bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterisation of Sodium Acetate Treatment on Acacia pennata Natural Fibres
Authors: Sheeba Kasirajan Rajam Jaya , Priya Retnam Krishna , Arunachalam Krishna Prakash , Avudaiappan Siva , Maureira-Carsalade Nelson , Roco-Videla Ángel
Published: April 2023

The present study concerns the physico-chemical, structural, mechanical and thermal characterization of Acacia pennata, a natural and almost inexpensive fibre, as a potential reinforcement in polymer composites. The effect of treating the fibre with sodium acetate to increase its qualities has been seen through the use of thermogravimetric analysis, scanning electron microscope (SEM) analysis, X-ray diffraction (XRD), mechanical property tester, and Fourier transform infrared spectroscopy (FTIR). According to XRD analysis, the elimination of lignin and wax-like impurities resulted in an increase in the AP fibre’s crystalline index (79.73%). The fibre’s thermal stability was also discovered to be 365 °C. Tensile strength (557.58 MPa) and elongation at break both increased by 2.9% after treatment with sodium acetate. The surface nature and quality of AP fibres improved after sodium acetate treatment. It was confirmed by the reduction of chemical compositions (such as hemicellulose, lignin and pectin). Given its density, the fibre can be suggested as a reinforcement in polymer composites for light-weight applications because its lightweight property will be more useful for composite manufacturing.
High Tg, Bio-Based Isosorbide Methacrylate Resin Systems for Vat Photopolymerization
Authors: Chu Xi , Tu Jianwei , Berensmann Heather R. , La Scala John J. , Palmese Giuseppe R.
Published: April 2023

The use of isosorbide-derived polymers has garnered significant attention in recent decades as a high-performance, renewable material sourced from biomass. Of particular interest is isosorbide methacrylate, which possesses low viscosity (<500 cps), high thermal properties (Tg ≈ 220 °C), and high modulus (>4 GPa). These characteristics present a promising opportunity to replace BPA-derived methacrylate compounds in various applications. This investigation aims to synthesize and characterize isosorbide-based low-viscosity resin systems for 3D printing. The resin blends are composed of isosorbide methacrylate and two bio-renewable methacrylates, furfuryl methacrylate (FM) and bis-hydroxymethyl-furan methacrylate (BHMF-M), polymerized through a digital light processing (DLP) technique. The addition of the bio-based co-monomers serves to enhance the fracture toughness of the brittle isosorbide methacrylate crosslinked homopolymer (GIc = 37 J/m2). The resulting polymers exhibit Tg values greater than 200 °C and GIc around 100 J/m2. These resin systems hold potential for imparting high bio-based content to polymers used in additive manufacturing for high-performance applications.
Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High Resolution Aerial Images
Authors: Khan Sultan Daud , Basalamah Saleh
Published: April 2023

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin.
Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing
Authors: Panhuber Reinhard
Published: April 2023

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed ℓ0-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms.
Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization
Authors: Zhao Jianwei , Zhai Qiang , Zhao Pengbo , Huang Rui , Cheng Hong
Published: April 2023

Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules—one for aerial-aware knowledge generation from ground-view semantics and vice versa—and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, i.e., CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models. Our code will be available upon acceptance.
Investigation of the Global Spatio-Temporal Characteristics of Astronomical Seeing
Authors: Bi Cuicui , Qing Chun , Qian Xianmei , Luo Tao , Zhu Wenyue , Weng Ningquan
Published: April 2023

Astronomical seeing is important for monitoring the atmospheric environment, observation scheduling and management, and selecting astronomical sites. This study first attempts to establish a near-global astronomical seeing map by employing the fifth European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) data combined with the estimated model. Then, some example sites’ results from ERA5 were compared against the astronomical seeing results from the balloon-borne microthermal measurements and the differential image motion monitor (DIMM) instrument. The global astronomical seeing variations exhibit large spatial dependence. The best seeing areas are generally discerned over the mid-latitude regions, consistent with the regions of the European Southern Observatory and Tibetan Plateau. In addition, the astronomical seeing values of the Tibetan Plateau in spring are better than in the other seasons. The site results from ERA5 show that the astronomical seeing values of some example sites are generally consistent with the measurements. Overall, the global astronomical seeing map presented in this study can provide a reference and basis to further understand the astronomy site selection and optoelectronics equipment observation path selection.
A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin
Authors: Jiang Xiaoman , Wang Guoqiang , Wang Yuntao , Yao Jiping , Xue Baolin , A Yinglan
Published: April 2023

Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC2021) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R2 = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.
Electromagnetic Field of a HED in the Spherical “Earth-Ionosphere” Model and Its Application in Geophysics
Authors: Gao Ya , Di Qing-Yun , Fu Chang-Min , Zhang Yi-Lang
Published: April 2023

The controlled source extremely low frequency (CSELF) method bears the potential for deep resource exploitation utilizing the skywave. The “Skywave” denotes the electromagnetic wave propagating through the waveguide formed by the Earth and ionosphere. It has a considerable penetration depth into the lithosphere due to its low-frequency band. Previous research on extremely low-frequency electromagnetic fields with the coupled lithosphere, atmosphere, and planar ionosphere models ignored the effect of the Earth’s curvature. Thus, we aimed to present the exact formulas for horizontal electric dipoles (HED) in a spherical “Earth-ionosphere” model. These new formulas consider the Earth’s curvature as a multilayer medium rather than a homogeneous underground. We introduce three techniques: function combination pairs, addition and subtraction terms, and Padé approximants, to handle slow convergence in numerical calculation. In the spherical waveguide, electromagnetic fields are mutually interfered with and produce oscillations, which is different from the planar model. The influence of Earth’s curvature cannot be neglected with the increase in source–receiver distance, though it is negligible within 3000 km. Furthermore, it is worth noting that apparent resistivity ρθφ enters the waveguide area earlier than ρφθ. This method can be used as Green’s function to simulate the electromagnetic field of actual antennas and 3-D models.
Determination of Bayesian Cramér–Rao Bounds for Estimating Uncertainties in the Bio-Optical Properties of the Water Column, the Seabed Depth and Composition in a Coastal Environment
Authors: Guillaume Mireille , Minghelli Audrey , Chami Malik , Lei Manchun
Published: April 2023

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds.
Reflective Fiber Temperature Probe Based on Localized Surface Plasmon Resonance towards Low-Cost and Wireless Interrogation
Authors: Su Yang-Duan , Leatherman Carter Neal , Wang Yuankang , Ohodnicki Paul Richard
Published: April 2023

Reflection fiber temperature sensors functionalized with plasmonic nanocomposite material using intensity-based modulation are demonstrated for the first time. Characteristic temperature optical response of the reflective fiber sensor is experimentally tested using Au-incorporated nanocomposite thin films deposited on the fiber tip, and theoretically validated using a thin-film-optic-based optical waveguide model. By optimizing the Au concentration in a dielectric matrix, Au nanoparticles (NP) exhibit a localized surface plasmon resonance (LSPR) absorption band in a visible wavelength that shows a temperature sensitivity ~0.025%/°C as a result of electron–electron and electron–phonon scattering of Au NP and the surrounding matrix. Detailed optical material properties of the on-fiber sensor film are characterized using scanning electron microscopy (SEM) and focused-ion beam (FIB)-assisted transmission electron microscopy (TEM). Airy’s expression of transmission and reflection using complex optical constants of layered media is used to model the reflective optical waveguide. A low-cost wireless interrogator based on a photodiode transimpedance-amplifier (TIA) circuit with a low-pass filter is designed to integrate with the sensor. The converted analog voltage is wirelessly transmitted via 2.4 GHz Serial Peripheral Interface (SPI) protocols. Feasibility is demonstrated for portable, remotely interrogated next-generation fiber optic temperature sensors with future capability for monitoring additional parameters of interest.
Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
Authors: Jamil Mamoona , Sarfraz Mubashar , Ghauri Sajjad A. , Khan Muhammad Asghar , Marey Mohamed , Almustafa Khaled Mohamad , Mostafa Hala
Published: April 2023

