bluuebunny/crossref_metadata_2025 huggingface dataset:
Algorithm of Additional Correction of Level 2 Remote Sensing Reflectance Data Using Modelling of the Optical Properties of the Black Sea Waters
Authors: Korchemkina Elena N. , Kalinskaya Daria V.
Published: February 2022

Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical depth of the atmosphere and the spectral slope of its spectrum, the so-called Angstrom parameter. Inaccurate determination of these parameters is one of the causes of errors in the retrieval of the remote sensing reflectance spectra. In this work, the obtained large array of field and satellite data for the northeastern part of the Black Sea is used, including ship-based measurements of atmospheric characteristics and sea reflectance, MODIS Aqua/Terra and OLCI Sentinel-3 A/B Level 2 remote sensing reflectance and atmospheric data. The purpose of this study is to show the numerical differences between the atmospheric parameters measured from the surface level and from the satellite and demonstrate their relationship with the differences between in situ and satellite remote sensing reflectance. Based on the information received, we propose an algorithm for the additional correction of satellite Level 2 data that uses a two-parametric model of the Black Sea remote sensing reflectance as a first approximation. This method does not require any in situ information. It is shown that additional correction significantly reduces the discrepancy between in situ and retrieved remote sensing reflectance, especially in short-wave spectral bands.
Learning Pairwise Potential CRFs in Deep Siamese Network for Change Detection
Authors: Zheng Dalong , Wei Zhihui , Wu Zebin , Liu Jia
Published: February 2022

Very high resolution (VHR) images change detection plays an important role in many remote sensing applications, such as military reconnaissance, urban planning and natural resource monitoring. Recently, fully connected conditional random field (FCCRF)-facilitated deep convolutional neural networks have shown promising results in change detection. However, the FCCRF in change detection currently is still postprocessing based on the output of the front-end network, which is not a convenient end-to-end network model and cannot combine front-end network knowledge with the knowledge of pairwise potential. Therefore, we propose a new end-to-end deep Siamese pairwise potential CRFs network (PPNet) for VHR images change detection. Specifically, this method adds a conditional random field recurrent neural network (CRF-RNN) unit into the convolutional neural network and integrates the knowledge of unary potential and pairwise potential in the end-to-end training process, aiming to refine the edges of changed areas and to remove the distant noise. In order to correct the front-end network identification errors, the method uses effective channel attention (ECA) to further effectively distinguish the change areas. Our experimental results on two data sets verify that the proposed method has more advanced capability with almost no increase in the number of parameters and effectively avoids the overfitting phenomenon in the training process.
GPM DPR Retrievals: Algorithm, Evaluation, and Validation
Authors: Liao Liang , Meneghini Robert
Published: February 2022

The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dm simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and Dm in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and Dm estimates to some extent as the range-independent assumption of the adjustable parameter (ε) of the R-Dm relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (δPIA) used in the DPR algorithm to find the best ε and range-independent ε assumption. A range-variable ε model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval.
HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles
Authors: Ullah Safi , Khan Muazzam A. , Ahmad Jawad , Jamal Sajjad Shaukat , e Huma Zil , Hassan Muhammad Tahir , Pitropakis Nikolaos , Arshad  , Buchanan William J.
Published: February 2022

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.
Usability Testing of Virtual Reality Applications—The Pilot Study
Authors: Kamińska Dorota , Zwoliński Grzegorz , Laska-Leśniewicz Anna
Published: February 2022

