bluuebunny/crossref_metadata_2025 huggingface dataset:
Differential Expression and Function of SVIP in Breast Cancer Cell Lines and In Silico Analysis of Its Expression and Prognostic Potential in Human Breast Cancer
Authors: Atalay Şahar Esra , Ballar Kirmizibayrak Petek
Published: May 2023

The heterogeneity of cancer strongly suggests the need to explore additional pathways to target. As cancer cells have increased proteotoxic stress, targeting proteotoxic stress-related pathways such as endoplasmic reticulum stress is attracting attention as a new anticancer treatment. One of the downstream responses to endoplasmic reticulum stress is endoplasmic reticulum-associated degradation (ERAD), a major degradation pathway that facilitates proteasome-dependent degradation of unfolded or misfolded proteins. Recently, SVIP (small VCP/97-interacting protein), an endogenous ERAD inhibitor, has been implicated in cancer progression, especially in glioma, prostate, and head and neck cancers. Here, the data of several RNA-sequencing (RNA-seq) and gene array studies were combined to evaluate the SVIP gene expression analysis on a variety of cancers, with a particular focus on breast cancer. The mRNA level of SVIP was found to be significantly higher in primary breast tumors and correlated well with its promoter methylation status and genetic alterations. Strikingly, the SVIP protein level was found to be low despite increased mRNA levels in breast tumors compared to normal tissues. On the other hand, the immunoblotting analysis showed that the expression of SVIP protein was significantly higher in breast cancer cell lines compared to non-tumorigenic epithelial cell lines, while most of the key proteins of gp78-mediated ERAD did not exhibit such an expression pattern, except for Hrd1. Silencing of SVIP enhanced the proliferation of p53 wt MCF-7 and ZR-75-1 cells but not p53 mutant T47D and SK-BR-3 cells; however, it increased the migration ability of both types of cell lines. Importantly, our data suggest that SVIP may increase p53 protein levels in MCF7 cells by inhibiting Hrd1-mediated p53 degradation. Overall, our data reveal the differential expression and function of SVIP on breast cancer cell lines together with in silico data analysis.
Effects of Oxygen Partial Pressure and Thermal Annealing on the Electrical Properties and High-Temperature Stability of Pt Thin-Film Resistors
Authors: Pang Yawen , Zhao Nan , Ruan Yong , Sun Limin , Zhang Congchun
Published: May 2023

The effects of oxygen partial pressure and annealing temperature on the microstructure, electrical properties, and film adhesion of Pt thin-film resistors with PtxOy as the adhesion layer were investigated. Pt/PtxOy films were deposited on alumina substrates by radio frequency sputtering and annealed in a muffle furnace at temperatures in the range of 800–1000 °C. The microstructure and chemical composition of Pt thin-film resistors were examined by optical microscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and time-of-flight secondary ion mass spectrometry. The experimental results show that annealing will lead to the formation of bubbles on the surface of the film, and the film prepared at 20% oxygen partial pressure has the least bubbles. The Pt thin-film resistors with a PtxOy adhesion layer sputtered with 10% oxygen partial pressure had the highest TCR (temperature coefficient of resistance) of 3434 ppm/°C, and the TCR increased with increasing annealing temperature. Repeated experiments show that Pt thin-film resistors have better stability at annealing temperatures of 800 °C and 900 °C. Comprehensively considering the TCR and stability, the optimal adhesion layer of Pt thin-film resistors was prepared at an oxygen partial pressure of 10% and an annealing temperature of 900 °C.
Changes in Cranial Shape and Developmental Quotient at 6 Months of Age in Preterm Infants
Authors: Nakanomori Aya , Miyabayashi Hiroshi , Tanaka Yukari , Maedomari Taishin , Mukai Chihiro , Saito Katsuya , Okahashi Aya , Nagano Nobuhiko , Morioka Ichiro
Published: May 2023

