bluuebunny/crossref_metadata_2025 huggingface dataset:
Cell-Free Nucleic Acids: Physico-Chemical Properties, Analytical Considerations, and Clinical Applications
Authors: Bronkhorst Abel J. , Holdenrieder Stefan
Published: July 2023

Human body fluids are rich sources of cell-free nuclear material, which exhibits unique characteristics [...]
[18F]FDG-PET/CT in Idiopathic Inflammatory Myopathies: Retrospective Data from a Belgian Cohort
Authors: Yildiz Halil , Lepere Charlotte , Zorzi Giulia , Gheysens Olivier , Roodhans Fabien , Pothen Lucie
Published: July 2023

[18F]FDG-PET/CT is a useful tool for diagnosis and cancer detection in idiopathic inflammatory myopathies (IIMs), especially polymyositis (PM) and dermatomyositis (DM). Data deriving from Europe are lacking. We describe [18F]FDG-PET/CT results in a Belgian cohort with IIMs, focusing on patients with PM and DM. All of the cases of IIMs admitted between December 2010 and January 2023 to the Cliniques Universitaires Saint-Luc (Belgium) were retrospectively reviewed. In total, 44 patients were identified with suspected IIMs; among them, 29 were retained for final analysis. The mean age of the retained patients was 48.7 years; 19 patients were female (65.5%). Twenty-two patients had DM and seven had PM. The mean serum creatinine kinase (CK) and the mean CRP levels were 3125 UI/L and 30.3 mg/L, respectively. [18F]FDG-PET/CT imaging was performed for 27 patients, detecting interstitial lung diseases (ILDs) in 7 patients (25.9%), cancer in 3 patients (11.1%), and abnormal muscle FDG uptake compatible with myositis in 13 patients (48.1%). All of the patients who were detected to have ILDs via PET/CT imaging were confirmed using a low-dose lung CT scan. Among the patients who were detected to have abnormal muscle FDG uptake via PET/CT scans (13/28), the EMG was positive in 12 patients (p = 0.004), while the MRI was positive in 8 patients (p = 0.02). We further observed that there was a significantly higher level of CK in the group with abnormal muscle FDG uptake (p = 0.008). Our study showed that PET/CT is useful for detecting cancer and ILDs. We showed that the detection of abnormal muscle uptake via PET/CT was in accordance with EMG and MRI results, as well as with the mean CK value, and that the presence of dyspnea was significantly associated with the presence of ILDs detected via PET/CT imaging (p = 0.002).
Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications
Authors: Di Dio Michele , Barbuto Simona , Bisegna Claudio , Bellin Andrea , Boccia Mario , Amparore Daniele , Verri Paolo , Busacca Giovanni , Sica Michele , De Cillis Sabrina , Piramide Federico , Zaccone Vincenzo , Piana Alberto , Alba Stefano , Volpi Gabriele , Fiori Cristian , Porpiglia Francesco , Checcucci Enrico
Published: July 2023

Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models’ reconstruction.
SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning
Authors: Wang Wentao , Ye Chen , Tian Jun
Published: July 2023

The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.
On IRS-Assisted Covert Communication with a Friendly UAV
Authors: Xu Xiaobei , Hu Linzi , Wei Sha , Qian Yuwen , Yan Shihao , Shu Feng , Li Jun
Published: July 2023

Driven by the rapidly growing demand for information security, covert wireless communication has become an essential technology and attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations, creating challenges around deployment in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS)-assisted covert communication system (CCS) for communicating with a friendly unmanned aerial vehicle (UAV) in which the UAV generates artificial noise (AN) to interfere with monitoring. Furthermore, we model the power of AN emitted by the UAV using an uncertainty model, through which the closed-form detection error probability (DEP) of the covert wireless communication for monitoring is derived. Under the derived DEP, we formulate the optimization problem to maximize the covert rate, then design an iterative algorithm to solve the optimization problem and obtain the optimal covert rate using Dinkelbach method. Simulation results show that the proposed system achieves the maximum covert rate when the phase of the IRS units and the trajectory and transmit power of the UAV are optimized jointly.
Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties
Authors: Hamdane Yassine , Segarra Joel , Buchaillot Maria Luisa , Rezzouk Fatima Zahra , Gracia-Romero Adrian , Vatter Thomas , Benfredj Nermine , Hameed Rana Arslan , Gutiérrez Nieves Aparicio , Torró Torró Isabel , Araus José Luis , Kefauver Shawn Carlisle
Published: July 2023

