bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Edible Insects in Rural Livelihoods, and Identified Challenges in Vhembe District, Limpopo, South Africa
Authors: Hlongwane Zabentungwa T. , Slotow Rob , Munyai Thinandavha C.
Published: December 2021

Edible insects are an important natural commodity in rural areas that is used for household consumption and to generate income through trade. As a result, edible-insect trading is a profitable business that provides employment and improves the livelihoods of impoverished rural people. This study aimed at determining the socioeconomic benefits of and reasons for trading insects, and to assess if edible insects are included in economic development strategies in the Vhembe district of Limpopo province, South Africa. We conducted 72 questionnaire interviews targeting traders in 5 towns across the district. Five insect groups belonging to four insect orders are traded in informal markets of the district. Mopane worms (Gonimbrasia belina) were the most traded (42%) edible insects. Unemployment (45%) and the demand for edible insects (34%) were the major reasons for trading insects. Insect trading has numerous benefits; however, the provision of income (60%) and financial support (35%) were stated as the primary benefits. Despite several benefits associated with trading in insects, there are many challenges such as insect spoilage and a decline in the availability of edible insects in the wild. Edible insects play an important role in food security and the rural economy by generating employment opportunities for unemployed traders. Trading in insects is a traditional practice based on indigenous knowledge, which has persisted as an economic practice that improves rural livelihoods by reducing poverty and increases the human dignity of rural citizens. Only four governmental organisations in Limpopo included edible insects in economic development strategies. Trading insects is primarily an informal activity. The government could stimulate the activity and broaden and deepen the community benefits by providing infrastructure, access to harvest areas, financial support, and business training as part of a rural empowerment strategy to end hunger and poverty while creating employment opportunities in rural areas.
Assessment and Attribution of Mangrove Forest Changes in the Indian Sundarbans from 2000 to 2020
Authors: Samanta Sourav , Hazra Sugata , Mondal Partho P. , Chanda Abhra , Giri Sandip , French Jon R. , Nicholls Robert J.
Published: December 2021

The Indian Sundarbans, together with Bangladesh, comprise the largest mangrove forest in the world. Reclamation of the mangroves in this region ceased in the 1930s. However, they are still subject to adverse environmental influences, such as sediment starvation due to migration of the main river channels in the Ganges–Brahmaputra delta over the last few centuries, cyclone landfall, wave action from the Bay of Bengal—changing hydrology due to upstream water diversion—and the pervasive effects of relative sea-level rise. This study builds on earlier work to assess changes from 2000 to 2020 in mangrove extent, genus composition, and mangrove ‘health’ indicators, using various vegetation indices derived from Landsat and MODIS satellite imagery by performing maximum likelihood supervised classification. We show that about 110 km2 of mangroves disappeared within the reserve forest due to erosion, and 81 km2 were gained within the inhabited part of Sundarbans Biosphere Reserve (SBR) through plantation and regeneration. The gains are all outside the contiguous mangroves. However, they partially compensate for the losses of the contiguous mangroves in terms of carbon. Genus composition, analyzed by amalgamating data from published literature and ground-truthing surveys, shows change towards more salt-tolerant genus accompanied by a reduction in the prevalence of freshwater-loving Heiritiera, Nypa, and Sonneratia assemblages. Health indicators, such as the enhanced vegetation index (EVI) and normalized differential vegetation index (NDVI), show a monotonic trend of deterioration over the last two decades, which is more pronounced in the sea-facing parts of the mangrove forests. An increase in salinity, a temperature rise, and rainfall reduction in the pre-monsoon and the post-monsoon periods appear to have led to such degradation. Collectively, these results show a decline in mangrove area and health, which poses an existential threat to the Indian Sundarbans in the long term, especially under scenarios of climate change and sea-level rise. Given its unique values, the policy process should acknowledge and address these threats.
A Residual Attention and Local Context-Aware Network for Road Extraction from High-Resolution Remote Sensing Imagery
Authors: Liu Ziwei , Wang Mingchang , Wang Fengyan , Ji Xue
Published: December 2021

