bluuebunny/crossref_metadata_2025 huggingface dataset:
Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function
Authors: Ławryńczuk Maciej , Nebeluk Robert
Published: September 2021

Model Predictive Control (MPC) algorithms typically use the classical L2 cost function, which minimises squared differences of predicted control errors. Such an approach has good numerical properties, but the L1 norm that measures absolute values of the control errors gives better control quality. If a nonlinear model is used for prediction, the L1 norm leads to a difficult, nonlinear, possibly non-differentiable cost function. A computationally efficient alternative is discussed in this work. The solution used consists of two concepts: (a) a neural approximator is used in place of the non-differentiable absolute value function; (b) an advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is obtained in place of the nonlinear one. Advantages of the presented solution are discussed for a simulated neutralisation benchmark. It is shown that the obtained trajectories are very similar, practically the same, as those possible in the reference scheme with nonlinear optimisation. Furthermore, the L1 norm even gives better performance than the classical L2 one in terms of the classical control performance indicator that measures squared control errors.
PEA Electromagnetic Distortion Reduction by Impedance Grounding and Pulsed Voltage Electrode Configurations
Authors: Mier Escurra Guillermo , Rodrigo Mor Armando , Castro Luis Carlos , Vaessen Peter
Published: September 2021

Space charges are one of the main challenges facing the constantly increasing use of extruded high voltage direct current (HVDC) cables. The Pulsed Electro-Acoustic (PEA) method is one of the most common procedures for space charge measurements of insulation. One issue with the PEA method is distortion due to the crosstalk between the applied voltage pulse and the acoustic sensor. This work analyzed two factors involved in the reduction in this distortion: the influence of the exposed semiconductor distance between the injection electrodes and PEA test cell, and the influence of adding a reactance at the grounding circuit of the PEA test cell. The interaction of these two factors with the distortion was analyzed through a series of experimental testing. Moreover, the performance regarding distortion after applying a developed coaxial injection was compared with the standard non-coaxial injection configuration. It was observed that these two factors had a direct impact on distortion and can be utilized for the reduction in distortion arising from the crosstalk of the applied pulsed voltage. The results can be utilized for the consideration of practical aspects during the construction of a PEA test setup for the measurement of full-size HVDC cables.
A Key Management Protocol Based on the Hash Chain Key Generation for Securing LoRaWAN Networks
Authors: Hakeem Shimaa A. Abdel , El-Kader Sherine M. Abd , Kim HyungWon
Published: September 2021

Recently, many Low Power Wide Area Network (LPWAN) protocols have been proposed for securing resource-constrained Internet of Things (IoT) devices with negligible power consumption. The Long Range Wide Area Network (LoRaWAN) is a low power communication protocol that supports message authentication, integrity, and encryption using two-session preshared secret keys. However, although the LoRaWAN supports some security functions, it suffers from session key generation and key update problems. This motivates us to introduce a new key management protocol that resolves the LoRaWAN problems and supports key updates. The proposed protocol is based on hash chain generation using a one-way hash function. Network entities share a common hash chain of n key elements to allow using a unique signing key per message. We also propose a salt hashing algorithm that encrypts the original keys into a different form to avoid the physical attacks at the end device side. We analyzed the proposed key generation performance in terms of the computation time, the required storage, and the communication overhead. We implemented and tested the proposed key generation protocol using the NS-3 network simulator. The proposed lightweight key generation protocol significantly enhances the security of the original LoRaWAN at a negligible overhead. The proposed protocol reduces the power consumption and transmission time by two times compared with some previous protocols. In addition, the proposed key generation protocol can resist attacks, such as key compromising attacks and replay attacks, and it supports the Perfect Forward Secrecy, which was not supported by LoRaWAN.
Carbon Nanotube Wearable Sensors for Health Diagnostics
Authors: Rdest Monika , Janas Dawid
Published: September 2021

