bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Pre-Tension on Free-End Torsion Behavior and Mechanical Properties of an Extruded Magnesium Alloy
Authors: Chen Hongbing , Shen Zhikang , Song Bo , She Jia
Published: July 2023

In this study, the influence of pre-tension on free-end torsion behavior and compression mechanical properties and micro-hardness of an extruded AZ31 Mg alloy was investigated using electron backscatter diffraction (EBSD), compression testing and micro-hardness testing. The result indicates that pre-tension can cause significant dislocation strengthening, which can increase the torsion yield strength and make the shear stress–shear strain curve of the pre-tension sample almost parallel to that of the as-extruded sample during plastic deformation stage. Texture in edge position on the cross-section of both the pre-tension and as-extruded samples can be rotated towards the extrusion direction by about ~30° by free-end torsion. The Swift effect is mainly responsible for the occurrence of massive extension twins in the central region. In contrast, normal stress is the main cause of extension twins occurring in the edge region. However, the effect of extension twins on micro-hardness is less than that of dislocations. The micro-hardness of both free-end torsion specimens increases almost linearly with increasing distance from center to edge on the cross-section. Nevertheless, the increase in micro-hardness of the pre-tension and then torsion sample is inconspicuous because pre-tension leads to dislocation proliferation and dislocation accumulation saturation. The result also indicates that both pre-tension and free-end torsion can lead to dislocation strengthening, which can obviously increase the micro-hardness and compressive yield stress. The underlying mechanisms were explored and discussed in detail.
Preparation a High-Performance of Gangue-Based Geopolymer Backfill Material: Recipes Optimization Using the Taguchi Method
Authors: Yang Sen , Zhu Hongguang , Pang Sen , Ruan Zaijie , Lin Sinuo , Ding Yi , Cao Pengpeng , Shen Zhengyan
Published: July 2023

The strip filling method in underground reservoir needs high strength to achieve the requirements of water storage. In order to address the challenges associated with costly and weak filling materials, this study aimed to develop an economically efficient and high-strength gangue-based geopolymer backfill material (GBGBM). To achieve this, the Taguchi method was employed to design a series of 25 experiments, each consisting of four factors and five levels. This study focused on investigating the effects of different gangue gradation levels, sand ratios, water binder ratios (w/b), and aggregate binder ratios (a/b) on the working characteristics and unconfined compressive strength (UCS) of the GBGBM. The optimal combination of the GBGBM was determined by employing a signal-to-noise ratio (S/N)-based extreme difference and variance analysis. The results revealed that the w/b ratio exerted the most substantial influence on both the slump and UCS. Specifically, when employing a gradation of 50%, a sand ratio of 55%, an a/b ratio of 2.5, and a w/b ratio of 0.64, the slump measured 251.2 mm, the UCS at 3d reached 5.27 MPa, and the UCS at 28d amounted to 17.65 MPa. These findings indicated a remarkable improvement in early UCS by 131.14% and the late UCS by 49.45% compared to gangue-based cement backfill material (GBCBM). Additionally, this study examined the hydration products and microstructures of both GBGBM and GBCBM using X-ray diffraction (XRD), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) analyses. Significantly, the GBGBM exhibited notable advantages over the GBCBM, including a 78.16% reduction in CO2 emissions, a 73.45% decrease in energy consumption, and a 24.82% reduction in cost. These findings underscore the potential of GBGBM as a sustainable and cost-effective alternative to GBCBM.
Influence of Grout Properties on the Tensile Performance of Rockbolts Based on Modified Cable Elements
Authors: Chen Jianhang , Wang Shiji , Zhao Yiqiang , Liu Lei , Skrzypkowski Krzysztof , Zagórski Krzysztof , Zagórska Anna
Published: July 2023

The grout annulus (GA) has a significant effect on the tensile performance of rockbolts in mining engineering. However, little research has been conducted to use modified cable elements to study this effect quantitatively. This paper used the modified cable elements in FLAC3D to study the effect of the GA on the tensile performance of rockbolts. The two-stage coupling law was used to simulate the behaviour of the GA. The stress had a linear relation with the slippage before the shear strength (SS). After the SS, the stress decreased exponentially. Numerical in situ roadway reinforcement cases were used to study the influence of the grout annulus on the tensile performance of rockbolts. The results showed that, when the SS of the GA increased from 3.2 MPa to 6.4 Mpa, the peak force of rockbolts increased from 247 kN to 425 kN. Moreover, when the SS of the GA increased from 3.2 Mpa to 6.4 Mpa, the distance between the position of the maximum tensile capacity and the external end decreased from 1.17 m to 0.81 m. Last, for the circular roadway, the peak force in rockbolts installed in the lateral side was 171.7 kN, which was significantly larger than the top side of 72.3 kN.
A Review of the Antibacterial, Fungicidal and Antiviral Properties of Selenium Nanoparticles
Authors: Serov Dmitry A. , Khabatova Venera V. , Vodeneev Vladimir , Li Ruibin , Gudkov Sergey V.
Published: July 2023