Extracting road information from high-resolution remote sensing images (HRI) can provide crucial geographic information for many applications. With the improvement of remote sensing image resolution, the image data contain more abundant feature information. However, this phenomenon also enhances the spatial heterogeneity between different types of roads, making it difficult to accurately discern the road and non-road regions using only spectral characteristics. To remedy the above issues, a novel residual attention and local context-aware network (RALC-Net) is proposed for extracting a complete and continuous road network from HRI. RALC-Net utilizes a dual-encoder structure to improve the feature extraction capability of the network, whose two different branches take different feature information as input data. Specifically, we construct the residual attention module using the residual connection that can integrate spatial context information and the attention mechanism, highlighting local semantics to extract local feature information of roads. The residual attention module combines the characteristics of both the residual connection and the attention mechanism to retain complete road edge information, highlight essential semantics, and enhance the generalization capability of the network model. In addition, the multi-scale dilated convolution module is used to extract multi-scale spatial receptive fields to improve the model’s performance further. We perform experiments to verify the performance of each component of RALC-Net through the ablation study. By combining low-level features with high-level semantics, we extract road information and make comparisons with other state-of-the-art models. The experimental results show that the proposed RALC-Net has excellent feature representation ability and robust generalizability, and can extract complete road information from a complex environment.
Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications
Authors: Bernhard Maximilian , Schubert Matthias
Published: December 2021

Object detection on aerial and satellite imagery is an important tool for image analysis in remote sensing and has many areas of application. As modern object detectors require accurate annotations for training, manual and labor-intensive labeling is necessary. In situations where GPS coordinates for the objects of interest are already available, there is potential to avoid the cumbersome annotation process. Unfortunately, GPS coordinates are often not well-aligned with georectified imagery. These spatial errors can be seen as noise regarding the object locations, which may critically harm the training of object detectors and, ultimately, limit their practical applicability. To overcome this issue, we propose a co-correction technique that allows us to robustly train a neural network with noisy object locations and to transform them toward the true locations. When applied as a preprocessing step on noisy annotations, our method greatly improves the performance of existing object detectors. Our method is applicable in scenarios where the images are only annotated with points roughly indicating object locations, instead of entire bounding boxes providing precise information on the object locations and extents. We test our method on three datasets and achieve a substantial improvement (e.g., 29.6% mAP on the COWC dataset) over existing methods for noise-robust object detection.
Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility
Authors: Liu Ru , Peng Jianbing , Leng Yanqiu , Lee Saro , Panahi Mahdi , Chen Wei , Zhao Xia
Published: December 2021

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.
Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information
Authors: Chakhar Amal , Hernández-López David , Ballesteros Rocío , Moreno Miguel A.
Published: December 2021

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R2 equaled 0.5 in the principal growth stage (leaf development), but the crop R2 drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages.
Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning
Authors: Badreldin Nasem , Prieto Beatriz , Fisher Ryan
Published: December 2021

Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.
HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU
Authors: Al-azzawi Sana Sabah , Khaksar Siavash , Hadi Emad Khdhair , Agrawal Himanshu , Murray Iain
Published: December 2021

Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children’s lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2–5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up.
Evaluation of Polytyramine Film and 6-Mercaptohexanol Self-Assembled Monolayers as the Immobilization Layers for a Capacitive DNA Sensor Chip: A Comparison
Authors: Mahadhy Ally , Mattiasson Bo , StåhlWernersson Eva , Hedström Martin
Published: December 2021

