bluuebunny/crossref_metadata_2025 huggingface dataset:
Federated Learning for 5G Radio Spectrum Sensing
Authors: Wasilewska Małgorzata , Bogucka Hanna , Kliks Adrian
Published: December 2021

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.
Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review
Authors: Mennella Ciro , Alloisio Susanna , Novellino Antonio , Viti Federica
Published: December 2021

Technology-aided hand functional assessment has received considerable attention in recent years. Its applications are required to obtain objective, reliable, and sensitive methods for clinical decision making. This systematic review aims to investigate and discuss characteristics of technology-aided hand functional assessment and their applications, in terms of the adopted sensing technology, evaluation methods and purposes. Based on the shortcomings of current applications, and opportunities offered by emerging systems, this review aims to support the design and the translation to clinical practice of technology-aided hand functional assessment. To this end, a systematic literature search was led, according to recommended PRISMA guidelines, in PubMed and IEEE Xplore databases. The search yielded 208 records, resulting into 23 articles included in the study. Glove-based systems, instrumented objects and body-networked sensor systems appeared from the search, together with vision-based motion capture systems, end-effector, and exoskeleton systems. Inertial measurement unit (IMU) and force sensing resistor (FSR) resulted the sensing technologies most used for kinematic and kinetic analysis. A lack of standardization in system metrics and assessment methods emerged. Future studies that pertinently discuss the pathophysiological content and clinimetrics properties of new systems are required for leading technologies to clinical acceptance.
Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm
Authors: Wang Qingyan , Zhang Qi , Liang Xintao , Wang Yujing , Zhou Changyue , Mikulovich Vladimir Ivanovich
Published: December 2021

For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism. The shallow feature enhancement mechanism is used to extract features from the network and improve the network’s ability to locate small objects and color resolution by merging two shallow features at different stages with the high-level semantic features obtained after two rounds of upsampling. Uncertainty is introduced in the bounding box prediction mechanism to improve the reliability of the prediction of the bounding box by modeling the output coordinates of the prediction bounding box and adding the Gaussian model to calculate the uncertainty of the coordinate information. The LISA traffic light data set is used to perform detection and recognition experiments separately. The Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of traffic lights. In the detection experiment, the area under the PR curve value of the Improved YOLOv4 algorithm is found to be 97.58%, which represents an increase of 7.09% in comparison to the 90.49% score gained in the Vision for Intelligent Vehicles and Applications Challenge Competition. In the recognition experiment, the mean average precision of the Improved YOLOv4 algorithm is 82.15%, which is 2.86% higher than that of the original YOLOv4 algorithm. The Improved YOLOv4 algorithm shows remarkable advantages as a robust and practical method for use in the real-time detection and recognition of traffic signal lights.
Reduction of Coil-Crack Angle Sensitivity Effect Using a Novel Flux Feature of ACFM Technique
Authors: Huang Ruochen , Lu Mingyang , Chen Ziqi , Yin Wuliang
Published: December 2021

Alternating current field measurement (ACFM) testing is one of the promising techniques in the field of non-destructive testing with advantages of the non-contact capability and the reduction of lift-off effects. In this paper, a novel crack detection approach was proposed to reduce the effect of the angled crack (cack orientation) by using rotated ACFM techniques. The sensor probe is composed of an excitation coil and two receiving coils. Two receiving coils are orthogonally placed in the center of the excitation coil where the magnetic field is measured. It was found that the change of the x component and the peak value of the z component of the magnetic field when the sensor probe rotates around a crack followed a sine wave shape. A customized accelerated finite element method solver programmed in MATLAB was adopted to simulate the performance of the designed sensor probe which could significantly improve the computation efficiency due to the small crack perturbation. The experiments were also carried out to validate the simulations. It was found that the ratio between the z and x components of the magnetic field remained stable under various rotation angles. It showed the potential to estimate the depth of the crack from the ratio detected by combining the magnetic fields from both receiving coils (i.e., the x and z components of the magnetic field) using the rotated ACFM technique.
Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy
Authors: Tariq Hassan , Rashid Muhammad , Javed Asfa , Zafar Eeman , Alotaibi Saud S. , Zia Muhammad Yousuf Irfan
Published: December 2021