The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R2 = 0.61–0.7 and R2 = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R2 = 0.25–0.35 and R2 = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties.
Joint Deployment and Coverage Path Planning for Capsule Airports with Multiple Drones
Authors: Sun Weichang , Luo Zhihao , Huang Kuihua , Shi Jianmai
Published: July 2023

Due to the advantages of low cost and high flexibility, drones have been applied to urban surveillance, vegetation monitoring, and other fields with the need for coverage of regions. To expand UAVs’ coverage, we designed the Capsule Airport (CA) to recharge and restore drones and provide take-off and landing services. Meanwhile, the combination of drones’ coverage path planning (CPP) and the deployment of CAs is a crucial problem with few relevant studies. We propose a solution approach to the CPP problem based on selecting scanning patterns and trapezoidal decomposition. In addition, we construct a 0–1 integer programming model to minimize the cost of the distance between CAs and the scanning missions. Specifically, a solution approach based on greedy and clustering heuristics is designed to solve this problem. Furthermore, we then develop a local-search-based algorithm with the operators of CA location exchange and drone scanning mission exchange to further optimize the solution. Random instances at different sizes are used to validate the performance of proposed algorithms, through which the sensitivity analysis is conducted with some factors. Finally, a case study based on the Maolichong forest park in Changsha, China, is presented to illustrate the application of the proposed method.
Hybridized Hierarchical Watermarking and Selective Encryption for Social Image Security
Authors: Ye Conghuan , Tan Shenglong , Wang Zheng , Shi Binghua , Shi Li
Published: July 2023

With the advent of cloud computing and social multimedia communication, more and more social images are being collected on social media platforms, such as Facebook, TikTok, Flirk, and YouTube. The amount of social images produced and disseminated is rapidly increasing. Meanwhile, cloud computing-assisted social media platforms have made social image dissemination more and more efficient. There exists an unstoppable trend of fake/unauthorized social image dissemination. The growth of social image sharing underscores potential security risks for illegal use, such as image forgery, malicious copying, piracy exposure, plagiarism, and misappropriation. Therefore, secure social image dissemination has become urgent and critical on social media platforms. The authors propose a secure scheme for social image dissemination on social media platforms. The main objective is to make a map between the tree structure Haar (TSH) transform and the hierarchical community structure of a social network. First, perform the TSH transform on a social image using social network analysis (SNA). Second, all users in a social media platform are coded using SNA. Third, watermarking and encryption are performed in a compressed domain for protecting social image dissemination. Finally, the encrypted and watermarked contents are delivered to users via a hybrid multicast–unicast scheme. The use of encryption along with watermarking can provide double protection for social image dissemination. The theory analysis and experimental results demonstrate the effectiveness of the proposed scheme.
“Girls Hold All the Power in the World”: Cultivating Sisterhood and a Counterspace to Support STEM Learning with Black Girls
Authors: Edwards Erica B. , King Natalie S.
Published: July 2023

For far too long, schools have been violent places where Black girls are often adultified, overdisciplined, and overlooked. In school science and mathematics specifically, Black girls have been isolated, tokenized, and made to feel invisible. This qualitative study leveraged the Multidimensionality of Black Girls’ STEM Learning conceptual framework to explore the roles of two Black women middle school science and mathematics teachers on the STEM learning experiences of 12 Black girls who live in the U.S. Midwest and how the girls engage with culturally relevant lessons in an afterschool program—SISTERHOOD I AM STEM. Data sources included a demographic questionnaire, program artifacts, and semi-structured transformative dialogic interviews with student and teacher participants. Findings revealed the significance and benefits of single-gender STEM learning environments for Black girls who struggle to connect with school and the role of Black women teachers in creating safe spaces for STEM engagement. In addition, the afterschool STEM program served as a mechanism to promote self-visualization and confidence for Black girls in science with the HyFlex model fostering a communal experience for the girls and their families. This STEM learning space organized and facilitated by Black women educators resisted Black girls’ pathologization and cultivated their sense of belonging. It holds promise for developing the social bonds that are critically important to their persistence in the field and a new narrative where “Girls hold all the power in the world”.
Anti-Similar Visual Target Tracking Algorithm Based on Filter Peak Guidance and Fusion Network
Authors: Wang Jing , Wei Yuan , Wu Xueyi , Huang Weichao , Yu Lu
Published: July 2023

Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference.
Identifying and Modeling Resonance-Related Fluctuations on the Experimental Characteristic Impedance for PCB and On-Chip Transmission Lines
Authors: Rodríguez-Velásquez Yojanes , Torres-Torres Reydezel , Murphy-Arteaga Roberto
Published: July 2023

It is well known that the fluctuations in experimentally obtained characteristic impedance versus frequency curves are associated with resonances originated by standing waves bouncing back and forth between the transitions at the transmission line terminations. In fact, microwave engineers are aware of the difficulty to completely remove the parasitic effect of these transitions, which makes obtaining smooth and physically expected frequency-dependent curves for the characteristic impedance a tough task. Here, we point out for the first time that these curves exhibit additional fluctuations within the microwave range due to standing waves taking place within the transition itself. Experimental verification of this fact was carried out by extracting this fundamental parameter from measurements performed on on-chip and printed circuit board (PCB) lines using probe pad adapters and coaxial connectors. We demonstrate that the lumped circuit approach to represent the transitions lacks validity when the additional fluctuations due to the connectors become apparent, and we propose a new model including transmission line effects within the transition.
A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism
Authors: Shi Yan , Jiang Chun , Liu Changhua , Li Wenhan , Wu Zhiyong
Published: July 2023

High-quality space target images are important for space surveillance and space attack defense confrontation. To obtain space target images with higher resolution and sharpness, this paper proposes an image super-resolution reconstruction network based on dual regression and a deformable convolutional attention mechanism (DCAM). Firstly, the mapping space is constrained by dual regression; secondly, deformable convolution is used to expand the perceptual field and extract the high-frequency features of the image; finally, the convolutional attention mechanism is used to calculate the saliency of the channel domain and the spatial domain of the image to enhance the useful features and suppress the useless feature responses. The experimental results show that the method outperforms the comparison algorithm in both objective quality evaluation index and localization accuracy on the space target image dataset compared with the current mainstream image super-resolution algorithms.
Track False-Target Deception Method Based on Phase-Switched Screen
Authors: Hao Guoqing , Feng Dejun , Wang Junjie , Zhou Zimeng , Wang Ling
Published: July 2023

Track processing is the foundation of radar multi-target tracking, and the processing performance for jamming has particular research significance when it comes to protecting high-value targets. At present, passive jamming using a modulated metasurface exhibits a fast response and a flexible operation mode. However, most research in this area has been carried out at the radar signal processing level and less at the data processing level. In this paper, a range of false target track deception method based on a phase-switched screen (PSS) is proposed, and the relationship between the matched filtering output, radar detection, and track processing is derived. This method uses PSS to generate multiple false targets with controlled spatial distribution and magnitude, which can form high-fidelity false tracking tracks. The number of false tracking tracks can be flexibly altered by controlling the modulation parameters. The simulation results validate the effectiveness of the proposed method.
Avoid Bogie Bearing Failure of IGBT Inverter Fed EMUs and Locomotives
Authors: Wang Liguo , Yang Xiujuan , Yan Xiangzhen
Published: July 2023

Three current paths are proposed, and theoretical analysis and laboratory tests are carried out to investigate the root causes of bearing failure in IGBT inverter-fed locomotives and EMUs. The three types of current paths that run through the drive unit bearings and axle box bearings used on EMUs and electric locomotives are classified as the primary side current path, the main traction system current path, and the current path between the vehicles of the EMU or electric locomotive and the vehicles it hauls. The research found that the EDM current path in the main traction system caused by common mode voltage is distinguished as the main cause resulting in the failure of the bogie motor bearings or the bearings of the load connected to the motor shaft. The cause of common mode voltage is analyzed, and the thresholds of current density and voltage without causing bearing damage are analyzed and presented. The lab tests carried out on the bearings on the main traction system’s current path verified that the current path does exist. The proof to identify electric erosion, such as craters and washboards, and corresponding measures to prevent the failure of bogie bearings are proposed. Further research about the other two current paths is urgent and necessary.
The DC Inductor Current Ripple Reduction Method for a Two-Stage Power Conversion System
Authors: Kim Hyeong-Jin , Park Yong-Min , Son Yung-Deug , Kang Jae-Beom , Lee Ji-Young , Kim Jang-Mok
Published: July 2023

