bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Roasting, Boiling, and Frying Processing on 29 Polyphenolics and Antioxidant Activity in Seeds and Shells of Sweet Chestnut (Castanea sativa Mill.)
Authors: Mustafa Ahmed M. , Abouelenein Doaa , Acquaticci Laura , Alessandroni Laura , Abd-Allah Rehab H. , Borsetta Germana , Sagratini Gianni , Maggi Filippo , Vittori Sauro , Caprioli Giovanni
Published: October 2021

Sweet chestnuts (Castanea sativa Mill.) are highly prized nuts, and the consumption of fresh chestnuts is usually preceded by roasting, boiling, and frying. The aim of this work was to simultaneously analyze 29 polyphenolic compounds for the first time in raw, boiled, roasted, and fried chestnut seeds and shells using HPLC-MS/MS. Principal component analysis depending on the HPLC-MS/MS results showed that roasting, boiling, and frying affected the contents of 25 detected phenolic compounds in a unique way, of which the most notable phenolics were gallic acid, ellagic acid, and (+)-catechin. Additionally, total polyphenolic content (TPC) was measured via the Folin–Ciocalteu method, and TPC in seeds and inner and outer shells was increased in all treatments except for microwave-roasted seeds. Furthermore, the higher TPC in the inner and outer shells when compared to seeds supported their higher antioxidant activity (AOA) determined via the DPPH experiment. AOA of seeds was increased in all treatments, while the AOA of shells was higher in roasting and lower in boiling and frying treatments. The assessment of these changes is necessary so that chestnut seed consumption and the recycling of their shells as a natural source of antioxidants can be maximized.
Seed Priming Boost Adaptation in Pea Plants under Drought Stress
Authors: Arafa Sally A. , Attia Kotb A. , Niedbała Gniewko , Piekutowska Magdalena , Alamery Salman , Abdelaal Khaled , Alateeq Talal K. , A. M. Ali Mohamed , Elkelish Amr , Attallah Shreen Y.
Published: October 2021

In the present investigation, we study the effect of Bacillus thuringiensis MH161336 (106–8 CFU/cm3), silicon (25 mL L−1), and carrot extract (75 mL L−1) as seed primers, individually or in combination, on morphological, physio-biochemical and yield components of drought-stressed pea plants (Master B) during 2019/2020 and 2020/2021 seasons. Our results indicated that drought causes a remarkable reduction in plant height, leaf area, number of leaves per plant, and number of flowers per plant in stressed pea plants during two seasons. Likewise, number of pods, pod length, seeds weight of 10 dried plants, and dry weight of 100 seeds were decreased significantly in drought-stressed pea plants. Nevertheless, seed priming with the individual treatments or in combination boosted the morphological, physio-biochemical, and yield characters of pea plants. The best results were obtained with the Bacillus thuringiensis + carrot extract treatment, which led to a remarkable increase in the number of leaves per plant, leaf area, plant height, and number of flowers per plant in stressed pea plants in both seasons. Moreover, pod length, number of seeds per pod, seeds weight of 10 dried plants, and dry weight of 100 seeds were significantly increased as well. Bacillus thuringiensis + carrot extract treatment led to improved biochemical and physiological characters, such as relative water content, chlorophyll a, chlorophyll b, regulated the up-regulation of antioxidant enzymes, increased seed yield, and decreased lipid peroxidation and reactive oxygen species, mainly superoxide and hydrogen peroxide, in drought-stressed pea plants.
Acidic and Heat Processing of Egg Yolk Dispersions
Authors: Aguilar José Manuel , Felix Manuel , López-González Yolanda , Cordobés Felipe , Guerrero Antonio
Published: October 2021