Diabetic retinopathy (DR) is a human eye disease that affects people who are suffering from diabetes. It causes damage to their eyes, including vision loss. It is treatable; however, it takes a long time to diagnose and may require many eye exams. Early detection of DR may prevent or delay the vision loss. Therefore, a robust, automatic and computer-based diagnosis of DR is essential. Currently, deep neural networks are being utilized in numerous medical areas to diagnose various diseases. Consequently, deep transfer learning is utilized in this article. We employ five convolutional-neural-network-based designs (AlexNet, GoogleNet, Inception V4, Inception ResNet V2 and ResNeXt-50). A collection of DR pictures is created. Subsequently, the created collections are labeled with an appropriate treatment approach. This automates the diagnosis and assists patients through subsequent therapies. Furthermore, in order to identify the severity of DR retina pictures, we use our own dataset to train deep convolutional neural networks (CNNs). Experimental results reveal that the pre-trained model Se-ResNeXt-50 obtains the best classification accuracy of 97.53% for our dataset out of all pre-trained models. Moreover, we perform five different experiments on each CNN architecture. As a result, a minimum accuracy of 84.01% is achieved for a five-degree classification.
Generative Adversarial Networks for Morphological–Temporal Classification of Stem Cell Images
Authors: Witmer Adam , Bhanu Bir
Published: December 2021

Frequently, neural network training involving biological images suffers from a lack of data, resulting in inefficient network learning. This issue stems from limitations in terms of time, resources, and difficulty in cellular experimentation and data collection. For example, when performing experimental analysis, it may be necessary for the researcher to use most of their data for testing, as opposed to model training. Therefore, the goal of this paper is to perform dataset augmentation using generative adversarial networks (GAN) to increase the classification accuracy of deep convolutional neural networks (CNN) trained on induced pluripotent stem cell microscopy images. The main challenges are: 1. modeling complex data using GAN and 2. training neural networks on augmented datasets that contain generated data. To address these challenges, a temporally constrained, hierarchical classification scheme that exploits domain knowledge is employed for model learning. First, image patches of cell colonies from gray-scale microscopy images are generated using GAN, and then these images are added to the real dataset and used to address class imbalances at multiple stages of training. Overall, a 2% increase in both true positive rate and F1-score is observed using this method as compared to a straightforward, imbalanced classification network, with some greater improvements on a classwise basis. This work demonstrates that synergistic model design involving domain knowledge is key for biological image analysis and improves model learning in high-throughput scenarios.
Extracting Rectified Building Footprints from Traditional Orthophotos: A New Workflow
Authors: Chen Qi , Zhang Yuanyi , Li Xinyuan , Tao Pengjie
Published: December 2021

Deep learning techniques such as convolutional neural networks have largely improved the performance of building segmentation from remote sensing images. However, the images for building segmentation are often in the form of traditional orthophotos, where the relief displacement would cause non-negligible misalignment between the roof outline and the footprint of a building; such misalignment poses considerable challenges for extracting accurate building footprints, especially for high-rise buildings. Aiming at alleviating this problem, a new workflow is proposed for generating rectified building footprints from traditional orthophotos. We first use the facade labels, which are prepared efficiently at low cost, along with the roof labels to train a semantic segmentation network. Then, the well-trained network, which employs the state-of-the-art version of EfficientNet as backbone, extracts the roof segments and the facade segments of buildings from the input image. Finally, after clustering the classified pixels into instance-level building objects and tracing out the roof outlines, an energy function is proposed to drive the roof outline to maximally align with the building footprint; thus, the rectified footprints can be generated. The experiments on the aerial orthophotos covering a high-density residential area in Shanghai demonstrate that the proposed workflow can generate obviously more accurate building footprints than the baseline methods, especially for high-rise buildings.
Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data
Authors: Muntean Maria Viorela
Published: December 2021

Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city’s traffic management.
A Novel Calibration Method for Gyro-Accelerometer Asynchronous Time in Foot-Mounted Pedestrian Navigation System
Authors: Chen Tianyu , Yang Gongliu , Cai Qingzhong , Wen Zeyang , Zhang Wenlong
Published: December 2021

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.
Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery
Authors: Lin Maohua , Abd Moaed A. , Taing Alex , Tsai Chi-Tay , Vrionis Frank D. , Engeberg Erik D.
Published: December 2021