The need for objective data-driven usability testing of VR applications is becoming more tangible with the rapid development of numerous VR applications and their increased accessibility. Traditional methods of testing are too time and resource consuming and might provide results that are highly subjective. Thus, the aim of this article is to explore the possibility of automation of usability testing of VR applications by using objective features such as HMD built-in head and hands tracking, EEG sensor, video recording, and other measurable parameters in addition to automated analysis of subjective data provided in questionnaires. For this purpose, a simple VR application was created which comprised relatively easy tasks that did not generate stress for the users. Fourteen volunteers took part in the study and their signals were monitored to acquire objective automated data. At the same time the observer was taking notes of subjects’ behaviour, and their subjective opinions about the experience were recorded in a post-experiment questionnaire. The results acquired from signal monitoring and questionnaires were juxtaposed with observation and post-interview results to confirm the validity and efficacy of automated usability testing. The results were very promising, proving that automated usability testing of VR applications is potentially achievable.
A Novel Data Sampling Driven Kalman Filter Is Designed by Combining the Characteristic Sampling of UKF and the Random Sampling of EnKF
Authors: Cui Tipo , Sun Xiaohui , Wen Chenglin
Published: February 2022

In order to improve the performance of the Kalman filter for nonlinear systems, this paper contains the advantages of UKF statistical sampling and EnKF random sampling, respectively, and establishes a new design method of sampling a driven Kalman filter in order to overcome the shortcomings of UKF and EnKF. Firstly, a new sampling mechanism is proposed. Based on sigma sampling with UKF statistical constraints, random sampling similar to EnKF is carried out around each sampling point, so as to obtain a large sample data ensemble that can better describe the characteristics of the system variables to be evaluated. Secondly, by analyzing the spatial distribution characteristics of the obtained large sample ensemble, a sample weight selection and assignment mechanism with the centroid of the data ensemble as the optimization goal are established. Thirdly, a new Kalman filter driven by large data sample ensemble is established. Finally, the effectiveness of the new filter is verified by computer numerical simulation experiments.
IoT Device Integration and Payment via an Autonomic Blockchain-Based Service for IoT Device Sharing
Authors: Dawod Anas , Georgakopoulos Dimitrios , Jayaraman Prem Prakash , Nirmalathas Ampalavanapillai , Parampalli Udaya
Published: February 2022

The Internet of Things (IoT) incorporates billions of IoT devices (e.g., sensors, cameras, wearables, smart phones, as well as other internet-connected machines in homes, vehicles, and industrial plants), and the number of such connected IoT devices is currently growing rapidly. This paper proposes a novel Autonomic Global IoT Device Discovery and Integration Service (which we refer to as aGIDDI) that permits IoT applications to find IoT devices that are owned and managed by other parties in IoT (which we refer to as IoT device providers), integrate them, and pay for using their data observations. aGIDDI incorporates a suite of interacting sub-services supporting IoT device description, query, integration, payment (via a pay-as-you-go payment model), and access control that utilise a special-purpose blockchain to manage all information needed for IoT applications to find, pay and use the IoT devices they need. The paper describes aGIDDI’s novel protocol that allows any IoT application to discover and automatically integrate and pay for IoT devices and their data that are provided by other parties. The paper also presents aGIDDI’s architecture and proof-of-concept implementation, as well as an experimental evaluation of the performance and scalability of aGIDDI in variety of IoT device integration and payment scenarios.
Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging
Authors: Cabeza-Ruiz Robin , Velázquez-Pérez Luis , Linares-Barranco Alejandro , Pérez-Rodríguez Roberto
Published: February 2022

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.
Single-Molecule Detection of Nucleic Acids via Liposome Signal Amplification in Mass Spectrometry
Authors: Lin Xiangcheng , Zhao Mengmeng , Li Mingyue , Long Juan , Zhang Jing , Yu Fang , Xu Fen , Sun Lixian
Published: February 2022

A single-molecule detection method was developed for nucleic acids based on mass spectrometry counting single liposome particles. Before the appearance of symptoms, a negligible amount of nucleic acids and biomarkers for the clinical diagnosis of the disease were already present. However, it is difficult to detect extremely low concentrations of nucleic acids using the current methods. Hence, the establishment of an ultra-sensitive nucleic acid detection technique is urgently needed. Herein, magnetic beads were used to capture target nucleic acids, and liposome particles were employed as mass tags for single-particle measurements. Liposomes were released from magnetic beads via photocatalytic cleavage. Hence, one DNA molecule corresponded to one liposome particle, which could be counted using mass spectrometric measurement. The ultrasensitive detection of DNA (10–18 M) was achieved using this method.
An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors
Authors: Cuperman Rafael , Jansen Kaspar M. B. , Ciszewski Michał G.
Published: February 2022

