bluuebunny/crossref_metadata_2025 huggingface dataset:
Modified Time-Frequency Marginal Features for Detection of Seizures in Newborns
Authors: Khan Nabeel Ali , Ali Sadiq , Choi Kwonhue
Published: April 2022

The timely detection of seizure activity in the case of newborns can help save lives. Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording acquired from 36 newborns admitted to Royal Women’s Hospital, Brisbane, Australia. A novel set of time-frequency marginal features are defined to detect seizure activity in newborns. The proposed set is based on the observation that EEG seizure signals appear either as a train of spikes or as a summation of frequency-modulated chirps with slow variation in the instantaneous frequency curve. The proposed set of features is obtained by extracting the time-frequency (TF) signature of seizure spikes and frequency-modulated chirps by exploiting the direction of ridges in the TF plane. Based on extracted TF signature of spikes, the modified time-marginal is computed whereas based on the extracted TF signature of frequency-modulated chirps, the modified frequency-marginal is computed. It is demonstrated that features extracted from the modified time-domain marginal and frequency-domain marginal in combination with TF statistical and frequency-related features lead to better accuracy than the existing TF signal classification method, i.e., the proposed method achieves an F1 score of 70.93% which is 5% greater than the existing method.
Passive Landmark Geometry Optimization and Evaluation for Reliable Autonomous Navigation in Mining Tunnels Using 2D Lidars
Authors: Torres-Torriti  Miguel , Nazate-Burgos  Paola , Paredes-Lizama  Fabián , Guevara Javier , Auat Cheein Fernando
Published: April 2022

Autonomous navigation in mining tunnels is challenging due to the lack of satellite positioning signals and visible natural landmarks that could be exploited by ranging systems. Solutions requiring stable power feeds for locating beacons and transmitters are not accepted because of accidental damage risks and safety requirements. Hence, this work presents an autonomous navigation approach based on artificial passive landmarks, whose geometry has been optimized in order to ensure drift-free localization of mobile units typically equipped with lidar scanners. The main contribution of the approach lies in the design and optimization of the landmarks that, combined with scan matching techniques, provide a reliable pose estimation in modern smoothly bored mining tunnels. A genetic algorithm is employed to optimize the landmarks’ geometry and positioning, thus preventing that the localization problem becomes ill-posed. The proposed approach is validated both in simulation and throughout a series of experiments with an industrial skid-steer CAT 262C robotic excavator, showing the feasibility of the approach with inexpensive passive and low-maintenance landmarks. The results show that the optimized triangular and symmetrical landmarks improve the positioning accuracy by 87.5% per 100 m traveled compared to the accuracy without landmarks. The role of optimized artificial landmarks in the context of modern smoothly bored mining tunnels should not be understated. The results confirm that without the optimized landmarks, the localization error accumulates due to odometry drift and that, contrary to the general intuition or belief, natural tunnel features alone are not sufficient for unambiguous localization. Therefore, the proposed approach ensures grid-based SLAM techniques can be implemented to successfully navigate in smoothly bored mining tunnels.
Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm
Authors: Chaudhri Shiv Nath , Rajput Navin Singh , Alsamhi Saeed Hamood , Shvetsov Alexey V. , Almalki Faris A.
Published: April 2022

Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system using Convolutional Neural Network (CNN) with a smaller number of gas sensor elements. We have identified redundant gas sensor elements in a gas sensor array and removed them to reduce the power consumption without significant deviation in the node’s performance. The inevitable variation in the performance due to removing redundant sensor elements has been compensated using specialized data pre-processing (zero-padded virtual sensors and spatial augmentation) and CNN. The experiment is demonstrated to classify and quantify the four hazardous gases, viz., acetone, carbon tetrachloride, ethyl methyl ketone, and xylene. The performance of the unoptimized gas sensor array has been taken as a “baseline” to compare the performance of the optimized gas sensor array. Our proposed approach reduces the power consumption from 10 Watts to 5 Watts; classification performance sustained to 100 percent while quantification performance compensated up to a mean squared error (MSE) of 1.12 × 10−2. Thus, our power-efficient optimization paves the way to “computation on edge”, even in the resource-constrained 6G-IoT paradigm.
Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions
Authors: Holzinger Andreas , Saranti Anna , Angerschmid Alessa , Retzlaff Carl Orge , Gronauer Andreas , Pejakovic Vladimir , Medel-Jimenez Francisco , Krexner Theresa , Gollob Christoph , Stampfer Karl
Published: April 2022

