.
Inter-Zone Differences of Convective Development in a Convection Outbreak Event over Southeastern Coast of China: An Observational Analysis
Authors: Huang Yipeng , Zhang Murong , Zhao Yuchun , Jou Ben Jong-Dao , Zheng Hui , Luo Changrong , Chen Dehua
Published: December 2021

Among the densely-populated coastal areas of China, the southeastern coast has received less attention in convective development despite having been suffering from significantly increasing thunderstorm activities. The convective complexity under such a region with extremely complex underlying and convective conditions deserves in-depth observational surveys. This present study examined a high-impact convection outbreak event with over 40 hail reports in the southeastern coast of China on 6 May 2020 by focusing on contrasting the convective development (from convective initiation to supercell occurrences) among three adjacent convection-active zones (north (N), middle (M), and south (S)). The areas from N to S featured overall flatter terrain, higher levels of free convection, lower relative humidity, larger convective inhibition, more convective available potential energy, and greater vertical wind shears. With these mesoscale environmental variations, distinct inter-zone differences in the convective development were observed with the region’s surveillance radar network and the Himawari-8 geostationary satellite. Convection initiated in succession from N to S and began with more warm-rain processes in N and M and more ice-phase processes in S. The subsequent convection underwent more vigorous vertical growth from N to S. The extremely deep convection in S was characterized by the considerably strong precipitation above the freezing level, echo tops of up to 18 km, and a great amount of deep (even overshooting) and thick convective clouds with significant cloud-top glaciation. Horizontal anvil expansion in convective clouds was uniquely apparent over S. From N to S, more pronounced mesocyclone and weak-echo region signatures indicated high risks of severe supercell hailstorms. These results demonstrate the strong linkage between the occurrence likelihood of severe convection and associated weather (such as supercells and hailstones) and the early-stage convective development that can be well-captured by high-resolution observations and may facilitate fine-scale convection nowcasting.
Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2
Authors: Le Quilleuc Alyson , Collin Antoine , Jasinski Michael F. , Devillers Rodolphe
Published: December 2021

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites.
RGB-D-Based Method for Measuring the Angular Range of Hip and Knee Joints during Home Care Rehabilitation
Authors: Uccheddu Francesca , Furferi Rocco , Governi Lapo , Carfagni Monica
Published: December 2021

Home-based rehabilitation is becoming a gold standard for patient who have undergone knee arthroplasty or full knee replacement, as it helps healthcare costs to be minimized. Nevertheless, there is a chance of increasing adverse health effects in case of home care, primarily due to the patients’ lack of motivation and the doctors’ difficulty in carrying out rigorous supervision. The development of devices to assess the efficient recovery of the operated joint is highly valued both for the patient, who feels encouraged to perform the proper number of activities, and for the doctor, who can track him/her remotely. Accordingly, this paper introduces an interactive approach to angular range calculation of hip and knee joints based on the use of low-cost devices which can be operated at home. First, the patient’s body posture is estimated using a 2D acquisition method. Subsequently, the 3D posture is evaluated by using the depth information coming from an RGB-D sensor. Preliminary results show that the proposed method effectively overcomes many limitations by fusing the results obtained by the state-of-the-art robust 2D pose estimation algorithms with the 3D data of depth cameras by allowing the patient to be correctly tracked during rehabilitation exercises.
Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles
Authors: Horváth Ernő , Pozna Claudiu , Unger Miklós
Published: December 2021

Road and sidewalk detection in urban scenarios is a challenging task because of the road imperfections and high sensor data bandwidth. Traditional free space and ground filter algorithms are not sensitive enough for small height differences. Camera-based or sensor-fusion solutions are widely used to classify drivable road from sidewalk or pavement. A LIDAR sensor contains all the necessary information from which the feature extraction can be done. Therefore, this paper focuses on LIDAR-based feature extraction. For road and sidewalk detection, the current paper presents a real-time (20 Hz+) solution. This solution can also be used for local path planning. Sidewalk edge detection is the combination of three algorithms working parallelly. To validate the result, the de facto standard benchmark dataset, KITTI, was used alongside our measurements. The data and the source code to reproduce the results are shared publicly on our GitHub repository.
Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings
Authors: Wang Qinghua , Wang Lijuan , Yu Hongtao , Wang Dong , Nandi Asoke K.
Published: December 2021