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we investigated the possibility to extract these statistics from acceleration sensor data generated by a previously developed sensor garment. We used deep learning-based models to recognize five football-related activities (jogging, sprinting, passing, shooting and jumping) in an accurate, robust, and fast manner. A combination of convolutional (CNN) layers followed by recurrent (bidirectional) LSTM layers achieved up to 98.3% of accuracy. Our results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. In addition to an increase in accuracy, the proposed deep learning architecture showed to be 2.7 to 3.4 times faster in evaluation time than traditional machine learning methods. This demonstrated that deep learning models are accurate as well as time-efficient and are thus highly suitable for cost-effective, fast, and accurate human activity recognition tasks.
Non-Contact Smart Sensing of Physical Activities during Quarantine Period Using SDR Technology
Authors: Khan Muhammad Bilal , Mustafa Ali , Rehman Mubashir , AbuAli Najah Abed , Yuan Chang , Yang Xiaodong , Shah Fiaz Hussain , Abbasi Qammer H.
Published: February 2022

The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases.
Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement
Authors: Schiopu Ionut , Munteanu Adrian
Published: February 2022

The paper proposes a novel post-filtering method based on convolutional neural networks (CNNs) for quality enhancement of RGB/grayscale images and video sequences. The lossy images are encoded using common image codecs, such as JPEG and JPEG2000. The video sequences are encoded using previous and ongoing video coding standards, high-efficiency video coding (HEVC) and versatile video coding (VVC), respectively. A novel deep neural network architecture is proposed to estimate fine refinement details for full-, half-, and quarter-patch resolutions. The proposed architecture is built using a set of efficient processing blocks designed based on the following concepts: (i) the multi-head attention mechanism for refining the feature maps, (ii) the weight sharing concept for reducing the network complexity, and (iii) novel block designs of layer structures for multiresolution feature fusion. The proposed method provides substantial performance improvements compared with both common image codecs and video coding standards. Experimental results on high-resolution images and standard video sequences show that the proposed post-filtering method provides average BD-rate savings of 31.44% over JPEG and 54.61% over HEVC (x265) for RGB images, Y-BD-rate savings of 26.21% over JPEG and 15.28% over VVC (VTM) for grayscale images, and 15.47% over HEVC and 14.66% over VVC for video sequences.
Synergistic Evaluation of Passive Microwave and Optical/IR Data for Modelling Vegetation Transmissivity towards Improved Soil Moisture Retrieval
Authors: Moradizadeh Mina , Srivastava Prashant K. , Petropoulos George P.
Published: February 2022

Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representativeness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval.
Systematic Review of Authentication and Authorization Advancements for the Internet of Things
Authors: Trnka Michal , Abdelfattah Amr S. , Shrestha Aishwarya , Coffey Michael , Cerny Tomas
Published: February 2022

Technologies for the Internet of Things (IoT) are maturing, yet no common standards dictate their direction, leaving space for a plethora of research directions and opportunities. Among the most important IoT topics is security. When we design a robust system, it is important to know the available options for facing common tasks related to access control, authentication, and authorization. In this review, we systematically analyze 1622 peer-reviewed publications from October 2017 to December 2020 to find the taxonomy of security solutions. In addition, we assess and categorize current practices related to IoT security solutions, commonly involved technologies, and standards applied in recent research. This manuscript provides a practical road map to recent research, guiding the reader and providing an overview of recent research efforts.
Development and Optimization of Hybrid Polymeric Nanoparticles of Apigenin: Physicochemical Characterization, Antioxidant Activity and Cytotoxicity Evaluation
Authors: Zafar Ameeduzzafar , Alruwaili Nabil K. , Imam Syed Sarim , Alsaidan Omar Awad , Ahmed Mohammed Muqtader , Yasir Mohd , Warsi Musarrat Husain , Alquraini Ali , Ghoneim Mohammed M. , Alshehri Sultan
Published: February 2022