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.
Multi-Incidence Holographic Profilometry for Large Gradient Surfaces with Sub-Micron Focusing Accuracy
Authors: Idicula Moncy Sajeev , Kozacki Tomasz , Józwik Michal , Mitura Patryk , Martinez-Carranza Juan , Choo Hyon-Gon
Published: December 2021

Surface reconstruction for micro-samples with large discontinuities using digital holography is a challenge. To overcome this problem, multi-incidence digital holographic profilometry (MIDHP) has been proposed. MIDHP relies on the numerical generation of the longitudinal scanning function (LSF) for reconstructing the topography of the sample with large depth and high axial resolution. Nevertheless, the method is unable to reconstruct surfaces with large gradients due to the need of: (i) high precision focusing that manual adjustment cannot fulfill and (ii) preserving the functionality of the LSF that requires capturing and processing many digital holograms. In this work, we propose a novel MIDHP method to solve these limitations. First, an autofocusing algorithm based on the comparison of shapes obtained by the LSF and the thin tilted element approximation is proposed. It is proven that this autofocusing algorithm is capable to deliver in-focus plane localization with submicron resolution. Second, we propose that wavefield summation for the generation of the LSF is carried out in Fourier space. It is shown that this scheme enables a significant reduction of arithmetic operations and can minimize the number of Fourier transforms needed. Hence, a fast generation of the LSF is possible without compromising its accuracy. The functionality of MIDHP for measuring surfaces with large gradients is supported by numerical and experimental results.
Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation
Authors: Wang Quanzeng , Zhou Yangling , Ghassemi Pejman , McBride David , Casamento Jon P. , Pfefer T. Joshua
Published: December 2021

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (Δcb), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δcb between ±0.03 °C with standard deviation (σΔcb) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37–38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.
Systematic Approach for Remote Sensing of Historical Conflict Landscapes with UAV-Based Laserscanning
Authors: Storch Marcel , Jarmer Thomas , Adam Mirjam , de Lange Norbert
Published: December 2021

In order to locate historical traces, drone-based Laserscanning has become increasingly popular in archaeological prospection and historical conflict landscapes research. The low resolution of aircraft-based Laserscanning is not suitable for small-scale detailed analysis so that high-resolution UAV-based LiDAR data are required. However, many of the existing studies lack a systematic approach to UAV-LiDAR data acquisition and point cloud filtering. We use this methodology to detect anthropogenic terrain anomalies. In this study, we systematically investigated different influencing factors on UAV-LiDAR data acquisition. The flight parameters speed and altitude above ground were systematically varied. In addition, different vegetation cover and seasonal acquisition times were compared, and we evaluated three different types of filter algorithms to separate ground from non-ground. It could be seen from our experiments that for the detection of subsurface anomalies in treeless open terrain, higher flight speeds like 6 m/s were feasible. Regarding the flight altitude, we recommend an altitude of 50–75 m above ground. At higher flight altitudes of 100–120 m above ground, there is the risk that terrain characteristics smaller than 50 cm will be missed. Areas covered with deciduous forest should only be surveyed during leaf-off season. In the presence of low-level vegetation (small bushes and shrubs with a height of up to 2 m), it turned out that the morphological filter was the most suitable. In tree-covered areas with total absence of near ground vegetation, however, the choice of filter algorithm plays only a subordinate role, especially during winter where the resulting ground point densities have a percentage deviation of less than 6% from each other.
Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review
Authors: Cho Gookbin , Azzouzi Sawsen , Zucchi Gaël , Lebental Bérengère
Published: December 2021

Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial technologies. In the present review, we show across 20 water monitoring analytes and 90 references that carbon nanotube-based electrochemical sensors, chemistors and field-effect transistors (chemFET) can meet these needs. A set of 126 additional references provide context and supporting information. After introducing water quality monitoring challenges, the general operation and fabrication principles of CNT water quality sensors are summarized. They are sorted by target analytes (pH, micronutrients and metal ions, nitrogen, hardness, dissolved oxygen, disinfectants, sulfur and miscellaneous) and compared in terms of performances (limit of detection, sensitivity and detection range) and functionalization strategies. For each analyte, the references with best performances are discussed. Overall, the most frequently investigated analytes are H+ (pH) and lead (with 18% of references each), then cadmium (14%) and nitrite (11%). Micronutrients and toxic metals cover 40% of all references. Electrochemical sensors (73%) have been more investigated than chemistors (14%) or FETs (12%). Limits of detection in the ppt range have been reached, for instance Cu(II) detection with a liquid-gated chemFET using SWCNT functionalized with peptide-enhanced polyaniline or Pb(II) detection with stripping voltammetry using MWCNT functionalized with ionic liquid-dithizone based bucky-gel. The large majority of reports address functionalized CNTs (82%) instead of pristine or carboxyl-functionalized CNTs. For analytes where comparison is possible, FET-based and electrochemical transduction yield better performances than chemistors (Cu(II), Hg(II), Ca(II), H2O2); non-functionalized CNTs may yield better performances than functionalized ones (Zn(II), pH and chlorine).
Sandwich Integration Technique for the Pressure Sensor Detection of Occlusal Force In Vitro
Authors: Gao Jinxia , Liu Longjun , Su Zhiwen , Wang Haitao
Published: December 2021