In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode decomposition (VMD). SVD is used to determine the structure of the underlying model, which is referred to as signal and noise subspaces, and VMD is used to decompose the original signal into several band-limited modes. Then the effective components are selected from these modes to reconstruct the denoised signal according to the difference spectrum (DS) of singular values and kurtosis values. Simulated signals and experimental signals of roller bearing faults have been analyzed using this proposed method and compared with SVD-DS. The results demonstrate that the proposed method can effectively retain the useful signals and denoise the bearing signals in extremely noisy backgrounds.
Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression
Authors: Dumic Emil , Bjelopera Anamaria , Nüchter Andreas
Published: December 2021

In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.
Application and Development of Noncontact Detection Method of α-Particles Based on Radioluminescence
Authors: Wu Zeqian , Cheng Jinxing , Xu Mei , Wang Qingbo , Yu Ai , Zhang Yue , Wen Weiwei , Wu Youpeng , Tang Zhongfeng
Published: December 2021

The detection of α particles is of great significance in military and civil nuclear facility management. At present, the contact method is mainly used to detect α particles, but its shortcomings limit the broad application of this method. In recent years, preliminary research on noncontact α-particle detection methods has been carried out. In this paper, the theory of noncontact α-particles detection methods is introduced and studied. We also review the direct detection and imaging methods of α particles based on the different wavelengths of fluorescence photons, and analyze the application and development of this method, providing an important reference for researchers to carry out related work.
A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping
Authors: Munguia Rodrigo , Trujillo Juan-Carlos , Guerra Edmundo , Grau Antoni
Published: December 2021

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.
Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization
Authors: Zakia Umme , Menon Carlo
Published: December 2021

Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes time consuming and impractical. In real scenarios, a pretrained transfer learning model predicting forces quickly once fine-tuned to target distribution would be a favorable choice and hence needs to be examined. Therefore, in this study a unified supervised FMG-based deep transfer learner (SFMG-DTL) model using CNN architecture was pretrained with multiple sessions FMG source data (Ds, Ts) and evaluated in estimating forces in separate target domains (Dt, Tt) via supervised domain adaptation (SDA) and supervised domain generalization (SDG). For SDA, case (i) intra-subject evaluation (Ds ≠ Dt-SDA, Ts ≈ Tt-SDA) was examined, while for SDG, case (ii) cross-subject evaluation (Ds ≠ Dt-SDG, Ts ≠ Tt-SDG) was examined. Fine tuning with few “target training data” calibrated the model effectively towards target adaptation. The proposed SFMG-DTL model performed better with higher estimation accuracies and lower errors (R2 ≥ 88%, NRMSE ≤ 0.6) in both cases. These results reveal that interactive force estimations via transfer learning will improve daily HRI experiences where “target training data” is limited, or faster adaptation is required.
Effects of Optical Beams on MIMO Visible Light Communication Channel Characteristics
Authors: Ding Jupeng , I Chih-Lin , Wang Jintao , Yang Hui
Published: December 2021

Under 5G envision, for pushing visible light communication (VLC) channel model evolution to various non-Lambertian beams, this paper introduces the typical commercial non-Lambertian beams, such as Luxeon rebel and side emitter, into the conventional analytical VLC channel model. The numerical results illustrate that the non-Lambertian beams can significantly affect the VLC channel frequency response characteristics. Compared with the traditional Lambertian beam, Side Emitter optical beam could naturally bring up to about 56.8% VLC multi input multi output channel capacity deviation, which objectively opens a new discussion dimension for enhancing VLC transmission performance.
Modular Piezoresistive Smart Textile for State Estimation of Cloths
Authors: Proesmans Remko , Verleysen Andreas , Vleugels Robbe , Veske Paula , De Gusseme Victor-Louis , Wyffels Francis
Published: December 2021