This perspective article highlights a recent surge of interest in the application of textiles containing carbon nanotube (CNT) sensors for human health monitoring. Modern life puts more and more pressure on humans, which translates into an increased number of various health disorders. Unfortunately, this effect either decreases the quality of life or shortens it prematurely. A possible solution to this problem is to employ sensors to monitor various body functions and indicate an upcoming disease likelihood at its early stage. A broad spectrum of materials is currently under investigation for this purpose, some of which already entered the market. One of the most promising materials in this field are CNTs. They are flexible and of high electrical conductivity, which can be modulated upon several forms of stimulation. The article begins with an illustration of techniques for how wearable sensors can be built from them. Then, their application potential for tracking various health parameters is presented. Finally, the article ends with a summary of this field’s progress and a vision of the key directions to domesticate this concept.
ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
Authors: Kim Cheonyong , Kim Joobum , Kim Ki-Hyeon , Lee Sang-Kwon , Kim Kiwook , Shah Syed Asif Raza , Goo Young-Hoon
Published: September 2021

Here, we introduce the current stage and future directions of the wireless infrastructure of the Korea Research Environment Open NETwork (KREONET), a representative national research and education network in Korea. In 2018, ScienceLoRa, a pioneering wireless network infrastructure for scientific applications based on low-power wide-area network technology, was launched. Existing in-service applications in monitoring regions, research facilities, and universities prove the effectiveness of using wireless infrastructure in scientific areas. Furthermore, to support the more stringent requirements of various scientific scenarios, ScienceLoRa is evolving toward ScienceIoT by employing high-performance wireless technology and distributed computing capability. Specifically, by accommodating a private 5G network and an integrated edge computing platform, ScienceIoT is expected to support cutting-edge scientific applications requiring high-throughput and distributed data processing.
Evaluating Localization Accuracy of Automated Driving Systems
Authors: Rehrl Karl , Gröchenig Simon
Published: September 2021

Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.
American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation
Authors: Shin Jungpil , Matsuoka Akitaka , Hasan Md. Al Mehedi , Srizon Azmain Yakin
Published: September 2021

Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due to the cost-effectiveness of vision-based approaches, researchers have been conducted here also despite the accuracy drop. The purpose of this research is to recognize American sign characters using hand images obtained from a web camera. In this work, the media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: one is the distances between the joint points and the other one is the angles between vectors and 3D axes. The classifiers utilized to classify the characters were support vector machine (SVM) and light gradient boosting machine (GBM). Three character datasets were used for recognition: the ASL Alphabet dataset, the Massey dataset, and the finger spelling A dataset. The results obtained were 99.39% for the Massey dataset, 87.60% for the ASL Alphabet dataset, and 98.45% for Finger Spelling A dataset. The proposed design for automatic American sign language recognition is cost-effective, computationally inexpensive, does not require any special sensors or devices, and has outperformed previous studies.
Modeling, Simulation Methods and Characterization of Photon Detection Probability in CMOS-SPAD
Authors: Panglosse Aymeric , Martin-Gonthier Philippe , Marcelot Olivier , Virmontois Cédric , Saint-Pé Olivier , Magnan Pierre
Published: September 2021

Single-Photon Avalanche Diodes (SPAD) in Complementary Metal-Oxide Semiconductor (CMOS) technology are potential candidates for future “Light Detection and Ranging” (Lidar) space systems. Among the SPAD performance parameters, the Photon Detection Probability (PDP) is one of the principal parameters. Indeed, this parameter is used to evaluate the SPAD sensitivity, which directly affects the laser power or the telescope diameter of space-borne Lidars. In this work, we developed a model and a simulation method to predict accurately the PDP of CMOS SPAD, based on a combination of measurements to acquire the CMOS process doping profile, Technology Computer-Aided Design (TCAD) simulations, and a Matlab routine. We compare our simulation results with a SPAD designed and processed in CMOS 180 nm technology. Our results show good agreement between PDP predictions and measurements, with a mean error around 18.5%, for wavelength between 450 and 950 nm and for a typical range of excess voltages between 15 and 30% of the breakdown voltage. Due to our SPAD architecture, the high field region is not entirely insulated from the substrate, a comparison between simulations performed with and without the substrate contribution indicates that PDP can be simulated without this latter with a moderate loss of precision, around 4.5 percentage points.
Processing and Analysis of Long-Range Scans with an Atomic Force Microscope (AFM) in Combination with Nanopositioning and Nanomeasuring Technology for Defect Detection and Quality Control
Authors: Ortlepp Ingo , Stauffenberg Jaqueline , Manske Eberhard
Published: September 2021