The performance of a biosensor is associated with the properties of an immobilization layer on a sensor chip. In this study, gold sensor chips were modified with two different immobilization layers, polytyramine film and 6-mercaptohexanol self-assembled monolayer. The physical, electrochemical and analytical properties of polytyramine film and mercaptohexanol self-assembled monolayer modified gold sensor chips were studied and compared. The study was conducted using atomic force microscopy, cyclic voltammetry and a capacitive DNA-sensor system (CapSenze™ Biosystem). The results obtained by atomic force microscopy and cyclic voltammetry indicate that polytyramine film on the sensor chip surface possesses better insulating properties and provides more spaces for the immobilization of the capture probe than a mercaptohexanol self-assembled monolayer. A capacitive DNA sensor hosting a polytyramine single-stranded DNA-modified sensor chip displayed higher sensitivity and larger signal amplitude than that of a mercaptohexanol single-stranded DNA-modified sensor chip. The linearity responses for polytyramine single-stranded DNA- and mercaptohexanol single-stranded DNA-modified sensor chips were obtained at log concentration ranges, equivalent to 10−12 to 10−8 M and 10−10 to 10−8 M, with detection limits of 4.0 × 10−13 M and 7.0 × 10−11 M of target complementary single-stranded DNA, respectively. Mercaptohexanol single-stranded DNA- and polytyramine single-stranded DNA-modified sensor chips exhibited a notable selectivity at an elevated hybridization temperature of 50 °C, albeit the signal amplitudes due to the hybridization of the target complementary single-stranded DNA were reduced by almost 20% and less than 5%, respectively.
Refractive Index-Based Terahertz Sensor Using Graphene for Material Characterization
Authors: Veeraselvam Aruna , Mohammed Gulam Nabi Alsath , Savarimuthu Kirubaveni , Anguera Jaume , Paul Jessica Constance , Krishnan Ram Kumar
Published: December 2021

In this paper, a graphene-based THz metamaterial has been designed and characterized for use in sensing various refractive index profiles. The proposed single-band THz sensor was constructed using a graphene-metal hybridized periodic metamaterial wherein the unit cell had a footprint of 1.395λeff × 1.395λeff and resonated at 4.4754 THz. The realized peak absorption was 98.88% at 4.4754 THz. The sensitivity of the proposed metamaterial sensor was estimated using the absorption characteristics of the unit cell. The performance of the sensor was analyzed under two different categories, viz. the random dielectric loading and chemical analytes, based on the refractive index. The proposed THz sensor offered a peak sensitivity of 22.75 GHz/Refractive Index Unit (RIU) for the various sample loadings. In addition, the effect of the sample thickness on the sensor performance was analyzed and the results were presented. From the results, it can be inferred that the proposed metamaterial THz sensor that was based on a refractive index is suitable for THz sensing applications.
Fabrication and Evaluation of a Flexible MEMS-Based Microthermal Flow Sensor
Authors: Cho Myoung-Ock , Jang Woojin , Lim Si-Hyung
Published: December 2021

Based on the results of computational fluid dynamics simulations, this study designed and fabricated a flexible thermal-type micro flow sensor comprising one microheater and two thermistors using a micro-electromechanical system (MEMS) process on a flexible polyimide film. The thermistors were connected to a Wheatstone bridge circuit, and the resistance difference between the thermistors resulting from the generation of a flow was converted into an output voltage signal using LabVIEW software. A mini tube flow test was conducted to demonstrate the sensor’s detection of fluid velocity in gas and liquid flows. A good correlation was found between the experimental results and the simulation data. However, the results for the gas and liquid flows differed in that for gas, the output voltage increased with the fluid’s velocity and decreased against the liquid’s flow velocity. This study’s MEMS-based flexible microthermal flow sensor achieved a resolution of 1.1 cm/s in a liquid flow and 0.64 cm/s in a gas flow, respectively, within a fluid flow velocity range of 0–40 cm/s. The sensor is suitable for many applications; however, with some adaptations to its electrical packaging, it will be particularly suitable for detecting biosignals in healthcare applications, including measuring respiration and body fluids.
WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation
Authors: Gadipudi Nivesh , Elamvazuthi Irraivan , Lu Cheng-Kai , Paramasivam Sivajothi , Su Steven
Published: December 2021

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.
Real-Time Jellyfish Classification and Detection Based on Improved YOLOv3 Algorithm
Authors: Gao Meijing , Bai Yang , Li Zhilong , Li Shiyu , Zhang Bozhi , Chang Qiuyue
Published: December 2021

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system.
Thin-Film-Based SAW Magnetic Field Sensors
Authors: Meyer Jana , Schell Viktor , Su Jingxiang , Fichtner Simon , Yarar Erdem , Niekiel Florian , Giese Thorsten , Kittmann Anne , Thormählen Lars , Lebedev Vadim , Moench Stefan , Žukauskaitė Agnė , Quandt Eckhard , Lofink Fabian
Published: December 2021