The intelligent reflecting surface (IRS) is a cutting-edge technology for cost-effectively achieving future spectrum- and energy-efficient wireless communication. In particular, an IRS comprises many low-cost passive devices that can independently reflect the incident signal with a configurable phase shift to produce three-dimensional (3D) passive beamforming without transmitting Radio-Frequency (RF) chains. Thus, the IRS can be utilized to greatly improve wireless channel conditions and increase the dependability of communication systems. This article proposes a scheme for an IRS-equipped GEO satellite signal with proper channel modeling and system characterization. Gabor filter networks (GFNs) are jointly proposed for the extraction of distinct features and the classification of these features. Hybrid optimal functions are used to solve the estimated classification problem, and a simulation setup was designed along with proper channel modeling. The experimental results show that the proposed IRS-based methodology provides higher classification accuracy than the benchmark without the IRS methodology.
Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework
Authors: Jiao Zedong , Du Xiuli , Liu Zhansheng , Liu Liang , Sun Zhe , Shi Guoliang
Published: April 2023

Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional ‘factor-energy consumption’ to three parts of ‘factor-event-energy consumption’, which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.
Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing
Authors: Schmitt Anna-Maria , Sauer Christian , Höfflin Dennis , Schiffler Andreas
Published: April 2023

Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception–style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end.
Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning
Authors: Vrahatis Aristidis G. , Skolariki Konstantina , Krokidis Marios G. , Lazaros Konstantinos , Exarchos Themis P. , Vlamos Panagiotis
Published: April 2023

Alzheimer’s disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.
Development and Core Technologies for Intelligent SWaP3 Infrared Cameras: A Comprehensive Review and Analysis
Authors: Jiao Jingjie , Zhao Lixing , Pan Wenhao , Li Xiaoyan
Published: April 2023

With the development of infrared detection and imaging technology, infrared cameras (IRCs) play an important role in many fields, such as military, industry, and civilian. Additionally, the requirements for the size, performance, and intelligence of IRCs are becoming more and more strict. Consequently, the associated research and development (R&D) of IRCs is gradually focused on the aspects of miniaturization, high performance, intelligence, low power consumption, and low cost, involving many frontier fields, including artificial intelligence, new materials, new optical systems, and electronics systems. In fact, there are continual studies on intelligent SWaP3 IRCs, but unfortunately, a systematic arrangement and analysis are lacking. Therefore, a systematical and comprehensive review for the developments and core technologies of the intelligent SWaP3 IRCs is really needed. In this paper, in terms of the aforementioned requirements, we conduct a review and analysis of current intelligent SWaP3 IRCs based on 90 literature and statistics in recent decades to provide the relevant developers with a helpful reference for facilitating the indicator optimization of intelligent SWaP3 IRCs with new developed technologies. We analyze the development of SWaP3 IRCs in the aspects of lightweight, miniaturization, low price, and high performance, including hyperspectral resolution, high spatial resolution, large field of view (FOV), and wide dynamic elaborately. Moreover, the development in low power consumption and intelligence is also discussed in detail. Additionally, we briefly summarize the primary applications of intelligent SWaP3 IRCs in military, scientific, and civil. Then, the core technologies comprising high-integration, lightweight, hyperspectral imaging (HSI), low-power consumption, as well as the realization of high performance such as high-resolution, high-frame, and wide-dynamic range of SWaP3 IRCs are discussed and analyzed in detail. Finally, we prospect for the intelligent SWaP3 IRCs that it is necessary to continuously expand the concept of SWaP3 by reliability, stability, extensibility, and safety. In addition, it is useful to embed cutting-edge technologies such as small pixel pitch array, multi-sensors fusion, and deploy intelligent algorithms to IRCs. Additionally, the improvement of the whole machine from multi-dimension such as chip, camera, and system is expected and needs to be taken more seriously. It is hoped that this paper can provide a reference for the R&D of intelligent SWaP3 IRCs in the future.
Fast Deployment of a UWB-Based IPS for Emergency Response Operations
Authors: Adame Toni , Igual Julia , Catalan Marisa
Published: April 2023