This paper deals with a planar nanopositioning and -measuring machine, the so-called nanofabrication machine (NFM-100), in combination with a mounted atomic force microscope (AFM). This planar machine has a circular moving range of 100 mm. Due to the possibility of detecting structures in the nanometre range with an atomic force microscope and the large range of motion of the NFM-100, structures can be analysed with high resolution and precision over large areas by combining the two systems, which was not possible before. On the basis of a grating sample, line scans over lengths in the millimetre range are demonstrated on the one hand; on the other hand, the accuracy as well as various evaluation methods are discussed and analysed.
Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System
Authors: Wang Qiupeng , Sun Xiaohui , Wen Chenglin
Published: September 2021

This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical properties. Firstly, the transfer function and measurement function are transformed into a nonlinear random dynamic model with a polynomial form via system identification through the multidimensional Taylor network. Secondly, the higher order polynomials in the transformed state model and measurement model are defined as implicit variables of the system. At the same time, the state model and the measurement model are equivalent to the pseudolinear model based on the combination of the original variable and the hidden variable. Thirdly, higher order hidden variables are treated as additive parameters of the system; then, we establish an extended dimensional linear state model and a measurement model combining state and parameters via the previously used random dynamic model. Finally, as we only know the results of the limited sampling of the random modeling error, we use the combination of the maximum correlation estimator and the Kalman filter to establish a new higher order extended Kalman filter. The effectiveness of the new filter is verified by digital simulation.
A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum
Authors: Purbowaskito Widagdo , Lan Chen-Yang , Fuh Kenny
Published: September 2021

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.
EXecutive-Functions Innovative Tool (EXIT 360°): A Usability and User Experience Study of an Original 360°-Based Assessment Instrument
Authors: Borgnis Francesca , Baglio Francesca , Pedroli Elisa , Rossetto Federica , Isernia Sara , Uccellatore Lidia , Riva Giuseppe , Cipresso Pietro
Published: September 2021

Over the last few decades, several studies have shown the feasibility, acceptability, and efficacy of VR-based instruments in the early evaluation of executive dysfunction (ED) in psychiatric and neurologic conditions. Due to the negative impact of ED in everyday functioning, identifying innovative strategies for evaluating ED allows clinicians to detect executive impairment early and minimize its effects. This work aimed to test the usability and user experience (UX) of EXecutive-functions Innovative Tool 360° (EXIT 360°), a 360°-based tool for assessing ED. Seventy-six healthy subjects underwent an evaluation that involved (1) usability assessment using System Usability Scale and (2) evaluation of UX using the ICT-Sense of Presence and UX Questionnaire. Results showed a satisfactory level of usability (mean = 75.9 ± 12.8), with good scores for usability and learnability. As regards UX, EXIT 360° showed an absence of negative effects (mean = 1.79 ± 0.95) and high scores in ecological validity (mean = 4.32 ± 0.54) and engagement (mean = 3.76 ± 0.56). Moreover, it obtained good scores in efficiency (mean = 1.84 ± 0.84), originality (mean = 2.49 ± 0.71), and attractiveness (mean = 1.93 ± 0.98). Interestingly, demographic characteristics and technological expertise had no impact on the performance (p > 0.05). Overall, EXIT 360° appeared to be a usable, learn-to-use, engaging, and creative tool with irrelevant negative effects. Further studies will be conducted to evaluate these aspects in the clinical population.
The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions
Authors: Sharifi Renani Mohsen , Eustace Abigail M. , Myers Casey A. , Clary Chadd W.
Published: September 2021