The purpose of this study was to investigate changes in cranial shape among preterm neonates aged 1–6 months and the relationship between developmental quotient (DQ) and cranial shape at 6 months of age. Preterm infants who were hospitalized in our hospital were prospectively followed for 6 months. The cephalic index (CI) and cranial vault asymmetry index (CVAI) were evaluated at 1 (T1), 3 (T2), and 6 months (T3) of age and compared with those of the full-term infants. The relationship between CI or CVAI and DQ at T3 was analyzed using the Enjoji Scale of Infant Analytical Development. A total of 26 participants born at 34.7 ± 1.9 weeks of gestation were included. The CI increased with age (T1: 77.2%, T2: 82.9%, T3: 85.4%, p < 0.01). The prevalence of dolichocephaly at T3 did not significantly differ from that in full-term infants (15.4% vs. 4.5%, p = 0.08). CVAI did not significantly differ between preterm and full-term infants. The DQ showed no significant correlation with either the CI or CVAI (correlation coefficients: 0.23 for CI, −0.01; CVAI). Dolichocephaly improved over time in preterm infants and no relationship between cranial shape and development was observed in preterm infants at 6 months of age.
Bilayer Coating Composed of Starch and Methyl Cellulose-Nanoscale TiO2 for the Protection of Historic Paper from UV
Authors: Aleksić Gabriela , Cigula Tomislav , Vukoje Marina , Itrić Ivanda Katarina
Published: May 2023

Among the agents of deterioration considered particularly damaging to cultural heritage objects are light, ultraviolet (UV) and infrared. The high-energy UV is the most damaging of the three, as it can cause irreversible visual, chemical and structural changes in cellulose-based materials. Known for its photocatalytic properties, TiO2 can absorb UV and is thermally and chemically stable. In this study, we propose an innovative bilayer coating composed of starch, methyl cellulose and nano-TiO2 to utilize UV blocking properties of TiO2. The results of the treatments were assessed by determining physical, optical and surface properties, as well as the degradation degree of the samples, prior and post accelerated ageing. The results show that an increase in the weight concentration of TiO2 enhances the colour difference, but the colour of samples coated by lower concentrations of TiO2 after accelerated ageing is closer to the original value than the colour of the uncoated ones. An increase in both the contact angle and the absorption time is also present, but after ageing, both parameters significantly decrease due to the presence of TiO2. To conclude, the presented nanocomposite coating can protect historic paper from UV, but one should bear in mind that a higher weight concentration could lead to a higher water sensitivity after exposure to UV.
High-Temperature Solid Particle Erosion of Environmental and Thermal Barrier Coatings
Authors: Presby Michael J. , Stokes Jamesa L. , Harder Bryan J. , Lee Kang N. , Hoffman Leland C.
Published: May 2023

Solid particle erosion (SPE) is a common phenomenon observed in gas turbine engines. Particles entrained in the gas flow impact engine hardware, resulting in micro-scale damage that leads to deleterious effects such as material removal. For protective coatings, damage due to SPE is a key concern, since it can negatively affect the durability of the coating and subsequently the life of the underlying component. In this work, the high-temperature SPE behavior of two state-of-the-art environmental barrier coatings (EBCs) deposited via air plasma spray (APS) is investigated using alumina erodent to understand the effect of particle kinetic energy, impingement angle, and temperature. The SPE behavior of the EBCs is also compared to APS and electron beam–physical vapor deposition (EB-PVD) thermal barrier coatings (TBCs) to elucidate similarities and differences in the erosion response. The EBCs were more susceptible to SPE than the EB-PVD TBC but had greater SPE resistance compared to the APS TBC. Coating microstructure and porosity were shown to have a strong influence on the observed behavior.
Comparative Study of Resin and Silane Coupling Agents Coating Treatments on Bonding Strength Improvement of Titanium and Carbon Fiber Composites
Authors: Zuo Shihao , Wang Lin , Zhang Jinheng , Yang Guangming , Xu Yang , Hu Yunsen , Cheng Fei , Hu Xiaozhi
Published: May 2023

In this study, anodizing treatment was utilized to etch titanium (Ti) substrates’ surface to prefabricate nano-cavities. Resin pre-coating (RPC) and three silane coupling agents’ coating (CAC) techniques were further applied to porous Ti substrates surface to compare the reinforcement effect of adhesive bonding strength. SEM images show that nano-cavities have been prepared to create a greater contact area and vertical volume on Ti substrate surface, fully covered by resin coatings via RPC. A higher surface roughness and better surface wetting are also obtained by the testing results of atomic force microscope and contact angles. Single lap shear tests results indicate that specimens with “anodizing + RPC” treatment yield the best average shear strength of 20.73 MPa, increased by 31.7% compared to anodizing base strength and at least 63.0% higher than silane KH-550/560/792-coated specimens. A dominant cohesive failure and fiber-tearing on CFRP’s shallow surface, instead of adhesive debonding failure, are shown in the appearances of damaged specimens, proving that the RPC technique has a more effective bonding strength reinforcement in titanium and carbon fiber-reinforced polymer (Ti-CFRP) composites’ toughening. Thus, the simple RPC technique can be regarded as a new-type alternative to adhesive joint toughening to manufacture high-performance composites for aerospace applications.
Acne Vulgaris Topical Therapies: Application of Probiotics as a New Prevention Strategy
Authors: Dapkevicius Ieva , Romualdo Vânia , Marques Ana Camila , Lopes Carla M. , Amaral Maria Helena
Published: May 2023