A wide range of applications from multiple sectors already use ultra-wideband (UWB) technology to locate and track assets precisely. This is not the case, however, for first responder localization during emergency response (ER) operations, which are highly conditioned by procedural and environmental constraints. After analyzing these limitations and reviewing the current state-of-the-art solutions, this work presents a UWB-based indoor positioning system (IPS) that relies on the global navigation satellite system real-time kinematic (GNSS-RTK) technology to quickly, accurately, and safely deploy its required infrastructure on site. A set of tests conducted on a two-story building prove the suitability of such a system, providing an average accuracy of less than 1 meter for static targets and the ability to faithfully reproduce the path followed by a mobile target inside the building. The obtained results strengthen the presented approach and pave the way for more sophisticated UWB-based IPSs that would include unmanned aerial vehicles (UAVs) and/or mobile robots to speed up network deployment even more while offering additional ER services.
Provably Secure Receiver-Unrestricted Group Key Management Scheme for Mobile Ad Hoc Networks
Authors: Zhang Rui , Han Wendie , Zhang Lei , Wang Lulu , Meng Xinyu
Published: April 2023

Mobile ad hoc networks (MANETs) are self-configuring networks of wireless nodes, i.e., mobile devices. Since communications in MANETs occur via wireless channels, it is of significance to secure communications among wireless and mobile nodes. Group key management, as a widely used method for securing group communications, has potentially been used in MANETs for years. Most recently, a secure receiver-unrestricted group key management scheme for MANETs has been proposed, which is used to establish a secure channel among a group of wireless nodes without a trusted dealer, which has some advantages such as eliminating the certificate management problem and receiver restriction. However, a formal security analysis of this scheme is still lacking. Therefore, in this paper, we propose the complete security proof to demonstrate that the scheme satisfies the essential security properties including authentication, message confidentiality, known-key security and dynamic secrecy. We also give a brief discussion about the efficiency of the scheme.
Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks
Authors: Bhattacharya Pronaya , Verma Ashwin , Prasad Vivek Kumar , Tanwar Sudeep , Bhushan Bharat , Florea Bogdan Cristian , Taralunga Dragos Daniel , Alqahtani Fayez , Tolba Amr
Published: April 2023

The aim of the peer-to-peer (P2P) decentralized gaming industry has shifted towards realistic gaming environment (GE) support for game players (GPs). Recent innovations in the metaverse have motivated the gaming industry to look beyond augmented reality and virtual reality engines, which improve the reality of virtual game worlds. In gaming metaverses (GMs), GPs can play, socialize, and trade virtual objects in the GE. On game servers (GSs), the collected GM data are analyzed by artificial intelligence models to personalize the GE according to the GP. However, communication with GSs suffers from high-end latency, bandwidth concerns, and issues regarding the security and privacy of GP data, which pose a severe threat to the emerging GM landscape. Thus, we proposed a scheme, Game-o-Meta, that integrates federated learning in the GE, with GP data being trained on local devices only. We envisioned the GE over a sixth-generation tactile internet service to address the bandwidth and latency issues and assure real-time haptic control. In the GM, the GP’s game tasks are collected and trained on the GS, and then a pre-trained model is downloaded by the GP, which is trained using local data. The proposed scheme was compared against traditional schemes based on parameters such as GP task offloading, GP avatar rendering latency, and GS availability. The results indicated the viability of the proposed scheme.
RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach
Authors: Alam Syed Samiul , Chakma Arbil , Rahman Md Habibur , Bin Mofidul Raihan , Alam Md Morshed , Utama Ida Bagus Krishna Yoga , Jang Yeong Min
Published: April 2023

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today’s society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification.
Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP
Authors: Liao Jun , Lin Yuanchang , Ma Tengyun , He Songxiying , Liu Xiaofang , He Guotian
Published: April 2023