This paper proposes a method for minimizing the inductor current ripple of a DC–DC converter in a two-stage power conversion system consisting of a grid-connected PWM converter and an interleaved multiphase three-level DC–DC converter. To reduce the output voltage ripple, the three-level DC–DC converter is configured in parallel and operated interleaved. However, a circulating current generated by the interleaved operation increases the inductor current ripple of each DC–DC converter and causes system loss and inductor saturation. In this paper, the inductor and output current ripple of the interleaved three-phase three-level DC–DC converter is mathematically analyzed and the effect of the DC–DC converter’s duty ratio and output voltage on each current ripple is described. Based on this analysis, a method is proposed for controlling the optimal DC link voltage through the PWM converter, so that the DC–DC converter is controlled with the duty ratio that minimizes the inductor current ripple. The simulation and experimental results under various operating conditions are presented to verify the feasibility of the proposed control method.
Investigating the Factors Influencing the Adoption of Blockchain Technology across Different Countries and Industries: A Systematic Literature Review
Authors: Marengo Agostino , Pagano Alessandro
Published: July 2023

Despite the reported disruptive nature of blockchain technology in the extant literature, its adoption is slower than its potential. This difference between the technology’s promises and its current adoption has sparked interest in understanding the factors impeding widespread adoption. This systematic literature review (SLR), drawn from 1786 studies published between 2008 and May 2023, seeks to address this gap. Specifically, our research explores the influence of factors and their differences and commonalities on blockchain adoption. The SLR, examining individual and organisational perspectives, identifies 152 unique factors influencing 25 industries across 21 countries. This review also highlights distinct commonalities and variations in these factors across industries and countries. For instance, while regulatory issues and costs were universal concerns, the importance of technical understanding diverged between industries. Furthermore, country-specific factors, including local regulations and cultural aspects, emerged as significantly influenced insights that provide a comprehensive perspective on the dynamics of blockchain adoption, offering valuable guidance to industry practitioners and researchers striving to navigate the complexities of blockchain integration.
An Algorithm Based on DAF-Net++ Model for Wood Annual Rings Segmentation
Authors: Ge Zhedong , Zhang Ziheng , Shi Liming , Liu Shuai , Gao Yisheng , Zhou Yucheng , Sun Qiang
Published: July 2023

The semantic segmentation of annual rings is a research topic of interest in wood chronology. To solve the problem of wood annual rings being difficult to segment in dense areas and being greatly affected by defects such as cracks and wormholes, this paper builds a DAF-Net++ model which is based on U-Net whose backbone network is VGG16 and filled with dense jump links, CBAM and DCAM. In this model, VGG16 is used to enhance the extraction ability of image features, dense jump links are used to fuse semantic information of different levels, DCAM provides weighting guidance for shallow features, and CBAM solves the loss of down-sampling information. Taking a Chinese fir wood as the experimental object, 1700 CT images of wood transverse section were obtained by medical CT equipment and 120 of them were randomly selected as the dataset, which was expanded by cropping and rotation, among others. DAF-Net++ was used for training the model and segmentation of the annual rings, and finally the performance of the model was evaluated. The training method is freeze training followed by thaw training, and takes Focal Loss as the loss function, ReLU as the activation function, and Adam as the optimizer. The experimental results show that, in the segmentation of CT images of Chinese fir annual rings, the MIoU of DAF-Net++ is 93.67%, the MPA is 96.76%, the PA is 96.63%, and the Recall is 96.76%. Compared with other semantic segmentation models such as U-Net, U-Net++, DeepLabv3+, etc., DAF-Net++ has better segmentation performance.
A Novel UAV-Assisted Multi-Mobility Channel Model for Urban Transportation Emergency Communications
Authors: Liang Jinfan , Huang Xun , Xu Qiwang , Liu Yu , Zhang Jingfan , Huang Jie
Published: July 2023