Egg yolk is a multifunctional ingredient widely used in many food products, wherein proteins are the dominant component contributing to this functionality. However, the potential risk of foodborne illness associated with egg use forces us to ensure that foodstuffs based on egg yolk are managed in a safe and sanitary manner. Lowering the pH under a certain value by adding acids could serve this purpose, but it can also greatly modify the rheological and functional properties of egg yolk. This research aims to assess the influence of citric acid on the rheological properties and microstructure of chicken egg yolk dispersions and their heat-set gels. The dispersions were prepared from fresh hen’s eggs yolks by adding water or citric acid to obtain a technical yolk (45 wt.% in solids) at the desired pH value. Viscoelastic measurements were carried out using a control stress rheometer, and microstructure was evaluated by cryo-scanning electronic microscopy (CryoSEM). An evolution of the viscoelastic properties of egg yolk dispersions from fluid to gel behavior was observed as the pH decreased until 2 but showing a predominantly fluid behavior at pH 3. The profile of viscoelastic properties along the thermal cycle applied is modified to a great extent, also showing a strong dependence on pH. Thus, the sol–gel transition can be modulated by the pH value.
A Pragmatics of Ritual: The Yoshida Goma at the Interface of Shintō and Shingon
Authors: Payne Richard K.
Published: October 2021

Drawing on practices and teachings from Daoism, neo-Confucianism, and tantric Buddhism, Yoshida Kanetomo (1435–1511) created the system of Yuiitsu Shintō, also known eponymously as Yoshida Shintō, all the while making claims for Shintō as the world’s original religion. Important for the establishment of Yoshida Shintō was the creation of a program of rituals. This essay examines one of the three rituals created for the Yoshida ritual program, the Yoshida Shintō goma ritual, which hybridizes tantric Buddhist ritual organization and Daoist symbolism. A pragmatics of ritual is developed as a means of identifying the factors that Yoshida felt were salient in presenting the goma as a Yoshida Shintō ritual.
Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time
Authors: Gholami Akram , Homayouni Taymaz , Ehsani Reza , Sun Jian-Qiao
Published: October 2021

This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.
Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set
Authors: Krasteva Vessela , Christov Ivaylo , Naydenov Stefan , Stoyanov Todor , Jekova Irena
Published: October 2021

Considering the significant burden to patients and healthcare systems globally related to atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the view of prominent perspectives for fast and accurate point-of-care arrhythmia detection, our study optimizes an artificial neural network (NN) classifier and ranks the importance of enhanced 137 diagnostic ECG features computed from time and frequency ECG signal representations of short single-lead strips available in 2017 Physionet/CinC Challenge database. Based on hyperparameters’ grid search of densely connected NN layers, we derive the optimal topology with three layers and 128, 32, 4 neurons per layer (DenseNet-3@128-32-4), which presents maximal F1-scores for classification of Normal rhythms (0.883, 5076 strips), AF (0.825, 758 strips), Other rhythms (0.705, 2415 strips), Noise (0.618, 279 strips) and total F1 relevant to the CinC Challenge of 0.804, derived by five-fold cross-validation. DenseNet-3@128-32-4 performs equally well with 137 to 32 features and presents tolerable reduction by about 0.03 to 0.06 points for limited input sets, including 8 and 16 features, respectively. The feature reduction is linked to effective application of a comprehensive method for computation of the feature map importance based on the weights of the activated neurons through the total path from input to specific output in DenseNet. The detailed analysis of 20 top-ranked ECG features with greatest importance to the detection of each rhythm and overall of all rhythms reveals DenseNet decision-making process, noticeably corresponding to the cardiologists’ diagnostic point of view.
Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles
Authors: Roberge Vincent , Tarbouchi Mohammed
Published: October 2021

This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k-means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source–Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3× speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU.
Embedded Transdermal Alcohol Detection via a Finger Using SnO2 Gas Sensors
Authors: Annanouch Fatima Ezahra , Martini Virginie , Fiorido Tomas , Lawson Bruno , Aguir Khalifa , Bendahan Marc
Published: October 2021