Bite force measurement is an important parameter when checking the function and integrity of the masticatory system, whereas it is currently very difficult to measure bite force during functional movement. Hence, the purpose of this study is to explore the potential technique and device for the measurement and intervention of the continuous bite forces on functional and dynamic occlusal condition. A portable biosensor by sandwich technique was designed, and the validity, reliability, and sensitivity were determined by mechanical pressure loading tests; meanwhile, the pressure signal is acquired by, and transmitted to, voltage changes by the electrical measurements of the sensors. The result is that, when the mechanical stress detection device is thicker than 3.5 mm, it shows relatively ideal mechanical properties; however, when the thickness is less than 3.0 mm, there is a risk of cracking. Mechanical stress changing and voltage variation had a regularity and positive relationship in this study. The mechanical stress-measuring device made by medical and industrial cross has a good application prospect for the measurement of bite force during function.
Irriman Platform: Enhancing Farming Sustainability through Cloud Computing Techniques for Irrigation Management
Authors: Forcén-Muñoz Manuel , Pavón-Pulido Nieves , López-Riquelme Juan Antonio , Temnani-Rajjaf Abdelmalek , Berríos Pablo , Morais Raul , Pérez-Pastor Alejandro
Published: December 2021

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier.
5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0
Authors: Chinchilla-Romero Lorena , Prados-Garzon Jonathan , Ameigeiras Pablo , Muñoz Pablo , Lopez-Soler Juan M.
Published: December 2021

Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.
Spearmint (Mentha spicata) Cultivation in Decoupled Aquaponics with Three Hydro-Components (Grow Pipes, Raft, Gravel) and African Catfish (Clarias gariepinus) Production in Northern Germany
Authors: Knaus Ulrich , Zimmermann Julia , Appelbaum Samuel , Palm Harry W.
Published: December 2021

Mint (Mentha spicata) was cultivated in different hydroponic components: grow pipes, a raft and an ebb-and-flood gravel substrate system irrigated with aquaculture effluents from intensive African catfish (Clarias gariepinus) production under decoupled aquaponic conditions in northern Germany. The spearmint grew well and plant heights above ground were not significantly different between the gravel (57.7 ± 13.1 cm), raft (58.0 ± 17.7 cm) and grow pipe components (63.6 ± 9.9 cm). Root lengths and root fresh weights were two-fold and four-fold higher in raft (64.3 ± 20.5 cm; 42.8 ± 29.9 g) and grow pipes (59.4 ± 15.2 cm; 41.3 ± 25.7 g) compared with gravel substrate (29.7 ± 7.8 cm; 9.4 ± 9.4 g; raft = grow pipes > gravel). Spearmint leaf number was significantly higher in the grow pipes (770.0 ± 224.4) than in the gravel substrate (499.8 ± 228.4) with intermediate values in the raft. Significantly highest mean fresh biomass was found in the raft (1275.6 ± 33.4 g), followed by grow pipes (1042.0 ± 35.8 g) and gravel substrate (686.3 ± 98.2 g; raft > grow pipes > gravel). M. spicata, under aquaponics, grows best in grow pipe and raft components. An increase in pipe diameter for the grow pipes and a reduction in the channel height for the raft components could optimize aquaponic culture conditions for both industrial production and the hobby sector in the future.
Mitigating Harmful Effects of Climate Warming on Ceiling Paintings by Ceiling Insulation: An Evaluation Using Timed IR Imaging and Numeric Modelling
Authors: Kain Günther , Idam Friedrich , Huber Alfons , Mudri Martin , Petutschnigg Alexander , Goldsteiner Markus
Published: December 2021