With the increasing requirements for unmanned aerial vehicle (UAV) communication in various application scenarios, the UAV-assisted emergency communication in urban transportation scenario has received great attention. In this paper, a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) for the urban traffic communication scenario is proposed. The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are considered by introducing the time-variant acceleration and velocity correspondingly. The velocity variation of the clusters is used to simulate the motion of vehicles around the Rx. Moreover, to describe the vehicles’ moving states, Markov chain is adopted to analyze the changes in cluster motion states, including survival, death, dynamic, and static states. By adjusting the scenario-specific parameters, such as the vehicle density (ρ) and dynamic–static ratio (Ω), the model can support various urban traffic scenarios. Based on the proposed model, several key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval, under different clusters and antenna accelerations are obtained and analyzed. The accuracy of the proposed model is verified by the measured data. The results demonstrate the usability of our model, which can be provided as a reference for the design, evaluation, and optimization of future communication networks between UAV and vehicles in urban transportation emergency communications.
Enabling Efficient and Privacy-Preserving Task Allocation with Temporal Access Control for Mobile Crowdsensing
Authors: Song Fuyuan , Liu Yiwei , Ma Siyao , Jiang Qin , Zhang Xiang , Fu Zhangjie
Published: July 2023

The increasing proliferation of GPS-enabled mobile devices, including Unmanned Aerial Vehicles (UAVs), smartphones, and laptops, has resulted in a significant upsurge in the outsourcing of spatial data to cloud servers for storage and computation purposes, such as task allocation and location-based services. However, the reliance on untrusted cloud servers introduces the risk of privacy breaches, as these servers possess the ability to deduce and access users’ private information based on task content and query requirements. Existing privacy-preserving task-allocation schemes offer only coarse-grained and non-temporal access control, which restricts their applicability in scenarios involving multiple users and time-series data, such as trajectory and time-related routes. To overcome these challenges, this paper proposes an Efficient and Privacy-Preserving Task Allocation with Temporal Access Control (EPTA-T) scheme for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, EPTA-T achieves efficient and privacy-preserving task allocation in mobile crowdsensing. Specifically, EPTA-T supports fine-grained and temporal access control through the utilization of an attribute-based access tree and function integration. The formal security analysis demonstrated that EPTA-T effectively guarantees data privacy and query privacy throughout the task allocation process. Extensive experiments conducted using a real-world dataset indicated that the EPTA-T scheme surpassed the performance of the state-of-the-art scheme.
Enhanced-Deep-Residual-Shrinkage-Network-Based Voiceprint Recognition in the Electric Industry
Authors: Zhang Qingrui , Zhai Hongting , Ma Yuanyuan , Sun Lili , Zhang Yantong , Quan Weihong , Zhai Qi , He Bangwei , Bai Zhiquan
Published: July 2023

Voiceprint recognition can extract voice features and identity the speaker through the voice information, which has great application prospects in personnel identity verification and voice dispatching in the electric industry. The traditional voiceprint recognition algorithms work well in a quiet environment. However, noise interference inevitably exists in the electric industry, degrading the accuracy of traditional voiceprint recognition algorithms. In this paper, we propose an enhanced deep residual shrinkage network (EDRSN)-based voiceprint recognition by combining the traditional voiceprint recognition algorithms with deep learning (DL) in the context of the noisy electric industry environment, where a dual-path convolution recurrent network (DPCRN) is employed to reduce the noise, and its structure is also improved based on the deep residual shrinkage network (DRSN). Moreover, we further use a convolutional block attention mechanism (CBAM) module and a hybrid dilated convolution (HDC) in the proposed EDRSN. Simulation results show that the proposed network can enhance the speaker’s vocal features and further distinguish and eliminate the noise features, thus reducing the noise influence and achieving better recognition performance in a noisy electric environment.
Development- and Validation-Improved Metrological Methods for the Determination of Inorganic Impurities and Ash Content from Biofuels
Authors: Stratulat Camelia , Ginghina Raluca Elena , Bratu Adriana Elena , Isleyen Alper , Tunc Murat , Hafner-Vuk Katarina , Frey Anne Mette , Kjeldsen Henrik , Vogl Jochen
Published: July 2023