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven models require comprehensive and representative training datasets to be generalizable across the movement variability seen in the population at large. Bottlenecks in model development frequently occur due to the lack of sufficient training data and the significant time and resources necessary to acquire these datasets. Reliable methods to generate synthetic biomechanical training data could streamline model development and potentially improve model performance. In this study, we developed a methodology to generate synthetic kinematics and the associated predicted IMU signals using open source musculoskeletal modeling software. These synthetic data were used to train neural networks to predict three degree-of-freedom joint rotations at the hip and knee during gait either in lieu of or along with previously measured experimental gait data. The accuracy of the models’ kinematic predictions was assessed using experimentally measured IMU signals and gait kinematics. Models trained using the synthetic data out-performed models using only the experimental data in five of the six rotational degrees of freedom at the hip and knee. On average, root mean square errors in joint angle predictions were improved by 38% at the hip (synthetic data RMSE: 2.3°, measured data RMSE: 4.5°) and 11% at the knee (synthetic data RMSE: 2.9°, measured data RMSE: 3.3°), when models trained solely on synthetic data were compared to measured data. When models were trained on both measured and synthetic data, root mean square errors were reduced by 54% at the hip (measured + synthetic data RMSE: 1.9°) and 45% at the knee (measured + synthetic data RMSE: 1.7°), compared to measured data alone. These findings enable future model development for different activities of clinical significance without the burden of generating large quantities of gait lab data for model training, streamlining model development, and ultimately improving model performance.
Automatic Measurement of the Carotid Blood Flow for Wearable Sensors: A Pilot Study
Authors: Matera Riccardo , Ricci Stefano
Published: September 2021

The assessment of the velocity of blood flowing in the carotid, in modern clinical practice, represents an important exam performed both in emergency situations and as part of scheduled screenings. It is typically performed by an expert sonographer who operates a complex and costly clinical echograph. Unfortunately, in developing countries, in rural areas, and even in crowded modern cities, the access to this exam can be limited by the lack of suitable personnel and ultrasound equipment. The recent availability of low-cost, handheld devices has contributed to solving part of the problem, but a wide access to the exam is still hampered by the lack of expert sonographers. In this work, an automated procedure is presented with the hope that, in the near future, it can be integrated into a low-cost, handheld instrument that is also suitable for self-measurement, for example, as can be done today with the finger oximeter. The operator should only place the probe on the neck, transversally with respect to the common tract of the carotid. The system, in real-time, automatically locates the vessel lumen, places the sample volume, and performs an angle-corrected velocity measurement of the common carotid artery peak velocity. In this study, the method was implemented for testing on the ULA-OP 256 scanner. Experiments on flow phantoms and volunteers show a performance in sample volume placement similar to that achieved by expert operators, and an accuracy and repeatability of 3.2% and 4.5%, respectively.
Supramolecular Atropine Potentiometric Sensor
Authors: Ferreira Catarina , Palmeira Andreia , Sousa Emília , Amorim Célia G. , Araújo Alberto Nova , Montenegro Maria Conceição
Published: September 2021

A supramolecular atropine sensor was developed, using cucurbit[6]uril as the recognition element. The solid-contact electrode is based on a polymeric membrane incorporating cucurbit[6]uril (CB[6]) as an ionophore, 2-nitrophenyl octyl ether as a solvent mediator, and potassium tetrakis (4-chlorophenyl) borate as an additive. In a MES-NaOH buffer at pH 6, the performance of the atropine sensor is characterized by a slope of (58.7 ± 0.6) mV/dec with a practical detection limit of (6.30 ± 1.62) × 10−7 mol/L and a lower limit of the linear range of (1.52 ± 0.64) × 10−6 mol/L. Selectivity coefficients were determined for different ions and excipients. The obtained results were bolstered by the docking and spectroscopic studies which demonstrated the interaction between atropine and CB[6]. The accuracy of the potentiometric analysis of atropine content in certified reference material was evaluated by the t-Student test. The herein proposed sensor answers the need for reliable methods providing better management of this hospital drug shelf-life while reducing its flush and remediation costs.
Comprehensive Optimization of the Tripolar Concentric Ring Electrode Based on Its Finite Dimensions Model and Confirmed by Finite Element Method Modeling
Authors: Makeyev Oleksandr , Ye-Lin Yiyao , Prats-Boluda Gema , Garcia-Casado Javier
Published: September 2021