Smart textiles have found numerous applications ranging from health monitoring to smart homes. Their main allure is their flexibility, which allows for seamless integration of sensing in everyday objects like clothing. The application domain also includes robotics; smart textiles have been used to improve human-robot interaction, to solve the problem of state estimation of soft robots, and for state estimation to enable learning of robotic manipulation of textiles. The latter application provides an alternative to computationally expensive vision-based pipelines and we believe it is the key to accelerate robotic learning of textile manipulation. Current smart textiles, however, maintain wired connections to external units, which impedes robotic manipulation, and lack modularity to facilitate state estimation of large cloths. In this work, we propose an open-source, fully wireless, highly flexible, light, and modular version of a piezoresistive smart textile. Its output stability was experimentally quantified and determined to be sufficient for classification tasks. Its functionality as a state sensor for larger cloths was also verified in a classification task where two of the smart textiles were sewn onto a piece of clothing of which three states are defined. The modular smart textile system was able to recognize these states with average per-class F1-scores ranging from 85.7 to 94.6% with a basic linear classifier.
Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review
Authors: Tambare Parkash , Meshram Chandrashekhar , Lee Cheng-Chi , Ramteke Rakesh Jagdish , Imoize Agbotiname Lucky
Published: December 2021

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.
Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
Authors: Lee Kyung Hyun , Min Ji Young , Byun Sangwon
Published: December 2021

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.
Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
Authors: Zhu Jinlin , Jiang Muyun , Liu Zhong
Published: December 2021

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.
Modified Functional Reach Test: Upper-Body Kinematics and Muscular Activity in Chronic Stroke Survivors
Authors: Marchesi Giorgia , Ballardini Giulia , Barone Laura , Giannoni Psiche , Lentino Carmelo , De Luca Alice , Casadio Maura
Published: December 2021

Effective control of trunk muscles is fundamental to perform most daily activities. Stroke affects this ability also when sitting, and the Modified Functional Reach Test is a simple clinical method to evaluate sitting balance. We characterize the upper body kinematics and muscular activity during this test. Fifteen chronic stroke survivors performed twice, in separate sessions, three repetitions of the test in forward and lateral directions with their ipsilesional arm. We focused our analysis on muscles of the trunk and of the contralesional, not moving, arm. The bilateral activations of latissimi dorsi, trapezii transversalis and oblique externus abdominis were left/right asymmetric, for both test directions, except for the obliquus externus abdominis in the frontal reaching. Stroke survivors had difficulty deactivating the contralesional muscles at the end of each trial, especially the trapezii trasversalis in the lateral direction. The contralesional, non-moving arm had muscular activations modulated according to the movement phases of the moving arm. Repeating the task led to better performance in terms of reaching distance, supported by an increased activation of the trunk muscles. The reaching distance correlated negatively with the time-up-and-go test score.
Empirical Analysis of Evidence-Based Policymaking in R&D Programmes
Authors: Shim Hyensup , Shin Kiyoon
Published: December 2021

