bluuebunny/crossref_metadata_2025 huggingface dataset:
Quinoa Phenotyping Methodologies: An International Consensus
Authors: Stanschewski Clara S. , Rey Elodie , Fiene Gabriele , Craine Evan B. , Wellman Gordon , Melino Vanessa J. , S. R. Patiranage Dilan , Johansen Kasper , Schmöckel Sandra M. , Bertero Daniel , Oakey Helena , Colque-Little Carla , Afzal Irfan , Raubach Sebastian , Miller Nathan , Streich Jared , Amby Daniel Buchvaldt , Emrani Nazgol , Warmington Mark , Mousa Magdi A. A. , Wu David , Jacobson Daniel , Andreasen Christian , Jung Christian , Murphy Kevin , Bazile Didier , Tester Mark ,   on behalf of the Quinoa Phenotyping Consortium
Published: August 2021

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
Altering Nitrogen Sources Affects Growth Carbon Costs in Vachellia nilotica Growing in Nutrient-Deficient Grassland Soils
Authors: Sithole Nonkululeko , Tsvuura Zivanai , Kirkman Kevin , Magadlela Anathi
Published: August 2021

Vachellianilotica (L.) Willd. Ex Del. is a multipurpose leguminous tree that is common in grassland and savanna ecosystems in southern and eastern Africa. These ecosystem soils are reported to be acidic and nutrient-limited, specifically with regards to nitrogen (N) and phosphorus (P). The presence of this plant in these terrestrial ecosystems improves soil fertility benefiting the surrounding vegetation due to its ability to fix atmospheric N. This study seeks to understand the N-fixing bacteria symbiosis and physiological adaptations of V. nilotica in these acidic and nutrient-deficient KwaZulu-Natal soils. The soils used for this study were collected from the Ukulinga Grassland Nutrient Experiment located at the Ukulinga research farm of the University of KwaZulu-Natal, Pietermaritzburg, South Africa. Due to long-term soil nutrient addition treatments, these soils offered a diverse nutrient variation for better understanding the effects of acidity and nutrient variation on microbial symbiosis, plant nutrition, and biomass accumulation of V. nilotica. V. nilotica was able to maintain growth by relying on both atmospheric and soil-derived N across all treatments decreasing carbon (C) growth costs. There was an increased reliance on atmospheric-derived N of un-nodulated high N-treated plants. The plants grown in high N + P soils were able to nodulate with various species from the Mesorhizobium genus, which resulted in increased biomass compared to other plants. The results of this study show that V. nilotica can alter N sources to reduce C growth costs. In addition, both nodulating and free-living soil N2 fixing bacteria such as Caulobacter rhizosphaerae, Sphingomonas sp. and Burkholderia contaminans identified in the experimental soils may play an important role under P-deficient conditions.
Generation of Negative Air Ions by Use of Piezoelectric Cold Plasma Generator
Authors: Korzec Dariusz , Neuwirth Daniel , Nettesheim Stefan
Published: August 2021

The negative air ions (NAI) are used for the removal of particles or droplets from the air. In this study, three types of piezoelectric cold plasma generators (PCPG), in combination with cylindrical electrostatic ion filters, are applied for NAI production. The high voltage on the filter cylinder is induced by the electric field from the piezoelectric transformer of the PCPG. To achieve the dc bias, the cylinder of the electrostatic filter is connected to the ground over ultrafast switching diodes. The ion concentrations are measured for different airflows, PCPG powers, and electrostatic filter geometries. The NAI concentration in the order of magnitude of 107 cm−3, and a negative-to-positive ion concentration ratio of over 200 is reached. The production of ozone is evaluated and the PCPG configuration with a minimum ozone production rate is proposed. The ozone concentration below 60 ppb is reached in the airflow of 90 m3/h.
Structural Characterisation and Assessment of the Novel Bacillus amyloliquefaciens RK3 Exopolysaccharide on the Improvement of Cognitive Function in Alzheimer’s Disease Mice
Authors: Gangalla Ravi , Gattu Sampath , Palaniappan Sivasankar , Ahamed Maqusood , Macha Baswaraju , Thampu Raja Komuraiah , Fais Antonella , Cincotti Alberto , Gatto Gianluca , Dama Murali , Kumar Amit
Published: August 2021