In this paper, we report the fabrication and characterization of a portable transdermal alcohol sensing device via a human finger, using tin dioxide (SnO2) chemoresistive gas sensors. Compared to conventional detectors, this non-invasive technique allowed us the continuous monitoring of alcohol with low cost and simple fabrication process. The sensing layers used in this work were fabricated by using the reactive radio frequency (RF) magnetron sputtering technique. Their structure and morphology were investigated by means of X-ray spectroscopy (XRD) and scanning electron microscopy (SEM), respectively. The results indicated that the annealing time has an important impact on the sensor sensitivity. Before performing the transdermal measurements, the sensors were exposed to a wide range of ethanol concentrations and the results displayed good responses with high sensitivity, stability, and a rapid detection time. Moreover, against high relative humidity (50% and 70%), the sensors remained resistant by showing a slight change in their gas sensing performances. A volunteer (an adult researcher from our volunteer group) drank 50 mL of tequila in order to realize the transdermal alcohol monitoring. Fifteen minutes later, the volunteer’s skin started to evacuate alcohol and the sensor resistance began to decline. Simultaneously, breath alcohol measurements were attained using a DRAGER 6820 certified breathalyzer. The results demonstrated a clear correlation between the alcohol concentration in the blood, breath, and via perspiration, which validated the embedded transdermal alcohol device reported in this work.
Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors
Authors: Khaloufi Hayat , Abouelmehdi Karim , Beni-Hssane Abderrahim , Rustam Furqan , Jurcut Anca Delia , Lee Ernesto , Ashraf Imran
Published: October 2021

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.
Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models
Authors: Islam Muhammad Rafiqul , Paul Manoranjan
Published: October 2021

Video analytics and computer vision applications face challenges when using video sequences with low visibility. The visibility of a video sequence is degraded when the sequence is affected by atmospheric interference like rain. Many approaches have been proposed to remove rain streaks from video sequences. Some approaches are based on physical features, and some are based on data-driven (i.e., deep-learning) models. Although the physical features-based approaches have better rain interpretability, the challenges are extracting the appropriate features and fusing them for meaningful rain removal, as the rain streaks and moving objects have dynamic physical characteristics and are difficult to distinguish. Additionally, the outcome of the data-driven models mostly depends on variations relating to the training dataset. It is difficult to include datasets with all possible variations in model training. This paper addresses both issues and proposes a novel hybrid technique where we extract novel physical features and data-driven features and then combine them to create an effective rain-streak removal strategy. The performance of the proposed algorithm has been tested in comparison to several relevant and contemporary methods using benchmark datasets. The experimental result shows that the proposed method outperforms the other methods in terms of subjective, objective, and object detection comparisons for both synthetic and real rain scenarios by removing rain streaks and retaining the moving objects more effectively.
MoHRiPA—An Architecture for Hybrid Resources Management of Private Cloud Environments
Authors: Andreazi Gabriel Tomiatti , Estrella Júlio Cezar , Bruschi Sarita Mazzini , Immich Roger , Guidoni Daniel , Alves Pereira Júnior Lourenço , Meneguette Rodolfo Ipolito
Published: October 2021

The high demand for data processing in web applications has grown in recent years due to the increased computing infrastructure supply as a service in a cloud computing ecosystem. This ecosystem offers benefits such as broad network access, elasticity, and resource sharing, among others. However, properly exploiting these benefits requires optimized provisioning of computational resources in the target infrastructure. Several studies in the literature improve the quality of this management, which involves enhancing the scalability of the infrastructure, either through cost management policies or strategies aimed at resource scaling. However, few studies adequately explore performance evaluation mechanisms. In this context, we present the MoHRiPA—Management of Hybrid Resources in Private cloud Architecture. MoHRiPA has a modular design encompassing scheduling algorithms, virtualization tools, and monitoring tools. The proposed architecture solution allows assessing the overall system’s performance by using complete factorial planning to identify the general behavior of architecture under high demand of requests. It also evaluates workload behavior, the number of virtualized resources, and provides an elastic resource manager. A composite metric is also proposed and adopted as a criterion for resource scaling. This work presents a performance evaluation by using formal techniques, which analyses the scheduling algorithms of architecture and the experiment bottlenecks analysis, average response time, and latency. In summary, the proposed MoHRiPA mapping resources algorithm (HashRefresh) showed significant improvement results than the analyzed competitor, decreasing about 7% percent in the uniform average compared to ListSheduling (LS).
Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework
Authors: Cheng Chen-Yang , Pourhejazy Pourya , Hung Chia-Yu , Yuangyai Chumpol
Published: October 2021