Due to climate change, ceiling paintings in many historic buildings are subjected to increasingly high short-term temperature change, resulting in high thermal tension caused by the construction assembly. This article focuses on the combined use of timed IR imaging and numeric modelling to evaluate insulation measures on the upper side of a ceiling to reduce thermal tensions in the painting layers, overheating in summer as well as cooling down in winter. As a model room, the southern splendour stair hall in the Burgtheater Vienna was chosen. Famous ceiling paintings created from 1886 to 1888 by Gustav Klimt and his brother Ernst Klimt can be found on this ceiling. The results show that timed IR imaging is an adequate tool to study the transient thermal behaviour of ceiling paintings which are not accessible to standard sensor measurements. Moreover, it could be shown that the presented measurement technique is well suited to validate a numeric model. The latter was applied to evaluate the potential insulation on the top of the ceiling. It was shown that cooling loads and energy loss in the room underneath can be reduced and most importantly the thermal stress in painting layers is reduced. The findings are relevant as, due to global warming, the current situation in many buildings is worsening. Considering the great intangible cultural value of many ceiling paintings, the application of the presented evaluation strategy for building physical boundaries on a ceiling with paintings seems to be appropriate.
Archimedes Optimization Algorithm Based Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter
Authors: Khan Rashid Ahmed , Farooqui Shoeb Azam , Sarwar Mohammad Irfan , Ahmad Seerin , Tariq Mohd , Sarwar Adil , Zaid Mohammad , Ahmad Shafiq , Shah Noor Mohamed Adamali
Published: December 2021

This paper presents the Archimedes optimization algorithm to eliminate selective harmonics in a cascaded H-bridge (CHB) multilevel inverter (MLI). The foremost objective of the selective harmonic elimination (SHE) is to eliminate lower order harmonics by finding the optimal switching angle combination which minimizes the objective function containing Total Harmonic Distortion (THD) and other specific harmonic terms. Consequently, the THD is also reduced. In this study, a recently proposed metaheuristic technique named the Archimedes optimization algorithm (AOA) is used to determine the optimal angles corresponding to the 5, 7 and 9 level CHB-MLI. AOA involves equations related to a physical law, the Archimedes Principle. It is based on the idea of a buoyant force acting upward on a body or object that is partially or completely submerged in a fluid, and the upward force is related to the weight of the fluid displaced. This optimization technique has been implemented on CHB-MLI to generate various level outputs, simulated on MATLAB™ R2021a version environment software. The simulation results reveal that AOA is a high-performance optimization technique in terms of convergence speed and exploitation-exploration balance and is well-suited to the solution of the SHE problem. Furthermore, the laboratory validated the simulation result on a hardware setup using DSP-TMS320F28379D.
Study of STEM for Sustainability in Design Education: Framework for Student Learning and Outcomes with Design for a Disaster Project
Authors: Chan Ming-Ni , Nagatomo Daisuke
Published: December 2021

STEM has successfully introduced an interdisciplinary education model that can be used for training students to develop skillsets for the 21st century. STEM Education for Sustainability (STEM4S) expands the scope of education to meet rapidly changing global challenges, such as climate change and SDGs by the United Nations, which require the multidisciplinary curriculum to be integrated into STEM. Design-based tasks play a significant role in STEM education by promoting students’ critical thinking and problem-solving abilities. While STEM successfully employs design, design education currently conducts subjective procedures and lacks the framework for adopting the critical-thinking process. Therefore, design education can develop students’ cognitive skills by reflecting on STEM learning experiences. This study articulates the framework for design education by investigating problem-based and project-based learning and the double-diamond diagram for innovation. The goal of this study was to apply these observations and formulate the framework for STEM4S. This study examined the framework adopted at the National Taiwan Normal University in the Department of Design, with qualitative analysis of participants and quantitative analysis of questionnaire results. Finally, the researchers discuss the research questions and future applications of this framework.
Unlocking the Potential of Fish to Improve Food and Nutrition Security in Sub-Saharan Africa
Authors: Muringai Rodney T. , Mafongoya Paramu , Lottering Romano T. , Mugandani Raymond , Naidoo Denver
Published: December 2021