In this work, the first surface acoustic-wave-based magnetic field sensor using thin-film AlScN as piezoelectric material deposited on a silicon substrate is presented. The fabrication is based on standard semiconductor technology. The acoustically active area consists of an AlScN layer that can be excited with interdigital transducers, a smoothing SiO2 layer, and a magnetostrictive FeCoSiB film. The detection limit of this sensor is 2.4 nT/Hz at 10 Hz and 72 pT/Hz at 10 kHz at an input power of 20 dBm. The dynamic range was found to span from about ±1.7 mT to the corresponding limit of detection, leading to an interval of about 8 orders of magnitude. Fabrication, achieved sensitivity, and noise floor of the sensors are presented.
Adaptive Observer Based Fault Tolerant Control for Sensor and Actuator Faults in Wind Turbines
Authors: Teng Jing , Li Changling , Feng Yizhan , Yang Taoran , Zhou Rong , Sheng Quan Z.
Published: December 2021

The installed wind energy generation capacity has been increasing dramatically all over the world. However, most wind turbines are installed in hostile environments, where regular operation needs to be ensured by effective fault tolerant control methods. An adaptive observer-based fault tolerant control scheme is proposed in this article to address the sensor and actuator faults that usually occur on the core subsystems of wind turbines. The fast adaptive fault estimation (FAFE) algorithm is adopted in the adaptive observers to accurately and rapidly located the faults. Based on the states and faults estimated by the adaptive observers, the state feedback fault tolerant controllers are designed to stabilize the system and compensate for the faults. The gain matrices of the controllers are calculated by the pole placement method. Simulation results illustrate that the proposed fault tolerant control scheme with the FAFE algorithm stabilizes the faulty system effectively and performs better than the baseline on the benchmark model of wind turbines.
The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results
Authors: ElNakieb Yaser , Ali Mohamed T. , Elnakib Ahmed , Shalaby Ahmed , Soliman Ahmed , Mahmoud Ali , Ghazal Mohammed , Barnes Gregory Neal , El-Baz Ayman
Published: December 2021

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview–Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.
Potassium Nitrate Treatment Is Associated with Modulation of Seed Water Uptake, Antioxidative Metabolism and Phytohormone Levels of Pea Seedlings
Authors: Hernández José A. , Díaz-Vivancos Pedro , Acosta-Motos José Ramón , Barba-Espín Gregorio
Published: December 2021

(1) Background: Seed treatment with potassium nitrate (KNO3) has been associated with dormancy breaking, improved germination and enhanced seedling growth and uniformity in a variety of plant species. However, the KNO3 effect seems to be dependent on plant species and treatment conditions. (2) Methods: We describe the effect of incubation of dry pea seeds with different KNO3 concentration on water uptake kinetic, early seedling growth, antioxidant metabolism and hormone profile in pea seedlings. (3) Results: Low (0.25 mM) KNO3 levels increased seedling water uptake and growth, whereas high (40 mM) levels decreased seedling growth. KNO3 treatment differentially affected the antioxidant defences. Low KNO3 levels maintained the activity of antioxidant enzymes, while high levels reduced the activity of H2O2-scavenging enzymes. KNO3 induced a progressive decline in ascorbate levels and reduced (GSH) and oxidised (GSSG) glutathione. Low KNO3 levels strongly increased GA1 and decreased ABA in both seedlings and cotyledons, resulting in a decline in the ABA/GAs ratio. (4) Conclusions: Pea seed treatment with a low KNO3 level promoted early seedling growth. In this process, an interaction among KNO3, antioxidant defences and ABA/GAs ratio is proposed.
COVID-19 and the Creeping Necropolitics of Crimmigration Control
Authors: Koulish Robert
Published: December 2021

The COVID-19 pandemic has had a drastic impact on migration and migrants and immigration policies worldwide [...]
Kinetic Analysis for the Catalytic Pyrolysis of Polypropylene over Low Cost Mineral Catalysts
Authors: Kim Young-Min , Pyo Sumin , Hakimian Hanie , Yoo Kyung-Seun , Rhee Gwang-Hoon , Park Young-Kwon
Published: December 2021