This study explores evidence-based policymaking (EBP) in public research and development (R&D) programmes. Governments develop and implement R&D programmes to promote innovation, the key driver of sustainable economic growth. For effective management, public policies should be planned, executed, and terminated based on their impact evaluation and feedback. Although many studies have investigated the impact of R&D support, few have analysed whether it evolves on the basis of evidence. Using a dataset of Korean programmes, this study adopted the propensity score matching with a difference-in-differences method. We distinguished four determinants of performance evidence: R&D intensity, assets, sales, and profits growth. The following are the main results: (1) while R&D intensity, sales, and profits growth reveal the efficiency of the EBP mechanism, the performance of assets do not differ across the abolished and continued programmes; (2) the EBP process classified by subsidy amount reveals no statistically significant differences in terms of R&D intensity and profits. This suggests that policymakers need to consider the amount of subsidy granted under the budget limitation. This study contributes to the empirical research on EBP using heterogeneous evidence indicators and describes how policymakers exploit policy evaluation to implement and monitor policies for sustainable development.
Spatio-Temporal Modeling of Small-Scale Ultrafine Particle Variability Using Generalized Additive Models
Authors: Gaeta Alessandra , Leone Gianluca , Di Menno di Bucchianico Alessandro , Cusano Mariacarmela , Gaddi Raffaela , Pelliccioni Armando , Reatini Maria Antonietta , Di Bernardino Annalisa , Cattani Giorgio
Published: December 2021

High-resolution measurements of ultrafine particle concentrations in ambient air are needed for the study of health human effects of long-term exposure. This work, carried out in the framework of the VIEPI project (Integrated Evaluation of Indoor Particulate Exposure), aims to extend current knowledge on small-scale spatio-temporal variability of Particle Number Concentration (PNC, considered a proxy of the ultrafine particles) at a local scale domain (1 km × 1 km). PNC measurements were made in the university district of San Lorenzo in Rome using portable condensation particle counters for 7 consecutive days at 21 sites in November 2017 and June 2018. Generalized Additive Models (GAMs) were performed in the area for winter, summer and the overall period. The log-transformed two-hour PNC averages constitute the response variable, and covariates were grouped by urban morphology, land use, traffic and meteorology. Winter PNC values were about twice the summer ones. PNC recorded in the university area were significantly lower than those observed in the external routes. GAMs showed a rather satisfactory result in order to capture the spatial variability, in accordance with those of other previous studies: variances were equal to 71.1, 79.7 and 84%, respectively, for winter, summer and the overall period.
Impact of Consumer Health Awareness on Dairy Product Purchase Behavior during the COVID-19 Pandemic
Authors: Xu Jiabin , Wang Jingjing , Li Cuixia
Published: December 2021

Corona Virus Disease 2019 (COVID-19) has led to a reduction in the overall consumption of dairy products in China. How to restore the consumption potential of dairy products and alleviate the serious impact on the dairy market in the post-epidemic period is an urgent problem that needs to be resolved. Based on the survey data of 1780 consumers in 31 provinces (municipalities and autonomous regions) of China, the Heckman two-stage model was used to empirically test the impact of consumer health awareness on dairy product purchase behavior during the COVID-19 pandemic and to further analyze the differences in factors affecting dairy product purchase behavior with the restriction of consumer health awareness. The results showed that the overall level of consumer health awareness after the outbreak of COVID-19 was relatively high. A total of 79% of consumers preferred to buy dairy products after the COVID-19 outbreak, and the proportion of purchased dairy products increased by an average of 17.49%, compared with that before the COVID-19 outbreak. Health change perception, health concern degree, and health habit development in consumer health awareness all have important impacts on the purchase behavior of dairy products. Among them, health change perception and health habit development both positively and significantly affected the purchase intention. Moreover, all three aspects of consumer health awareness positively increased the proportion of dairy product purchases. Difference analysis showed that there were obvious differences among consumer groups with different health awareness in dairy product purchase decisions. Component factor analysis found that, overall, consumer health awareness directly affected the purchase intention and increased the purchase proportion of dairy products. Therefore, policy recommendations are proposed to increase the consumption momentum of dairy products by raising consumer health awareness in the post-epidemic period.
The Impact of Agricultural Socialized Services to Promote the Farmland Scale Management Behavior of Smallholder Farmers: Empirical Evidence from the Rice-Growing Region of Southern China
Authors: Cai Baozhong , Shi Fang , Huang Yuangji , Abatechanie Meseret
Published: December 2021