In this study Bacillus amyloliquefaciens RK3 was isolated from a sugar mill effluent-contaminated soil and utilised to generate a potential polysaccharide with anti-Alzheimer’s activity. Traditional and molecular methods were used to validate the strain. The polysaccharide produced by B. amyloliquefaciens RK3 was purified, and the yield was estimated to be 10.35 gL−1. Following purification, the polysaccharide was structurally and chemically analysed. The structural analysis revealed the polysaccharide consists of α-d-mannopyranose (α-d-Manp) and β-d-galactopyranose (β-d-Galp) monosaccharide units connected through glycosidic linkages (i.e., β-d-Galp(1→6)β-d-Galp (1→6)β-d-Galp(1→2)β-d-Galp(1→2)[β-d-Galp(1→6)]β-d-Galp(1→2)α-d-Manp(1→6)α-d-Manp (1→6)α-d-Manp(1→6)α-d-Manp(1→6)α-d-Manp). The scanning electron microscopy and energy-dispersive X-ray spectroscopy imaging of polysaccharides emphasise their compactness and branching in the usual tubular heteropolysaccharide structure. The purified exopolysaccharide significantly impacted the plaques formed by the amyloid proteins during Alzheimer’s disease. Further, the results also highlighted the potential applicability of exopolysaccharide in various industrial and pharmaceutical applications.
Multi-Level Optimization Process for Rationalizing the Distribution Logistics Process of Companies Selling Dietary Supplements
Authors: Szentesi Szabolcs , Illés Béla , Cservenák Ákos , Skapinyecz Róbert , Tamás Péter
Published: August 2021

The commission sales form is a very significant channel of sales today, which is especially true in the field of dietary supplements. In parallel, the prevailing digitalization trends have opened up further new opportunities for this form of distribution. The multi-level optimization process presented in the publication makes it possible to optimize the distribution logistics processes of companies producing food supplements at a high level by exploiting these new possibilities. The operation of the procedure is also illustrated through a practical example.
Effect of Tip Clearance Size on Tubular Turbine Leakage Characteristics
Authors: Li Xinrui , Li Zhenggui , Zhu Baoshan , Wang Weijun
Published: August 2021

To study the effect of tip clearance on unsteady flow in a tubular turbine, a full-channel numerical calculation was carried out based on the SST k–ω turbulence model using a power-plant prototype as the research object. Tip leakage flow characteristics of three clearance δ schemes were compared. The results show that the clearance value is directly proportional to the axial velocity, momentum, and flow sum of the leakage flow but inversely proportional to turbulent kinetic energy. At approximately 35–50% of the flow direction, velocity and turbulent kinetic energy of the leakage flow show the trough and peak variation law, respectively. The leakage vortex includes a primary tip leakage vortex (PTLV) and a secondary tip leakage vortex (STLV). Increasing clearance increases the vortex strength of both parts, as the STLV vortex core overlaps Core A of PTLV, and Core B of PTLV becomes the main part of the tip leakage vortex. A “right angle effect” causes flow separation on the pressure side of the tip, and a local low-pressure area subsequently generates a separation vortex. Increasing the gap strengthens the separation vortex, intensifying the flow instability. Tip clearance should therefore be maximally reduced in tubular turbines, barring other considerations.
End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning
Authors: Cheng Chuen-Sheng , Ho Ying , Chiu Tzu-Cheng
Published: August 2021

Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control.
Machining of Inserts with PCD Cutting-Edge Technology and Determination of Optimum Machining Conditions Based on Roundness Deviation and Chip-Cross Section of AW 5083 AL-Alloy Verified with Grey Relation Analysis
Authors: Miškiv-Pavlík Martin , Jurko Jozef
Published: August 2021