Facial expression methods play a vital role in human–computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different datasets, that result in large variations in recognition rates and low accuracy rates for different categories of facial expression datasets. This study introduces RCL-Net, a method of recognizing wild facial expressions that is based on an attention mechanism and LBP feature fusion. The structure consists of two main branches, namely the ResNet-CBAM residual attention branch and the local binary feature (LBP) extraction branch (RCL-Net). First, by merging the residual network and hybrid attention mechanism, the residual attention network is presented to emphasize the local detail feature information of facial expressions; the significant characteristics of facial expressions are retrieved from both channel and spatial dimensions to build the residual attention classification model. Second, we present a locally improved residual network attention model. LBP features are introduced into the facial expression feature extraction stage in order to extract texture information on expression photographs in order to emphasize facial feature information and enhance the recognition accuracy of the model. Lastly, experimental validation is performed using the FER2013, FERPLUS, CK+, and RAF-DB datasets, and the experimental results demonstrate that the proposed method has superior generalization capability and robustness in the laboratory-controlled environment and field environment compared to the most recent experimental methods.
Circle Diameter Impacts Stride Frequency and Forelimb Stance Duration at Various Gaits in Horses
Authors: Logan Alyssa A. , Snyder Alyson J. , Nielsen Brian D.
Published: April 2023

The effects of gait and diameter have been studied independently, but rarely together in equine circular exercise studies. This study aimed to determine the impact of diameter (10-m or 15-m) at various gaits (walk, trot, and canter) on stride frequency or forelimb stance duration. Nine mature horses were outfitted with Tekscan™ Hoof Sensors on their forelimbs during circular and straight-line exercise at various gaits on a clay and sand arena surface. Statistical analysis was performed in SAS 9.4 with fixed effects of exercise type, recording, leg, and breed (PROC GLIMMIX, p < 0.05 significance). At walk (p < 0.0001) and trot (p < 0.001), stride frequency was lower during circular exercise. Stride frequency was similar between forelimbs at all gaits. At walk (p < 0.001) and canter (p = 0.01), stance duration was greatest during 10-m circle exercise. At walk (p = 0.0007), trot (p < 0.001), and canter (p < 0.0001), the inside forelimb had longer stance duration than the outside forelimb. Differences between forelimb stance durations may support asymmetrical travel while horses exercise on a circle at the walk, trot, and canter. These results demonstrate diameter and gait are important factors when evaluating forelimb kinematics during circular exercise.
Development of Methods of Quality Control of the Tablets «Ramipril»
Authors: Typlynska Kateryna , Kondratova Yuliya , Logoyda Liliya
Published: April 2023

Our main target was to develop methods for the quality control of the tablet «ramipril» according to the indicators of «Quantitative determination», «Impurities» and «Dissolution». New, precise, accurate and green HPLC methods were developed for the determination of ramipril and its impurities in tablets. The separation was accomplished using a diode array detector at 210 nm with an isocratic and gradient mobile phase consisting of a 0.2 g/L solution of sodium hexanesulfonate (pH 2.7) and the acetonitrile and chromatographic columns Acclaim 120 C18 and Inertsil ODS-3. The developed method was validated in accordance with ICH guidelines. The analysis of impurities was performed within a run duration of less than 25 min, which is about a two times shorter than that of the official Ph. Eur. method. The analysis of ramipril in tablets was performed with a run duration of less than 4.5 min, which is about three times shorter than that of the official USP method. The developed methods were successfully applied for the quality control of the tablet «ramipril» according to the indicators of «Quantitative determination», «Impurities» and «Dissolution». In addition, they proved its superiority over the reported methods in terms of greenness using different assessment tools.
Risks and Requirements in Sustainable App Development—A Review
Authors: Hunger Tom , Arnold Marlen , Pestinger Rico
Published: April 2023