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.
Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements
Authors: Dulău Marius , Oniga Florin
Published: October 2021

In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.
An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition
Authors: Kain Raslan , Hajj Hazem
Published: October 2021

Mobile devices and sensors have limited battery lifespans, limiting their feasibility for context recognition applications. As a result, there is a need to provide mechanisms for energy-efficient operation of sensors in settings where multiple contexts are monitored simultaneously. Past methods for efficient sensing operation have been hierarchical by first selecting the sensors with the least energy consumption, and then devising individual sensing schedules that trade-off energy and delays. The main limitation of the hierarchical approach is that it does not consider the combined impact of sensor scheduling and sensor selection. We aimed at addressing this limitation by considering the problem holistically and devising an optimization formulation that can simultaneously select the group of sensors while also considering the impact of their triggering schedule. The optimization solution is framed as a Viterbi algorithm that includes mathematical representations for multi-sensor reward functions and modeling of user behavior. Experiment results showed an average improvement of 31% compared to a hierarchical approach.
Binary Neural Network for Automated Visual Surface Defect Detection
Authors: Liu Wenzhe , Zhang Jiehua , Su Zhuo , Zhou Zhongzhu , Liu Li
Published: October 2021

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.
A Propagation Study of LoRa P2P Links for IoT Applications: The Case of Near-Surface Measurements over Semitropical Rivers
Authors: Gutiérrez-Gómez Amado , Rangel Víctor , Edwards Robert M. , Davis John G. , Aquino Raúl , López-De la Cruz Jesús , Mendoza-Cano Oliver , Lopez-Guerrero Miguel , Geng Yu
Published: October 2021

Internet of Things (IoT) radio networks are becoming popular in several scenarios for short-range applications (e.g., wearables and home security) and medium-range applications (e.g., shipping container tracking and autonomous farming). They have also been proposed for water monitoring in flood warning systems. IoT communications may use long range (LoRa) radios working in the 915 MHz industrial, scientific and medical (ISM) band. In this research, we study the propagation characteristics of LoRa chirp radio signals close to and over water in a tropical meadow region. We use as a case study the Colima River in Mexico. We develop a novel point-to-point IoT measurement sounding system that does not require decoding of LoRa propriety bursts and provides accurate power versus distance profiles along the riparian zone of a steeply dropping mountain river. We used this system to obtain the measurements reported in this work, which are also analyzed and modeled. The results show that the LoRa signal propagation over water exhibits a log-normal distribution. As a result of the chirp signal processing, two new experimental path loss models are presented. The path loss results show a considerable degradation of the received signal power over water within vegetation and less signal degradation at antenna heights closer to the water surface.
Dual Crisscross Attention Module for Road Extraction from Remote Sensing Images
Authors: Chen Chuan , Zhao Huilin , Cui Wei , He Xin
Published: October 2021

Traditional pixel-based semantic segmentation methods for road extraction take each pixel as the recognition unit. Therefore, they are constrained by the restricted receptive field, in which pixels do not receive global road information. These phenomena greatly affect the accuracy of road extraction. To improve the limited receptive field, a non-local neural network is generated to let each pixel receive global information. However, its spatial complexity is enormous, and this method will lead to considerable information redundancy in road extraction. To optimize the spatial complexity, the Crisscross Network (CCNet), with a crisscross shaped attention area, is applied. The key aspect of CCNet is the Crisscross Attention (CCA) module. Compared with non-local neural networks, CCNet can let each pixel only perceive the correlation information from horizontal and vertical directions. However, when using CCNet in road extraction of remote sensing (RS) images, the directionality of its attention area is insufficient, which is restricted to the horizontal and vertical direction. Due to the recurrent mechanism, the similarity of some pixel pairs in oblique directions cannot be calculated correctly and will be intensely dilated. To address the above problems, we propose a special attention module called the Dual Crisscross Attention (DCCA) module for road extraction, which consists of the CCA module, Rotated Crisscross Attention (RCCA) module and Self-adaptive Attention Fusion (SAF) module. The DCCA module is embedded into the Dual Crisscross Network (DCNet). In the CCA module and RCCA module, the similarities of pixel pairs are represented by an energy map. In order to remove the influence from the heterogeneous part, a heterogeneous filter function (HFF) is used to filter the energy map. Then the SAF module can distribute the weights of the CCA module and RCCA module according to the actual road shape. The DCCA module output is the fusion of the CCA module and RCCA module with the help of the SAF module, which can let pixels perceive local information and eight-direction non-local information. The geometric information of roads improves the accuracy of road extraction. The experimental results show that DCNet with the DCCA module improves the road IOU by 4.66% compared to CCNet with a single CCA module and 3.47% compared to CCNet with a single RCCA module.
Comparison of DOFS Attachment Methods for Time-Dependent Strain Sensing
Authors: Wang Shaoquan , Sæter Erik , Lasn Kaspar
Published: October 2021