A kinetic analysis of non-catalytic pyrolysis (NCP) and catalytic pyrolysis (CP) of polypropylene (PP) with different catalysts was performed using thermogravimetric analysis (TGA) and kinetic models. Three kinds of low-cost natural catalysts were used to maximize the cost-effectiveness of the process: natural zeolite (NZ), bentonite, olivine, and a mesoporous catalyst, Al-MCM-41. The decomposition temperature of PP and apparent activation energy (Ea) were obtained from the TGA results at multiple heating rates, and a model-free kinetic analysis was performed using the Flynn–Wall–Ozawa model. TGA indicated that the maximum decomposition temperature (Tmax) of the PP was shifted from 464 °C to 347 °C with Al-MCM-41 and 348 °C with bentonite, largely due to their strong acidity and large pore size. Although olivine had a large pore size, the Tmax of PP was only shifted to 456 °C, because of its low acidity. The differential TG (DTG) curve of PP over NZ revealed a two-step mechanism. The Tmax of the first peak on the DTG curve of PP with NZ was 376 °C due to the high acidity of NZ. On the other hand, that of the second peak was higher (474 °C) than the non-catalytic reaction. The Ea values at each conversion were also decreased when using the catalysts, except olivine. At <0.5 conversion, the Ea obtained from the CP of PP with NZ was lower than that with the other catalysts: Al-MCM-41, bentonite, and olivine, in that order. The Ea for the CP of PP with NZ increased more rapidly, to 193 kJ/mol at 0.9 conversion, than the other catalysts.
Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review
Authors: Murcia Nicolas , Cardin Olivier , Mohafid Abdelmoula , Senkel Marie-Pascale
Published: December 2021

Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers.
Mechanical Properties of High-Performance Hybrid Fibre-Reinforced Concrete at Elevated Temperatures
Authors: Mubarak Moawiah , Muhammad Rashid Raizal Saifulnaaz , Amran Mugahed , Fediuk Roman , Vatin Nikolai , Klyuev Sergey
Published: December 2021

Deterioration of concrete’s integrity under elevated temperature requires an alteration in its composition to have better thermal stability. Fibre-reinforced concrete has shown significant improvements in concrete strength and this paper aimed to investigate the influence of steel (ST) and polypropylene (PP) fibres on the behaviour of high-performance concrete (HPC) exposed to elevated temperatures. Six mixtures were prepared and cast by adding one or two types of polypropylene fibre (54 and 9 mm) at 0.25 or 0.5% and either singly or in a hybrid combination, along with a fixed volumetric content at 1% of five-dimensional hooked steel (5DH) fibres. At the age of 28 days, samples were heated to the targeted temperature of 800 °C and cooled down naturally to the laboratory temperature. Visual inspection, flexural, split tensile and compressive strengths were examined before and after the exposure to elevated temperatures. Results exhibited that the hybridization of long and short PP fibres, along with the ST fibres, has notably improved all residual mechanical properties of HPC and kept the integrity of concrete after exposure to elevated temperatures. In addition, PP fibres can significantly prevent spalling, but ST fibres were ineffective in mitigating explosive spalling in beams specimens.
The Phantom of the ‘Responsible Consumer’: Unmasking the Intention–Action Gap with an Indirect Questioning Technique
Authors: Kilian Sven , Mann Andreas
Published: December 2021

The intention-action gap stands out in research on sustainable consumption for decades. The current research explores the role of socially desirable responding (SDR) in the appearance of this gap by utilising an indirect questioning technique. Two online experiments (n=306 and n=334) demonstrate, in line with most market surveys, that consumers present themselves as highly responsible when being assessed with the standard survey measurement approach (i.e., direct questioning). However, the responses of participants toward the exact same measures of consumers’ social responsibility perceptions and behavioural intentions heavily drop when applying an indirect questioning technique, indicating a substantial overstatement of consumers’ social responsibility perceptions in traditional market surveys. Furthermore, this study provides novel evidence regarding the validity and underlying mechanism of the indirect questioning technique, thereby alleviating long-lasting concerns about this method. Implications for the intention–action gap discussion and consumer ethics research are proposed.
An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens
Authors: Ahmed Ghufran , Malick Rauf Ahmed Shams , Akhunzada Adnan , Zahid Sumaiyah , Sagri Muhammad Rabeet , Gani Abdullah
Published: December 2021

