bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Composite Structure on Temperature Distribution—An Analysis Using the Finite Difference Method
Authors: Kubacka Ewelina , Ostrowski Piotr
Published: July 2023

Among composites, we can distinguish periodic structures, biperiodic structures, and structures with a functional gradation of material properties made of two or more materials. The selection of the composite’s constituent materials and the way they are distributed affects the weight of the composite, its strength, and other properties, as well as the way it conducts heat. This work is about studying the temperature distribution in composites, depending on the type of component material and its location. For this purpose, the Tolerance Averaging Technique and the Finite Difference Method were used. Differential equations describing heat conduction phenomena were obtained using the Tolerance Averaging Technique, while the Finite Difference Method was used to solve them. In terms of results, temperature distribution plots were produced showing the effect of the structure of the composite on the heat transfer properties.
Exopolysaccharide Production from Marine-Derived Brevundimonas huaxiensis Obtained from Estremadura Spur Pockmarks Sediments Revealing Potential for Circular Economy
Authors: Catalão Marta , Fernandes Mafalda , Galdon Lorena , Rodrigues Clara F. , Sobral Rita G. , Gaudêncio Susana P. , Torres Cristiana A. V.
Published: July 2023

Marine environments represent an enormous biodiversity reservoir due to their numerous different habitats, being abundant in microorganisms capable of producing biomolecules, namely exopolysaccharides (EPS), with unique physical characteristics and applications in a broad range of industrial sectors. From a total of 67 marine-derived bacteria obtained from marine sediments collected at depths of 200 to 350 m from the Estremadura Spur pockmarks field, off the coast of Continental Portugal, the Brevundimonas huaxiensis strain SPUR-41 was selected to be cultivated in a bioreactor with saline culture media and glucose as a carbon source. The bacterium exhibited the capacity to produce 1.83 g/L of EPS under saline conditions. SPUR-41 EPS was a heteropolysaccharide composed of mannose (62.55% mol), glucose (9.19% mol), rhamnose (19.41% mol), glucuronic acid (4.43% mol), galactose (2.53% mol), and galacturonic acid (1.89% mol). Moreover, SPUR-41 EPS also revealed acyl groups in its composition, namely acetyl, succinyl, and pyruvyl. This study revealed the importance of research on marine environments for the discovery of bacteria that produce new value-added biopolymers for pharmaceutical and other biotechnological applications, enabling us to potentially address saline effluent pollution via a sustainable circular economy.
Marine Microbial Polysaccharides: An Untapped Resource for Biotechnological Applications
Authors: Jeewon Rajesh , Aullybux Aadil Ahmad , Puchooa Daneshwar , Nazurally Nadeem , Alrefaei Abdulwahed Fahad , Zhang Ying
Published: July 2023

As the largest habitat on Earth, the marine environment harbors various microorganisms of biotechnological potential. Indeed, microbial compounds, especially polysaccharides from marine species, have been attracting much attention for their applications within the medical, pharmaceutical, food, and other industries, with such interest largely stemming from the extensive structural and functional diversity displayed by these natural polymers. At the same time, the extreme conditions within the aquatic ecosystem (e.g., temperature, pH, salinity) may not only induce microorganisms to develop a unique metabolism but may also increase the likelihood of isolating novel polysaccharides with previously unreported characteristics. However, despite their potential, only a few microbial polysaccharides have actually reached the market, with even fewer being of marine origin. Through a synthesis of relevant literature, this review seeks to provide an overview of marine microbial polysaccharides, including their unique characteristics. In particular, their suitability for specific biotechnological applications and recent progress made will be highlighted before discussing the challenges that currently limit their study as well as their potential for wider applications. It is expected that this review will help to guide future research in the field of microbial polysaccharides, especially those of marine origin.
Synthesis and Antimicrobial Activity of New Mannich Bases with Piperazine Moiety
Authors: Janowska Sara , Andrzejczuk Sylwia , Gawryś Piotr , Wujec Monika
Published: July 2023