The skin microbiome is an essential barrier for preventing the invasion of pathogens and regulating the immune responses. When this barrier is disrupted, several dermatoses, including acne vulgaris, may arise. Most people will experience acne starting at the onset of puberty and continuing into adolescence; a significant percentage of those individuals continue to suffer from it into adulthood. Although common, this dermatosis usually has an enormous impact on the self-esteem and quality of life of individuals who suffer from it. An increase in consumer literacy regarding skincare leads buyers to seek out innovative products containing ingredients with proven benefits for their concerns. Probiotics have proven to be an alternative to the use of antibiotics, often associated with undesirable effects, in the treatment and prevention of dermatological disorders such as acne. This review provides a comprehensive analysis of the pathophysiology, risk factors, symptoms, conventional treatment recommendations and main studies emphasizing innovative topical products for acne-prone skin based on probiotics. In addition, the potential advantages, and limitations/challenges associated with the implementation and manufacturing of these innovative skin products are also highlighted.
Revisiting Multiple Ring Oscillator-Based True Random Generators to Achieve Compact Implementations on FPGAs for Cryptographic Applications
Authors: Parrilla Luis , García Antonio , Castillo Encarnación , López-Villanueva Juan Antonio , Meyer-Baese Uwe
Published: May 2023

The generation of random numbers is crucial for practical implementations of cryptographic algorithms. In this sense, hardware security modules (HSMs) include true random number generators (TRNGs) implemented in hardware to achieve good random number generation. In the case of cryptographic algorithms implemented on FPGAs, the hardware implementation of RNGs is limited to the programmable cells in the device. Among the different proposals to obtain sources of entropy and process them to implement TRNGs, those based in ring oscillators (ROs), operating in parallel and combined with XOR gates, present good statistical properties at the cost of high area requirements. In this paper, these TRNGs are revisited, showing a method for area optimization independently of the FPGA technology used. Experimental results show that three ring oscillators requiring only three LUTs are enough to build a TRNG on Artix 7 devices from Xilinx with a throughput of 33.3 Kbps, which passes NIST tests. A throughput of 50 Kbps can be achieved with four ring oscillators, also requiring three LUTs in Artix 7 devices, while 100 Kbps can be achieved using an structure with four ring oscillators requiring seven LUTs.
Carbonate Minerals’ Precipitation in the Presence of Background Electrolytes: Sr, Cs, and Li with Different Transporting Anions
Authors: Marin-Troya Pedro , Espinosa Carlos , Monasterio-Guillot Luis , Alvarez-Lloret Pedro
Published: May 2023

Carbonate minerals are largely associated with many geological and biological environments as well as several industrial and technological processes. The crystalline characteristics of these mineral phases can be modified by background salts present in the solution due to the effect of different electrolytes on the dynamics of ion-water interactions and ionic strength during precipitation. In the current research, we studied the effect of the presence of several electrolytes (i.e., Cs, Li, and Sr), combined with chloride and carbonate as transporting anions, on the growth and mineral evolution processes of carbonate precipitation in solution. The electrolyte composition during the reaction (experimental times from 24 h up to 30 days) determined the formation of specific calcium carbonate polymorphs. The Li presence induced the formation of vaterite which was progressively transformed into calcite during the reaction time, while Cs stabilized the calcite formation. The Sr presence in the system caused the precipitation of strontianite with modifications in its cell parameters and the structural arrangement of the carbonate molecular group. During the mineral evolution considering chloride and carbonate experimental set-ups, several compositional and cell parameters/crystallinity variations of the carbonated phases were also observed. A better understanding of the relationship between the compositional properties of the aqueous solvent and the crystallization mechanisms can contribute to a deeper comprehension of the mineral precipitation and transformation in different multicomponent solutions that occur in natural environments and in controlled synthesis processes.
The Crystal Structure of Manganotychite, Na6Mn2(CO3)4(SO4), and Structural Relations in the Northupite Group
Authors: Krivovichev Sergey V. , Panikorovskii Taras L. , Bazai Ayya V. , Sidorov Mikhail Yu.
Published: May 2023