The resistance of microorganisms to antimicrobial drugs is an important problem worldwide. To solve this problem, active searches for antimicrobial components, approaches and therapies are being carried out. Selenium nanoparticles have high potential for antimicrobial activity. The relevance of their application is indisputable, which can be noted due to the significant increase in publications on the topic over the past decade. This review of research publications aims to provide the reader with up-to-date information on the antimicrobial properties of selenium nanoparticles, including susceptible microorganisms, the mechanisms of action of nanoparticles on bacteria and the effect of nanoparticle properties on their antimicrobial activity. This review describes the most complete information on the antiviral, antibacterial and antifungal effects of selenium nanoparticles.
Mechanical Characterization of MWCNT-Reinforced Cement Paste: Experimental and Multiscale Computational Investigation
Authors: Kavvadias Ioannis E. , Tsongas Konstantinos , Bantilas Kosmas E. , Falara Maria G. , Thomoglou Athanasia K. , Gkountakou Fani I. , Elenas Anaxagoras
Published: July 2023

Computational approaches could provide a viable and cost-effective alternative to expensive experiments for accurately evaluating the nonlinear constitutive behavior of cementitious nanocomposite materials. In the present study, the mechanical properties of cement paste reinforced with multi-wall carbon nanotubes (MWCNTs) are examined experimentally and numerically. A multiscale computational approach is adopted in order to verify the experimental results. For this scope, a random sequential adsorption algorithm was developed to generate non-overlapping matrix-inclusion three-dimensional (3D) representative volume elements (RVEs), considering the inclusions as straight elements. Nonlinear finite element analyses (FEA) were performed, and the homogenized elastic and inelastic mechanical properties were computed. The use of a multiscale computational approach to accurately evaluate the nonlinear constitutive behavior of cementitious materials has rarely been explored before. For this purpose, the RVEs were analyzed both in pure tension and compression. Young’s modulus as well compressive and tensile strength results were compared and eventually matched the experimental values. Moreover, the effect of MWCNTs on the nonlinear stress–strain behavior of reinforced cement paste was noted. Subsequently, three-point bending tests were conducted, and the stress–strain behavior was verified with FEA in the macro scale. The numerical modeling reveals a positive correlation between the concentration of MWCNTs and improved mechanical properties, assuming ideal dispersion. However, it also highlights the impact of practical limitations, such as imperfect dispersion and potential defects, which can deteriorate the mechanical properties that are observed in the experimental results. Among the different cases studied, that with a 0.1 wt% MWCNTs/CP composite demonstrated the closest agreement between the numerical model and the experimental measurements. The numerical model achieved the best accuracy in estimating the Young’s modulus (underestimation of 13%), compressive strength (overestimation of 1%), and tensile strength (underestimation of 6%) compared to other cases. Overall, these numerical findings contribute significantly to understanding the mechanical behavior of the nanocomposite material and offer valuable guidance for optimizing cement-based composites for engineering applications.
Autoencoder Feature Residuals for Network Intrusion Detection: One-Class Pretraining for Improved Performance
Authors: Lewandowski Brian , Paffenroth Randy
Published: July 2023

The proliferation of novel attacks and growing amounts of data has caused practitioners in the field of network intrusion detection to constantly work towards keeping up with this evolving adversarial landscape. Researchers have been seeking to harness deep learning techniques in efforts to detect zero-day attacks and allow network intrusion detection systems to more efficiently alert network operators. The technique outlined in this work uses a one-class training process to shape autoencoder feature residuals for the effective detection of network attacks. Compared to an original set of input features, we show that autoencoder feature residuals are a suitable replacement, and often perform at least as well as the original feature set. This quality allows autoencoder feature residuals to prevent the need for extensive feature engineering without reducing classification performance. Additionally, it is found that without generating new data compared to an original feature set, using autoencoder feature residuals often improves classifier performance. Practical side effects from using autoencoder feature residuals emerge by analyzing the potential data compression benefits they provide.
2D Collagen Membranes from Marine Demosponge Chondrosia reniformis (Nardo, 1847) for Skin-Regenerative Medicine Applications: An In Vitro Evaluation
Authors: Tassara Eleonora , Oliveri Caterina , Vezzulli Luigi , Cerrano Carlo , Xiao Lian , Giovine Marco , Pozzolini Marina
Published: July 2023