A series of novel Mannich bases were designed, synthesized, and screened for their antimicrobial activity. The target compounds were synthesized from 4-(3-chlorophenyl)-5-(3-fluorophenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione and different piperazine derivatives. The structures of the products were confirmed by 1H and 13C NMR and elemental analysis. The activity of piperazine derivatives against bacteria (Gram-positive: Staphylococcus epidermidis, Staphylococcus aureus, Micrococcus luteus, Bacillus cereus, and Bacillus subtilis; Gram-negative: Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Proteus mirabilis) and yeasts (Candida glabrata, Candida krusei, and Candida parapsilosis) was determined by the minimum inhibitory concentration and minimum bactericidal concentration values. Significant activity was observed against Gram-positive bacteria, mainly staphylococci (PG7–PG8) and bacteria of the genes of Micrococcus and Bacillus (PG1-3), as well as selected strains of Gram-negative bacteria, including bacteria of the Enterobacteriaceae family (PG7), while all tested compounds showed high fungistatic activity against Candida spp. yeasts, especially C. parapsilosis, with MICs ranging from 0.49 µg/mL (PG7) to 0.98 µg/mL (PG8) and 62.5 µg/mL (PG1-3). In conclusion, the results obtained confirm the multidirectional antimicrobial activity of the newly synthesized piperazine derivatives. Furthermore, in silico studies suggest that the tested compounds are likely to have good oral bioavailability. The results obtained will provide valuable data for further research into this interesting group of compounds. The library of compounds obtained is still the subject of pharmacological research aimed at finding new interesting biologically active compounds.
Theoretical Insights on ORR Activity of Sn-N-C Single-Atom Catalysts
Authors: Zhang Yuhui , Li Boyang , Su Yaqiong
Published: July 2023

The advancement of efficient and stable single-atom catalysts (SACs) has become a pivotal pursuit in the field of proton exchange membrane fuel cells (PEMFCs) and metal-air batteries (MABs), aiming to enhance the utilization of clean and sustainable energy sources. The development of such SACs has been greatly significant in facilitating the oxygen reduction reaction (ORR) process, thereby contributing to the progress of these energy conversion technologies. However, while transition metal-based SACs have been extensively studied, there has been comparatively less exploration of SACs based on p-block main-group metals. In this study, we conducted an investigation into the potential of p-block main-group Sn-based SACs as a cost-effective and efficient alternative to platinum-based catalysts for the ORR. Our approach involved employing density functional theory (DFT) calculations to systematically examine the catalyst properties of Sn-based N-doped graphene SACs, the ORR mechanism, and their electrocatalytic performance. Notably, we employed an H atom-decorated N-based graphene matrix as a support to anchor single Sn atoms, creating a contrast catalyst to elucidate the differences in activity and properties compared to pristine Sn-based N-doped graphene SACs. Through our theoretical analysis, we gained a comprehensive understanding of the active structure of Sn-based N-doped graphene electrocatalysts, which provided a rational explanation for the observed high four-electron reactivity in the ORR process. Additionally, we analyzed the relationship between the estimated overpotential and the electronic structure properties, revealing that the single Sn atom was in a +2 oxidation state based on electronic analysis. Overall, this work represented a significant step towards the development of efficient and cost-effective SACs for ORR which could alleviate environmental crises, advance clean and sustainable energy sources, and contribute to a more sustainable future.
Investigation of Chemical Constituents and Antioxidant Activity of Biologically Active Plant-Derived Natural Products
Authors: Godlewska Katarzyna , Pacyga Paweł , Najda Agnieszka , Michalak Izabela
Published: July 2023

The aim of this publication is to present rapid screening methods (visual/colorimetric) that will enable quick identification of the presence of biologically active compounds in aqueous solutions. For this reason, 26 plant extracts obtained by ultrasound-assisted extraction were analysed for the content of these compounds. Higher plants, used as a raw material for extraction, are common in Europe and are easily available. The article proposes a comparison of various protocols for the identification of various compounds, e.g., phenolic compounds (phenols, tannins, anthocyanins, coumarins, flavones, flavonoids), vitamin C, quinones, quinines, resins, glycosides, sugars. Initial characterisation of the composition of plant extracts using fast and inexpensive methods allows you to avoid the use of time-consuming analyses with the use of advanced research equipment. In addition, the antioxidant activity of plant extracts using spectrophotometric methods (DPPH, ABTS, FRAP assay) and quantitative analysis of plant hormones such as abscisic acid, benzoic acid, gibberellic acid, indole acetic acid, jasmonic acid, salicylic acid, zeatin, zeatin riboside, and isipentenyl adenine was performed. The obtained results prove that the applied visual methods show different sensitivity in detecting the sought chemical compounds. Therefore, it is necessary to confirm the presence or absence of bioactive substances and their concentration using modern analytical methods.
Agricultural Solid Wastes Based Adsorbent Materials in the Remediation of Heavy Metal Ions from Water and Wastewater by Adsorption: A Review
Authors: Sen Tushar Kanti
Published: July 2023