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.
Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis
Authors: Abu Farha Nadia , Al-Shargie Fares , Tariq Usman , Al-Nashash Hasan
Published: April 2022

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color–Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.
An Efficient Framework for Securing the Smart City Communication Networks
Authors: Alfouzan Faisal Abdulaziz , Kim Kyounggon , Alzahrani Nouf M.
Published: April 2022

Recently, smart cities have increasingly been experiencing an evolution to improve the lifestyle of citizens and society. These emerge from the innovation of information and communication technologies (ICT) which are able to create a new economic and social opportunities. However, there are several challenges regarding our security and expectation of privacy. People are already involved and interconnected by using smart phones and other appliances. In many cities, smart energy meters, smart devices, and security appliances have recently been standardized. Full connectivity between public venues, homes, cares, and some other social systems are on their way to be applied, which are known as Internet of Things. In this paper, we aim to enhance the performance of security in smart city communication networks by using a new framework and scheme that provide an authentication and high confidentiality of data. The smart city system can achieve mutual authentication and establish the shared session key schemes between smart meters and the control center in order to secure a two-way communication channel. In our extensive simulation, we investigated and evaluated the security performance of the smart city communication network with and without our proposed scheme in terms of throughput, latency, load, and traffic received packet per seconds. Furthermore, we implemented and applied a man-in-the-middle (MITM) attack and network intrusion detection system (NIDS) in our proposed technique to validate and measure the security requirements maintaining the constrained resources.
Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images
Authors: Lin Mengchen , Bao Guidong , Sang Xiaoqian , Wu Yunfeng
Published: April 2022

With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a pivotal biomarker to assist in the clinical diagnosis of age-related macular diseases, diabetic macular edema, and retinal vein occlusion. In recent years, the advanced machine learning methods, such as deep learning paradigms, have attracted more and more attention from academia in the retinal fluid segmentation applications. The automatic retinal fluid segmentation based on deep learning can improve the semantic segmentation accuracy and efficiency of macular change analysis, which has potential clinical implications for ophthalmic pathology detection. This article summarizes several different deep learning paradigms reported in the up-to-date literature for the retinal fluid segmentation in OCT images. The deep learning architectures include the backbone of convolutional neural network (CNN), fully convolutional network (FCN), U-shape network (U-Net), and the other hybrid computational methods. The article also provides a survey on the prevailing OCT image datasets used in recent retinal segmentation investigations. The future perspectives and some potential retinal segmentation directions are discussed in the concluding context.
A Historical-Trajectories-Based Map Matching Algorithm for Container Positioning and Tracking
Authors: Li Wenfeng , Zhang Wenwen , Gao Cong
Published: April 2022

Positioning and tracking of containers is becoming an urgent demand of container transportation. Map matching algorithms have been widely applied to correct positioning errors. Because container trajectories have the characteristics of low sampling rate and missing GPS points, existing map matching algorithms based on the shortest path principle are not applicable for container positioning and tracking. To solve this problem, a historical-trajectories-based map matching algorithm (HTMM) is proposed. HTMM mines the travel time and the frequency in historical trajectories to help find the local path between two adjacent candidate points. HTMM first presents a path reconstruction method to calculate the travel time of historical trajectories on each road segment. A historical path index library based on a path tree is then developed to efficiently index historical paths. In addition, a location query and tracking method is introduced to determine the location of containers at given time. The performance of HTMM is validated on a real freight trajectory dataset. The experimental results show that HTMM has more than 3% and 5% improvement over the ST-Matching algorithm and HMM-based algorithm, respectively, at 60–300 s sampling intervals. The positioning error is reduced by half at a 60 s sampling interval.
One-Step Fabrication of Highly Sensitive Tris(2,2′-bipyridyl)ruthenium(II) Electrogenerated Chemiluminescence Sensor Based on Graphene-Titania-Nafion Composite Film
Authors: Lee Sang Jung , Lee Don Hui , Lee Won-Yong
Published: April 2022