Structural health monitoring (SHM) is a challenge for many industries. Over the last decade, novel strain monitoring methods using optical fibers have been implemented for SHM in aerospace, energy storage, marine, and civil engineering structures. However, the practical attachment of optical fibers (OFs) to the component is still problematic. While monitoring, the amount of substrate strain lost by the OF attachment is often unclear, and difficult to predict under long-term loads. This investigation clarifies how different attachment methods perform under time-dependent loading. Optical fibers are attached on metal, thermoset composite, and thermoplastic substrates for distributed strain sensing. Strains along distributed optical fiber sensors (DOFS) are measured by optical backscatter reflectometry (OBR) and compared to contact extensometer strains under tensile creep loading. The quality of the bondline and its influence on the strain transfer is analyzed. Residual strains and strain fluctuations along the sensor fiber are correlated to the fiber attachment method. Results show that a machine-controlled attachment process (such as in situ 3-D printing) holds great promise for the future as it achieves a highly uniform bondline and provides accurate strain measurements.
A Dynamic Threshold Cancellation Technique for a High-Power Conversion Efficiency CMOS Rectifier
Authors: Godinho António , Yang Zhaochu , Dong Tao , Gonçalves Luís , Mendes Paulo , Wen Yumei , Li Ping , Jiang Zhuangde
Published: October 2021

Power conversion efficiency (PCE) has been one of the key concerns for power management circuits (PMC) due to the low output power of the vibrational energy harvesters. This work reports a dynamic threshold cancellation technique for a high-power conversion efficiency CMOS rectifier. The proposed rectifier consists of two stages, one passive stage with a negative voltage converter, and another stage with an active diode controlled by a threshold cancellation circuit. The former stage conducts the signal full-wave rectification with a voltage drop of 1 mV, whereas the latter reduces the reverse leakage current, consequently enhancing the output power delivered to the ohmic load. As a result, the rectifier can achieve a voltage and power conversion efficiency of over 99% and 90%, respectively, for an input voltage of 0.45 V and for low ohmic loads. The proposed circuit is designed in a standard 130 nm CMOS process and works for an operating frequency range from 800 Hz to 51.2 kHz, which is promising for practical applications.
Adaptive Segmentation of Streaming Sensor Data on Edge Devices
Authors: Dębski Roman , Dreżewski Rafał
Published: October 2021

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.
Design and Implementation of Symmetric Legged Robot for Highly Dynamic Jumping and Impact Mitigation
Authors: Wang Lei , Meng Fei , Kang Ru , Sato Ryuki , Chen Xuechao , Yu Zhangguo , Ming Aiguo , Huang Qiang
Published: October 2021

Aiming at highly dynamic locomotion and impact mitigation, this paper proposes the design and implementation of a symmetric legged robot. Based on the analysis of the three-leg topology in terms of force sensitivity, force production, and impact mitigation, the symmetric leg was designed and equipped with a high torque density actuator, which was assembled by a custom motor and two-stage planetary. Under the kinematic and dynamic constraints of the robot system, a nonlinear optimization for high jumping and impact mitigation is proposed with consideration of the peak impact force at landing. Finally, experiments revealed that the robot achieved a jump height of 1.8 m with a robust landing, and the height was equal to approximately three times the leg length.
Modelling of Social Policy and Initiatives under COVID-19: Rural NEET Youth Case Study
Authors: Kvieskienė Giedrė , Ivanova Ilze , Trasberg Karmen , Stasytytė Viktorija , Celiešienė Eglė
Published: October 2021