The optimization performed in this study is based on the finite dimensions model of the concentric ring electrode as opposed to the negligible dimensions model used in the past. This makes the optimization problem comprehensive, as all of the electrode parameters including, for the first time, the radius of the central disc and individual widths of concentric rings, are optimized simultaneously. The optimization criterion used is maximizing the accuracy of the surface Laplacian estimation, as the ability to estimate the Laplacian at each electrode constitutes primary biomedical significance of concentric ring electrodes. For tripolar concentric ring electrodes, the optimal configuration was compared to previously proposed linearly increasing inter-ring distances and constant inter-ring distances configurations of the same size and based on the same finite dimensions model. The obtained analytic results suggest that previously proposed configurations correspond to almost two-fold and more than three-fold increases in the Laplacian estimation error compared with the optimal configuration proposed in this study, respectively. These analytic results are confirmed using finite element method modeling, which was adapted to the finite dimensions model of the concentric ring electrode for the first time. Moreover, the finite element method modeling results suggest that optimal electrode configuration may also offer improved sensitivity and spatial resolution.
Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot
Authors: Pérez-Reynoso Francisco David , Rodríguez-Guerrero Liliam , Salgado-Ramírez Julio César , Ortega-Palacios Rocío
Published: September 2021

People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.
Electrical Detection of Innate Immune Cells
Authors: Al Ahmad Mahmoud , Nasser Rasha A. , Olule Lillian J. A. , Ali Bassam R.
Published: September 2021

Accurately classifying the innate immune players is essential to comprehensively and quantitatively evaluate the interactions between the innate and the adaptive immune systems. In addition, accurate classification enables the development of models to predict behavior and to improve prospects for therapeutic manipulation of inflammatory diseases and cancer. Rapid development in technologies that provide an accurate definition of the type of cell in action, allows the field of innate immunity to the lead in therapy developments. This article presents a novel immunophenotyping technique using electrical characterization to differentiate between the two most important cell types of the innate immune system: dendritic cells (DCs) and macrophages (MACs). The electrical characterization is based on capacitance measurements, which is a reliable marker for cell surface area and hence cell size. We differentiated THP-1 cells into DCs and MACs in vitro and conducted electrical measurements on the three cell types. The results showed average capacitance readings of 0.83 µF, 0.93 µF, and 1.01 µF for THP-1, DCs, and MACs, respectively. This corresponds to increasing cell size since capacitance is directly proportional to area. The results were verified with image processing. Image processing was used for verification because unlike conventional techniques, especially flow cytometry, it avoids cross referencing and by-passes the limitation of a lack of specificity of markers used to detect the different cell types.
Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks
Authors: Figueroa Barraza Joaquín , López Droguett Enrique , Martins Marcelo Ramos
Published: September 2021

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.
A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments
Authors: Zhang Yu , Xu Xiping , Zhang Ning , Lv Yaowen
Published: September 2021

When a traditional visual SLAM system works in a dynamic environment, it will be disturbed by dynamic objects and perform poorly. In order to overcome the interference of dynamic objects, we propose a semantic SLAM system for catadioptric panoramic cameras in dynamic environments. A real-time instance segmentation network is used to detect potential moving targets in the panoramic image. In order to find the real dynamic targets, potential moving targets are verified according to the sphere’s epipolar constraints. Then, when extracting feature points, the dynamic objects in the panoramic image are masked. Only static feature points are used to estimate the pose of the panoramic camera, so as to improve the accuracy of pose estimation. In order to verify the performance of our system, experiments were conducted on public data sets. The experiments showed that in a highly dynamic environment, the accuracy of our system is significantly better than traditional algorithms. By calculating the RMSE of the absolute trajectory error, we found that our system performed up to 96.3% better than traditional SLAM. Our catadioptric panoramic camera semantic SLAM system has higher accuracy and robustness in complex dynamic environments.
An Ethnographic Perspective of Well-Being, Salutogenesis and Meaning Making among Refugees and Asylum Seekers in the Gambia and the United Kingdom
Authors: Wenning Brianne
Published: September 2021