This paper describes the important significance of cutting-edge technology in the machining of polycrystalline diamond (PCD) cutting inserts by comparing the evaluation criteria. The LASER technology of cutting-edge machining is compared with grinding and electrical discharge machining (EDM) technologies. To evaluate the data from the experiments, the Grey Relational Analysis (GRA) method was used to optimize the input factors of turning to achieve the required output parameters, namely the deviation of roundness and chip cross-section. The input factors of cutting speed, feed rate, depth of cut and corner radius were applied in the experiment for three different levels (minimum, medium and maximum). The optimal input factors for turning of aluminum alloy (AW 5083) were determined for the factorial plan according to Grey Relational Grade based on the GRA method for the multi-criteria of the output parameters. The results were confirmed by a verification test according to the GRA method and optimal values of input factors were recommended for the machining of Al-alloy (AW 5083) products. This material is currently being developed by engineers for forming selected components for the automotive and railway industries, mainly to reduce weight and energy costs. The best values of the output parameters were obtained at a cutting speed of 870 m/min, feed rate of 0.1 mm/min, depth of cut of 0.5 mm and a corner radius of 1.2 mm.
Combustion Efficiency in a Fluidized-Bed Combustor with a Modified Perforated Plate for Air Distribution
Authors: Erdiwansyah  , Mahidin  , Husin Husni , Nasaruddin  , Muhtadin  , Faisal Muhammad , Gani Asri , Usman  , Mamat Rizalman
Published: August 2021

Combustion efficiency is one of the most important parameters especially in the fluidized-bed combustor. Investigations into the efficiency of combustion in fluidized-bed combustor fuels using solid biomass waste fuels in recent years are increasingly in demand by researchers around the world. Specifically, this study aims to calculate the combustion efficiency in the fluidized-bed combustor. Combustion efficiency is calculated based on combustion results from the modification of hollow plates in the fluidized-bed combustor. The modified hollow plate aims to control combustion so that the fuel incorporated can burn out and not saturate. The combustion experiments were tested using palm oil biomass solid waste fuels such as palm kernel shell, oil palm midrib, and empty fruit bunches. The results of the measurements showed that the maximum combustion temperature for the palm kernel shell fuel reached 863 °C for M1 and 887 °C for M2. The maximum combustion temperature measurements for M1 and M2 from the oil palm midrib fuel testing reached 898 °C and 858 °C, respectively, while the maximum combustion temperature for M1 and M2 from the empty fruit bunches fuel was 667 °C and M2 847 °C, respectively. The rate of combustion efficiency with the modification of the hole plate in the fluidized-bed combustor reached 96.2%. Thermal efficiency in fluidized-bed combustors for oil palm midrib was 72.62%, for PKS was 70.03%, and for empty fruit bunches was 52.43%. The highest heat transfer rates for the oil palm midrib fuel reached 7792.36 W/m2, palm kernel shell 7167.38 W/m2, and empty fruit bunches 5127.83 W/m2. Thus, the modification of the holed plate in the fluidized-bed combustor chamber showed better performance of the plate than without modification.
Simple Gain-Scheduled Control System for Dissolved Oxygen Control in Bioreactors
Authors: Butkus Mantas , Levišauskas Donatas , Galvanauskas Vytautas
Published: August 2021

An adaptive control system for the set-point control and disturbance rejection of biotechnological-process parameters is presented. The gain scheduling of PID (PI) controller parameters is based on only controller input/output signals and does not require additional measurement of process variables for controller-parameter adaptation. Realization of the proposed system does not depend on the instrumentation-level of the bioreactor and is, therefore, attractive for practical application. A simple gain-scheduling algorithm is developed, using tendency models of the controlled process. Dissolved oxygen concentration was controlled using the developed control system. The biotechnological process was simulated in fed-batch operating mode, under extreme operating conditions (the oxygen uptake-rate’s rapidly and widely varying, feeding and aeration rate disturbances). In the simulation experiments, the gain-scheduled controller demonstrated robust behavior and outperformed the compared conventional PI controller with fixed parameters.
Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation
Authors: Alsayed Ahmad , Yunusa-Kaltungo Akilu , Quinn Mark K. , Arvin Farshad , Nabawy Mostafa R. A.
Published: August 2021