Research in tissue engineering and regenerative medicine has an ever-increasing need for innovative biomaterials suitable for the production of wound-dressing devices and artificial skin-like substitutes. Marine collagen is one of the most promising biomaterials for the production of such devices. In this study, for the first time, 2D collagen membranes (2D-CMs) created from the extracellular matrix extract of the marine demosponge Chondrosia reniformis have been evaluated in vitro as possible tools for wound healing. Fibrillar collagen was extracted from a pool of fresh animals and used for the creation of 2D-CMs, in which permeability to water, proteins, and bacteria, and cellular response in the L929 fibroblast cell line were evaluated. The biodegradability of the 2D-CMs was also assessed by following their degradation in PBS and collagenase solutions for up to 21 days. Results showed that C. reniformis-derived membranes avoided liquid and protein loss in the regeneration region and also functioned as a strong barrier against bacteria infiltration into a wound. Gene expression analyses on fibroblasts stated that their interaction with 2D-CMs is able to improve fibronectin production without interfering with the regular extracellular matrix remodeling processes. These findings, combined with the high extraction yield of fibrillar collagen obtained from C. reniformis with a solvent-free approach, underline how important further studies on the aquaculture of this sponge could be for the sustainable production and biotechnological exploitation of this potentially promising and peculiar biopolymer of marine origin.
Exploration of Vulcanodinium rugosum Toxins and their Metabolism Products in Mussels from the Ingril Lagoon Hotspot in France
Authors: Hort Vincent , Bastardo-Fernández Isabel , Nicolas Marina
Published: July 2023

Over the year 2018, we assessed toxin contamination of shellfish collected on a monthly basis in Ingril Lagoon, France, a site known as a hotspot for Vulcanodinium rugosum growth. This short time-series study gave an overview of the presence and seasonal variability of pinnatoxins, pteriatoxins, portimines and kabirimine, all associated with V. rugosum, in shellfish. Suspect screening and targeted analysis approaches were implemented by means of liquid chromatography coupled to both low- and high-resolution mass spectrometry. We detected pinnatoxin-A and pinnatoxin-G throughout the year, with maximum levels for each one observed in June (6.7 µg/kg for pinnatoxin-A; 467.5 µg/kg for pinnatoxin-G), whereas portimine-A was detected between May and September (maximum level = 75.6 µg/kg). One of the main findings was the identification of a series of fatty acid esters of pinnatoxin-G (n = 13) although the levels detected were low. The profile was dominated by the palmitic acid conjugation of pinnatoxin-G. The other 12 fatty acid esters had not been reported in European shellfish to date. In addition, after thorough investigations, two compounds were detected, with one being probably identified as portimine-B, and the other one putatively attributed to pteriatoxins. If available, reference materials would have ensured full identification. Monitoring of these V. rugosum emerging toxins and their biotransformation products will contribute towards filling the data gaps pointed out in risk assessments and in particular the need for more contamination data for shellfish.
Anti-Protozoan Activities of Polar Fish-Derived Polyalanine Synthetic Peptides
Authors: Nunes Ellynes Amancio Correia , da Silva Maria Cláudia , Cardoso Marlon Henrique , Preza Sergio Leandro Espíndola , de Oliveira Lucas Silva , Frihling Breno Emanuel Farias , Charneau Sébastien Olivier , Grellier Philippe , Franco Octávio Luiz , Migliolo Ludovico
Published: July 2023