Adsorption has become the most popular and effective separation technique that is used across the water and wastewater treatment industries. However, the present research direction is focused on the development of various solid waste-based adsorbents as an alternative to costly commercial activated carbon adsorbents, which make the adsorptive separation process more effective, and on popularising the sustainable options for the remediation of pollutants. Therefore, there are a large number of reported results available on the application of raw or treated agricultural biomass-based alternatives as effective adsorbents for aqueous-phase heavy metal ion removal in batch adsorption studies. The goal of this review article was to provide a comprehensive compilation of scattered literature information and an up-to-date overview of the development of the current state of knowledge, based on various batch adsorption research papers that utilised a wide range of raw, modified, and treated agricultural solid waste biomass-based adsorbents for the adsorptive removal of aqueous-phase heavy metal ions. Metal ion pollution and its source, toxicity effects, and treatment technologies, mainly via adsorption, have been reviewed here in detail. Emphasis has been placed on the removal of heavy metal ions using a wide range of agricultural by-product-based adsorbents under various physicochemical process conditions. Information available in the literature on various important influential physicochemical process parameters, such as the metal concentration, agricultural solid waste adsorbent dose, solution pH, and solution temperature, and importantly, the adsorbent characteristics of metal ion removal, have been reviewed and critically analysed here. Finally, from the literature reviewed, future perspectives and conclusions were presented, and a few future research directions have been proposed.
Therapeutic Role of Alkaloids and Alkaloid Derivatives in Cancer Management
Authors: Olofinsan Kolawole , Abrahamse Heidi , George Blassan P.
Published: July 2023

Cancer is a neoplastic disease that remains a global challenge with a reported prevalence that is increasing annually. Though existing drugs can be applied as single or combined therapies for managing this pathology, their concomitant adverse effects in human applications have led to the need to continually screen natural products for effective and alternative anticancer bioactive principles. Alkaloids are chemical molecules that, due to their structural diversity, constitute a reserve for the discovery of lead compounds with interesting pharmacological activities. Several in vitro studies and a few in vivo findings have documented various cytotoxic and antiproliferative properties of alkaloids. This review describes chaetocochin J, neopapillarine, coclaurine, reflexin A, 3,10-dibromofascaplysin and neferine, which belong to different alkaloid classes with antineoplastic properties and have been identified recently from plants. Despite their low solubility and bioavailability, plant-derived alkaloids have viable prospects as sources of viable lead antitumor agents. This potential can be achieved if more research on these chemical compounds is directed toward investigating ways of improving their delivery in an active form close to target cells, preferably with no effect on neighboring normal tissues.
pH-Responsive Cobalt(II)-Coordinated Assembly Containing Quercetin for Antimicrobial Applications
Authors: Santonoceta Giuseppina D. G. , Sgarlata Carmelo
Published: July 2023

The development of novel drug delivery systems (DDSs) with promising antibacterial properties is essential for facing the emergency of increasing resistance to antimicrobial agents. The antibacterial features of quercetin and its metal complexes have been broadly investigated. However, several drawbacks affect their activity and effectiveness. In this work, we propose a DDS based on a pH-responsive cobalt(II)-coordinated assembly containing quercetin and polyacrylic acid. This system is suggested to trigger the release of the model drug in a pH-dependent mode by exploiting the localized acidic environment at the bacterial infection sites under anaerobic conditions. The delivery system has been designed by accurately examining the species and the multiple equilibria occurring in solution among the assembly components. The formation of cobalt(II) complexes with quercetin in the absence or presence of the pH-responsive polyacrylic acid was investigated in buffered aqueous solution at pH 7.4 using spectrophotometric (UV-Vis) and calorimetric (ITC) techniques. The determined binding affinities and thermodynamic parameters that resulted are essential for the development of a DDS with improved binding and release capabilities. Furthermore, the affinity of the polymer–cobalt(II) complex toward the model antimicrobial flavonoid was explored at the solid–liquid interface by quartz crystal microbalance (QCM-D) experiments, which provided marked evidence for drug loading and release under pH control.
Matricaria chamomilla Essential Oils: Repellency and Toxicity against Imported Fire Ants (Hymenoptera: Formicidae)
Authors: Shah Farhan Mahmood , Guddeti Dileep Kumar , Paudel Pradeep , Chen Jian , Li Xing-Cong , Khan Ikhlas A. , Ali Abbas
Published: July 2023