Apps are a part of the everyday lives of many people around the world. Digital applications support many areas, including the private sphere and the working world. To strengthen social sustainability in app development, it is necessary to consider users’ needs. The aim of this paper is to identify users’ requirements and to explore possible concerns. Furthermore, we aim to investigate how sustainability can be integrated into app development. For this purpose, 25 studies were analyzed, in which users were interviewed. The papers were qualitatively evaluated. Our results reveal three main findings. First, specific requirements, such as value for monetary aspects, security, and privacy, play a crucial role for users. Second, users expressed concerns about the attractiveness, motivation, and accuracy of apps. Third, an absolute research gap was revealed in the integration of sustainability in app development. Based on the findings, the Sustainable App Development Model (SADM) was then developed. This model incorporates the most fundamental aspects of social, economic, and environmental sustainability and is characterized by a circular approach to app development.
Volumetric Modular Construction Risks: A Comprehensive Review and Digital-Technology-Coupled Circular Mitigation Strategies
Authors: Khan Ayaz Ahmad , Yu Rongrong , Liu Tingting , Gu Ning , Walsh James
Published: April 2023

Volumetric modular construction (VMC) has considerable benefits in providing better cost, time, quality, productivity, and sustainability performance. However, the adoption is low, owing to various associated risks. This study aims to identify VMC critical risk factors (CRFs) in project stages and project attributes by conducting a systematic literature review of 91 articles. In the project stages, 31 CRFs were identified based on frequency analyses, and divided into four stages: design and planning, offsite manufacturing, transportation and logistics, and onsite assembly. The project attribute risks were divided into the following categories: implementation and schedule, supply chain and financial, safety and ergonomic, and civil and structural. Overall, design and planning risks were critical, as they are crucial in maintaining the project’s upstream and downstream flow. Finally, the study proposed a mitigation framework for using digital technology-based circular strategies to overcome VMC risks. The framework includes disruptive and emergent digital technologies aiming to mitigate risks in VMC, keeping circularity in action. In terms of theoretical contributions, this research delivers a CRF register and categorical division for professionals to better understand the landscape of VMC risks. In terms of practical contributions, the study guides the practitioners towards strategies to overcome the pertinent risks.
Operating Leverage, Equity Incentive, and Enterprise Research and Development Investment
Authors: Tan Hui , Zhang Xinhua , Zeng Lili
Published: April 2023

Science and technology innovation plays a vital role in the sustainable development of enterprises, and even in the security and sustainable development of a nation. Against the background of China’s structural “deleveraging” macro policy, the following two aspects are considered in this research: First, should operating leverage be removed, and how does it affect the innovation investment of enterprises? Second, what will be the impact of the implementation of equity incentives on the relationship between operating leverage and innovation investment? Using a longitudinal panel dataset of Chinese A-share listed companies from 2010 to 2020, this study empirically tested the impact and mechanism of operating leverage on enterprise innovation investment. The findings show that operating leverage significantly contributes to an increase in enterprise innovation investment in general, but the positive correlation trend decreases with the increase in operating leverage. The implementation of equity incentives plays a positive role in moderating the relationship between operating leverage and innovation investment. Further heterogeneity analysis shows that the promotion effect of operating leverage on innovation investment is significant only in non-state owned enterprises (SOE), and the positive regulating effect of equity incentives in non-SOEs is more significant than that of the overall sample.
Spatial–Temporal Variation and Influencing Factors on Housing Prices of Resource-Based City: A Case Study of Xuzhou, China
Authors: Yao Qing , Hu Yingen
Published: April 2023

The steady development of the real estate market is an important link in the transformation of resource-based cities. Taking the main urban area of Xuzhou, a typical resource-based city in China, as an example, this study used a spatial–temporal geographically weighted regression (GTWR) method guided by characteristic price theory to analyze the evolution of the spatial–temporal pattern of house prices in resource-based cities and the influencing factors. The results of the study showed that, from the time trend, Xuzhou’s main city house price trend underwent the obvious stages; from June 2015 to September 2016, the trend was stable, and from March 2017 to June 2018, it was an “up–down–up” trend. With respect to the spatial distribution of house prices, from June 2015 to June 2017, the traditional business district and the major city in the Xuzhou metropolitan area were the areas of highest house prices, and after September 2017, the structural features of the traditional business district, along with the main city of the metropolitan area and the new city, formed the areas of highest importance. With regard to the factors influencing house prices, the type of dwelling, according to the building characteristics, had the largest impact on house prices, and the enrichment of housing types became an effective way to regulate housing prices. The impact of location characteristics on house prices varied depending on differences in the public infrastructure surrounding housing, with the contribution of the planned metro stations to house prices not effectively emerging. The impact of neighborhood characteristics on house prices varied, with tertiary care hospitals having a ‘neighborhood avoidance effect’ on house prices, given that hospital-generated waste was too densely distributed around houses, suppressing neighborhood house prices. The results of this study indicated that, in the process of real estate market development in resource-based cities, the planning department should consider the different functions and division of labor in each region and scientifically formulate urban development plans to provide a good external environment for the healthy development of the city’s property market.
Research on the Impact of Carbon Trading Policy on the Structural Upgrading of Marine Industry
Authors: Xu Sheng , Chen Jingxue , Wen Demei
Published: April 2023