The crystal structure of manganotychite has been refined using the holotype specimen from the Alluaiv Mountain, Lovozero massif, Kola peninsula, Russia. The mineral is cubic, Fd3¯, a = 14.0015(3) Å, V = 2744.88(18) Å3, Z = 8, R1 = 0.020 for 388 independently observed reflections. Manganotychite is isotypic to tychite and ferrotychite. Its crystal structure is based upon a three-dimensional infinite framework formed by condensation of MnO6 octahedra and CO3 groups by sharing common O atoms. The sulfate groups and Na+ cations reside in the cavities of the octahedral-triangular metal-carbonate framework. In terms of symmetry and basic construction of the octahedral-triangular framework, the crystal structure of manganotychite is identical to that of northupite, Na3Mg(CO3)2Cl. The transition northupite → tychite can be described as a result of the multiatomic 2Cl− → (SO4)2− substitution, where both chlorine and sulfate ions are the extra-framework constituents. However, the positions occupied by sulfate groups and chlorine ions correspond to different octahedral cavities within the skeletons of Na atoms. The crystal structure of northupite can be considered as an interpenetration of two frameworks: anionic [Mg(CO3)2]2− octahedral-triangular framework and cationic [ClNa3]2− framework with the antipyrochlore topology. Both manganotychite and northupite structure types can be described as a modification of the crystal structure of diamond (or the dia net) via the following steps: (i) replacement of a vertex of the dia net by an M4 tetrahedron (no symmetry reduction); (ii) attachment of (CO3) triangles to the triangular faces of the M4 tetrahedra (accompanied by the Fd3¯m → Fd3¯ symmetry reduction); (iii) filling voids of the resulting framework by Na+ cations (no symmetry reduction); and (iv) filling voids of the Na skeleton by either sulfate groups (in tychite-type structures) or chlorine atoms (in northupite). As a result, the information-based structural complexity of manganotychite and northupite exceeds that of the dia net.
Bryophytes in a Changing World: Understanding Distribution Patterns, Risks, and Conservation
Authors: Číhal Lukáš
Published: May 2023

Bryophytes are a group of small, non-vascular plants that include mosses, liverworts, and hornworts [...]
RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection
Authors: Strautiņa Sarmīte , Kalniņa Ieva , Kaufmane Edīte , Sudars Kaspars , Namatēvs Ivars , Nikulins Arturs , Edelmers Edgars
Published: May 2023

The RaspberrySet dataset is a valuable resource for those working in the field of agriculture, particularly in the selection and breeding of ecologically adaptable berry cultivars. This is because long-term changes in temperature and weather patterns have made it increasingly important for crops to be able to adapt to their environment. To assess the suitability of different cultivars or to make yield predictions, it is necessary to describe and evaluate berries’ characteristics at various growth stages. This process is typically carried out visually, but it can be time-consuming and labor-intensive, requiring significant expert knowledge. The RaspberrySet dataset was created to assist with this process, and it includes images of raspberry berries at five different stages of development. These stages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry images classified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated using ground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGB images, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). The images were taken in various weather conditions, at different times of the day, and from different angles, and they include fully visible buds, flowers, berries, and partially obscured buds. This dataset is intended to improve the efficiency of berry breeding and yield estimation and to identify the raspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allows for the reliable detection and phenotyping of yield components at different stages of development. By providing a homogenized dataset of images taken on-site at the Institute of Horticulture in Dobele, Latvia, the RaspberrySet dataset offers a valuable resource for those working in horticulture.
The Effect of Short-Term Transcutaneous Electrical Stimulation of Auricular Vagus Nerve on Parameters of Heart Rate Variability
Authors: Shvartz Vladimir , Sizhazhev Eldar , Sokolskaya Maria , Koroleva Svetlana , Enginoev Soslan , Kruchinova Sofia , Shvartz Elena , Golukhova Elena
Published: May 2023

Many previous studies have demonstrated that transcutaneous vagus nerve stimulation (VNS) has the potential to exhibit therapeutic effects similar to its invasive counterpart. An objective assessment of VNS requires a reliable biomarker of successful vagal activation. Although many potential biomarkers have been proposed, most studies have focused on heart rate variability (HRV). Despite the physiological rationale for HRV as a biomarker for assessing vagal stimulation, data on its effects on HRV are equivocal. To further advance this field, future studies investigating VNS should contain adequate methodological specifics that make it possible to compare the results between studies, to replicate studies, and to enhance the safety of study participants. This article describes the design and methodology of a randomized study evaluating the effect of short-term noninvasive stimulation of the auricular branch of the vagus nerve on parameters of HRV. Primary records of rhythmograms of all the subjects, as well as a dataset with clinical, instrumental, and laboratory data of all the current study subjects are in the public domain for possible secondary analysis to all interested researchers. The physiological interpretation of the obtained data is not considered in the article.
An Ensembled Framework for Human Breast Cancer Survivability Prediction Using Deep Learning
Authors: Mustafa Ehzaz , Jadoon Ehtisham Khan , Khaliq-uz-Zaman Sardar , Humayun Mohammad Ali , Maray Mohammed
Published: May 2023