Farmland scale management (FSM) is an essential strategy to establish an appropriate management scale for agricultural production, enhance smallholder farmer production efficiency, and improve the utilization rate of farmland. The Chinese government promotes farmland transfer as a tool to establish modern and moderate-scale agriculture. However, farmers remain unable to afford agricultural services and inputs required for appropriate FSM after farmland is transferred-in. This paper aims to examine the impact of agricultural socialized services (ASSs) on the FSM behavior of smallholder farmers through farmland transfer. A theoretical framework for the farmer household production aspect of this relationship is developed. A weighted least squares (WLS) model is applied to empirically examine smallholder farmers’ decisions to expand the scale of farmland induced by the promotion of ASSs based on data collected from 741 households in 2020 in the rice-growing region of southern China. The findings reveal that ASSs have a positive and significant impact on small farmers’ FSM. Small farmers’ behavior regarding farmland transfer is affected positively by the promotion of ASSs. The increase of ASSs encourages small farmers to transfer-in more farmland. However, the impact of ASSs on various steps of agricultural practice varied according to the FSM of smallholder farmers. Our findings imply that the government should take the development of ASSs as one of the main methods for promoting the establishment of moderate and large-scale agriculture and rural revitalization. Strengthening policies and financial support for both private and public ASS providers through financial innovation subsidies and preferential tax policies will help smallholder farmers reduce input costs and increase the scale of production and profits. The findings of this paper will provide a scientific basis and reference for the development of moderate-scale agriculture and rural revitalization.
Characteristics of Smoldering on Moist Rice Husk for Silica Production
Authors: Yan Shengtai , Yin Dezheng , He Fang , Cai Junmeng , Schliermann Thomas , Behrendt Frank
Published: December 2021

In order to assess the possibility of silica production via smoldering of moist rice husk, experiments of washed (moist) rice husk (7 kg with moisture content of 51%) in a newly designed smoldering apparatus was performed. The temperature inside the fuel bed during smoldering was recorded, and characteristics of ash were analyzed. Results showed that the highest temperature in the middle of the naturally piled fuel bed was about 560.0 °C, lower than those in most of combustors. Some volatiles from the lower part of the fuel bed adhere to its upper ash during piled smoldering. Silica content and specific surface area of ash from smoldering of washed (moist) rice husk were 86.4% and 84.9 m2/g, respectively. Compared to our experiments, they are close to smoldering of unwashed rice husk (89.0%, 67.7 m2/g); different from muffle furnace burning (600 °C, 2 h) of washed (93.4%, 164.9 m2/g) and un-washed (90.2%, 45.7 m2/g) rice husk. The specific surface area is higher than those from most industrial methods (from 11.4 to 39.3 m2/g). After some improvements, the smoldering process has great potential in mass product of high quality silica directly from moist rice husk.
Solid-State Transformers: Fundamentals, Topologies, Applications, and Future Challenges
Authors: Khan Saniya , Rahman Khaliqur , Tariq Mohd , Hameed Salman , Alamri Basem , Babu Thanikanti Sudhakar
Published: December 2021

Solid-state transformers (SSTs) have emerged as a superior alternative to conventional transformers and are regarded as the building block of the future smart grid. They incorporate power electronics circuitry and high-frequency operation, which allows high controllability and enables bi-directional power flow, overcoming the limitations of conventional transformers. This paper presents a detailed analysis of the solid-state transformer, expounding the fundamentals, converter topologies, applications, and future challenges of the SST in a systematic manner. The paper discusses the necessity of improved replacement of the low-frequency transformers (LFTs) and presents the configuration of SST. It presents SST fundamentals in individual stages and explores its origin and evolution. The basic topologies, their specifications, and control strategies are also described. The applications of SST as a replacement of LFTs are discussed along with recent applications. The future challenges for real-time implementation of SSTs are explored, and research directions are proposed.
Circularity in the New Gravity—Re-Thinking Vernacular Architecture and Circularity
Authors: Dabaieh Marwa , Maguid Dalya , El-Mahdy Deena
Published: December 2021