A highly sensitive tris(2,2′-bipyridyl)ruthenium(II) (Ru(bpy)32+) electrogenerated chemiluminescence (ECL) sensor based on a graphene-titania-Nafion composite film has been prepared in a simple one-step manner. In the present work, a highly concentrated 0.1 M Ru(bpy)32+ solution was mixed with an as-prepared graphene-titania-Nafion composite solution (1:20, v/v), and then a small aliquot (2 µL) of the resulting mixture solution was cast on a glassy carbon electrode surface. This one-step process for the construction of an ECL sensor shortens the fabrication time and leads to reproducible ECL signals. Due to the synergistic effect of conductive graphene and mesoporous sol-gel derived titania-Nafion composite, the present ECL sensor leads to a highly sensitive detection of tripropylamine from 1.0 × 10−8 M to 2.0 × 10−3 M with a detection limit of 0.8 nM (S/N = 3), which is lower in comparison to that of the ECL sensor based on the corresponding ECL sensor based on the titania-Nafion composite containing carbon nanotube. The present ECL sensor also shows a good response for nicotinamide adenine dinucleotide hydrogen (NADH) from 1.0 × 10−6 M to 1.0 × 10−3 M with a detection limit of 0.4 µM (S/N = 3). Thus, the present ECL sensor can offer potential benefits in the development of dehydrogenase-based biosensors.
Rethinking Power Efficiency for Next-Generation Processor-Free Sensing Devices
Authors: Song Yihang , Li Songfan , Zhang Chong , Li Shengyu , Lu Li
Published: April 2022

The last decade has seen significant advances in power optimization for IoT sensors. The conventional wisdom considers that if we reduce the power consumption of each component (e.g., processor, radio) into μW-level of power, the IoT sensors could achieve overall ultra-low power consumption. However, we show that this conventional wisdom is overturned, as bus communication can take significant power for exchanging data between each component. In this paper, we analyze the power efficiency of bus communication and ask whether it is possible to reduce the power consumption for bus communication. We observe that existing bus architectures in mainstream IoT devices can be classified into either push-pull or open-drain architecture. push-pull only adapts to unidirectional communication, whereas open-drain inherently fits for bidirectional communication which benefits simplifying bus topology and reducing hardware costs. However, open-drain consumes more power than push-pull due to the high leakage current consumption while communicating on the bus. We present Turbo, a novel approach introducing low power to the open-drain based buses by reducing the leakage current created on the bus. We instantiate Turbo on I2C bus and evaluate it with commercial off-the-shelf (COTS) sensors. The results show a 76.9% improvement in power efficiency in I2C communication.
Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network
Authors: Tamang Lakpa Dorje , Kim Byung-Wook
Published: April 2022

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves a high-quality reconstruction of the ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while providing compelling SR reconstruction time.
Butterfly: μW Level ULP Sensor Nodes with High Task Throughput
Authors: Zhang Chong , Lu Li , Song Yihang , Meng Qianhe , Zhang Junqin , Shao Xiandong , Zhang Guangyuan , Hou Mengshu
Published: April 2022

The rapid development of Internet of Things (IoT) applications calls for light-weight IoT sensor nodes with both low-power consumption and excellent task execution efficiency. However, in the existing system framework, designers must make trade-offs between these two. In this paper, we propose an “edge-to-end integration” design paradigm, Butterfly, which assists sensor nodes to perform sensing tasks more efficiently with lower power consumption through their (high-performance) network infrastructures (i.e., a gateway). On the one hand, to optimize the power consumption, Butterfly offloads the energy-intensive computational tasks from the nodes to the gateway with only microwatt-level power budget, thereby eliminating the power-consuming Microcontroller (MCU) from the node. On the other hand, we address three issues facing the optimization of task execution efficiency. To start with, we buffer the frequently used instructions and data to minimize the volume of data transmitted on the downlink. Furthermore, based on our investigation on typical sensing data structures, we present a novel last-bit transmission and packaging mechanism to reduce the data amount on the uplink. Finally, we design a task prediction mechanism on the gateway to support efficient scheduling of concurrent tasks on multiple MCU-free Butterfly nodes. The experiment results show that Butterfly can speed up the task rate by 4.91 times and reduce the power consumption of each node by 94.3%, compared to the benchmarks. In addition, Butterfly nodes have natural security advantages (e.g., anti-capture) as they offload the control function with all application information up to the gateway.
Standing-Wave Feeding for High-Gain Linear Dielectric Resonator Antenna (DRA) Array
Authors: Abdalmalak Kerlos Atia , Althuwayb Ayman Abdulhadi , Lee Choon Sae , Botello Gabriel Santamaría , Falcón-Gómez Enderson , García-Castillo Luis Emilio , García-Muñoz Luis Enrique
Published: April 2022