Research on refugees and asylum seekers largely focuses on the negative impacts that forced migration has on well-being. Though most individuals do not experience poor long-term mental health because of forced migration, less attention has been given to what factors promote positive well-being. Using an ethnographic approach, I elucidate how the concept of salutogenesis can be applied to African refugees and asylum seekers living in the greater Serrekunda area of the Gambia and in Newcastle-upon-Tyne in the United Kingdom. Specifically, I explore what resources impact on the sense of coherence construct and its three components—comprehensibility, manageability and meaningfulness—and how these are embedded in everyday discussions and understandings. In total, I spent twenty months conducting ethnographic fieldwork between the two sites and conducted forty individual interviews. Amongst my interlocutors, the three most common resources that people spoke positively about, particularly as it relates to meaning making, are work, education and religion. Further research in this area is crucial in order to identify, promote and strengthen those factors facilitating positive well-being amongst those who have been forcibly displaced.
Associations between Testing and Game Performance in Ice Hockey: A Scoping Review
Authors: Huard Pelletier Vincent , Glaude-Roy Julien , Daigle André-Philipe , Brunelle Jean-François , Bissonnette Antoine , Lemoyne Jean
Published: September 2021

Background: Despite the exhaustive body of literature on the demands of ice hockey, less is known about the relationships between functional performance testing protocols (on ice and off ice) and performance in a game situation. The objective of this review is to provide an overview of these associations. Methods: This review aims to identify on- and off-ice testing currently used in the scientific literature and their possible transfer to game performance as well as identifying research gaps in this field. Results: The 17 selected studies showed that off-ice and on-ice fitness test results can be modestly transferred to the player’s selection as well as global and advanced performance indicators. Conclusion: This review of the literature reinforces the importance of strength and conditioning coaches administering previously validated fitness tests. Regarding the academic research, it is also proposed to use performance markers that are directly related to the players’ on-ice performance to represent more accurately the relationship between the players’ fitness level and their work output. Three research gaps were also identified in relation to targeted populations, choice of performance markers and data measurement methods.
A Study on the Development of Reduction Facilities’ Management Standards for Agricultural Drainage for Disaster Reduction
Authors: Song Youngseok , Park Moojong
Published: September 2021

The agricultural drainage in rural area plays important roles in water supply and drainage for crop cultivation. Various kinds of debris near agricultural drainage, however, causes sedimentation in the drainage during rainfall. The debris introduced into the agricultural drainage moves out of the drainage under a high flow rate. This causes a reduction in the flow rate, which may affect the discharge capacity, resulting in crop damage. This study developed a reduction facility to reduce debris entering agricultural drainage and analyzed the performance by measuring the capture efficacy in the hydraulic experiment. A total of 648 runs were performed for 216 experiment conditions where three replications and error ranges were calculated depending on the inflow characteristics of debris. This study also evaluated the performance of the reduction facility and established the design criteria by developing a capture efficacy equation by flow rate and type of reduction facility.
A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification
Authors: Alimi Oyeniyi Akeem , Ouahada Khmaies , Abu-Mahfouz Adnan M. , Rimer Suvendi , Alimi Kuburat Oyeranti Adefemi
Published: September 2021

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.
Diversity of Sources of Income for Smallholder Farming Communities in Malawi: Importance for Improved Livelihood
Authors: Bhatti Muhammad Azher , Godfrey Sosheel Solomon , Ip Ryan H. L. , Kachiwala Chipo , Hovdhaugen Håvard , Banda Liveness J. , Limuwa Moses , Wynn Peter C. , Ådnøy Tormod , Eik Lars Olav
Published: September 2021

Agriculture is vital to global food production. Around 550 million smallholding households produce most of the world’s food, and many rely on livestock rearing for a living. Smallholder farms must survive and thrive to maintain and increase food production. Baseline information is vital for further extension service interventions. The goal of this Malawian study was to collect quantitative baseline data on crop and livestock production, agriproduct sales, and other indicators through a household survey, and to compare the efficacy (in terms of income) of using the concept of “Lead and Follow” farmer training programs. The baseline study survey was carried out in 44 sections of 11 extension planning areas from Malawi’s five districts (Dowa, Kasungu, Mchinji, Mzimba, and Rumphi). In total, 1131 smallholder households were interviewed. Crop production, livestock farming, and providing casual labor for others were all identified as significant sources of income for smallholders, implying that all agriproducts (the whole-farm approach) is equally important for improving smallholder livelihoods. On the one hand, the whole-farm approach should improve smallholders’ resilience regarding climate change and poverty. Lower agriproduct sales, on the other hand, indicated that links to the market were frequently poor but an increased market focus should help smallholders sell their produce at a fair margin. In terms of best practices adoption, both Lead and Follow farmers adopted similar farm practices (crops and livestock) to increase income. In general, no significant difference in income was calculated from many farm enterprises for both Lead and Follow farmers. However, the income from pigs and firewood was significantly higher for Follow farmers than for Lead farmers. Lead farmers reported significantly higher off-farm income sources. Significant changes are proposed to the “Lead farmer extension approach”.
Evaluation of Cowpea (Vigna unguiculata) in an Intercropping System as Pollinator Enhancer for Increased Crop Yield
Authors: Dingha Beatrice N. , Omaliko Paul C. , Amoah Barbara A. , Jackai Louis E. , Shrestha Deepak
Published: September 2021