In this study, five laboratories, namely, BRML (Romania), TUBITAK UME (Turkey), IMBIH (Bosnia and Herzegovina), BAM (Germany), and DTI (Denmark), developed and validated analytical procedures by ICP-MS, ICP-OES, MWP-AES, WD-XRF, and ID-MS for the determination of inorganic impurities in solid and liquid biofuels, established the budget of uncertainties, and developed the method for determining the amount of ash in the measurement range 0–1.2% with absolute repeatability less than 0.1% and absolute reproducibility of 0.2% (according to EN ISO 18122). In order to create homogeneous certified reference materials, improved methodologies for the measurement and characterization of solid and liquid biofuels were developed. Thus, information regarding the precision, accuracy, and bias of the method, and identifying the factors that intervened in the measurement of uncertainty were experimentally determined, supplementing the information from the existing standards in the field.
A Comprehensive Review of a Decade of Field PV Soiling Assessment in QEERI’s Outdoor Test Facility in Qatar: Learned Lessons and Recommendations
Authors: Aïssa Brahim , Isaifan Rima J. , Figgis Benjamin W. , Abdallah Amir A. , Bachour Dunia , Perez-Astudillo Daniel , Sanfilippo Antonio , Lopez-Garcia Juan , Bermudez Benito Veronica
Published: July 2023

Soiling of photovoltaic (PV) modules is a major issue due to its critical impact on PV performance and reliability, especially in the desert and arid regions such as the state of Qatar. Soiling frequently results in a severe reduction in PV power generation, which drastically affects the economical profitability of the PV plant, and therefore, must be mitigated. The most common way of mitigating PV soiling is surface cleaning. However, the latter could consequently increase the associated operation and maintenance (O&M) cost of the PV site. However, previous studies indicated that even if the best-optimized cleaning schemes are used, the actual global solar-power production can still be reduced by about 4%, which is associated with at least EUR 5 billion in annual revenue losses worldwide. This loss is expected to reach a conservative value of EUR 7 billion in 2023. Accordingly, investigating the interplayed physics phenomena related to the various soiling processes, the site-specific O&M costs, along with a techno-economical assessment of state-of-the-art soiling mitigation strategies (including innovative anti-soiling coating materials) is of paramount importance. The goal of this comprehensive report is to provide the solar community at large, and those focusing on the desert environment in particular, with real field measurements that provide key findings and challenges in addressing soiling research obtained from multiyear testing at the Outdoor Test Facility (OTF) field station, located in the desert environment of the city of Doha, in the state of Qatar.
Enhanced Heat Transfer of a Heat Exchanger Tube Installed with V-Shaped Delta-Wing Baffle Turbulators
Authors: Samruaisin Prachya , Maza Rangsan , Thianpong Chinaruk , Chuwattanakul Varesa , Maruyama Naoki , Hirota Masafumi , Eiamsa-ard Smith
Published: July 2023

The influences of V-shaped delta-wing baffles on the thermohydraulic performance characteristics in a round tube were experimentally tested. The V-shaped delta-wing baffles having a set number of wings (N = 4, 6, and 8) were comparatively tested. The V-shaped delta-wing baffles with various pitch ratios of P/D = 2.0, 2.5, and 3.0 were thoroughly fitted inside a tube. In the present work, the baffles were responsible for both the recirculation/reverse flow behind the solid baffle and the longitudinal vortex flow behind the V-shaped wing. The V-shaped winged baffles with N = 8 produced high heat transfer rates by promoting the development of reverse and vortex flows. These currents aid in fluid mixing between the two streams. Experimental results suggested that utilizing V-shaped delta-wing baffles having N = 4, 6, and 8 led to Nusselt number enhancement of up to 97–105.6%, 105.8–127.8% and 114.8–138.9%, respectively. When N was 8, the V-shaped wings baffles created additional multi vortex flows, which resulted in some fluid mixing between the vortex and the reverse flow. It was discovered that a greater turbulent intensity is imparted to the flow that was occurring between the V-shaped delta-wing baffles, which led to an increase in the rate of heat transfer when the pitch ratio was decreased. The increase in Nusselt number was up to 118.26–151.3% more than it was in a tube with the lowest pitch ratio (P/D = 2.0). It was also found that the baffles with N = 8 wings and P/D = 3.0 offered a maximum aerothermal performance factor (APF) of 1.01. Furthermore, the V-shaped delta-wing baffles have the potential for energy savings at low Re ≤ 6000, indicated by the APF beyond unity.
Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory
Authors: Ma Ning , Yin Huaixian , Wang Kai
Published: July 2023