Chagas disease, sleeping sickness and malaria are infectious diseases caused by protozoan parasites that kill millions of people worldwide. Here, we performed in vitro assays of Pa-MAP, Pa-MAP1.9, and Pa-MAP2 synthetic polyalanine peptides derived from the polar fish Pleuronectes americanus toward Trypanosoma cruzi, T. brucei gambiense and Plasmodium falciparum activities. We demonstrated that the peptides Pa-MAP1.9 and Pa-MAP2 were effective to inhibit T. brucei growth. In addition, structural analyses using molecular dynamics (MD) studies showed that Pa-MAP2 penetrates deeper into the membrane and interacts more with phospholipids than Pa-MAP1.9, corroborating the previous in vitro results showing that Pa-MAP1.9 acts within the cell, while Pa-MAP2 acts via membrane lysis. In conclusion, polyalanine Pa-MAP1.9 and Pa-MAP2 presented activity against bloodstream forms of T. b. gambiense, thus encouraging further studies on the application of these peptides as a treatment for sleeping sickness.
Training in Aortic Arch Surgery as a Blueprint for a Structured Educational Team Approach: A Review
Authors: Discher Philipp , Kreibich Maximilian , Berger Tim , Kondov Stoyan , Eschenhagen Matthias , Schibilsky David , Bork Magdalena , Walter Tim , Chikvatia Salome , Gottardi Roman , Rylski Bartosz , Siepe Matthias , Czerny Martin
Published: July 2023

Background and objectives: The treatment of pathologies of the aortic arch is a complex field of cardiovascular surgery that has witnessed enormous progress recently. Such treatment is mainly performed in high-volume centres, and surgeons gain great experience in mastering potential difficulties even under emergency circumstances, thereby ensuring the effective therapy of more complex pathologies with lower complication rates. As the numbers of patients rise, so does the need for well-trained surgeons in aortic arch surgery. But how is it possible to learn surgical procedures in a responsible way that, in addition to surgical techniques, also places particular demands on the overall surgical management such as perfusion strategy and neuro-protection? This is why a good training programme teaching young surgeons without increasing the risk for patients is indispensable. Our intention was to highlight the most challenging aspects of aortic arch surgery teaching and how young surgeons can master them. Materials and Methods: We analysed the literature to find out which methods are most suitable for such teaching goals and what result they reveal when serving as teaching procedures. Results: Several studies were found comparing the surgical outcome of young trainees with that of specialists. It was found that the results were comparable whether the procedure was performed by a specialist or by a trainee assisted by the specialist. Conclusions: We thus came to the conclusion that even for such a complex type of intervention, the responsible training of young surgeons by experienced specialists is possible. However, it requires a clear strategy and team approach to ensure a safe outcome for the patient.
Investigation of Inkjet-Printed Masks for Fast and Easy Photolithographic NIL Masters Manufacturing
Authors: Burkert Selina , Eder Christian , Heinrich Andreas
Published: July 2023

Modern optical systems often require small, optically effective structures that have to be manufactured both precisely and cost-effectively. One option to do this is using nanoimprint lithography (NIL), in which the optical structures are replicated as masters using a stamping process. It would also be advantageous to manufacture the master structures quickly and easily. A master manufacturing process based on a photolithographic image of an inkjet-printed mask is presented and investigated in this paper. An essential element is that a deliberate blurring of the printed structure edge of the mask is used in the photolithographic process. Combined with the use of a non-linear photoresist, this allows for improved edge geometries of the master structure. We discuss the inkjet-printed photomask, the custom photolithography system to prevent imaging of the printing dot roughness and the manufacturing processes of NIL polymer masks as well as their subsequent stamp imprinting. Finally, it was shown that stamp geometries with a width of 1.7 µm could be realised using inkjet-printed photomasks in the master manufacturing process. This methodology opens up the potential of fast and simple master manufacturing for the development and manufacturing of optical elements.
Differences in Ratio of Carbon Stable Isotopes among Barley Grain Milling Fractions with Various Concentrations of Beta-Glucans
Authors: Levanič Tom , Cigić Blaž , Germ Mateja , Polišenská Ivana , Vaculová Kateřina , Pravst Igor , Kocjan Ačko Darja , Kreft Ivan
Published: July 2023

The grains of three barley varieties were milled and sieved to obtain respective milling fractions with a content of beta-glucans (b-G) from 1.4 to 10.7%. The enriched fraction obtained by the extraction and precipitation contained 24.7% of b-G. The differences between the ratio of stable C carbon isotopes were established. Milling fractions with coarse particles had more beta-glucans and a more negative ratio of δ13C isotope in comparison to the respective intact barley grain. However, the enriched fraction had a less negative isotope ratio. So, it is not expected that the deviation from the stable isotope ratio of grain in milling fractions is the result of the content of b-G, but it depends on other barley grain constituents. In different parts of barley grain, there are substances with different stable isotope ratios, and by milling and sieving, they are assorted to the same milling fraction with most of the b-G. The method for determining the ratio of a stable carbon isotope in diverse barley grain fractions, applied in this investigation, is potentially opening the possibility for an additional method of screening the concentration of bioactive constituents in barley grain.
[125I]INFT: Synthesis and Evaluation of a New Imaging Agent for Tau Protein in Post-Mortem Human Alzheimer’s Disease Brain
Authors: Limpengco Roz R. , Liang Christopher , Sandhu Yasmin K. , Mukherjee Jogeshwar
Published: July 2023