Matricaria chamomilla flower essential oils (EOs) blue Egyptian (EO-1), chamomile German CO2 (EO-2), and chamomile German (EO-3) and the pure compound α-bisabolol were evaluated against red imported fire ants (RIFA), Solenopsis invicta Buren, black imported fire ants, S. richteri Forel (BIFA), and hybrid imported fire ants (HIFA) for their repellency and toxicity. A series of serial dilutions were tested starting from 125 µg/g until the failure of the treatment. Based on the amount of sand removed, EO-1 showed significant repellency at dosages of 7.8, 7.8, and 31.25 µg/g against RIFA, BIFA, and HIFA, respectively. EO-3 was repellent at 3.9, 7.8, and 31.25 µg/g against BIFA, RIFA, and HIFA, whereas α-bisabolol was active at 7.8, 7.8, and 31.25 µg/g against BIFA, HIFA, and RIFA, respectively. DEET (N, N-diethyl-meta-toluamide) was active at 31.25 µg/g. Toxicity of EOs and α-bisabolol was mild to moderate. For EO-1, LC50 values were 93.6 and 188.11 µg/g against RIFA and BIFA; 98.11 and 138.4 µg/g for EO-2; and 142.92 and 202.49 µg/g for EO-3, respectively. The LC50 of α-bisabolol was 159.23 µg/g against RIFA. In conclusion, M. chamomilla EOs and α-bisabolol offer great potential to be developed as imported fire ant repellents.
Pancreatic Stellate Cells and the Targeted Therapeutic Strategies in Chronic Pancreatitis
Authors: Chang Man , Chen Wenjuan , Xia Ruting , Peng Yangyue , Niu Pandi , Fan Hui
Published: July 2023

Chronic pancreatitis (CP) is a disease characterized by inflammatory recurrence that accompanies the development of pancreatic fibrosis. As the mystery of CP pathogenesis is gradually revealed, accumulating evidence suggests that the activation of pancreatic stellate cells (PSCs) and the appearance of a myofibroblast-like phenotype are the key gatekeepers in the development of CP. Targeting PSCs to prevent their activation and conversion to a myofibroblast-like phenotype, as well as increasing antioxidant capacity to counteract ongoing oxidative stress, are effective strategies for preventing or treating CP. Therefore, we reviewed the crosstalk between CP and pancreatic fibrosis, summarized the activation mechanisms of PSCs, and investigated potential CP therapeutic strategies targeting PSCs, including, but not limited to, anti-fibrosis therapy, antioxidant therapy, and gene therapy. Meanwhile, the above therapeutic strategies are selected in order to update the available phytopharmaceuticals as novel complementary or alternative approaches for the prevention and treatment of CP to clarify their potential mechanisms of action and their relevant molecular targets, aiming to provide the most comprehensive therapeutic treatment direction for CP and to bring new hope to CP patients.
Recent Advances and Future Prospects of Mycosporine-like Amino Acids
Authors: Peng Jiahui , Guo Fangyu , Liu Sishi , Fang Haiyan , Xu Zhenshang , Wang Ting
Published: July 2023

Mycosporine-like amino acids (MAAs) are a class of water-soluble active substances produced by various aquatic organisms. However, due to the limitations of low accumulation of MAAs in organisms, the cumbersome extraction process, difficult identification, and high cost, MAAs have not yet been widely used in human life. Recently, there has been an emergence of heterologous synthesis for MAAs, making increasing yield the key to the quantification and application of MAAs. This review summarizes the latest research progress of MAAs, including: (1) introducing the biodistribution of MAAs and the content differences among different species to provide a reference for the selection of research subjects; (2) elaborating the species and molecular information of MAAs; (3) dissecting the synthesis mechanism and sorting out the synthesis pathways of various MAAs; (4) summarizing the methods of extraction and identification, summarizing the advantages and disadvantages, and providing a reference for the optimization of extraction protocols; (5) examining the heterologous synthesis method; and (6) summarizing the physiological functions of MAAs. This paper comprehensively updates the latest research status of MAAs and the various problems that need to be addressed, especially emphasizing the potential advantages of heterologous synthesis in the future production of MAAs.
Efficient Fe3C-CF Cathode Catalyst Based on the Formation/Decomposition of Li2−xO2 for Li-O2 Batteries
Authors: Yi Guanyu , Li Gaoyang , Jiang Shuhuai , Zhang Guoliang , Guo Liang , Zhang Xiuqi , Zhao Zhongkui , Zou Zhongping , Ma Hailong , Fu Xiaojiao , Liu Yan , Dang Feng
Published: July 2023

Lithium-oxygen batteries have attracted considerable attention in the past several years due to their ultra-high theoretical energy density. However, there are still many serious issues that must be addressed before considering practical applications, including the sluggish oxygen redox kinetics, the limited capacity far from the theoretical value, and the poor cycle stability. This study proposes a surface modification strategy that can enhance the catalytic activity by loading Fe3C particles on carbon fibers, and the microstructure of Fe3C particle-modified carbon fibers is studied by multiple materials characterization methods. Experiments and density functional theory (DFT) calculations show that the discharge products on the Fe3C carbon fiber (Fe3C-CF) cathode are mainly Li2−xO2. Fe3C-CF exhibits high catalytic ability based on its promotion of the formation/decomposition processes of Li2−xO2. Consequently, the well-designed electrode catalyst exhibits a large specific capacity of 17,653.1 mAh g−1 and an excellent cyclability of 263 cycles at a current of 200 mA g−1.
Torah Trumps Life: Reflections on Uncivil Religion and Haredi Response to the COVID-19 Pandemic
Authors: Braiterman Zachary J.
Published: July 2023