Breast cancer is categorized as an aggressive disease, and it is one of the leading causes of death. Accurate survival predictions for both long-term and short-term survivors, when delivered on time, can help physicians make effective treatment decisions for their patients. Therefore, there is a dire need to design an efficient and rapid computational model for breast cancer prognosis. In this study, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks. Specifically, we design a convolutional neural network (CNN) for clinical modalities, a deep neural network (DNN) for copy number variations (CNV), and a long short-term memory (LSTM) architecture for gene expression modalities to effectively handle multi-dimensional data. The independent models’ results are then used for binary classification (long term > 5 years and short term < 5 years) based on survivability using the random forest method. The EBCSP model’s successful application outperforms models that utilize a single data modality for prediction and existing benchmarks.
A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography
Authors: Franceschini Stefano , Autorino Maria Maddalena , Ambrosanio Michele , Pascazio Vito , Baselice Fabio
Published: May 2023

In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.
Different BD BACTEC™ Blood Culture Bottle Types for the Detection of Fungi in Simulated Sterile Body Fluid Samples
Authors: Tomazin Rok , Pliberšek Tadej , Oštrbenk Valenčak Anja , Matos Tadeja
Published: May 2023

Blood culture systems are a potential alternative to classical cultivation of fungi on mycological media, but there are limited data on the suitability of these systems for culturing other sample types (e.g., sterile body fluids). We conducted a prospective study to evaluate different types of blood culture (BC) bottles for the detection of different fungal species in non-blood samples. A total of 43 fungal isolates were tested for their ability to grow in BD BACTEC Mycosis-IC/F (Mycosis bottles), BD BACTEC Plus Aerobic/F (Aerobic bottles) and BD BACTEC Plus Anaerobic/F (Anaerobic bottles) (Becton Dickinson, East Rutherford, NJ, USA) BC bottles inoculated with spiked samples without the addition of blood or fastidious organism supplement. Time to detection (TTD) was determined for all BC types tested and compared between groups. In general, Mycosis and Aerobic bottles were similar (p > 0.05). The Anaerobic bottles failed to support growth in >86% of cases. The Mycosis bottles were superior in detecting Candida glabrata, Cryptococcus spp. and Aspergillus spp. (p < 0.05). The performance of Mycosis and Aerobic bottles was similar, but if cryptococcosis or aspergillosis is suspected, the use of Mycosis bottles is recommended. Anaerobic bottles are not recommended for fungal detection.
Improved Radar Detection of Small Drones Using Doppler Signal-to-Clutter Ratio (DSCR) Detector
Authors: Gong Jiangkun , Yan Jun , Hu Huiping , Kong Deyong , Li Deren
Published: May 2023

The detection of drones using radar presents challenges due to their small radar cross-section (RCS) values, slow velocities, and low altitudes. Traditional signal-to-noise ratio (SNR) detectors often fail to detect weak radar signals from small drones, resulting in high “Missed Target” rates due to the dependence of SNR values on RCS and detection range. To overcome this issue, we propose the use of a Doppler signal-to-clutter ratio (DSCR) detector that can extract both amplitude and Doppler information from drone signals. Theoretical calculations suggest that the DSCR of a target is less dependent on the detection range than the SNR. Experimental results using a Ku-band pulsed-Doppler surface surveillance radar and an X-band marine surveillance radar demonstrate that the DSCR detector can effectively extract radar signals from small drones, even when the signals are similar to clutter levels. Compared to the SNR detector, the DSCR detector reduces missed target rates by utilizing a lower detection threshold. Our tests include quad-rotor, fixed-wing, and hybrid vertical take-off and landing (VTOL) drones, with mean SNR values comparable to the surrounding clutter but with DSCR values above 10 dB, significantly higher than the clutter. The simplicity and low radar requirements of the DSCR detector make it a promising solution for drone detection in radar engineering applications.
Study on the Differences between the Extraction Results of the Structural Parameters of Individual Trees for Different Tree Species Based on UAV LiDAR and High-Resolution RGB Images
Authors: You Haotian , Tang Xu , You Qixu , Liu Yao , Chen Jianjun , Wang Feng
Published: May 2023