Aggregation of Tau protein into paired helical filaments causing neurofibrillary tangles (NFT) is a neuropathological feature in Alzheimer’s disease (AD). This study aimed to develop and evaluate the effectiveness of a novel radioiodinated tracer, 4-[125I]iodo-3-(1H-pyrrolo[2,3-c]pyridine-1-yl)pyridine ([125I]INFT), for binding to Tau protein in postmortem human AD brain. Radiosynthesis of [125I]INFT was carried out using electrophilic destannylation by iodine-125 and purified chromatographically. Computational modeling of INFT binding on Tau fibril was compared with IPPI. In vitro, autoradiography studies were conducted with [125I]INFT for Tau in AD and cognitively normal (CN) brains. [125I]INFT was produced in >95% purity. Molecular modeling of INFT revealed comparable binding energies to IPPI at site-1 of the Tau fibril with an affinity of IC50 = 7.3 × 10−8 M. Binding of [125I]INFT correlated with the presence of Tau in the AD brain, confirmed by anti-Tau immunohistochemistry. The ratio of average grey matter (GM) [125I]INFT in AD versus CN was found to be 5.9, and AD GM/white matter (WM) = 2.5. Specifically bound [125I]INFT to Tau in AD brains was displaced by IPPI (>90%). Monoamine oxidase inhibitor deprenyl had no effect and clorgyline had little effect on [125I]INFT binding. [125I]INFT is a less lipophilic imaging agent for Tau in AD.
Structure Characterization and Immunomodulatory Activity of Misgurnus anguillicaudatus Carbohydrates
Authors: Yun Liyuan , Han Conglin , He Xiaoqing , Li Qian , Fersht Viktor , Zhang Min
Published: July 2023

Misgurnus anguillicaudatus, also known as oriental weather loach, is widely consumed and favored in East Asia due to its superior nutritional values and excellent flavor. In this study, a crude Misgurnus anguillicaudatus carbohydrates (MAC) was isolated from Misgurnus anguillicaudatus. Subsequently, two parts, which were named MAO and MAP, respectively, were separated from MAC, and their primary structures and immunomodulatory activity were investigated. The results showed that MAO had a molecular weight of 2854 Da, and principally consisted of arabinose (77.11%) and rhamnose (21.97%), together with minor levels of fucose (0.92%); MAP, with a molecular weight of 3873 Da, was mainly composed of fucose (87.55%) and a small amount of rhamnose (8.86%) and galactose (3.59%). The in vitro assay showed that MAC could significantly enhance the proliferation of macrophages without cytotoxicity and increase the production of immune substances (TNF-α, IL-6). Together with Western blot results, we speculated that MAC could stimulate RAW264.7 murine macrophage cells to secrete TNF-α and IL-6 through up-regulating TLR4-MAPK-p38 signaling pathways. The results indicated that MAC could be a potential immune agent and might provide meaningful information for further chain conformation and immune mechanism research.
Evaluation and Prediction of the Effect of Fabric Wetting on Coolness
Authors: Wu Zijiang , Shi Yunlong , Qian Xiaoming , Lei Haiyang
Published: July 2023

As an important parameter of garment comfort, the thermal sensation of fabrics changes with factors such as sweat-induced humidity, making it a crucial area of research. To explore the coolness sensation of fabrics under different humidities, we tested heat transfer between fabrics and skin for 20 different fabrics with varying thermal absorption rates using fuzzy comprehensive evaluation to objectively assess their coolness levels. Subjective evaluation was then obtained by having subjects touch the fabrics and provide feedback, resulting in a subjective evaluation of their coolness levels. We compared the objective and subjective evaluations and found them to be highly consistent (R2 = 0.909), indicating accurate objective classification of fabric coolness levels. Currently, random forest regression models are widely used in the textile industry for classification, identification, and performance predictions. These models enable the prediction of fabric coolness levels by simultaneously considering the impact of all fabric parameters. We established a random forest regression model for predicting the coolness of wet fabrics, obtaining a high accuracy between predicted and tested thermal absorption coefficients (R2 = 0.872, RMSE = 0.305). Therefore, our random forest regression model can successfully predict the coolness of wet fabrics.
Individual and Unity (Heti): The Generative Structure of Human Relations from the Confucian Perspective
Authors: Meng Qingnan
Published: July 2023