The accuracy of stockpile estimations is of immense criticality to process optimisation and overall financial decision making within manufacturing operations. Despite well-established correlations between inventory management and profitability, safe deployment of stockpile measurement and inspection activities remain challenging and labour-intensive. This is perhaps owing to a combination of size, shape irregularity as well as the health hazards of cement manufacturing raw materials and products. Through a combination of simulations and real-life assessment within a fully integrated cement plant, this study explores the potential of drones to safely enhance the accuracy of stockpile volume estimations. Different types of LiDAR sensors in combination with different flight trajectory options were fully assessed through simulation whilst mapping representative stockpiles placed in both open and fully confined areas. During the real-life assessment, a drone was equipped with GPS for localisation, in addition to a 1D LiDAR and a barometer for stockpile height estimation. The usefulness of the proposed approach was established based on mapping of a pile with unknown volume in an open area, as well as a pile with known volume within a semi-confined area. Visual inspection of the generated stockpile surface showed strong correlations with the actual pile within the open area, and the volume of the pile in the semi-confined area was accurately measured. Finally, a comparative analysis of cost and complexity of the proposed solution to several existing initiatives revealed its proficiency as a low-cost robotic system within confined spaces whereby visibility, air quality, humidity, and high temperature are unfavourable.
On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection
Authors: Kolberg Jascha , Gläsner Daniel , Breithaupt Ralph , Gomez-Barrero Marta , Reinhold Jörg , von Twickel Arndt , Busch Christoph
Published: August 2021

Within the last few decades, the need for subject authentication has grown steadily, and biometric recognition technology has been established as a reliable alternative to passwords and tokens, offering automatic decisions. However, as unsupervised processes, biometric systems are vulnerable to presentation attacks targeting the capture devices, where presentation attack instruments (PAI) instead of bona fide characteristics are presented. Due to the capture devices being exposed to the public, any person could potentially execute such attacks. In this work, a fingerprint capture device based on thin film transistor (TFT) technology has been modified to additionally acquire the impedances of the presented fingers. Since the conductance of human skin differs from artificial PAIs, those impedance values were used to train a presentation attack detection (PAD) algorithm. Based on a dataset comprising 42 different PAI species, the results showed remarkable performance in detecting most attack presentations with an APCER = 2.89% in a user-friendly scenario specified by a BPCER = 0.2%. However, additional experiments utilising unknown attacks revealed a weakness towards particular PAI species.
Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots
Authors: Huang Stanley Y. B. , Lee Chih-Jen , Lee Shih-Chin
Published: August 2021

With the popularity of financial technology (fintech) chatbots equipped with artificial intelligence, understanding the user’s response mechanism can help bankers formulate precise marketing strategies, which is a crucial issue in the social science field. Nevertheless, the user’s response mechanism towards financial technology chatbots has been relatively under-investigated. To fill these literature gaps, latent growth curve modeling was adopted by the present research to survey Taiwanese users of fintech chatbots. The present study proposed a customer continuance model to predict continuance intention for fintech chatbots and that cognitive and emotional dimensions positively influence the growth in a user’s attitude toward fintech chatbots, which in turn, positively influences continuance intention over time. In total, 401 customers of fintech chatbots were surveyed through three time points to examine the relationship between these variables over six months. The results support the theoretical model of this research and can advance the literature of fintech chatbots and the information technology adoption model.
Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
Authors: He Jialuan , Xing Zirui , Xiang Tianqi , Zhang Xin , Zhou Yinghai , Xi Chuanyu , Lu Hai
Published: August 2021

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.
Evaluating the Quality of Experience Performance Metric for UAV-Based Networks
Authors: Khakimov Abdukodir , Mokrov Evgeny , Poluektov Dmitry , Samouylov Konstantin , Koucheryavy Andrey
Published: August 2021

In this work, we consider a UAV-assisted cell in a single user scenario. We consider the Quality of Experience (QoE) performance metric calculating it as a function of the packet loss ratio. In order to acquire this metric, a radio-channel emulation system was developed and tested under different conditions. The system consists of two independent blocks, separately emulating connections between the User Equipment (UE) and unmanned aerial vehicle (UAV) and between the UAV and Base station (BS). In order to estimate scenario usage constraints, an analytical model was developed. The results show that, in the described scenario, cell coverage can be enhanced with minimal impact on QoE.
An Extensive Literature Review on Underwater Image Colour Correction
Authors: Vlachos Marinos , Skarlatos Dimitrios
Published: August 2021