The poultry industry contributes majorly to the food industry. The demand for poultry chickens raises across the world quality concerns of the poultry chickens. The quality measures in the poultry industry contribute towards the production and supply of their eggs and their meat. With the increasing demand for poultry meat, the precautionary measures towards the well-being of the chickens raises the concerns of the industry stakeholders. The modern technological advancements help the poultry industry in monitoring and tracking the health of poultry chicken. These advancements include the identification of the chickens’ sickness and well-being using video surveillance, voice observations, ans feces examinations by using IoT-based wearable sensing devices such as accelerometers and gyro devices. These motion-sensing devices are placed over a chicken and transmit the chicken’s movement data to the cloud for further analysis. Analyzing such data and providing more accurate predictions about chicken health is a challenging issue. In this paper, an IoT based predictive service framework for the early detection of diseases in poultry chicken is proposed. The proposed study contributes by extending the dataset through generating the synthetic data using Generative Adversarial Networks (GAN). The experimental results classify the sick and healthy chicken in a poultry farms using machine learning classification modeling on the synthetic data and the real dataset. Theoretical analysis and experimental results show that the proposed system has achieved an accuracy of 97%. Moreover, the accuracy of the different classification models are compared in the proposed study to provide more accurate and best performing classification technique. The proposed study is mainly focused on proposing an Industrial IoT-based predictive service framework that can classify poultry chickens more accurately in real time.
The Compatibility between the Takeover Process in Conditional Automated Driving and the Current Geometric Design of the Deceleration Lane in Highway
Authors: Chen Cihe , Lin Zijian , Zhang Shuguang , Chen Feng , Chen Peiyan , Zhang Lin
Published: December 2021

In recent years, the takeover process of conditional automated driving has attached a great deal of attention. However, most of the existing research has focused on the effects of human-machine interactions or driver-related features (e.g., non-driving-related tasks), while there is little knowledge about the compatibility between the takeover process and existing road geometric design. As there is a high possibility that drivers must take over the vehicle before they diverge from the mainline of the highway, this explanatory study aimed to examine the compatibility between the takeover process and the current deceleration lane geometric design. The distribution range of existing deceleration lanes’ lengths were obtained through a geo-based survey. Nine scenarios were recreated in the driving simulator which were designed with various deceleration lane lengths and driving modes (different takeover time budgets and manual driving as the baseline group). A total of 31 participants were recruited to take part in the experiment, their gaze behaviors were recorded simultaneously. Results showed that, compared with manual driving, both drivers’ horizontal and vertical gaze dispersion increased, while drivers adopted higher deceleration in the mainline and merged into the deceleration lane later under takeover conditions. Moreover, a longer deceleration lane could benefit vehicle control. However, its marginal effect was reduced with the increase of deceleration lane length. These findings can help automated vehicle manufacturers design dedicated takeover schemes for different deceleration lane lengths.
Ballistic Impact Resistance of UHPC Plates Made with Hybrid Fibers and Low Binder Content
Authors: Dapper Paulo Rodrigo , Ehrendring Hinoel Zamis , Pacheco Fernanda , Christ Roberto , Menegussi Giovanna Costella , Oliveira Maria Fernanda de , Tutikian Bernardo Fonseca
Published: December 2021

This study assesses the ballistic impact strength of thin plates made of ultra-high-performance concrete (UHPC) with low cement content (250 kg/m3) and volumes of 80% steel and 20% polypropylene (PP) hybrid fibers. The plates were prepared with thicknesses of 30, 50, and 70 mm and fiber volume ratios of 1.5% and 3.0%. Compressive strength, flexural tensile strength, residual strength, and ballistic impact strength were determined using experimental methods. Test results showed that regardless of fiber content, the UHPC specimens prepared with the hybrid fibers showed similar performance against ballistic impact, exerting relatively low impact energy below 1000 J. The UHPC3.0 mixture made with 3.0% hybrid fiber content exhibited the best performance in terms of energy absorption and spalling resistance at impact energy levels greater than 4000 J. Plate sections with thicknesses of 7 mm showed class III performance (highest level), as recommended for military-based applications.
Does Economic Policy Intervention Inhibit the Efficiency of China’s Green Energy Economy?
Authors: Fang Zhiyu , Jiang Ling , Fang Zhong
Published: December 2021