Light Detection and Ranging (LiDAR) points and high-resolution RGB image-derived points have been successfully used to extract tree structural parameters. However, the differences in extracting individual tree structural parameters among different tree species have not been systematically studied. In this study, LiDAR data and images were collected using unmanned aerial vehicles (UAVs) to explore the differences in digital elevation model (DEM) and digital surface models (DSM) generation and tree structural parameter extraction for different tree species. It was found that the DEMs generated based on both forms of data, LiDAR and image, exhibited high correlations with the field-measured elevation, with an R2 of 0.97 and 0.95, and an RMSE of 0.24 and 0.28 m, respectively. In addition, the differences between the DSMs are small in non-vegetation areas, whereas the differences are relatively large in vegetation areas. The extraction results of individual tree crown width and height based on two kinds of data are similar when all tree species are considered. However, for different tree species, the Cinnamomum camphora exhibits the greatest accuracy in terms of crown width extraction, with an R2 of 0.94 and 0.90, and an RMSE of 0.77 and 0.70 m for LiDAR and image points, respectively. In comparison, for tree height extraction, the Magnolia grandiflora exhibits the highest accuracy, with an R2 of 0.89 and 0.90, and an RMSE of 0.57 and 0.55 m for LiDAR and image points, respectively. The results indicate that both LiDAR and image points can generate an accurate DEM and DSM. The differences in the DEMs and DSMs between the two data types are relatively large in vegetation areas, while they are small in non-vegetation areas. There are significant differences in the extraction results of tree height and crown width between the two data sets among different tree species. The results will provide technical guidance for low-cost forest resource investigation and monitoring.
IH-TCGAN: Time-Series Conditional Generative Adversarial Network with Improved Hausdorff Distance for Synthesizing Intention Recognition Data
Authors: Wang Siyuan , Wang Gang , Fu Qiang , Song Yafei , Liu Jiayi
Published: May 2023

As military technology continues to evolve and the amount of situational information available on the battlefield continues to increase, data-driven deep learning methods are becoming the primary method for air target intention recognition. Deep learning is based on a large amount of high quality data; however, in the field of intention recognition, it often faces key problems such as low data volume and unbalanced datasets due to insufficient real-world scenarios. To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The innovation of the method is mainly reflected in three aspects: (1) Use of a transverter to map real and synthetic data into the same manifold so that they have the same intrinsic dimension; (2) Addition of a restorer and a classifier in the network structure to ensure that the model can generate high-quality multiclass temporal data; (3) An improved Hausdorff distance is proposed that can measure the time order differences between multivariate time-series data and make the generated results more reasonable. We conduct experiments using two time-series datasets, evaluate the results using various performance metrics, and visualize the results using visualization techniques. The experimental results show that IH-TCGAN is able to generate synthetic data similar to the real data and has significant advantages in the generation of time series data.
Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm
Authors: Zhou Wei , Wang Limin , Han Xuming , Wang Yizhang , Zhang Yufei , Jia Zhiyao
Published: May 2023

The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm’s performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.
Latin America and the Caribbean’s Productivity: The Role of Pro-Market Policies, Institutions, Infrastructure, and Natural Resource Endowments
Authors: Le Clech Néstor , Guevara-Pérez Juan Carlos
Published: May 2023

This paper examines the role played by pro-market policies, institutions, natural resources, and infrastructure on the total factor productivity (TFP) of twenty Latin American and Caribbean countries over the period 2000–2018. In estimating the TFP, we use the Färe-Primont index based on DEA. We have verified cointegration and estimated the long-run parameters by the grouped-mean FMOLS estimator. For measuring the impact of institutions, natural resources, and infrastructure, we have used the Productive Capacity Indexes of UNCTAD. We found a positive effect of all these variables on the TFP. Natural resources and energy play the most important role. Followed by information and communication technology and institutions quality. Transport infrastructure has played a minor role. The pro-market policies have been measured by the Fraser Institute indexes. We verified the positive impact of pro-market policies in the area of international trade and financial openness through six different variables. Finally, we considered three areas to measure the impact of domestic regulations and policies; credit, labor and business. We could verify a positive impact of credit and business pro-market policies, but we did not get statistically significant results from the labor regulations index.
The Impact of Augmented Reality (AR) on the Academic Performance of High School Students
Authors: Amores-Valencia Antonio , Burgos Daniel , Branch-Bedoya John W.
Published: May 2023