As a novel type of energy storage element, supercapacitors have been extensively used in power systems, transportation and industry due to their high power density, long cycle life and good low-temperature performance. The health status of supercapacitors is of vital importance to the safe operation of the entire energy storage system. In order to improve the prediction accuracy of the remaining useful life (RUL) of supercapacitors, this paper proposes a method based on the Harris hawks optimization (HHO) algorithm and long short-term memory (LSTM) recurrent neural networks (RNNs). The HHO algorithm has the advantages of a wide global search range and a high convergence speed. Therefore, the HHO algorithm is used to optimize the initial learning rate of LSTM RNNs and the number of hidden-layer units, so as to improve the stability and reliability of the system. The root mean square error (RMSE) between the predicted result and the observed result reduced to 0.0207, 0.026 and 0.0341. The prediction results show that the HHO-LSTM has higher accuracy and robustness than traditional LSTM and GRU (gate recurrent unit) models.
CFD Modeling and Thermal Analysis of a Cold Plate Design with a Zig-Zag Serpentine Flow Pattern for Li-Ion Batteries
Authors: Jayarajan Sarath Arangat , Azimov Ulugbek
Published: July 2023

Heavy-duty vehicles, such as trucks or buses, typically have larger battery packs compared to passenger electric vehicles (EVs). These batteries generate more heat due to the increased power demands of the vehicle. Effective thermal management is therefore crucial to prevent excessive heat buildup and maintain optimal battery performance. This paper aimed to develop a dynamic and efficient cooling system for larger Li-ion batteries used in electric vehicles. In this study, we propose a novel cold plate design featuring a zig-zag serpentine flow pattern within a rectangular profile channel. The chosen design maximizes the coolant coverage over the cold plate’s surface area. To investigate the performance of the cold plate design, we designed and modeled a total of six different cold plates with varying numbers of channels (3, 5, 7, 9, 11, and 13). Preliminary simulations were conducted using Star CCM+ software. The cold plate material selected for its high thermal conductivity was aluminum, while water served as the coolant. Several parameters were optimized, including adjustments to channel width, mass flow rate, heat flux, and inlet coolant temperature. The optimization was conducted to determine the optimal design for the cold plate. We found that the best design configurations were five-channel with an 18 mm channel width and a seven-channel with a 16 mm channel width. It was found that the temperature rapidly increased and reached its maximum in the outlet region. In the design with three channels, the maximum temperature attained at the exit region was 330.84 K. The temperature gradually decreased at the exit region when the number of the channels increased from 3 channels to 13 channels and achieved a minimum temperature of 316 K for the design with 13 channels. For these configurations, heat fluxes of 2 °C and 3 °C were found to be optimal, while a discharge rate of 4 °C was deemed acceptable. The zig-zag design and the obtained results are instrumental in designing and evaluating the performance of cold plates by exploring various parameters. This research contributes to the development of an effective cooling system for large Li-ion batteries in EVs, potentially enhancing their efficiency and reliability.
Rapid Calculation of Active Distribution Network Reliability Based on Dynamic Equipment Failure Rate
Authors: Liu Zifa , Li Jiong , Zhang Ting , Shao Junteng
Published: July 2023