A novel feeding method for linear DRA arrays is presented, illuminating the use of the power divider, transitions, and launchers, and keeping uniform excitation to array elements. This results in a high-gain DRA array with low losses with a design that is simple, compact and inexpensive. The proposed feeding method is based on exciting standing waves using discrete metallic patches in a simple design procedure. Two arrays with two and four DRA elements are presented as a proof of concept, which provide high gains of 12 and 15dBi, respectively, which are close to the theoretical limit based on array theory. The radiation efficiency for both arrays is about 93%, which is equal to the array element efficiency, confirming that the feeding method does not add losses as in the case of standard methods. To facilitate the fabrication process, the entire array structure is 3D-printed, which significantly decreases the complexity of fabrication and alignment. Compared to state-of-the-art feeding techniques, the proposed method provides higher gain and higher efficiency with a smaller electrical size.
Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements
Authors: Mekruksavanich Sakorn , Jitpattanakul Anuchit
Published: April 2022

Wearable technology has advanced significantly and is now used in various entertainment and business contexts. Authentication methods could be trustworthy, transparent, and non-intrusive to guarantee that users can engage in online communications without consequences. An authentication system on a security framework starts with a process for identifying the user to ensure that the user is permitted. Establishing and verifying an individual’s appearance usually requires a lot of effort. Recent years have seen an increase in the usage of activity-based user identification systems to identify individuals. Despite this, there has not been much research into how complex hand movements can be used to determine the identity of an individual. This research used a one-dimensional residual network with squeeze-and-excitation (SE) configurations called the 1D-ResNet-SE model to investigate hand movements and user identification. According to the findings, the SE modules have enhanced the one-dimensional residual network’s identification ability. As a deep learning model, the proposed methodology is capable of effectively identifying features from the input smartwatch sensor and could be utilized as an end-to-end model to clarify the modeling process. The 1D-ResNet-SE identification model is superior to the other models. Hand movement assessment based on deep learning is an effective technique to identify smartwatch users.
Minimizing Global Buffer Access in a Deep Learning Accelerator Using a Local Register File with a Rearranged Computational Sequence
Authors: Lee Minjae , Zhang Zhongfeng , Choi Seungwon , Choi Jungwook
Published: April 2022

We propose a method for minimizing global buffer access within a deep learning accelerator for convolution operations by maximizing the data reuse through a local register file, thereby substituting the local register file access for the power-hungry global buffer access. To fully exploit the merits of data reuse, this study proposes a rearrangement of the computational sequence in a deep learning accelerator. Once input data are read from the global buffer, repeatedly reading the same data is performed only through the local register file, saving significant power consumption. Furthermore, different from prior works that equip local register files in each computation unit, the proposed method enables sharing a local register file along the column of the 2D computation array, saving resources and controlling overhead. The proposed accelerator is implemented on an off-the-shelf field-programmable gate array to verify the functionality and resource utilization. Then, the performance improvement of the proposed method is demonstrated relative to popular deep learning accelerators. Our evaluation indicates that the proposed deep learning accelerator reduces the number of global-buffer accesses to nearly 86.8%, consequently saving up to 72.3% of the power consumption for the input data memory access with a minor increase in resource usage compared to a conventional deep learning accelerator.
Assessment for Different Neural Networks with FeatureSelection in Classification Issue
Authors: Chen Joy Iong-Zong , Pi Chung-Sheng
Published: April 2022