Breast cancer is the most common cancer in females and ranked second after skin cancer. The use of natural compounds is a good alternative for the treatment of breast cancer with less toxicity than synthetic drugs. The aim of the present study is to develop and characterize hybrid Apigenin (AN) Nanoparticles (NPs) for oral delivery (AN-NPs). The hybrid AN-NPs were prepared by the self-assembly method using lecithin, chitosan and TPGS. Further, the NPs were optimized by Box-Behnken design (3-factor, 3-level). The hybrid NPs were evaluated for particle size (PS), entrapment efficiency (EE), zeta potential (ZP), and drug release. The optimized hybrid NPs (ON2), were further evaluated for solid state characterization, permeation, antioxidant, cytotoxicity and antimicrobial study. The formulation (ON2) exhibited small PS of 192.6 ± 4.2 nm, high EE 69.35 ± 1.1%, zeta potential of +36.54 mV, and sustained drug release (61.5 ± 2.5% in 24 h), as well as significantly (p < 0.05) enhanced drug permeation and antioxidant activity. The IC50 of pure AN was found to be significantly (p < 0.05) lower than the formulation (ON2). It also showed significantly greater (p < 0.05) antibacterial activity than pure AN against Bacillus subtilis and Salmonella typhimurium. From these findings, it revealed that a hybrid AN polymeric nanoparticle is a good carrier for the treatment of breast cancer.
Aircraft Landing Gear Retraction/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network
Authors: Chen Jie , Xu Qingshan , Guo Yingchao , Chen Runfeng
Published: February 2022

The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system.
A New Labeling Approach for Proportional Electromyographic Control
Authors: Hagengruber Annette , Leipscher Ulrike , Eskofier Bjoern M. , Vogel Jörn
Published: February 2022

Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to create labels for decoder calibration. In certain scenarios, where ground truth, such as additional sensor data, can not be measured, e.g., with people suffering from physical disabilities, these methods come with the challenge of generating appropriate labels. We introduce a new approach that uses the EMG-feature stream recorded during a simple training procedure to generate continuous labels. The method avoids synchronization mismatches in the labels and has no need for additional sensor data. Furthermore, we investigated the influence of the transient phase of the muscle contraction when using the new labeling approach. For this purpose, we performed a user study involving 10 subjects performing online 2D goal-reaching and tracking tasks on a screen. In total, five different labeling methods were tested, including three variations of the new approach as well as methods based on binary labels, which served as a baseline. Results of the evaluation showed that the introduced labeling approach in combination with the transient phase leads to a proportional command that is more accurate than using only binary labels. In summary, this work presents a new labeling approach for proportional EMG control without the need of a complex training procedure or additional sensors.
Unpacking the ‘15-Minute City’ via 6G, IoT, and Digital Twins: Towards a New Narrative for Increasing Urban Efficiency, Resilience, and Sustainability
Authors: Allam Zaheer , Bibri Simon Elias , Jones David S. , Chabaud Didier , Moreno Carlos
Published: February 2022

The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart Cities network technologies to provide tailored pathways to respond to contextualised challenges through the advent of data mining and processing to better inform urban decision-making processes. We argue that the ‘15-minute city’ concept can value-add from Smart City network technologies in particular through Digital Twins, Internet of Things (IoT), and 6G. The data gathered by these technologies, and processed via Machine Learning techniques, can unveil new patterns to understand the characteristics of urban fabrics. Collectively, those dimensions, unpacked to support the ‘15-minute city’ concept, can provide new opportunities to redefine agendas to better respond to economic and societal needs as well as align more closely with environmental commitments, including the United Nations’ Sustainable Development Goal 11 and the New Urban Agenda. This perspective paper presents new sets of opportunities for cities arguing that these new connectivities should be explored now so that appropriate protocols can be devised and so that urban agendas can be recalibrated to prepare for upcoming technology advances, opening new pathways for urban regeneration and resilience crafting.
Automatic Detection of Real Damage in Operating Tie-Rods
Authors: Lucà Francescantonio , Manzoni Stefano , Cigada Alfredo , Barella Silvia , Gruttadauria Andrea , Cerutti Francesco
Published: February 2022