NEET (Not in Education, Employment, or Training) youth rates in Europe are generally higher in rural regions than in urban areas and the share in rural regions is constantly increasing. During the COVID-19 pandemic, young people became even more vulnerable as they experienced social exclusion and mental health problems. The objective of this paper is to analyse NEET youth-related statistics in Europe and distinguish positive initiatives for young people in rural areas of the Baltic countries to encourage positive emotions and willingness to learn. Statistical analysis and case study methods were employed. Data on youth unemployment, NEET youth by age and gender, and poverty and social exclusion of young people, is analysed. Social policy initiatives in Lithuania, Latvia, and Estonia, mainly from rural municipalities, are presented and discussed. This research determines the key issues related to NEET youth and proposes initiatives to overcome existing problems among young people. Such social initiatives aim to promote positive social emotions of youth, promote their inclusion in society, and foster regional sustainability.
Conditional Inference in Small Sample Scenarios Using a Resampling Approach
Authors: Draxler Clemens , Kurz Andreas
Published: October 2021

This paper discusses a non-parametric resampling technique in the context of multidimensional or multiparameter hypothesis testing of assumptions of the Rasch model. It is based on conditional distributions and it is suggested in small sample size scenarios as an alternative to the application of asymptotic or large sample theory. The exact sampling distribution of various well-known chi-square test statistics like Wald, likelihood ratio, score, and gradient tests as well as others can be arbitrarily well approximated in this way. A procedure to compute the power function of the tests is also presented. A number of examples of scenarios are discussed in which the power function of the test does not converge to 1 with an increasing deviation of the true values of the parameters of interest from the values specified in the hypothesis to be tested. Finally, an attempt to modify the critical region of the tests is made aiming at improving the power and an R package is provided.
Knowledge Management Infrastructure Framework for Enhancing Knowledge-Intensive Business Processes
Authors: Aviv Itzhak , Hadar Irit , Levy Meira
Published: October 2021

In this age of digital transformation, knowledge-intensive organizations strive to improve business outcomes and sustainability by improving their knowledge-intensive business processes (KIBPs) to obtain a competitive advantage. Many researchers have claimed that KIBP enhancement is possible through knowledge management (KM) initiatives supported by an effective KM infrastructure. Current KM infrastructures deal with formal KM procedures, where knowledge is created, stored, assimilated, and disseminated. Such activities, however, are designed to be performed outside of the business process. KM infrastructures are expected to be more effective when they include specific real-time knowledge procedures integrated into the operational flow of KIBPs. This paper explores how modern KM infrastructures can support KIBPs, considering both formal and operational KM procedures. Our study’s essential contribution is the conceptual KM infrastructure framework (KMIF) developed based on grounded theory research. This infrastructure provides a systematic and robust approach, starting from the ground up, for structuring organizational knowledge assets across a range of KIBP environments. We define operational knowledge procedures directly involved in KIBPs, adding a layer to KM infrastructures beyond the formal knowledge procedures. A mixed-method case study was conducted to demonstrate and evaluate the proposed KMIF for enhancing business outcomes and sustainability of knowledge-intensive organizations.
A Framework of Unsustainable Behaviors to Support Product Eco-Design
Authors: Balikci Anil , Borgianni Yuri , Maccioni Lorenzo , Nezzi Chiara
Published: October 2021