Due to the different focus of policies in different regions, China’s energy efficiency has been unstable in recent years. The changing focus of policies at the same time has also impacted the energy system, and therefore, it is very important to explore the impact of China’s new energy policy on its oil and gas energy efficiency. The practical significance of this research is to integrate three policy intervention factors: incentive economic policy intervention, government financial intervention, and mandatory policy intervention. Through the regression of the Stochastic Frontier Approach model, the influence of these policy intervention factors on the efficiency evaluation of decision-making units is eliminated. We calculate the environmental pollution index as an undesired output to measure the efficiency of policy intervention in the green economy of China’s oil and gas energy, use Luenberger model to explore total factor productivity, and find the main reasons that affect the productivity of the green energy economy. The results show that China’s oil and gas energy construction is currently in the stage of scale economy, but the heavy dependence of China’s energy consumption on foreign imports leads to difficulties and urgency in the present stage of technological progress. After excluding the factors of policy intervention, China’s overall energy is in a slightly insufficient policy environment, and energy efficiency is in an unbalanced state.
Pathways to Improving Nutrition among Upland Farmers through Nutrition-Sensitive Agriculture Interventions: A Case from Northern Laos
Authors: Sharma Indu K. , Essink Dirk , Fumado Victoria , Shrestha Ranjan , Susanto Zefanya D. , Broerse Jacqueline E. W.
Published: December 2021

While there is an increased awareness of the role of nutrition-sensitive agriculture (NSA) interventions on nutrition, the studies that investigate holistic pathways from interventions to nutrition outcomes are inadequate. We aimed to understand these pathways to improved nutrition from the Enhancing Nutrition of Upland Farming Families (ENUFF) Project implemented in northern Laos. We applied an embedded case study design by recruiting 101 participants representing implementers, school and private sector representatives, and beneficiaries, of which 34 participated in interviews and 68 participated in 11 FGDs. This was supplemented by a desk review of project documents. We analyzed the data using a directed content analysis across five pathways using a published framework on impact pathways from NSA interventions to nutrition outcomes. The project contributed to nutrition mainly through three core pathways-food production, nutrition and WASH-related knowledge, and agricultural income, supported by strengthening local institutions within the project’s scope. While it is evident that the project contributed to empowering women by saving their time and increasing income, further study is needed to investigate the translation of these aspects to nutrition-related practices. We also suggest the need to sustain the capacity of local institutions and their engagement beyond the project cycle. To enhance the effectiveness of NSA interventions on nutrition, there is a need to design and implement intervention package with multiple pathways and tailored strategies based on nutrition outcomes, the envisaged pathways, geographical context, and factors affecting these.
Evaluating the Impact of Positive Implicit Followership towards Employees’ Feedback-Seeking: Based on the Social Information Processing Perspective
Authors: Zhang Wei , Wang Xuejun , Xie Dingnan
Published: December 2021

Amid the growth of COVID-19 pandemic, SMEs are facing greater uncertainties and pressures to survive because even though they are efficiently managed, their human resource organizations lack a large number of resources and a well-developed training system to foster the sustainable development of employees. Employees are important assets of the company, and their continuous growth and development are keys to the survival of the company. In this context, the individual worker’s assessment of his or her job role and how the assessments drive the employee to exhibit an appropriate proactive work behavior are particularly important. Previous research has typically focused on how organizations and leaders perceive employees but has rarely explored employees’ own implicit followership cognitive states. This study integrates the traits of positive implicit followership of employees, namely, industry trait, enthusiasm trait, and good citizen trait, with perceived supervisor support (PSS) and feedback-seeking behavior (FSB) into one research framework. In this study, 207 valid questionnaires were collected by using offline convenience sampling, and structural equation modeling (SEM) analysis was conducted. The results show that employees’ industry traits directly and positively influence FSB, while enthusiasm traits and good citizen traits have no direct effect on promoting FSB. In addition, industry trait, enthusiasm trait, and good citizen trait significantly and positively influence PSS, with good citizen trait having the greatest positive effect on PSS. Furthermore, PSS has a significant positive effect on FSB. Finally, PSS was found to mediate between industry traits and FSB. Corresponding to the results of the study, the actions shaping employees’ positive implicit followership cognition and forming a good supportive atmosphere to promote employees’ performance of more feedback-seeking behaviors are recommended.