Many researchers have proposed vibration-based damage-detection approaches for continuous structural health monitoring. Translation to real applications is not always straightforward because the proposed methods have mostly been developed and validated in controlled environments, and they have not proven to be effective in detecting real damage when considering real scenarios in which environmental and operational variations are not controlled. This work was aimed to develop a fully-automated strategy to detect damage in operating tie-rods that only requires one sensor and that can be carried out without knowledge of physical variables, e.g., the axial load. This strategy was created by defining a damage feature based on tie-rod eigenfrequencies and developing a data-cleansing strategy that could significantly improve performance of outlier detection based on the Mahalanobis squared distance in real applications. Additionally, the majority of damage-detection algorithms presented in the literature related to structural health monitoring were validated in controlled environments considering simulated damage conditions. On the contrary, the approach proposed in this paper was shown to allow for the early detection of real damage associated with a corrosion attack under the effects of an intentionally uncontrolled environment.
Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke
Authors: Iosa Marco , Benedetti Maria Grazia , Antonucci Gabriella , Paolucci Stefano , Morone Giovanni
Published: February 2022

Many recent studies have highlighted that the harmony of physiological walking is based on a specific proportion between the durations of the phases of the gait cycle. When this proportion is close to the so-called golden ratio (about 1.618), the gait cycle assumes an autosimilar fractal structure. In stroke patients this harmony is altered, but it is unclear which factor is associated with the ratios between gait phases because these relationships are probably not linear. We used an artificial neural network to determine the weights associable to each factor for determining the ratio between gait phases and hence the harmony of walking. As expected, the gait ratio obtained as the ratio between stride duration and stance duration was found to be associated with walking speed and stride length, but also with hip muscle forces. These muscles could be important for exploiting the recovery of energy typical of the pendular mechanism of walking. Our study also highlighted that the results of an artificial neural network should be associated with a reliability analysis, being a non-deterministic approach. A good level of reliability was found for the findings of our study.
Plasmonic Strain Sensors Based on Au-TiO2 Thin Films on Flexible Substrates
Authors: Rodrigues Marco S. , Borges Joel , Vaz Filipe
Published: February 2022

This study aimed at introducing thin films exhibiting the localized surface plasmon resonance (LSPR) phenomenon with a reversible optical response to repeated uniaxial strain. The sensing platform was prepared by growing gold (Au) nanoparticles throughout a titanium dioxide dielectric matrix. The thin films were deposited on transparent polymeric substrates, using reactive magnetron sputtering, followed by a low temperature thermal treatment to grow the nanoparticles. The microstructural characterization of the thin films’ surface revealed Au nanoparticle with an average size of 15.9 nm, an aspect ratio of 1.29 and an average nearest neighbor nanoparticle at 16.3 nm distance. The plasmonic response of the flexible nanoplasmonic transducers was characterized with custom-made mechanical testing equipment using simultaneous optical transmittance measurements. The higher sensitivity that was obtained at a maximum strain of 6.7%, reached the values of 420 nm/ε and 110 pp/ε when measured at the wavelength or transmittance coordinates of the transmittance-LSPR band minimum, respectively. The higher transmittance gauge factor of 4.5 was obtained for a strain of 10.1%. Optical modelling, using discrete dipole approximation, seems to correlate the optical response of the strained thin film sensor to a reduction in the refractive index of the matrix surrounding the gold nanoparticles when uniaxial strain is applied.
North Korea’s Cyber Capabilities and Their Implications for International Security
Authors: Kim Min-hyung
Published: February 2022