Production of pollinator-dependent crops (PDCs) is increasing. However, pollinators are declining partly due to loss of floral resources. There is urgency to mitigate this decline and the potential risks to the production of PDCs and food security. One way is by promoting farming systems that enhance flower-rich habitats. In a two-year study, Pinkeye Purple Hull and Whippoorwill cowpea varieties attractive to pollinators were intercropped with three PDCs (squash, okra, and watermelon). We evaluated whether cowpea intercrop increases the abundance and diversity of pollinators, other beneficial insects, and crop yield, and decreases the abundance of the brown marmorated stink bug (BMSB). Diverse pollinators were recorded in both years for a total of 80,379, representing seven pollinator families (Andrenidae, Apidae, Crabronidae, Formicidae, Halictidae, Tachinidae, and Vespidae) captured in pan traps, six families (Apidae, Crabronidae, Halictidae, Pyralidae, Tachinidae, and Vespidae) from sticky traps, and five pollinator types (bumble bees, carpenter bees, honeybees, butterflies and moths, and wasps) through direct visual count. Pollinator abundance and diversity was highest on the cowpea-intercropped treatments than controls. PDCs intercropped with cowpea recorded more beneficial insects than BMSB. Okra, squash, and watermelon intercrops produced 7%, 27%, and 54% more fruits than the control, respectively. Our findings indicate that intercropping cowpeas with PDCs attracted more and diverse pollinators and resulted in increased crop yield. However, to optimize pollination, factors such as planting dates to synchronize the flowering of both cowpeas and PDCs should be taken into consideration.
Influence of Combustion Chamber Shapes and Nozzle Geometry on Performance, Emission, and Combustion Characteristics of CRDI Engine Powered with Biodiesel Blends
Authors: Teja K. M. V. Ravi , Prasad P. Issac , Reddy K. Vijaya Kumar , Banapurmath N. R. , Soudagar Manzoore Elahi M. , Khan T. M. Yunus , Badruddin Irfan Anjum
Published: September 2021

Environmentally friendly, renewable, and green fuels have many benefits over fossil fuels, particularly regarding energy efficiency, in addition to addressing environmental and socioeconomic problems. As a result, green fuels can be used in transportation and power generating applications. Furthermore, being green can ably address the emission-related issues of global warming. In view of the advantages of renewable fuels, two B20 fuel blends obtained from methyl esters of cashew nutshell (CHNOB), jackfruit seed (JACKFSNOB), and jamun seed oils (JAMSOB) were selected to evaluate the performance of a common rail direct injection (CRDI) engine. Compatibility of the nozzle geometry (NG) and combustion chamber shape (CCS) were optimized for increased engine performance. The optimized CCS matched with an increased number of injector nozzle holes in NG showed reasonably improved brake thermal efficiency (BTE), reduced emissions of smoke, HC, and CO, respectively, while NOx increased. Further combustion parameters, such as ignition delay (ID) and combustion duration (CD) reduced, while peak pressure (PP) and heat release rates (HRR) increased at the optimized injection parameters. The CRDI engine powered with JAMSOB B20 showed an increase in BTE of 4–5%, while a significant reduction in HC and CO emissions was obtained compared to JACKFSNOB B20 and CHNOB B20, with increased NOx.
Solar Irradiance Reduction Using Optimized Green Infrastructure in Arid Hot Regions: A Case Study in El-Nozha District, Cairo, Egypt
Authors: Elbardisy Wesam M. , Salheen Mohamed A. , Fahmy Mohammed
Published: September 2021