Eco-designed products can contribute to sustainable development if consumers choose them rather than the less environmentally friendly alternatives and if they are used properly. However, eco-design methods have so far failed to address the issue of unsustainable behaviors, whose sources have not been recognized. In light of this deficiency, the authors have analyzed a large number of eco-designed products with the aim to capture the possible unsustainable behaviors arising from their use and consumption. The subsequent characterization of unsustainable behaviors has led to the creation of a framework of unsustainable behaviors, which has been subjected to the evaluation of a pool of experts in the field. In its final version, the framework includes nine classes of unsustainable behaviors, which are categorized into the corresponding product lifecycle phases (purchase, use, end of life), and different kinds of undesired effects (harmful, insufficient, excessive) based on the TRIZ-oriented functional analysis. The classes, whose significance has been checked in the literature, include frequent causes of unsustainable behaviors and corresponding examples. Through the framework, designers can take into due account the possible circumstances that would prevent their developed products from being prone to unsustainable behaviors. In a future step, the classes of unsustainable behaviors are to be linked with indications arising from Design for Sustainable Behavior.
Determinants of Green Regeneration of Industrial Brownfield under Ecological Security Constraints: Model and Empirical Analysis Based on Grounded Theory
Authors: Zhang Yang , Ye Fangfang , Zhang Guangmin
Published: October 2021

Green regeneration of industrial brownfields (GRIB) is an inevitable choice under the collision of industrial structure adjustment and ecological civilization construction. Due to vegetation destruction and industrial pollution, the integrity and health of the ecosystem in the industrial brownfield have been destroyed and ecological security has become a primary factor in restricting GRIB. In order to explore the impact mechanism of GRIB under ecological security constraints, based on the original data obtained from in-depth interviews with 21 professionals, this study examines the applicability of DPSIR model in GRIB by using the grounded theory method to sort the determinants and explore the impact mechanism of GRIB under ecological security constraints from five dimensions: driving forces (incentive factor), pressure (external factor), state (internal factor), influence (produced comprehensive result), and response (substantive response of human society). Suggestions are made to strengthen the investigation and remediation of environmental pollution in industrial brownfield, cultivate the concept and awareness of green regeneration, and formulate incentive policies. The research conclusions effectively improve the problems existing in the reconstruction of industrial brownfield as well as provide a theoretical basis and targeted reference for the promotion of GRIB.
Reliability Enhancement of Electric Distribution Network Using Optimal Placement of Distributed Generation
Authors: Ahmad Sanaullah , Asar Azzam ul
Published: October 2021

As energy demand is increasing, power systems’ complexities are also increasing. With growing energy demand, new ways and techniques are formulated by researchers to increase the efficiency and reliability of power systems. A distribution system, which is one of the most important entities in a power system, contributes up to 90% of reliability problems. For a sustainable supply of power to customers, the distribution system reliability must be enhanced. Distributed generation (DG) is a new way to improve distribution system reliability by bringing generation nearer to the load centers. Artificial intelligence (AI) is an area in which much innovation and research is going on. Different scientific areas are utilizing AI techniques to enhance system performance and reliability. This work aims to apply DG as a distributed source in a distribution system to evaluate its impacts on reliability. The location of the DG is a design criteria problem that has a relevant effect on the reliability of the distribution system. As the distance of load centers from the feeder increases, outage durations also increase. The reliability was enhanced, as the SAIFI value was reduced by almost 40%, the SAIDI value by 25%, and the EENS value by 25% after injecting DG into the distribution network. The artificial neural network (ANN) technique was utilized to find the optimal location of the DG; the results were validated by installing DG at prescribed localities. The results showed that the injection of DG at proper locations enhances the reliability of a distribution system. The proposed approach was applied to thte Roy Billinton Test System (RBTS). The implementation of the ANN technique is a unique approach to the selection of a location for a DG unit, which confirms that applying this computational technique could decrease human errors that are associated with the hit and trial methods and could also decrease the computational complexities and computational time. This research can assist distribution companies in determining the reliability of an actual distribution system for planning and expansion purposes, as well as in injecting a DG at the most optimal location in order to enhance the distribution system reliability.
Application of Global Reporting Initiative (GRI) Principles for Measuring Quality of Corporate Social Responsibility (CSR) Disclosure: Evidence from Pakistan
Authors: Ismail Hina , Saleem Muhammad A. , Zahra Sadaf , Tufail Muhammad S. , Ali Rao Akmal
Published: October 2021