The mounting climate change crisis and the rapid urbanization of cities have pressured many practitioners, policymakers, and even private investors to develop new policies, processes, and methods for achieving more sustainable construction methods. Buildings are considered to be among the main contributors to harmful environmental impacts, resource consumption, and waste generation. The concept of a circular economy (CE), also referred to as “circularity”, has gained a great deal of popularity in recent years. CE, in the context of the building industry, is based on the concept of sustainable construction, which calls for reducing negative environmental impacts while providing a healthier indoor environment and closing material loops. Both vernacular architecture design strategies and circular economy principles share many of the same core concepts. This paper aims at investigating circular economy principles in relation to vernacular architecture principles in the built environment. The study demonstrates how circular principles can be achieved through the use of vernacular construction techniques and using local building materials. This paper will focus on Egypt as one of the oldest civilizations in the world, with a wide vernacular heritage, exploring how circularity is rooted in old vernacular settlements and how it can inspire contemporary circular practices.
Modeling the Impact of High Temperature on Mortality in Pakistan
Authors: Khan Barakzai Mushtaq Ahmad , Burney S.M. Aqil
Published: December 2021

The objective of this paper is to model and study the impact of high temperature on mortality in Pakistan. For this purpose, we have used mortality and climate data consisting of maximum temperature, variation in monthly temperature, average rainfall, humidity, dewpoint, as well as average air pressure in the country over the period from 2000 to 2019. We have used the Generalized Linear Model with Quasi-Poisson link function to model the number of deaths in the country and to assess the impact of maximum temperature on mortality. We have found that the maximum temperature in the country has a significant impact on mortality. The number of deaths in Pakistan increases as the maximum temperature increases. We found that, as the maximum temperature increase beyond 30 °C, mortality increases significantly. Our results indicate that mortality increases by 27% when the maximum temperature in the country increases from medium category to a very high level. Similarly, the number of deaths in the country increases by 11% when the temperature increases from medium temperature to high level. Furthermore, our study found that when the maximum temperature in the country decreases from a medium level to a low level, the number of deaths in the country decreases by 23%. This study does not consider the impact of other factors on mortality, such as age, medical conditions, gender, geographical location, as well as variability of temperature across the country.
Simulating Potential Impacts of Future Climate Change on Post-Rainy Season Sorghum Yields in India
Authors: Chadalavada Keerthi , Gummadi Sridhar , Kundeti Koteswara Rao , Kadiyala Dakshina Murthy , Deevi Kumara Charyulu , Dakhore Kailas Kamaji , Bollipo Diana Ranjitha Kumari , Thiruppathi Senthil Kumar
Published: December 2021

Given the wide use of the multi-climate model mean (MMM) for impact assessment studies, this work examines the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating the features of Indian summer monsoons as well as the post-rainy seasons for assessing the possible impacts of climate change on post-rainy season sorghum crop yields across India. The MMM simulations captured the spatial patterns and annual cycles of rainfall and surface air temperatures. However, bias was observed in the precipitation amounts and daily rainfall intensity. The trends in the simulations of MMM for both precipitation and temperatures were less satisfactory than the observed climate means. The Crop Environment Resource Synthesis (CERES)-sorghum model was used to estimate the potential impacts of future climate change on post-rainy season sorghum yield values. On average, post-rainy season sorghum yields are projected to vary between −4% and +40% as well as +10% and +59% in the near future (2040–2069) for RCP 4.5 and RCP 8.5, respectively, and between +20% and +70% (RCP 4.5) as well as +38% and +89% (RCP 8.5) in the far future (2070–2099). Even though surface air temperatures are increasing in future climate change projections, the findings suggest that an increase in the post-rainy season sorghum yields was due to an increase in the rainfall amounts up to 23% and an increase in the atmospheric CO2 levels by the end of the 21st century. The results suggest that the projected climate change during the post-rainy season over India is an opportunity for smallholders to capitalize on the increase in rainfall amounts and further increase sorghum yields with appropriate crop management strategies.