As if by design, crisis reveals basic structural fault lines. In the middle of the COVID-19 crisis, non-Haredi Jews expressed surprise and even outrage about the ultra-orthodox Haredi response to the pandemic. It was not understood how large-scale violations of public health protocols comported with the legal-halakhic principle of Pikuaḥ Nefesh (saving human life). In this essay, I explore Hasidic response to COVID-19 as reported in the secular and Haredi press and in emergent social science literature about this crisis. I place Haredi response to crisis in relation to the clash between two sets of values: the value of saving human life and the value of intensive Talmud study (talmud Torah) and ritual-communal practice. In what Robert Cover called a paideic nomos, there are more important things than human life. What we see already in the Babylonian Talmud is the profound ambiguity of paideic norms vis-à-vis the larger public good.
Condition Rating of Bridge Decks with Fuzzy Sets Modeling for SF-GPR Surveys
Authors: Gagarin Nicolas , Goulias Dimitrios , Mekemson James
Published: July 2023

Highway agencies monitor the condition of thousands of bridge decks every year. Even though Ground Penetrating Radar (GPR) has been used in bridge-deck evaluation, Step-Frequency GPR (SF-GPR) provides advanced condition assessment yet requires extensive and complex post-processing analysis. An SF-GPR analysis system was recently developed and used for monitoring the condition of all the bridge decks in the state of Maryland. The objective of this study was to develop a bridge deck condition rating approach using fuzzy sets modeling on the SF-GPR data and analysis. The fuzzy sets membership functions needed to reflect rating score categories similar to those considered in the National Bridge Inventory (NBI) database for uniformity. Thus, the fuzzy sets modeling was built considering nine condition membership functions. The overall bridge deck condition score leading to each of the nine condition states was based on both physical and condition-related bridge deck parameters as obtained from the SF-GPR analysis. The modeling approach is presented herein, along with two bridge deck examples. The proposed novel fuzzy sets modeling can be considered for possible adoption elsewhere where similar GPR systems are used.
Virtual Sensor of Gravity Centres for Real-Time Condition Monitoring of an Industrial Stamping Press in the Automotive Industry
Authors: Peinado-Asensi Ivan , Montés Nicolás , García Eduardo
Published: July 2023

This article proposes the development of a novel tool that allows real-time monitoring of the balance of a press during the stamping process. This is performed by means of a virtual sensor that, by using the tonnage information in real time, allows us to calculate the gravity centre of a virtual load that moves the slide up and down. The present development follows the philosophy shown in our previous work for the development of industrialised predictive systems, that is, the use of the information available in the system to develop IIoT tools. This philosophy is defined as I3oT (industrializable industrial Internet of Things). The tonnage data are part of a set of new criteria, called Criterion-360, used to obtain this information. This criterion stores data from a sensor each time the encoder indicates that the position of the main axis has rotated by one degree. Since the main axis turns in a complete cycle of the press, this criterion allows us to obtain information on the phases of the process and easily shows where the measured data are in the cycle. The new system allows us to detect anomalies due to imbalance or discontinuity in the stamping process by using the DBSCAN algorithm, which allows us to avoid unexpected stops and serious breakdowns. Tests were conducted to verify that our system actually detects minimal imbalances in the stamping process. Subsequently, the system was connected to normal production for one year. At the end of this work, we explain the anomalies detected as well as the conclusions of the article and future works.
Fully-Metallic Additively Manufactured Monolithic Double-Ridged Waveguide Rotman Lens in the K/Ka-Band
Authors: Fonseca Nelson J. G. , Gomanne Sophie-Abigaël , Rico-Fernández José , Jankovic Petar , Galdeano Jaione , Toso Giovanni , Angeletti Piero , Arrebola Manuel , Quevedo-Teruel Oscar
Published: July 2023