CSR Reporting is an essential mechanism for ensuring the transparency and accountability of companies towards sustainability performance. To further promote that sustainable development agenda, CSR-related regulations and policies have emerged worldwide, including in Pakistan. Therefore this study assesses the quality of corporate social responsibility in annual reports issued by firms listed at the Pakistan Stock Exchange. This study has operationalized the Global Reporting Initiative (GRI) principles for examining the quality of CSR disclosures. The paper sample comprised 540 annual reports of 90 financial or non-financial companies from the years 2012 to 2017. Content analysis is performed to look for six quality principles and measures, i.e., balance, comparability, accuracy, clarity, reliability, and timeliness. Results suggested that most Pakistani firms provide precise and on-time information and put less emphasis on the balance of information and comparable information. Moreover, this study also highlighted that organizations should implement the GRI principle for disclosing qualitative CSR report.
A Systematic Review on Adaptation Practices in Aquaculture towards Climate Change Impacts
Authors: Abu Samah Asnarulkhadi , Shaffril Hayrol Azril Mohamed , Fadzil Mohd Fauzi , Ahmad Nobaya , Idris Khairuddin
Published: October 2021

The impact of climate change is one of the many challenges faced by aquaculture communities nowadays. Only a limited number of articles have attempted to systematically review available literature in this field, which has led to the current study, aiming to develop a systematic review related to the practice of adapting to climate change among aquaculture communities. This systematic review was guided by the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) in its systematic searching strategy on Scopus, Web of Science, and Google Scholar, resulting in the selection of 20 related studies. All selected articles were assessed using the Mixed Method Appraisal Tool (MMAT). Employing thematic analysis, five main themes resulted, namely Governmental (five subthemes), Community (three subthemes), Facilities (five subthemes), Temperature (two subthemes) and Financial (three subthemes), along with 18 subthemes. Based on the pattern of previous studies, the review presented several recommendations for scholars, agencies, and communities to consider in future.
Child-to-Parent Violence during Confinement Due to COVID-19: Relationship with Other Forms of Family Violence and Psychosocial Stressors in Spanish Youth
Authors: Cano-Lozano M. Carmen , Navas-Martínez María J. , Contreras Lourdes
Published: October 2021

This study analyzes child-to-parent violence during strict confinement due to COVID-19 as well as its association with other forms of family violence (parent-to-child violence and exposure to violence between parents) and with different psychosocial stressors: Academics/work, family coexistence, finances, COVID-19 and physical and psychological health. The study included 2245 young people (52.8% females) aged between 18 and 25 years (M = 21.52 years, SD = 2.07 years). The results show that more than half of the young people reported having performed at least one violent behavior toward their parents. Child-to-parent violence is significantly related to violence from parents to their young adult children, to exposure to violence between parents and to different psychosocial stressors: Academics/work, family coexistence and psychological health. The individual and additive predictive value of other forms of family violence and psychosocial stressors in child-to-parent violence is confirmed. Prevention and intervention programs for child-to-parent violence must take into account the role of other forms of family violence present as well as the influence of psychosocial stressors.
Waste Electrical and Electronic Equipment Reutilization in China
Authors: Wang Rong , Deng Yi , Li Shuyuan , Yu Keli , Liu Yi , Shang Min , Wang Jiqin , Shu Jiancheng , Sun Zhi , Chen Mengjun , Liang Qian
Published: October 2021

Waste electrical and electronic equipment (WEEE), also called electronic waste or e-waste, the core of “urban mining”, is attracting more and more attention to its pollution control and circular recycling. Hence, we defined WEEE, preliminarily discussed its history in China and pointed out that China has made great achievements in WEEE circular reutilization and pollution control. Meanwhile, we analyzed the four levels of circular WEEE recycling: repair, reuse and remanufacture, waste-to-materials, waste-to-products and waste-to-energy, and also put forward questions during this process. Moving forward, WEEE management will turn to intelligent management targeted on hazardous waste and other pollution, not merely the guidelines. Meanwhile, WEEE technology will transfer to value-added and automated reutilization, not just simple dismantling.