North Korea’s economic and technological backwardness does not seem to allow Pyongyang to possess proficient cyberwarfare capabilities. Yet, North Korea’s cyber offensive capabilities are a major security threat in a new convergence space called the cyber–physical space (CPS) that connects the real world and the virtual world. How has North Korea become a formidable actor in the CPS, despite economic and technological disadvantages? Put differently, what makes North Korea a global cyber power despite its disconnect from international society? What are North Korea’s motivations behind strengthening its cyber capabilities in recent decades and what implications do these hold for international security? The primary objective of this article is to examine North Korea’s motivations for strengthening its cyber capabilities and analyze their implications for the sustainability of stability and peace on the Korean peninsula and beyond. By investigating the exemplary cases of North Korea’s recent cyberattacks, it seeks to explore the effective ways to manage the risks that North Korea’s enhanced cyber proficiencies pose in the current and future CPS.
Transitory Courtyards as a Feature of Sustainable Urbanism on the East African Coast
Authors: Baumanova Monika
Published: February 2022

The tropical urbanism of coastal East Africa has a thousand-year-long history, making it a recognized example of sustainable urbanism. Although economically dependent on trade, the precolonial Islamic towns of the so-called Swahili coast did not feature markets or other public buildings dedicated to mercantile activities before the European colonial involvement. In this regard, Swahili urban tradition differed from other tropical Islamic cities, such as in Morocco, Mali, Egypt or the Middle East, where markets fulfilled the role of social and economic hubs and, in terms of movement, major transitory/meeting spaces in the trading towns. Yet, the Swahili urban tradition thrived for centuries as a well-connected cosmopolitan type of tropical urbanism. As research has suggested, the public role of spaces associated with trade might have been fulfilled by houses. Using approaches of space syntax and network analysis, this article studies the morphology of the houses considering whether it could have been the courtyards that simulated the role of markets thanks to their transitory spatial configuration. The results are discussed reflecting on other models of houses with courtyards, especially the modern Swahili house appearing later in the colonial era when markets began to be established, and Islamic houses known from elsewhere.
Factors Affecting the Evolution of Technical Cooperation among “Belt and Road Initiative” Countries Based on TERGMs and ERGMs
Authors: Gao Jun , Yu Xiang
Published: February 2022

The Belt and Road Initiative (B&R), was initiated by China in 2013, and it covers over 60% and 30% of the world’s population and GDP, respectively. The initiative has directed a great deal of investment in energy, transportation, and 5G. Whilst much attention has been focused on cooperation in international trade, technological cooperation in the form of patents has been largely ignored. This paper investigates the formation of technological cooperative relationships among B&R countries within a technical cooperation network. Comprehensive consideration of various factors between participating countries was undertaken, using statistical methods from social network analysis theory. The node attributes and relations between countries and the network structure were studied in a sequence of network analyses using temporal exponential random graph models (TERGMs) and static exponential random graph models (ERGMs). The main findings suggest that research ability, financial ability, technological innovation ability, technological spillover proximity, geographical proximity, and technical proximity have an impact on participation in technical cooperation between B&R countries. Research ability, technological spillover proximity, geographical proximity, and technical proximity have a positive influence on the development of cooperation. However, the financial and technological innovation ability of a country does not actively promote the development of cooperation. The entire cooperation network structure does not have a greater aggregation effect compared with a random network, and intermediary multi-path cooperation is not obvious. This paper analyzes the driving factors for technological cooperation through a systematic study of the characteristics and relationships of B&R countries, and also of the network characteristics among B&R countries. The results of our analysis show that the characteristics of technical cooperation in the B&R region provide a reference for the study of international technical cooperation.
Sustainable Tourism Development in the Protected Areas of Maramureș, Romania: Destinations with High Authenticity
Authors: Mateoc-Sîrb Nicoleta , Albu Simona , Rujescu Ciprian , Ciolac Ramona , Țigan Eugenia , Brînzan Oana , Mănescu Camelia , Mateoc Teodor , Milin Ioana Anda
Published: February 2022