This paper reports on the design and experimental validation of a fully-metallic double-ridged waveguide 10 × 10 Rotman lens additively manufactured as a single part. The wide band operation of this quasi-optical beamformer enables us to cover the uplink and downlink frequencies allocated to satellite communications in the K/Ka-band, from 17.3 GHz to 30 GHz. The feeding port design was adjusted to enable vertical printing, thus minimizing the use of supporting structures. A prototype was manufactured and tested. The reported results indicate losses in the range of 0.5 dB in the lower-frequency band and 0.8 dB in the upper-frequency band, including the waveguide transitions added for test purposes. The measured reflection and coupling coefficients remain below −11.5 dB over the operating band. The standard deviation of the residual phase error across the array ports is below 5° in simulation and below 10° in measurements. Array factors synthesized using the scattering parameters confirm the good stability of the beamforming functionality over the wide frequency band analyzed. This monolithic design is a promising step toward more integrated antenna systems, such as a compact dual-stack configuration for planar array design.
Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning
Authors: Saemaldahr Raghdah , Ilyas Mohammad
Published: July 2023

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy. The determination of the preictal state is influenced by global and local model-assisted decision making by modeling the two-level edge layer. The Spiking Encoder (SE), integrated with the Graph Convolutional Neural Network (Spiking-GCNN), works as the local model trained using a bi-timescale approach. Each local model utilizes the aggregated seizure knowledge obtained from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. Thus, the proposed approach achieved 96.33% sensitivity and 96.14% specificity when tested on the CHB-MIT EEG dataset when modeling was performed using the bi-timescale approach and Spiking-GCNN-based epileptic pattern learning. Moreover, the adoption of federated learning greatly assists the proposed system, yielding a 96.28% higher accuracy as a result of addressing data scarcity.
Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN
Authors: Inomata Soichiro , Yoshimura Takaaki , Tang Minghui , Ichikawa Shota , Sugimori Hiroyuki
Published: July 2023

Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured. This study aims to estimate the indices by learning the short-axis images and the known left and right ventricular ejection fractions and to confirm the accuracy and whether each index is captured as a feature. A total of 100 patients with publicly available short-axis cine images were used. The dataset was divided into training:test = 8:2, and a regression model was built by training with the 3D-ResNet50. Accuracy was assessed using a five-fold cross-validation. The correlation coefficient, MAE (mean absolute error), and RMSE (root mean squared error) were determined as indices of accuracy evaluation. The mean correlation coefficient of the left ventricular ejection fraction was 0.80, MAE was 9.41, and RMSE was 12.26. The mean correlation coefficient of the right ventricular ejection fraction was 0.56, MAE was 11.35, and RMSE was 14.95. The correlation coefficient was considerably higher for the left ventricular ejection fraction. Regression modeling using the 3D-CNN indicated that the left ventricular ejection fraction was estimated more accurately, and left ventricular systolic function was captured as a feature.
FPGA Implementation of Image Registration Using Accelerated CNN
Authors: Aydin Seda Guzel , Bilge Hasan Şakir
Published: July 2023

Background: Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN applications. Methods: This study proposes an FPGA-based ultrasound IR CNN (FUIR-CNN) to regress three rigid registration parameters from image pairs. To speed up the estimation process, the proposed design makes use of fixed-point data and parallel operations carried out by unrolling and pipelining techniques. Experiments were performed on three US datasets in real time using the xc7z020, and the xcku5p was also used during implementation. Results: The FUIR-CNN produced results for the inference phase 139 times faster than the software-based network while retaining a negligible drop in regression performance of under 200 MHz clock frequency. Conclusions: Comprehensive experimental results demonstrate that the proposed end-to-end FPGA-based accelerated CNN achieves a negligible loss, a high speed for registration parameters, less power when compared to the CPU, and the potential for real-time medical imaging.
Stratospheric Night Sky Imaging Payload for Space Situational Awareness (SSA)
Authors: Kunalakantha Perushan , Baires Andrea Vallecillo , Dave Siddharth , Clark Ryan , Chianelli Gabriel , Lee Regina S. K.
Published: July 2023

Space situational awareness (SSA) refers to collecting, analyzing, and keeping track of detailed knowledge of resident space objects (RSOs) in the space environment. With the rapidly increasing number of objects in space, the need for SSA grows as well. Traditional methods rely heavily on imaging RSOs from large, narrow field-of-view (FOV), ground-based telescopes. This research outlines the technology demonstration payload, Resident Space Object Near-space Astrometric Research (RSONAR)—a star tracker-like, wide FOV camera combined with commercial off-the-shelf (COTS) hardware to image RSOs from the stratosphere, overcoming the disadvantages of ground-based observations. The hardware components and software algorithm are described and evaluated. The eligibility of the payload for SSA is proven by the image processing algorithms, which detect the RSOs in the images captured during flight and the survival of the COTS components in the near-space environment. The payload features a low-resolution, wide FOV camera coupled with a Field Programmable Gate Array (FPGA)-based platform that houses the altitude and time-based image capture algorithm. The newly developed payload in a 2U-CubeSat form factor was flown as a space-ready payload on the CSA/CNES stratospheric balloon research platform to carry out algorithm and functionality tests in August 2022.
Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat
Authors: Liang Kun , Song Jinpeng , Yuan Rui , Ren Zhizhou
Published: July 2023