In general, the investigation of NN (neural network) computing systems requires the management of a significant number of simultaneous distinct algorithms, such as parallel computing, fault tolerance, classification, and data optimization. Supervised learning for NN originally comes from certain parameters, such as self-revised learning, input learning datasets, and multiple second learning processes. Specifically, the operation continues to adjust the NN connection synapses’ weight to achieve a self-learning computer system. The current article is aimed at developing the CC (correlation coefficient) assignment scheme adaptively joint with the FS (feature selection) categories to pursue the solutions utilized in solving the restrictions of NN computing. The NN computing system is expected to solve high-dimensional data, data overfitting, and strict FS problems. Hence, the Fruits-360 dataset is applied in the current article, that is, the variety of fruits, the sameness of color, and the differences in appearance features are utilized to examine the NN system accuracy, performance, and loss rate. Accordingly, there are 120 different kinds with a total of 20,860 fruit image datasets collected from AlexNet, GoogLeNet, and ResNet101, which were implemented in the CC assignment scheme proposed in this article. The results are employed to verify that the accuracy rate can be improved by reducing strict FS. Finally, the results of accuracy rate from the training held for the three NN frameworks are discussed. It was discovered that the GoogLeNet model presented the most significant FS performance. The demonstrated outcomes validate that the proposed CC assignment schemes are absolutely worthwhile in designing and choosing an NN training model for feature discrimination. From the simulation results, it has been observed that the FS-based CC assignment improves the accurate rate of recognition compared to the existing state-of-the-art approaches.
Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer’s Disease
Authors: Sadiq Alishba , Yahya Norashikin , Tang Tong Boon , Hashim Hilwati , Naseem Imran
Published: April 2022

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain’s neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer’s patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 × 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer’s vs. normal controls. The nonfractal-based approach provides a good representation of the brain’s neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively.
Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
Authors: Zhu Wenyao , Chen Yizhi , Ko Siu-Teing , Lu Zhonghai
Published: April 2022

The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions and rectify the socket design. In this case study, a clustering-based analysis method is presented to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are employed to find the clustering results of the sensor data, taking the pressure measurement of a predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout. The Jenson–Shannon Divergence (JSD) and the mean pressure are applied as posterior validation metrics that compare the pressure features before and after sensor removal. A case study of a clinical trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The sensors on the posterior and medial regions are suggested to be reduced, and the main pressure features are kept. The proposed method can help sensor designers optimize sensor configurations for intra-socket measurements and thus assist the prosthetists in improving the socket fitting.
Repurposing of Four Drugs as Anti-SARS-CoV-2 Agents and Their Interactions with Protein Targets
Authors: Vesga Luis C. , Ruiz-Hernández Camilo A. , Alvarez-Jacome Jeimmy J. , Duque Jonny E. , Rincon-Orozco Bladimiro , Mendez-Sanchez Stelia C.
Published: April 2022

Although there are existing vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), new COVID-19 cases are increasing due to low immunization coverage and the emergence of new variants. For this reason, new drugs to treat and prevent severe COVID-19 are needed. Here, we provide four different FDA-approved drugs against SARS-CoV-2 proteins involved in the entry and replication process, aiming to identify potential drugs to treat COVID-19. We use the main protease (Mpro), the spike glycoprotein (S protein), and RNA-dependent RNA polymerase (RdRp) as protein targets for anti- SARS-CoV-2 drugs. In our constructed database, we selected different drugs against each target (Mpro, S protein, and RdRp) based on their common interactions with relevant residues involved in viral entry at the host cell and replication. Furthermore, their stability inside the binding pocket, as well as their predicted binding-free energy, allow us to provide new insight into the possible drug repurposing of viomycin (interacting with Mpro) due to its interactions with key residues, such as Asn 143, Glu 166, and Gln 189 at the same time as hesperidin (interacting with the S protein) is interacting with residues Tyr 449, Ser 494, and Thr 500, keeping inside the predicted binding pocket, as well as interacting with residues in different variants of concern. Finally, we also suggest nystatin and elvitegravir (interacting with RdRp) as possible drugs due to their stability within the predicted pocket along the simulation and their interaction with key residues, such as Asp 760, Asp 761, and Asp 618. Altogether our results provide new knowledge about the possible mechanism of the inhibition of viomycin, hesperidin, elvitegravir, and nystatin to inhibit the viral life cycle of SARS-CoV-2 and some of its variants of concern (VOC). Additionally, some iodide-based contrast agents were also found to bind the S protein strongly, i.e., iohexol (−58.99 Kcal/mol), iotrolan (−76.19 Kcal/mol), and ioxilan (−62.37 Kcal/mol). Despite the information we report here as the possible strong interaction between these contrast agents and the SARS-CoV-2′s S protein, Mpro, and RdRp, we believe that further investigation, including chemical modifications in their structures, are needed for COVID-19 treatment.
Wet Process Phosphoric Acid Purification Using Functionalized Organic Nanofiltration Membrane
Authors: Khaless Khaoula , Chanouri Hamza , Amal Said , Ouaattou Abla , Mounir El Mahdi , Haddar Hicham , Benhida Rachid
Published: April 2022