The active distribution network has been fully developed because it can achieve efficient energy utilization through the effective control of distributed generation, electrical energy storage, and active loads. However, the research on the reliability of active distribution network operation still stays in the medium and long-term calculation, without considering the influence of dynamic equipment characteristics and active distribution network operation characteristics on reliability. In order to improve the comprehensiveness and calculation speed of the reliability calculation of an active distribution network, this paper proposes a fast calculation method for the reliability of the active distribution network based on dynamic equipment failure rates. Firstly, based on fuzzy-theory and data-driven methods, the cloud model and the Proportional Hazard Model are used to model the dynamic failure rates of exposed and enclosed types of equipment, respectively. This considers the impacts of the dynamic failure characteristics of the equipment on the reliability calculation and improves the comprehensiveness of the calculation. Then, the improved K-means algorithm is used to achieve a faster and more suitable scenario reduction and increase the computation rate. Finally, considering the island operation and contact line transfer under the active distribution network, simplifying the network by improving the minimum path method, the reliability calculation method of the active distribution network combining source–load uncertainty, switch fault information, and load transfer probability is further proposed. The cases show that the dynamic fault rate model is closer to an engineering reality and has generality. The improved K-means algorithm is faster and more accurate than the traditional algorithm. The final proposed fast reliability calculation method reduces the time by 72.76 times compared to the traditional method. It fully reflects the operational characteristics of the active distribution network and provides a reference for the optimal dispatch of the active distribution network.
Corporate Governance’s Impact on Sustainable Finance: An Analysis of Borsa Istanbul Energy Sector Companies
Authors: Zehir Cemal , Özyeşil Mustafa , Borodin Alex , Aktürk Esin Benhür , Faedfar Sara , Çikrikçi Mustafa
Published: July 2023

The main purpose of this study is to conduct an evaluation based on listed companies traded in the energy sector sub-market of Borsa Istanbul (BIST). This evaluation is conducted on a sample of 27 companies between 2016 and 2021. In this study, corporate governance indicators are used as independent variables, while financial performance indicators are used as dependent variables. Initially, descriptive statistics of the sample and correlations between variables were calculated and interpreted in the analysis, and the Panel Data Analysis method is applied for the interaction between variables. This study emphasizes the importance of global economic and social crises, rapid changes in communication technologies, and the concept of sustainability for businesses. The sustainability of financing is highlighted as vital for companies. The findings of the study may serve as a valuable resource for understanding the performance of companies operating in the energy sector sub-market of BIST and their relationships with sustainability.
The Evolution of Crop-Based Materials in the Built Environment: A Review of the Applications, Performance, and Challenges
Authors: Motamedi Sina , Rousse Daniel R. , Promis Geoffrey
Published: July 2023

The use of bio-based building materials as an alternative to replacing concrete or insulation materials is called to become a growing trend in the construction industry. On Science direct, publications concerning “bio-based materials” have increased from 4 in 2002 to 1073 twenty years later, demonstrating a growing interest in these materials However, among bio-based materials, crop or plant-based materials are not as popular. Due to their relative novelty, little is known about their potential applications, physical characteristics, and environmental impacts. The aim of this review is to qualitatively investigate the technical and environmental viability of crop-based materials in modern building applications. The specific objectives of the study consider greenhouse gas (GHG) emissions using life cycle assessment (LCA) approaches, contribution to the circular economy, and physical and hygrothermal characteristics. Another objective is to examine the progress of crop-based materials’ R&D, current bottlenecks, and a future roadmap for their evolution in state-of-the-art renewable buildings. The paper is broad enough to capture a large readership rather than experts in the domain. The review reveals that crop-based materials have the potential to replace traditional, highly emissive building materials. They offer low environmental impacts, in all stages of their life cycle.
Investigation of Post-Treatment Improving Perovskite Solar Cells Initial Performances and Study of Its Impact on the Durability
Authors: Perrin Lara , Flandin Lionel , Farha Cynthia , Narbey Stéphanie , Martineau David , Planès Emilie
Published: July 2023

Among alternative perovskite device architectures, mesoporous scaffolds based on metal oxide and carbon look highly promising due to their inherent high stability. In this study, the perovskite deposition has been realized on a 1.5 cm2 active surface area using inkjet infiltration through the mesoporous scaffold, affording a clean industrial process for large-scale and stable perovskite devices. A spectacular enhancement of photovoltaic performances from 10% to 14% was then obtained through the implementation of a maturation step. A study of the occurring mechanisms was conducted using a full set of characterization techniques including J-V measurements, UV–visible and PL spectroscopies, LBIC and PL imaging, XRD, and surface roughness measurements. In addition, the impact of this maturation step on the durability of the performances was investigated.