Sustainable development involves economic growth in line with the requirements of ecological balance and human development, involving people’s relations with the environment as well as the responsibility of the current generation over future generations. Sustainable tourism was designed and supported in an attempt to manage all resources, so that the economic, social, and aesthetic needs of an area are satisfied while maintaining cultural integrity, essential ecological processes, biological diversity, and life support systems. Sustainable tourism is not “a fashion”; it is a way of thinking and acting with long-term implications. At national level, we believe that Maramureş County, Romania, is one of the most gifted natural and socio-economic geographic areas in the Oriental Carpathians of Romania, in which the conditions of sustainable tourism can be met and supported long term. The purpose of this scientific paper is to identify, synthesize, process, and interpret data about the tourist qualification of some localities in the protected areas of Maramureş, so as to inform local decision-makers and tourists about tourism forms currently practiced (and) possibly practicable in the future, because since tourism, as an economic branch, leads to the growth of jobs, it can contribute to the sustainable development of the area. The mathematical model proposed in the paper allows the grouping of available resources from 25 settlements in order to establish the types of tourism that can be supported and developed in these localities.
Energy Efficiency of Multi-Technology PV Modules under Real Outdoor Conditions—An Experimental Assessment in Ghardaïa, Algeria
Authors: Fezzani Amor , Hadj-Mahammed Idriss , Kouzou Abdellah , Zaghba Layachi , Drid Said , Khennane Messaouda , Kennel Ralph , Abdelrahem Mohamed
Published: February 2022

Energy efficiency and ratio performance are two key parameters for the analysis of the performance of photovoltaic (PV) modules. The present paper focusses on the assessment of the efficiency of four different photovoltaic module technologies based on energy efficiency and ratio performance. These PV modules were installed at the Applied Research Unit in Renewable Energy (URAER) in Algeria and were used to provide experimental data to help local and international economical actors with performance enhancement and optimal choice of different technologies subject to arid outdoor conditions. The modules studied in this paper are: two thin-film modules of copper indium selenide (CIS), hetero-junction with intrinsic thin-layer silicon (HIT) and two crystalline silicon modules (polycrystalline (poly-Si), monocrystalline (mono-Si)). These technologies were initially characterized using a DC regulator based on their measured I-V characteristics under the same outdoor climate conditions as the location where the monitoring of the electrical energy produced from each PV module was carried out. The DC regulator allows for extracting the maximum electrical power. At the same time, the measurements of the solar radiation and temperature were obtained from a pyranometer type Kipp & ZonenTM CMP21 and a Pt-100 temperature sensor (Kipp & Zonen, Delft, Netherlands). These measurements were performed from July 2020 to June 2021. In this work, the monthly average performance parameters such as energy efficiency are given and analyzed. The average efficiency of the modules over 12 months was evaluated at 4.74%, 7.65%, 9.13% and 10.27% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The calculated percentage deviations in the efficiency of the modules were 8.49%, 18.88%, 19.74% and 23.57% for the HIT, CIS, mono-Si and poly-Si modules, respectively. The low variation in the efficiency of the HIT module can be attributed to the better operation of this module under arid outdoor conditions, which makes it a promising module for adaptation to the region concerned.
Variables That Affect Thermal Comfort and Its Measuring Instruments: A Systematic Review
Authors: Mamani Tamara , Herrera Rodrigo F. , Muñoz-La Rivera Felipe , Atencio Edison
Published: February 2022