The utilization of phosphoric acid in various sectors, e.g., food industry, is controlled by the authorized concentration limit of impurities. However, industrial phosphoric acid is contaminated with undesirable impurities (such as F, Al, Fe, Mg, etc.). Herein, this study aimed to evaluate the efficiency of the membrane purification process of pretreated industrial phosphoric acid using a premodified nanofiltration membrane. We demonstrated that the prior pretreatment steps for industrial phosphoric acid allowed the elimination of sulfate, fluorine and arsenic. Further purification of the obtained pretreated phosphoric acid using membrane cells reduced the concentrations of Cd, Al, Fe and rare earth elements by 94.81, 99.30, 99.63 and 96.67%, respectively. The membrane is functionalized by a deposit of a high molecular weight polycationic polymer of polyethyleneimines in order to produce a highly charged membrane surface to enhance the separation efficiency, selectivity and stability of the membrane. We found that the purification process relies on electrostatic repulsion between the functionalized membrane and highly charged ions, and the reduction rate of metals is a cation charge-dependent parameter. The laboratory and industrial pilot scale results showed that this process allows the production of food-grade phosphoric acid.
Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
Authors: Gasparini Francesca , Grossi Alessandra , Giltri Marta , Bandini Stefania
Published: April 2022

Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.
Can Low-Cost, Handheld Spectroscopy Tools Coupled with Remote Sensing Accurately Estimate Soil Organic Carbon in Semi-Arid Grazing Lands?
Authors: Goodwin Douglas Jeffrey , Kane Daniel A. , Dhakal Kundan , Covey Kristofer R. , Bettigole Charles , Hanle Juliana , Ortega-S. J. Alfonso , Perotto-Baldivieso Humberto L. , Fox William E. , Tolleson Douglas R.
Published: April 2022

Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils’ spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale.
How Much Photovoltaic Efficiency Is Enough?
Authors: Werner Jürgen
Published: April 2022

At present, the purchasing prices for silicon-based photovoltaic modules with 20% efficiency and more are between 20 and 40 EURct/Wp. These numbers correspond to 40 to 80 EUR/m2 and are in the same range as the mounting costs (material prices plus salaries) of such modules. Installers and operators of photovoltaic systems carefully balance the module and mounting costs when deciding among modules of different efficiencies. This contribution emulates the installer’s decision via a simple, analytical module mounting decision (Mo2De) model. A priori, the model, and the resulting conclusions are completely independent of the photovoltaically active material inside the modules. De facto, however, based on the present state (cost, efficiency, reliability, bankability, etc.) of modules fabricated from (single) crystalline Si cells, conclusions on other photovoltaic materials might also be drawn: On the one hand, the model suggests that lower-efficiency modules with efficiencies below 20% will be driven out of the market. Keeping in mind their installation costs, installers will ask for large discounts for lower-efficiency modules. Technologies based on organic semiconductors, CdTe, CIGS, and even multicrystalline Si, might not survive in the utility market, or in industrial and residential applications. Moreover, this 20% mark will soon reach 23%, and finally will stop at around 25% for the very best, large-area (square meter sized) commercial modules based on single crystalline silicon only. On the other hand, it also seems difficult for future higher-efficiency modules based on tandem/triple cells to compete with standard Si-based reference modules. Compared to their expected higher efficiency, the production costs of tandem/triple cell modules and, therefore, also their required markup in sales, might be too high. Depending on the mounting cost, the Mo2De-model predicts acceptable markup values of 1 EURct/Wp (for low mounting costs of around 10 EUR/m2) to 11 EURct/Wp (for high mounting costs of 100 EUR/m2) if the module efficiency increases from 23% to 30%. Therefore, a 23% to 24% module efficiency, which is possible with silicon cells alone, might be enough for many terrestrial photovoltaic applications.
A Statistically Based Model for the Characterization of Vehicle Interactions and Vehicle Platoons Formation on Two-Lane Roads
Authors: Mauro Raffaele , Pompigna Andrea
Published: April 2022