The topic of underwater (UW) image colour correction and restoration has gained significant scientific interest in the last couple of decades. There are a vast number of disciplines, from marine biology to archaeology, that can and need to utilise the true information of the UW environment. Based on that, a significant number of scientists have contributed to the topic of UW image colour correction and restoration. In this paper, we try to make an unbiased and extensive review of some of the most significant contributions from the last 15 years. After considering the optical properties of water, as well as light propagation and haze that is caused by it, the focus is on the different methods that exist in the literature. The criteria for which most of them were designed, as well as the quality evaluation used to measure their effectiveness, are underlined.
Win, Draw, or Lose? Global Positioning System-Based Variables’ Effect on the Match Outcome: A Full-Season Study on an Iranian Professional Soccer Team
Authors: Nobari Hadi , Banoocy Norbert Keshish , Oliveira Rafael , Pérez-Gómez Jorge
Published: August 2021

The aim of the study was to determine the between-match and between-halves match variability of various Global Positioning System (GPS) variables and metabolic power average (MPA) in competitions, based on the match results obtained by professional soccer players over a full season. Observations on individual match performance measures were undertaken on thirteen outfield players competing in the Iranian Premier League. The measures selected for analysis included total duration, accelerations in zones (AccZ1, 2, and 3), decelerations in zones (DecZ1, 2, and 3), and MPA collected by the Wearable Inertial Measurement Unit (WIMU). The GPS manufacturer set the thresholds for the variables analyzed as follows: AccZ1 (<2 m·s−2); AccZ2 (2 to 4 m·s−2); AccZ3 (>4 m·s−2); DecZ1 (<−2 m·s−2); DecZ2 (−2 to −4 m·s−2); DecZ3 (>−4 m·s−2). The results revealed significant differences between wins and draws for the duration of the match and draws compared to wins for the first- half duration (p ≤ 0.05; ES = 0.36 [−0.43, 1.12]), (p ≤ 0.05; ES = −7.0 [−8.78, −4.78], respectively. There were significant differences on AccZ1 during the first-half between draws and defeats (p ≤ 0.05; ES = −0.43 [−1.32, 0.46]), for AccZ3 in the second-half between draws and defeats (p ≤ 0.05; ES = 1.37 [0.48, 2.25]). In addition, there were significant differences between wins and draws (p ≤ 0.05; ES = 0.22 [−0.62, 1.10]), and wins and defeats for MPA in the first- half (p ≤ 0.05; ES = 0.34 [−0.65, 1.22]). MPA showed further differences between draws and defeats in the second- half (p ≤ 0.05; ES = 0.57 [−0.22, 1.35]). Descriptive analysis revealed differences between the first and second half for wins in AccZ2 (p = 0.005), DecZ2 (p = 0.029), and MPA (p = 0.048). In addition, draws showed significant differences between the first and second half in duration, AccZ1, AccZ2, and DecZ2 (p = 0.008), (p = 0.017), (p = 0.040), and (p = 0.037) respectively. Defeats showed differences between the first and second half in AccZ1, AccZ3, and MPA (p = 0.001), (p = 0.018), and (p = 0.003) respectively. In summary, the study reveals large variations between the match duration, accelerometer variables, and MPA both within and between matches. Regardless of the match outcome, the first half seems to produce greater outputs. The results should be considered when performing a half-time re-warm-up, as this may be an additional factor influencing the drop in the intensity markers in the second half in conjunction with factors such as fatigue, pacing strategies, and other contextual variables that may influence the results.
Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks
Authors: Zhou Meilun , Elmore Jared A. , Samiappan Sathishkumar , Evans Kristine O. , Pfeiffer Morgan B. , Blackwell Bradley F. , Iglay Raymond B.
Published: August 2021

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.
EduNet: A New Video Dataset for Understanding Human Activity in the Classroom Environment
Authors: Sharma Vijeta , Gupta Manjari , Kumar Ajai , Mishra Deepti
Published: August 2021