To promote greenhouse gas emission reduction, China has proposed a dual carbon target to achieve carbon peaking by 2030 and carbon neutrality by 2060. Since 2013, China has opened an increasing number of carbon emission trading pilot projects, and at this stage, China’s carbon emission trading policy has been gradually promoted to the whole country; therefore, how can marine economy be affected under the promotion of carbon trading policy? This paper uses the difference in differences method to study the data of marine industry structure of Chinese coastal provinces from 2010 to 2018. The study finds that carbon trading policies promote the upgrading of the marine industry structure, and further verifies that the impact of carbon trading policies on the upgrading of the marine industry structure is spatially heterogeneous. In other words, the carbon trading policy also has a significant promoting effect on the provinces within 160 km of the pilot provinces, but the effect will be weaker than that of the pilot provinces; at 160–320 km from the pilot provinces, the carbon trading policy has no significant promoting effect on the provinces within this range; at 320–960 km from the pilot provinces, the effect of the carbon trading policy on the provinces within this range becomes negative and significant, showing a suppressive effect. The experimental findings of this paper will provide a reference for China to achieve its carbon neutrality goal and realize a strong ocean state.
Research on Affective Interaction in Mini Public Transport Based on IPA-FMEA
Authors: Peng Qizhao , Wang Weiwei , Yang Xiaoyan , Wang Yi , Chen Jian
Published: April 2023

In the promotion of sustainable modes of transport, especially public transport, reasonable failure risk assessment at the critical moment in the process of service provider touch with users can improve the service quality to a certain extent. This study presents a product service touch point evaluation approach based on the importance–performance analysis (IPA) of user and failure mode and effect analysis (FMEA). Firstly, the authors capture service product service touch points in the process of user interaction with the product by observing the user behavior in a speculative design experiment, and perform the correlation analysis of the service product service touch point. Second, the authors use the IPA analysis method to evaluate and classify the product service touch points and identify the key product service touch points. Thirdly, the authors propose to analyze the failure of key product service touch points based on user-perceived affective interaction and clarify the priority of each key touch point. Finally, reluctant interpersonal communication, as the key failure caused by high risk, is derived according to the evaluation report, which leads to establishing new product service touch points and improving the overall user experience to promote sustainable transports with similar forms and characteristics.
Exploring Factors Influencing the HQDMI in the Yellow River Basin: A Grounded Theory Approach
Authors: Su Yongqiang , Shi Jinfa , Zhang Manman
Published: April 2023

The research on high-quality development of the manufacturing industry (HQDMI) in the Yellow River Basin is in the exploratory stage, and little literature has systematically analyzed its influencing factors. Based on the grounded theory, this paper makes an exploratory analysis of the influencing factors of the HQDMI in the Yellow River Basin. This study finds that the influencing factors of the HQDMI mainly include six aspects: innovation capability, organization construction, industry resources, institutional environment, industry collaboration, and market demand. By using the HQDMI’s endogenous and exogenous factors as the core, the realization mode of the HQDMI in the Yellow River basin is put forward, and the influencing factors and action model of the HQDMI in the Yellow River Basin are constructed. The findings have significant reference value for promoting the HQDMI in the Yellow River Basin and are helpful for improving the HQDMI research.