Thermal comfort can impact the general behavior of the occupants, and considering that humans currently perform 90% of their daily work indoors, it is necessary to improve the accuracy of thermal comfort assessments, and a correct selection of variables could make this possible. However, no review integrates all the variables that could influence thermal comfort evaluation, which relates them to their respective capture devices. For this reason, this research identifies all the variables that influence the thermal comfort of a building, together with the measurement tools for these variables, evaluating the relevance of each one in the research carried out to date. For this purpose, a systematic literature review was carried out by analyzing a set of articles selected under certain defined inclusion/exclusion criteria. In this way, it became evident that the most used variables to measure thermal comfort are the same as those used by the predicted mean vote (PMV) model; however, research focused on the behavior of the occupants has focused on new variables that seek to respond to individual differences in human thermal perception.
Towards Carbon Neutrality in Higher Education Institutions: Case of Two Private Universities in Colombia
Authors: Osorio Ana M. , Úsuga Luisa F. , Vásquez Rafael E. , Nieto-Londoño César , Rinaudo Maria E. , Martínez José A. , Leal Filho Walter
Published: February 2022

This paper addresses the path followed by two private higher education institutions (HEI) in Colombia towards achieving carbon neutrality. The methodology followed by these universities to achieve a carbon-neutral certification, based on the Greenhouse Gas (GHG) Protocol, is first described. The process of developing the GHG inventory, projected towards the carbon neutrality of these organisations while using the standard ISO 14064:2006, involved a series of steps that were consolidated in three phases: (i) definition of the scope, collection of data and emissions quantification; (ii) analysis of results and mitigation actions; and (iii) verification and compensation strategies. Results for the HEIs are shown in terms of the organisational context, carbon footprint measurement, reduction, verification, and compensation. The case is presented for Universidad Pontificia Bolivariana, a multi-campus university that became the first carbon-neutral university in Latin America in 2017, and Universidad Ean, a single-campus university that became the second carbon-neutral university in Colombia in 2021, as verified by the Colombian Institute of Technical Standards and Certification (ICONTEC). This work shows that universities can play a key role in regional and global agendas with their contribution through the incorporation of sustainability strategies, since HEIs can not only achieve carbon neutrality, but they can help other organisations by delivering graduates who are aware of sustainability and provide specific training towards building a sustainability culture, which is needed for regenerative development.
Fault Detection of Wind Turbine Blades Using Multi-Channel CNN
Authors: Wang Meng-Hui , Lu Shiue-Der , Hsieh Cheng-Che , Hung Chun-Chun
Published: February 2022

This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional neural network (CNN) models. The result obtained from each model is sent to the output layer, which is a maximum output network (MAXNET), to compute the most accurate state. First, in terms of wind turbine blade state detection, this paper builds blade models based on the normal state and three common fault types, including blade angle anomaly, blade surface damage, and blade breakage. Vibration signals are employed for fault detection. The proposed wind turbine fault diagnosis approach adopts a triaxial vibration transducer and frame grabber to capture vibration signals and then applies the new MCNN algorithm to identify the state. The test results show that the proposed approach could deliver up to 87.8% identification accuracy for four fault types of large wind turbine blades.
Circular Business Strategies and Quality of Life
Authors: Nielsen Iben Bolund , Hakala Henri
Published: February 2022

Circular Economy (CE) and Quality of Life (QoL) are trending topics that have been researched extensively at both the local, regional and global levels. CE is often described as one of the key drivers of sustainability, and sustainability is one of the key drivers of improving QoL. However, studies that investigate the relationships between CE and QoL are rare, and a clear research gap exists. Therefore, this paper aims to initiate this discussion and bring forward illustrative examples on areas where CE could potentially have an impact on QoL, both on an individual and a societal level. By asking the question of how circular business strategies may impact QoL and how they relate, we investigate how CE can influence various aspects of QoL. We utilize the framework consisting of six CE strategies known under the acronym ReSOLVE to discuss how these CE strategies can be leveraged to impact QoL. Our discussion indicates a potential for both environmental and social gains through the implementation of circular product and service solutions. We also suggest that unintended consequences may occur, especially at the societal level. Hence, we propose that, while the discussion on CE has been focused on the environmental aspects of sustainability, the broader implications for QoL and other aspects of sustainability should also be included within the domain of CE implications. Hence, we propose that further research is necessary to develop a framework explaining the relationship between CE and QoL, encompassing both the positive and negative aspects.