Human action recognition in videos has become a popular research area in artificial intelligence (AI) technology. In the past few years, this research has accelerated in areas such as sports, daily activities, kitchen activities, etc., due to developments in the benchmarks proposed for human action recognition datasets in these areas. However, there is little research in the benchmarking datasets for human activity recognition in educational environments. Therefore, we developed a dataset of teacher and student activities to expand the research in the education domain. This paper proposes a new dataset, called EduNet, for a novel approach towards developing human action recognition datasets in classroom environments. EduNet has 20 action classes, containing around 7851 manually annotated clips extracted from YouTube videos, and recorded in an actual classroom environment. Each action category has a minimum of 200 clips, and the total duration is approximately 12 h. To the best of our knowledge, EduNet is the first dataset specially prepared for classroom monitoring for both teacher and student activities. It is also a challenging dataset of actions as it has many clips (and due to the unconstrained nature of the clips). We compared the performance of the EduNet dataset with benchmark video datasets UCF101 and HMDB51 on a standard I3D-ResNet-50 model, which resulted in 72.3% accuracy. The development of a new benchmark dataset for the education domain will benefit future research concerning classroom monitoring systems. The EduNet dataset is a collection of classroom activities from 1 to 12 standard schools.
Indoor Positioning Using Magnetic Fingerprint Map Captured by Magnetic Sensor Array
Authors: Chen Ching-Han , Chen Pi-Wei , Chen Pi-Jhong , Liu Tzung-Hsin
Published: August 2021

By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m.
Sustainable Biological Ammonia Production towards a Carbon-Free Society
Authors: Watanabe Yukio , Aoki Wataru , Ueda Mitsuyoshi
Published: August 2021

A sustainable society was proposed more than 50 years ago. However, it is yet to be realised. For example, the production of ammonia, an important chemical widely used in the agriculture, steel, chemical, textile, and pharmaceutical industries, still depends on fossil fuels. Recently, biological approaches to achieve sustainable ammonia production have been gaining attention. Moreover, unlike chemical methods, biological approaches have a lesser environmental impact because ammonia can be produced under mild conditions of normal temperature and pressure. Therefore, in previous studies, nitrogen fixation by nitrogenase, including enzymatic ammonia production using food waste, has been attempted. Additionally, the production of crops using nitrogen-fixing bacteria has been implemented in the industry as one of the most promising approaches to achieving a sustainable ammonia economy. Thus, in this review, we described previous studies on biological ammonia production and showed the prospects for realising a sustainable society.
Monte Carlo Simulation of the Effect of Heterogeneous Too-Cheap Prices on the Average Price Preference for Remanufactured Products
Authors: Kwak Minjung
Published: August 2021

A prevailing assumption in research on remanufactured products is “the cheaper, the better”. Customers prefer prices that are as low as possible. Customer price preference is modeled as a linear function with the minimal price at customers’ willingness to pay (WTP), which is assumed to be homogeneous and constant in the market. However, this linearity assumption is being challenged, as recent empirical studies have testified to customer heterogeneity in price perception and demonstrated the existence of too-cheap prices (TC). This study is the first attempt to investigate the validity of the linearity assumption for remanufactured products. A Monte Carlo simulation was conducted to estimate how the average market preference changes with the price of the remanufactured product when TC and WTP are heterogeneous across individual customers. Survey data from a previous study were used to fit and model the distributions of TC and WTP. Results show that a linear or monotonically decreasing relationship between price and customer preference may not hold for remanufactured products. With heterogeneous TC and WTP, the average price preference revealed an inverted U shape with a peak between the TC and WTP, independent of product type and individual customers’ preference function form. This implies that a bell-shaped or triangular function may serve as a better alternative than a linear function can when modeling market-price preference in remanufacturing research.
Safety Failure Factors Affecting Dairy Supply Chain: Insights from a Developing Economy
Authors: Hassan Aisha , Cui-Xia Li , Ahmad Naveed , Iqbal Muzaffar , Hussain Kramat , Ishtiaq Muhammad , Abrar Maira
Published: August 2021

Safety issues in the dairy industry have attracted greater attention in recent years, and the public have showed an intensive concern regarding safety failure in the dairy supply chain. Since the dairy industry is closely associated with humans and fulfills basic necessities, it is necessary to explore safety failure factors (SFFs) affecting the supply chain of the dairy industry. This paper aims to explore the SFFs of the dairy supply chain using an interpretive structural modeling technique (ISM) and Matrice d’Impacts Croises Multiplication Appliques a un Classement (MICMAC) analysis in a Pakistani context. A total of twenty-five failure factors have been identified through literature reviews and the opinion of an expert team, including managerial and technical experts from the dairy industry, as well as academics. Interpretive structural modeling (ISM) is applied to analyze the mutual interaction among barriers and to develop a structural model. The MICMAC technique is used to identify the importance of SFFs based on their driving and dependence power. The results of this study will help decision-makers in the dairy industry to plan their supply chain activities more effectively and efficiently by managing the identified barriers.
Design, Modeling, and Differential Flatness Based Control of Permanent Magnet-Assisted Synchronous Reluctance Motor for e-Vehicle Applications
Authors: Sriprang Songklod , Poonnoy Nitchamon , Guilbert Damien , Nahid-Mobarakeh Babak , Takorabet Noureddine , Bizon Nicu , Thounthong Phatiphat
Published: August 2021