New technologies currently play a fundamental role in the educational context. As a result, augmented reality (AR) has recently gained a presence in educational centres. However, this educational technology has not been explored in depth at the secondary education level. Therefore, this research aims to analyse the impact of augmented reality on the academic performance of secondary education students, considering gender and the students’ attitudes toward this technology. In this mixed-method research based on convenient sampling, 321 students aged 14 to 17 years from the same secondary education school were assigned to an experimental group (n = 159) and a control group (n = 162). The control group used a traditional methodology in a slide-based learning environment, while the experimental group worked with an AR mobile application (ComputAR) designed with the same concepts. The data collection instruments used comprised a pre-test/post-test in both groups and semi-structured interviews in the experimental group. The results showed that the students who used augmented reality achieved better grades, highlighting the potential benefits of integrating this technology into the teaching process. No significant differences were observed regarding the gender of the students. In conclusion, this study provides findings that encourage the use of augmented reality in secondary schools.
Feature Cluster-Based Secure Data Transmission Method for Social Internet of Vehicles
Authors: Xing Ling , Liu Lulu , Wu Honghai , Jia Xiaofan , Ma Huahong , Zhang Xiaohui
Published: May 2023

Due to the high mobility of vehicles and the high dynamics of SIoV network topology, the communication between users will be frequently interrupted, thus affecting the service quality of users. In addition, due to the open nature of the SIoV wireless channel, any user can broadcast messages in the system. However, unreliable users pose serious security threats to other users on the network. In order to solve these problems, we propose a feature cluster-based secure data transmission method (FC-SDTM) to ensure safe and stable data transmission between vehicles. This method creates feature clusters according to the feature similarity of users, which provides the stability of communication between users and improves users’ reliability in the cluster. Second, consortium blockchains store the transmission data sent by the sender in the cluster for the receiver to verify, further ensuring the security of intra-cluster communication. Finally, the random number key reduces the running time of the proposed method and solves the security problem caused by cluster topology updates. The experimental results demonstrate that this method can reduce the system running time and the message exposure rate, while also improving transmission accuracy.
A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization
Authors: Zhao Henghui , Zhang Wensheng , Huang Mengxing , Feng Siling , Wu Yuanyuan
Published: May 2023

Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.
Moving toward Digital Transformation by Force: Students’ Preferences, Happiness, and Mental Health
Authors: Afshar Jahanshahi Asghar , Polas Mohammad Rashed Hasan
Published: May 2023

The COVID-19 pandemic accelerated and, somehow, forced the process of digital transformation within the higher education sector. Due to the COVID-19 pandemic, online modes of course delivery have become the only available way of teaching in almost all parts of the world. We conducted a study in Mexico to know about students’ preferences for these forced online class schedules, exams and assignments, and online teaching styles during this health crisis. Furthermore, this research sought to know about the impact of this forced digitalization on students’ mental health and happiness. To conduct a conjoint analysis, we collected survey-based data from 219 undergraduate Mexican students. The most preferred attributes for students for online classes were “having a short online class (50 min)”, “possibility to have 70% pre-exam assignments and 30% final exam”, and “having a humorous professor in online classes”. In terms of students’ mental health, the prevalence of “moderate” anxiety and depression was 21.8%, and the prevalence of “severe” anxiety and depression was 14.9%. In terms of happiness, male students and students with shorter online classes per day felt a higher level of happiness.
Signature and Log-Signature for the Study of Empirical Distributions Generated with GANs
Authors: de Curtò J. , de Zarzà I. , Roig Gemma , Calafate Carlos T.
Published: May 2023

In this paper, we address the research gap in efficiently assessing Generative Adversarial Network (GAN) convergence and goodness of fit by introducing the application of the Signature Transform to measure similarity between image distributions. Specifically, we propose the novel use of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) Signature, along with Log-Signature, as alternatives to existing methods such as Fréchet Inception Distance (FID) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Our approach offers advantages in terms of efficiency and effectiveness, providing a comprehensive understanding and extensive evaluations of GAN convergence and goodness of fit. Furthermore, we present innovative analytical measures based on statistics by means of Kruskal–Wallis to evaluate the goodness of fit of GAN sample distributions. Unlike existing GAN measures, which are based on deep neural networks and require extensive GPU computations, our approach significantly reduces computation time and is performed on the CPU while maintaining the same level of accuracy. Our results demonstrate the effectiveness of the proposed method in capturing the intrinsic structure of the generated samples, providing meaningful insights into GAN performance. Lastly, we evaluate our approach qualitatively using Principal Component Analysis (PCA) and adaptive t-Distributed Stochastic Neighbor Embedding (t-SNE) for data visualization, illustrating the plausibility of our method.
Development of a Solar-Tracking System for Horizontal Single-Axis PV Arrays Using Spatial Projection Analysis
Authors: Huang Bin , Huang Jialiang , Xing Ke , Liao Lida , Xie Peiling , Xiao Meng , Zhao Wei
Published: May 2023

Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the shading effects between arrays and reduce the electricity production loss due to terrain changes. Based on a uniaxial tracker on the sloping terrain of a PV farm located in Ningxia, this study established a uniaxial solar-tracking strategy for sloping terrain by integrating a spatial projection model with a dynamic shadow assessment method. In the proposed strategy, the optimal tilt angle of the PV array and related desirable adjustment are identified taking into consideration major parameters such as the shadow area ratio S and the average solar irradiance intensity G. A tool underpinned by Matlab Simulink has also been developed to realize the proposed solar-tracking strategy. With the input of a simulated ramp signal β and the dynamically changed time parameters, the tracking angle of PV arrays over the simulated duration is accurately predicted, followed by a series of experimental validations conducted on the winter solstice and a typical sunny day (15 September). Moreover, the study also explored the terrain impacts on solar tracking by comparing the sloping terrain and flat terrain applications. The analytic and experimental results indicate that (a) the maximum value of the G(β) function could serve as the input to identify the optimal tracking angle; (b) the application of the flat terrain tracking (FTT) strategy in sloping terrain would result in a reduction of average solar irradiance intensity harvested by the PV arrays with varying degrees; (c) in the context of an east–west −7° sloping terrain, compared with the FTT strategy, the sloping terrain tracking (STT) strategy enabled anti-shading tracking, and then increased the daily PV electricity yield by 0.094 kWh/kWp, which is around 1.48% of the daily energy production; (d) given a measurement with annual scale, the STT strategy could cause a 1.26% increase in the energy harvesting with a flat uniaxial PV array on a −7° slope terrain, achieving an annual increase of 25.16 kWh/kWp. The experimental comparative analysis validated the precision of the proposed solar-tracking model, which has far-reaching significance for achieving automatic solar-tracking of PV modules, as well as improving the capacity and efficiency of PV systems.
Review of Recent Advances in Transcritical CO2 Heat Pump and Refrigeration Cycles and Their Development in the Vehicle Field
Authors: Ji Hongzeng , Pei Jinchen , Cai Jingyang , Ding Chen , Guo Fen , Wang Yichun
Published: May 2023

Refrigerant substitution is an urgent need in the context of reducing carbon emissions and slowing global warming. CO2 is now being proposed as a promising solution based on its excellent properties and system performance, especially in low-temperature environments. This paper presents an overview of recent advances in system configuration and operation characteristics to improve the performance of transcritical CO2 heat pump and refrigeration systems. The paper first introduces the basic research background, system cycle, and thermodynamic characteristics. Secondly, CO2 cycle improvements with single modifications and modification combinations are reviewed. Then, some important operation characteristics and control methods are discussed. Additionally, the paper provides a detailed description of the development of transcritical CO2 heat pump and refrigeration systems in the vehicle field. At the end of this review, conclusions and opportunities for future work in this field are presented.
Design of a Three-Phase Shell-Type Distribution Transformer Using Evolutionary Algorithms
Authors: Olivares-Galvan Juan Carlos , Ascencion-Mestiza Hector , Maximov Serguei , Mezura-Montes Efrén , Escarela-Perez Rafael
Published: May 2023

In this paper, three metaheuristic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) are compared in terms of minimizing the total owning cost (TOC) of the active part of a three-phase shell-type distribution transformer. The three methods use six inputs: power rating, primary voltage, secondary voltage, primary and secondary winding connections, and frequency. The TOC of the transformer, which includes the cost of the basic materials of the transformer plus the cost of losses, is minimized under the imposed constraints (excitation current, impedance, no-load losses, load losses, and efficiency) usually specified in the standards. As a case study, the three algorithms are applied to optimize the design of a three-phase shell-type distribution transformer of 750 kVA. All applied metaheuristic algorithms provide good results, while DE avoids local optima leading to better TOC reduction. The results of the optimization algorithms used are superior to those of the manufacturer, showing a 6% TOC reduction. Optimization of the design of a power transformer may have important implications for reducing greenhouse gas emissions and extending the lifetime of the equipment.