In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.
A New Form of Train Detection as a Solution to Improve Level Crossing Closing Time
Authors: Zawodny Michał , Kruszyna Maciej , Szczepanek Wojciech Kazimierz , Korzeń Mariusz
Published: July 2023

The critical points on the rail and road network are their intersections, i.e., level crossings. During a train crossing, car traffic is stopped. This reduces the fluidity of traffic on the road and, consequently, can cause congestion. The problem increases with the number of cars and trains. Frequently, due to national regulations, level crossing closure times are long. It is mainly dictated by safety issues. Building two-level intersections is not always a good solution, mainly because of the high cost of implementation. In the article, the authors proposed the use of sensors to reduce level crossing closure times and improve the Level of Service on the road network. The analyzed railroad lines are local agglomeration lines, mainly due to safety (low speed of commuter trains) and high impact on the road network. The sensors proposed in the article are based on radar/LIDAR. Formulas similar to HCM methods are proposed, which can be implemented in a railroad crossing controller. Simulations using the PTV Vissim program are carried out and the results are worked out based on the obtained data. The considered method can reduce the level crossing closure time by 68.6%, thereby increasing the Level of Service on roads near railroads.
A Size, Weight, Power, and Cost-Efficient 32-Channel Time to Digital Converter Using a Novel Wave Union Method
Authors: Alshahry Saleh M. , Alshehry Awwad H. , Alhazmi Abdullah K. , Chodavarapu Vamsy P.
Published: July 2023

We present a Tapped Delay Line (TDL)-based Time to Digital Converter (TDC) using Wave Union type A (WU-A) architecture for applications that require high-precision time interval measurements with low size, weight, power, and cost (SWaP-C) requirements. The proposed TDC is implemented on a low-cost Field-Programmable Gate Array (FPGA), Artix-7, from Xilinx. Compared to prior works, our high-precision multi-channel TDC has the lowest SWaP-C requirements. We demonstrate an average time precision of less than 3 ps and a Root Mean Square resolution of about 1.81 ps. We propose a novel Wave Union type A architecture where only the first multiplexer is used to generate the wave union pulse train at the arrival of the start signal to minimize the required computational processing. In addition, an auto-calibration algorithm is proposed to help improve the TDC performance by improving the TDC Differential Non-Linearity and Integral Non-Linearity.
Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic
Authors: Jaisinghani Dheryta , Phutela Nishtha
Published: July 2023

A good night’s sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict “sleep” and “awake” periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models—K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.
Advancements in Forest Fire Prevention: A Comprehensive Survey
Authors: Carta Francesco , Zidda Chiara , Putzu Martina , Loru Daniele , Anedda Matteo , Giusto Daniele
Published: July 2023

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires.
Estimation Method of an Electrical Equivalent Circuit for Sonar Transducer Impedance Characteristic of Multiple Resonance
Authors: Jang Jejin , Choi Jaehyuk , Lee Donghun , Mok Hyungsoo
Published: July 2023

Improving the operational efficiency and optimizing the design of sound navigation and ranging (sonar) systems require accurate electrical equivalent models within the operating frequency range. The power conversion system within the sonar system increases power efficiency through impedance-matching circuits. Impedance matching is used to enhance the power transmission efficiency of the sonar system. Therefore, to increase the efficiency of the sonar system, an electrical-matching circuit is employed, and this necessitates an accurate equivalent circuit for the sonar transducer within the operating frequency range. In conventional equivalent circuit derivation methods, errors occur because they utilize the same number of RLC branches as the resonant frequency of the sonar transducer, based on its physical properties. Hence, this paper proposes an algorithm for deriving an equivalent circuit independent of resonance by employing multiple electrical components and particle swarm optimization (PSO). A comparative verification was also performed between the proposed and existing approaches using the Butterworth–van Dyke (BVD) model, which is a method for deriving electrical equivalent circuits.
Enhancing Speech Emotion Recognition Using Dual Feature Extraction Encoders
Authors: Pulatov Ilkhomjon , Oteniyazov Rashid , Makhmudov Fazliddin , Cho Young-Im
Published: July 2023