This paper presents the utilization of differential flatness techniques from nonlinear control theory to permanent magnet assisted (PMa) synchronous reluctance motor (SynRM). The significant advantage of the proposed control approach is the potentiality to establish the behavior of the state variable system during the steady-state and transients operations as well. The mathematical models of PMa-SynRM are initially proved by the nonlinear case to show the flatness property. Then, the intelligent proportional-integral (iPI) is utilized as a control law to deal with some inevitable modeling errors and uncertainties for the torque and speed of the motor. Finally, a MicroLab Box dSPACE has been employed to implement the proposed control scheme. A small-scale test bench 1-KW relying on the PMa-SynRM has been designed and developed in the laboratory to approve the proposed control algorithm. The experimental results reflect that the proposed control effectively performs high performance during dynamic operating conditions for the inner torque loop control and outer speed loop control of the motor drive compared to the traditional PI control.
Integrated Cycles for Urban Biomass as a Strategy to Promote a CO2-Neutral Society—A Feasibility Study
Authors: Meinusch Nicole , Kramer Susanne , Körner Oliver , Wiese Jürgen , Seick Ingolf , Beblek Anita , Berges Regine , Illenberger Bernhard , Illenberger Marco , Uebbing Jennifer , Wolf Maximilian , Saake Gunter , Benndorf Dirk , Reichl Udo , Heyer Robert
Published: August 2021

The integration of closed biomass cycles into residential buildings enables efficient resource utilization and avoids the transport of biowaste. In our scenario called Integrated Cycles for Urban Biomass (ICU), biowaste is degraded on-site into biogas that is converted into heat and electricity. Nitrification processes upgrade the liquid fermentation residues to refined fertilizer, which can be used subsequently in house-internal gardens to produce fresh food for residents. Our research aims to assess the ICU scenario regarding produced amounts of biogas and food, saved CO2 emissions and costs, and social–cultural aspects. Therefore, a model-based feasibility study was performed assuming a building with 100 residents. The calculations show that the ICU concept produces 21% of the annual power (electrical and heat) consumption from the accumulated biowaste and up to 7.6 t of the fresh mass of lettuce per year in a 70 m2 professional hydroponic production area. Furthermore, it saves 6468 kg CO2-equivalent (CO2-eq) per year. While the ICU concept is technically feasible, it becomes economically feasible for large-scale implementations and higher food prices. Overall, this study demonstrates that the ICU implementation can be a worthwhile contribution towards a sustainable CO2-neutral society and decrease the demand for agricultural land.
Tourism-Related Loans as a Driver of a Small Island Economy: A Case of Northern Cyprus
Authors: Shirkhani Setareh , Fethi Sami , Alola Andrew Adewale
Published: August 2021

Although the literature on the contribution of tourism to economic expansion vis-à-vis the tourism-led growth hypothesis has been widely explored, so far, limited attention has been paid to the specificity of the role of tourism-related loans or financial inducement in economic growth. This paper examines the short-run and long-run relationships between bank loan disbursements to the tourism sector and economic growth in Northern Cyprus. We structurally derive empirical equations for co-integration and error-correction models by extending the original Solow growth model, applying a cointegration approach that is strengthened by the autoregressive distributed lag and Granger causality approaches to reveal important findings. The empirical findings suggest unidirectional causality from loans disbursed to the tourism sector to economic growth in Northern Cyprus for the period under study. Additionally, we show that tourism-related loans and human and technological advancement all spur economic growth in the short- and long-run. These findings’ main policy implication is that long-term complementary policies in the domestic banking system can increase the access to financial sources for tourism enterprises and, consequently, promote tourism-led economic growth, especially in small tourism-dependent economies where capital sources are scarce.
Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators
Authors: Machesa Mosa , Tartibu Lagouge , Okwu Modestus
Published: August 2021

Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.
Modelling Critical Innovation Factors in Rural Agrifood Industries: A Case Study in Cuenca, Spain
Authors: Gallego Francisco José , Díaz-Puente José María , Quesada Daniel Francisco , Bettoni Maddalena
Published: August 2021

The agrifood industry contributes to sustaining the population and the economic growth in rural areas of Spain. Innovation in the agrifood sector has therefore become a necessity as a means of improving the competitiveness of companies and the territory, thus promoting sustainable rural development in areas currently characterised by social issues such as depopulation. Meeting this need requires the generation of specific knowledge on innovation in the rural agrifood industry to strategically steer the business management of innovation. This study aims to contribute to further improving the competitiveness of the agrifood industry through the interrelation of critical innovation factors in small and medium-sized agrifood enterprises, thus shedding light on the innovation environment of differentiated local products in depopulated rural regions. The qualitative Interpretive Structural Modelling (ISM) methodology was used with the participation of entrepreneurs and experts from the sector. The ISM was applied to a case study in Alcarria Conquense, a Spanish region that embodies the current problems of many rural territories. The results show four factors (cooperation, managerial skills, absorptive capacity, and market orientation) are binding variables with a high power of influence and dependence, and a fifth factor, funding, is the most dependent on the others. The work contributes to the literature by revealing the needs and opportunities for a potential strategic planning of rural development that can positively influence the problems of the region through innovation management in this industry.
Potential and Economic Analysis of Solar-to-Hydrogen Production in the Sultanate of Oman
Authors: Ahshan Razzaqul
Published: August 2021

Hydrogen production using renewable power is becoming an essential pillar for future sustainable energy sector development worldwide. The Sultanate of Oman is presently integrating renewable power generations with a large share of solar photovoltaic (PV) systems. The possibility of using the solar potential of the Sultanate can increase energy security and contribute to the development of the sustainable energy sector not only for the country but also for the international community. This study presents the hydrogen production potential using solar resources available in the Sultanate. About 15 locations throughout the Sultanate are considered to assess the hydrogen production opportunity using a solar PV system. A rank of merit order of the locations for producing hydrogen is identified. It reveals that Thumrait and Marmul are the most suitable locations, whereas Sur is the least qualified. This study also assesses the economic feasibility of hydrogen production, which shows that the levelized cost of hydrogen (LCOH) in the most suitable site, Thumrait, is 6.31 USD/kg. The LCOH in the least convenient location, Sur, is 7.32 USD/kg. Finally, a sensitivity analysis is performed to reveal the most significant influential factor affecting the future’s green hydrogen production cost. The findings indicate that green hydrogen production using solar power in the Sultanate is promising, and the LCOH is consistent with other studies worldwide.
Autogenous Healing of Cracked Mortar Using Modified Steady-State Migration Test against Chloride Penetration
Authors: Abro Fahad ul Rehman , Buller Abdul Salam , Ali Tariq , Ul-Abdin Zain , Ahmed Zaheer , Memon Noor Ahmed , Lashari Ali Raza
Published: August 2021

Concrete is a popular building material all over the world, but because of different physiochemical processes, it is susceptible to crack development. One of the primary deterioration processes of reinforced concrete buildings is corrosion of steel bars within the concrete through these cracks. In this regard, a self-healing technique for crack repair would be the best solution to reduce the penetration of chloride ions inside concrete mass. In this study, a rapid chloride migration (RCM) test was conducted to determine the self-healing capacity of cracked mortar. With the help of the RCM test, the steady-state migration coefficient of cracked and uncracked specimens incorporating expansive and crystalline admixtures was calculated. Based on the rate of change of the chloride ion concentrations in the steady-state condition, the migration coefficient was calculated. Furthermore, bulk electrical conductivity tests were also conducted before and after the migration test to understand the self-healing behavior. It was evident from the test results that the self-healing of cracks was helpful to reduce the penetration of chloride ions and that it enhanced the ability of cracked mortar to restrict the chloride ingress. Using this test method, the self-healing capacity of the new self-healing technologies can be evaluated. The RCM test can be an acceptable technique to assess the self-healing ability of cement-based materials in a very short period, and the self-healing capacity can be characterized in terms of the decrease of chloride migration coefficients.