Approximately one-third of the global population suffering from chronic hunger are in sub-Saharan Africa (SSA). In addition to high prevalence of chronic hunger, millions of people suffer from micronutrient deficiencies. Meanwhile, there is growing consensus across scientific disciplines concurring that fish plays a crucial role in improving food and nutrition security. Therefore, the present review aims to demonstrate the role of fish and the whole fisheries sector towards securing food and nutrition security in SSA by summarizing the existing literature. Fish is a treasure store of animal protein and essential micronutrients such as zinc, iodine, calcium, and vitamins, which are essential in human nutrition and have proven to help reduce the risks of both malnutrition and non-communicable diseases. Policymakers, development agencies, and society should recognize the role that the fisheries sector can play in combatting hunger and undernutrition, especially for the poor and marginalized people in SSA.
Effect of Compost and Titanium Dioxide Application on the Vegetative Yield and Essential Oil Composition of Coriander
Authors: Khater Rania M. R. , Sabry Reham M. , Pistelli Luisa , Abd-ElGawad Ahmed M. , Soufan Walid , El-Gendy Abdel Nasser G.
Published: December 2021

Coriander is one of the most popular and intensely used spices owing to its multipurpose uses worldwide. It is mainly cultivated for the production of its dried seed and fresh leaves. The present study aimed to evaluate the application of compost and foliar spraying of TiO2 on the yield and essential oil composition of coriander. Two field experiments were conducted during two successive seasons; after that, the yield parameters were determined, and the essential oil of the seeds was extracted and analyzed via gas chromatography-mass spectrometry (GC-MS). Results indicated that coriander growths at both years were significantly affected by compost application and foliar application of TiO2, and a significant interaction of these two factors also occurred. Compost application at 50 m3 caused significant increments of 55% and 46% in umbels number and 75% and 64% in seed yield in the first and second season, respectively, compared with control. The application of compost to the coriander plant significantly influenced oil percentage and oil yield per ha. The maximum oil percent was recorded in control plants. Foliar application of TiO2 resulted in significant improvement in plant height, number of umbels, and seed yield of coriander as compared with control and reached their maximum values at 6 g L−1 compared with the control; foliar application of TiO2 at 2 g L−1 enhanced numbers of umbels by 22% with no significant differences between 2, 4, and 6 g L−1 treatments in the first season and by 24, 33, and 48% in the second season. Increases in seed yield accounted for 34, 43, and 64% in the first season and 21, 36, and 45% in the second season due to titanium dioxide application of 2, 4, and 6 g L−1, respectively. The maximum content of linalool (87.61%) and minimum content of estragole (0.7%) was recorded at 4 g L−1 titanium dioxide with no compost.
Improvement of the EC Performance in LCP-MOF Electrode Materials by Succinic Anhydrate Addition to the Electrolyte
Authors: Moustafa Mohamed Gaber , Aboraia Abdelaziz Mohamed , Butova Vera , Guda Alexander , Elmasry Fatma , Soldatov Alexander
Published: December 2021

The optimization of the electrolyte composition for a canonical cathode such as LiCoPO4 olivine. The implemented succinic anhydride within a liquid electrolyte LiPF6 and dissolved in carbonate/diethyl considerably improves the discharge capacity of the electrode are shown. The introduction of succinic anhydride into the solid/electrolyte interphase (SEI) layer is responsible for the improved electrochemical performance of the electrode. We used LiCoPO4@C-ZrO2 as a cathode to prove the concept. The observed results could be applied for a wide range of cathodes. Moreover, the proposed additive to the electrolyte could help evaluate the performance of the materials without the side effects of the electrolyte.
Changes in the Physico-Chemical Properties of Degraded Soils in Response to the ReviTec Approach Applied at Gawel (Far-North Cameroon)
Authors: Tchida Pascal Blaise , Ngakou Albert , Kesel Raimund , Koehler Hartmut
Published: December 2021