In the Middle East and North Africa (MENA) region, studies focused on the relationship between urban planning practice and climatology are still lacking, despite the fact that the latter has nearly three decades of literature in the region and the former has much more. However, such an unfounded relationship that would consider urban sustainability measures is a serious challenge, especially considering the effects of climate change. The Greater Cairo Region (GCR) has recently witnessed numerous serious urban vehicular network re-development, leaving the city less green and in need of strategically re-thinking the plan regarding, and the role of, green infrastructure. Therefore, this study focuses on approaches to the optimization of the urban green infrastructure, in order to reduce solar irradiance in the city and, thus, its effects on the urban climatology. This is carried out by studying one of the East Cairo neighborhoods, named El-Nozha district, as a representative case of the most impacted neighborhoods. In an attempt to quantify these effects, using parametric simulation, the Air Temperature (Ta), Mean Radiant Temperature (Tmrt), Relative Humidity (RH), and Physiological Equivalent Temperature (PET) parameters were calculated before and after introducing urban trees, acting as green infrastructure types that mitigate climate change and the Urban Heat Island (UHI) effect. Our results indicate that an optimized percentage, spacing, location, and arrangement of urban tree canopies can reduce the irradiance flux at the ground surface, having positive implications in terms of mitigating the urban heat island effect.
Differences in Health-Related Fitness Variables between Adult Athletes and Non-Athletes with Down Syndrome: A Descriptive Study
Authors: Castejón-Riber Cristina , Requena Francisco , Benítez-Sillero Juan de Dios , Camacho Rocío
Published: September 2021

The main purpose of this study is to present objective data on the differences in anthropometrics and fitness variables between adult athletes and non-athletes with Down Syndrome (DS) members of the “Down Cordoba Association” (DCA), after adjusting for confounders. Twenty-seven adults, 7 athletes and 20 non-athletes with DS, participated in this study. Health-related fitness variables were measured with tests validated for this population. First, the characteristics of the subjects according to sex and sports practice are presented. Athletes present significantly higher values for height (p = 0.010) and waist-to-hip ratio (p = 0.005). Furthermore, male athletes compared to male non-athletes have lower values in % of body fat (p = 0.030), and in waist (p = 0.031) and hip (p = 0.041) circumferences. Furthermore, athletes have better values in tests of strength, cardiovascular endurance (66.60 ± 47.66 vs. 32.31 ± 24.49 (s); p = 0.037), and agility, but not in flexibility. Significant differences were only found in cardiovascular endurance. As a conclusion, the practice of sports among the users of the “Down Córdoba Association” in general leads to better health-related fitness values. Therefore, we encourage the guardians of people with DS in this association to motivate them to practice sports because this practice will more likely improve their overall health.
Modeling the Influence of Information Systems on Sustainable Business Performance and Competitiveness
Authors: Djalic Natasa , Nikolic Milan , Bakator Mihalj , Erceg Zivko
Published: September 2021

The necessity for sustainable development, the coronavirus pandemic, and conducting business within the frameworks of the fourth industrial revolution—Industry 4.0 create a challenging environment where enterprises have difficulties to achieve and maintain competitiveness. Information is becoming a core construct when it comes to conducting business in the modern, globalized business environment. In this paper, the influence of information systems (IS) on sustainable business performance and competitiveness is analyzed. Additionally, the influence of human resource management (HRM) and decision making (DM) on sustainable business performance and competitiveness, as two crucial constructs in the new paradigm of conducting business, are addressed. The relations are discussed from the aspect of sustainable development and future post-pandemic business trends. The main goal is to determine the relations between the noted constructs in a transitional setting. In sum, 184 manufacturing enterprises from Republika Srpska were surveyed via a structured survey, where the focus was on quantifying and modeling the influence of IS, HRM, and DM on sustainable business performance. The results indicate that IS, HRM, DM can affect business performance in a transitional economic setting. This approach is novel, as the existing body of literature does not address all the noted influencing factors on sustainable business performance and competitiveness in a transition economy. Hence, this paper significantly contributes to the existing body of literature and provides a solid basis for future research in this domain.