Two-lane roads are the most significant part of the road network in many countries, and are widely used for systematic and non-systematic daily travels. Traffic on a two-lane road typically involves a high level of interaction among vehicles, with the formation of platoons. As a part of the road network of a country, they represent a crucial development factor from a social and economic point of view, because they ensure the close accessibility of the innermost areas and local markets, and favor the connection between the nodal points of the logistic system and the last mile of the supply and distribution chain. Thus, the estimation of the presence of vehicle platoons makes it possible to develop significant indicators for performance analysis on this type of road, which in turn is the basis for planning, upgrading, and improving transport programs to find a sustainable balance between environmental, social, and economic qualities. This paper presents a statistically based model, the Two-Lane Roads Statistical Platooning Model (TLR-SPM), which allows for evaluating the percentage of vehicles which are free to travel at the desired speed and of non-free vehicles constrained to travel in platoons at lower speeds than desired, as a function of the traffic flow. Based on a data-driven methodological approach, TLR-SPM allows for going beyond the critical threshold value for time headways, such as the widespread 3 s threshold, but lacks the need to hypothesize, identify, or estimate the probability laws for speed and time headway. From the formal treatment of the general statistical method, the paper shows the data processing procedure through its application to a real case. As shown by the application case and the comparisons with the results of other methods, the proposed model can significantly adapt to the experimental data and can support in analyzing a two-lane road in its operating conditions to promote its safety and efficiency as part of a sustainable transport system.
Influences of Environmental Regulations on Industrial Green Technology Innovation Efficiency in China
Authors: Shen Wanfang , Shi Jianing , Meng Qinggang , Chen Xiaolan , Liu Yufei , Cheng Ken , Liu Wenbin
Published: April 2022

The Paris Agreement marks global response to climate change after 2020 and China has proposed the dual carbon goals, carbon peaking and carbon neutrality, in response. This paper analyses the contribution to dual carbon goals by analyzing the impact of environmental regulations (ERs) on green technology innovation (GTI) in China. First, considering variances in energy consumption structure across provinces and industries, industrial CO2 emission is calculated and set as an undesirable output of industrial GTI. Then, industrial green technology innovation efficiencies (GTIE) of 29 provinces in China between 2005–2017 are calculated using a non-oriented two-stage network SBM-DEA model assuming variable returns to scale. Last, dynamic evolution and regional differences of industrial GTIE during green technology R&D, green technology commercialization, and overall GTI stages are respectively observed, and the influences from different types of ERs, command-based (CER), market-based (MER), and voluntary (VER), on industrial GTIE are analyzed. We identify China is overall experiencing relatively low but gradually increasing industrial GTIE and Industrial GTIE present gradient changes across provinces with increasingly prominent regional difference. It is found that influences of types of ERs on industrial GTIE present dynamic effect, threshold effect, lag effect and regional differences.
Application Studies for the Implementation of the Sustainability Charter in the Metropolitan City of Genoa
Authors: Pirlone Francesca , Spadaro Ilenia , Arzà Cristiana , Lonati Giovanna , Garibaldi Piero
Published: April 2022

Starting from Agenda 2030 and existing tools in the field of sustainability, this research defines the guidelines for a new Sustainability Charter created for a metropolitan-level city. These guidelines are then applied to the case study of the metropolitan city of Genoa. The paper reports, therefore, application studies for the implementation of the Sustainability Charter in the metropolitan city of Genoa. Funded by the Ministry of the Environment and the Protection of Territory and Sea, the Sustainability Charter of the Metropolitan City of Genoa, which we present here, is developed as part of “Agenda 2030, the Sustainable Metropolitan Agenda of the Metropolitan City of Genoa: moving towards sustainable metropolitan spaces”. This research has led to the implementation of a concrete product the entire citizenship can benefit from. The new proposed tool is oriented towards the application of sustainability in urban planning and management in order to reduce environmental impacts and promote a proper and better quality of life: a driving force for sustainable urban development. Sustainability as a tool to safeguard the cultural and environmental heritage and the economic system, which can represent a new opportunity for the development of competitiveness, innovation and employment.