A heavily degraded hardé area in the community of Gawel (Extreme Nord) was rehabilitated with a ReviTec approach. To evaluate the efficiency of introduced plants for rehabilitation, the site was monitored with the BioSoilPlot experiment over two years during the dry and rainy seasons (January 2018/2019, June 2018/2019, respectively). ReviTec was applied to accelerate ecological succession and improve the establishment of vegetation on these degradation-prone sites (Gawel 1 and Gawel 2). Growth volume, height and percentage cover of the herbaceous plants and soil physical and chemical parameters were assessed. Growth volume increased in both sites between 2018 and 2019 with Pennisetum pedicellatum exhibiting the highest values (Gawel 1: 3.41 dm3/m2 and 3.50 dm3/m2; Gawel 2: 3.47 dm3/m2 and 3.62 dm3/m2). Bunds were suitable for herbaceous growth, Sesbania sesban having the highest growth height (Gawel 1: 1.91 cm and 1.95 cm; Gawel 2: 1.95 cm and 1.97 cm) and Pennisetum pedicellatum the best percentage cover (Gawel 1: 53% and 58.33%; Gawel 2: 40.67% and 56.67%). Soil changed from sandy and strongly acid to clay-loam and slightly acid at Gawel 1, and from sandy and strongly acid to clay-loam and alkaline at Gawel 2. Soil water content and soil nutrient had increased within the ReviTec site compared to the outside with the application of compost-biochar-mycorrhiza treatment as the most promising over the two years of monitoring. Such results suggest that ReviTec approach can be used in sustainable restoration of soil hardé.
Hybrid Bayesian Network Models to Investigate the Impact of Built Environment Experience before Adulthood on Students’ Tolerable Travel Time to Campus: Towards Sustainable Commute Behavior
Authors: Chen Yu , Aghaabbasi Mahdi , Ali Mujahid , Anciferov Sergey , Sabitov Linar , Chebotarev Sergey , Nabiullina Karina , Sychev Evgeny , Fediuk Roman , Zainol Rosilawati
Published: December 2021

This present study developed two predictive and associative Bayesian network models to forecast the tolerable travel time of university students to campus. This study considered the built environment experiences of university students during their early life-course as the main predictors of this study. The Bayesian network models were hybridized with the Pearson chi-square test to select the most relevant variables to predict the tolerable travel time. Two predictive models were developed. The first model was applied only to the variables of the built environment, while the second model was applied to all variables that were identified using the Pearson chi-square tests. The results showed that most students were inclined to choose the tolerable travel time of 0–20 min. Among the built environment predictors, the availability of residential buildings in the neighborhood in the age periods of 14–18 was the most important. Taking all the variables into account, distance from students’ homes to campuses was the most important. The findings of this research imply that the built environment experiences of people during their early life-course may affect their future travel behaviors and tolerance. Besides, the outcome of this study can help planners create more sustainable commute behaviors among people in the future by building more compact and mixed-use neighborhoods.
Industrial Wastes-Cum-Strength Enhancing Additives Incorporated Lightweight Aggregate Concrete (LWAC) for Energy Efficient Building: A Comprehensive Review
Authors: Kumar Rajesh , Srivastava Abhishek , Lakhani Rajni
Published: December 2021

Lightweight aggregate concrete (LWAC) exhibits the advantages of thermal insulation, reduces energy consumption building costs, improves building efficiency and easy construction. Furthermore, the utilization of industrial wastes in concrete is advantageous in terms of environmental sustainability. In order to explore this, several researchers investigated the idea of integrating industrial wastes in LWAC. However, the lack of knowledge regarding the performance of industrial waste-based lightweight aggregate concrete hinders the adaptation of this concept and application of LWAC in the construction sector. Therefore, this paper summarizes the research in relation to the sustainable LWACs containing oil palm shell (OPS), lightweight expanded clay aggregate (LECA), vermiculite, perlite, pumice and sintered fly ash as lightweight aggregate, along with industrial wastes and strength-enhancing additives (viz. fibers, polymers, etc.). Firstly, desirable physical, chemical, morphological and mineralogical characterization of different lightweight aggregates are presented, and then a comprehensive overview on fresh, hardened, durability and thermal properties of LWAC incorporating industrial wastes are discussed in comparison with normal weight concrete. The review also highlights the current challenges and suggests the research gaps for further development of eco-friendly LWAC. It is concluded that vermiculite, perlite, pumice, OPS, sintered fly ash and LECA with some suitable industrial waste materials have the potential to be used in the construction sector. Moreover, LWAC with industrial waste has 50–65% lower carbon emission (kg CO2 eq/m3) in the environment. The scientific contribution of this paper provides insights into different LWACs and the knowledge base for future research and paradigm shift of using LWACs as more common alternative building materials.