Understanding and identifying emotional cues in human speech is a crucial aspect of human–computer communication. The application of computer technology in dissecting and deciphering emotions, along with the extraction of relevant emotional characteristics from speech, forms a significant part of this process. The objective of this study was to architect an innovative framework for speech emotion recognition predicated on spectrograms and semantic feature transcribers, aiming to bolster performance precision by acknowledging the conspicuous inadequacies in extant methodologies and rectifying them. To procure invaluable attributes for speech detection, this investigation leveraged two divergent strategies. Primarily, a wholly convolutional neural network model was engaged to transcribe speech spectrograms. Subsequently, a cutting-edge Mel-frequency cepstral coefficient feature abstraction approach was adopted and integrated with Speech2Vec for semantic feature encoding. These dual forms of attributes underwent individual processing before they were channeled into a long short-term memory network and a comprehensive connected layer for supplementary representation. By doing so, we aimed to bolster the sophistication and efficacy of our speech emotion detection model, thereby enhancing its potential to accurately recognize and interpret emotion from human speech. The proposed mechanism underwent a rigorous evaluation process employing two distinct databases: RAVDESS and EMO-DB. The outcome displayed a predominant performance when juxtaposed with established models, registering an impressive accuracy of 94.8% on the RAVDESS dataset and a commendable 94.0% on the EMO-DB dataset. This superior performance underscores the efficacy of our innovative system in the realm of speech emotion recognition, as it outperforms current frameworks in accuracy metrics.
Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery
Authors: Kavran Domen , Mongus Domen , Žalik Borut , Lukač Niko
Published: July 2023

Multispectral satellite imagery offers a new perspective for spatial modelling, change detection and land cover classification. The increased demand for accurate classification of geographically diverse regions led to advances in object-based methods. A novel spatiotemporal method is presented for object-based land cover classification of satellite imagery using a Graph Neural Network. This paper introduces innovative representation of sequential satellite images as a directed graph by connecting segmented land region through time. The method’s novel modular node classification pipeline utilises the Convolutional Neural Network as a multispectral image feature extraction network, and the Graph Neural Network as a node classification model. To evaluate the performance of the proposed method, we utilised EfficientNetV2-S for feature extraction and the GraphSAGE algorithm with Long Short-Term Memory aggregation for node classification. This innovative application on Sentinel-2 L2A imagery produced complete 4-year intermonthly land cover classification maps for two regions: Graz in Austria, and the region of Portorož, Izola and Koper in Slovenia. The regions were classified with Corine Land Cover classes. In the level 2 classification of the Graz region, the method outperformed the state-of-the-art UNet model, achieving an average F1-score of 0.841 and an accuracy of 0.831, as opposed to UNet’s 0.824 and 0.818, respectively. Similarly, the method demonstrated superior performance over UNet in both regions under the level 1 classification, which contains fewer classes. Individual classes have been classified with accuracies up to 99.17%.
Predicting the Early-Age Time-Dependent Behaviors of a Prestressed Concrete Beam by Using Physics-Informed Neural Network
Authors: Park Hyun-Woo , Hwang Jin-Ho
Published: July 2023

This paper proposes a physics-informed neural network (PINN) for predicting the early-age time-dependent behaviors of prestressed concrete beams. The PINN utilizes deep neural networks to learn the time-dependent coupling among the effective prestress force and the several factors that affect the time-dependent behavior of the beam, such as concrete creep and shrinkage, tendon relaxation, and changes in concrete elastic modulus. Unlike traditional numerical algorithms such as the finite difference method, the PINN directly solves the integro-differential equation without the need for discretization, offering an efficient and accurate solution. Considering the trade-off between solution accuracy and the computing cost, optimal hyperparameter combinations are determined for the PINN. The proposed PINN is verified through the comparison to the numerical results from the finite difference method for two representative cross sections of PSC beams.
Research on the Impact of Financial Deepening on Digital Economy Development: An Empirical Analysis from China
Authors: Shan Shuai , Liu Chuanzhe
Published: July 2023

Financial deepening aims to promote economic development. In the past years, China’s digital economy has achieved remarkable development achievements, and its role in guiding and supporting sustainable economic and social development has become increasingly prominent. Therefore, it is worth considering whether financial deepening can effectively support the development of the digital economy and what the impact pathway is of financial deepening on digital economy development. Based on panel data from 2013 to 2021 at the provincial level in China, this study constructs a comprehensive indicator system for digital economy development using the entropy method. The indicator comprises four dimensions: digital infrastructure, digital industrialization, industrial digitalization, and digital services. By utilizing fixed effects models and mediation effects models, this study investigates the impact and transmission mechanisms of financial deepening on digital economy development. The research results indicate that financial deepening significantly improves the level of digital economy development and passes the robustness test. Heterogeneity tests indicate that financial deepening contributes more to promoting digital economy development in the central and western regions compared to the eastern region. Financial deepening promotes the digital economy development through the intermediary pathway of technological innovation, where technological innovation serves as a mediator between the two. In the future, it is crucial to fully leverage the supportive role of financial deepening, promote technological innovation, and further drive the sustainable development of the digital economy.