In terms of the comprehension of modern order and values, understanding individuals and their relationship with the community has always been an important perspective. Scholars have noticed that traditional Confucian thought in China contains a profound understanding of individuals and their relationship with the community. In the Confucian perspective, an individual’s significance lies in human relations, with the “family” constituting the foundation and core of these. This study presents the unique structure of the “family” in Confucian ethics and its generative significance. A literature analysis of Confucianism reveals that it places significant importance on the husband–wife relationship. The husband–wife relationship is a unity (heti) in the order of human relations, where spouses enjoy equal status and respect. The union of husband and wife propels the generational progression of the entire human relation system, making the husband–wife relationship a core node in the continuity of human ethical order. In this generative structure woven by human relations, the individuals are always subordinate to the human relationships they are involved in. The primary significance of the individual lies in the roles and corresponding responsibilities assumed within various human relations. Previous discussions on Confucian ethics have failed to clearly unveil the generative structure inherent in the concept of the family. The Confucian understanding of the individual and their relationship with the community can only be accurately grasped by clarifying this aspect.
Detection and Analysis of Pavement-Section Based on Laser Displacement Sensor
Authors: Han Jin , Xiong Gao , Liu Jia
Published: July 2023

The section detection of the pavement is the data basis for measuring the road smoothness, rutting, lateral slope, and structural depth. The detection of the Pavement-Section includes longitudinal-section inspection and cross-section inspection. In this paper, based on multiple laser displacement sensors, fused accelerometers and attitude sensors, and using vehicle-mounted high-speed detection, we design a sensor-fused pavement section data acquisition method, establish the relevant mathematical model, and realize the automatic acquisition of pavement longitudinal and transverse sections. The acceleration sensor is filtered to improve the accuracy of data acquisition, and the error of the detection system is calculated and analyzed. Through the actual measurement, the vehicle-mounted high-speed pavement profile detection method adopted in this paper can not only accurately detect the profile of the pavement profile, but also improve the detection efficiency, providing a cost-effective detection mode for road surface detection.
NFC-Enabled Dual-Channel Flexible Printed Sensor Tag
Authors: Choi Jonghyun , Visagie Ian , Chen Yi , Abbel Robert , Parker Kate
Published: July 2023

Wireless sensor tags in flexible formats have numerous applications; some are commercially available for specific target applications. However, most of these wireless sensor tags have been used for single-sensing applications. In this study, we designed a printed circuit board (PCB) module (13 mm × 13 mm) for near-field communication-enabled sensor tags with both electrical resistance and capacitance read-out channels that enables dual-channel sensing. As part of the wireless sensor tag, a square antenna pattern was printed directly on a flexible poly(ethylene terephthalate) (PET) substrate and integrated into the PCB module to demonstrate a dual-channel temperature and ethylene gas sensor. The temperature and ethylene sensors were printed using a positive temperature coefficient ink and a tin oxide (SnO2) nanoparticle ink, respectively. With dual sensing capabilities, this type of sensor tag can be used in smart packaging for the quality monitoring of fresh produce (e.g., bananas) by tracking temperature and ethylene concentration in the storage/transport environment.
Effect of Load on Quartz Crystal Microbalance Sensor Response Addressed Using Fractional Order Calculus
Authors: Burda Ioan
Published: July 2023

To accurately model the effect of the load caused by a liquid medium as a function of its viscosity, the fractional order Butterworth–Van Dyke (BVD) model of the QCM sensor is proposed in this study. A comprehensive understanding of the fractional order BVD model followed by a simulation of situations commonly encountered in experimental investigations underpins the new QCM sensor approach. The Levenberg–Marquardt (LM) algorithm is used in two fitting steps to extract all parameters of the fractional order BVD model. The integer-order electrical parameters were determined in the first step and the fractional order parameters were extracted in the second step. A parametric investigation was performed in air, water, and glycerol–water solutions in ten-percent steps for the fractional order BVD model. This indicated a change in the behavior of the QCM sensor when it swapped from air to water, modeled by the fractional order BVD model, followed by a specific dependence with increasing viscosity of the glycerol–water solution. The effect of the liquid medium on the reactive motional circuit elements of the BVD model in terms of fractional order calculus (FOC) was experimentally demonstrated. The experimental results demonstrated the value of the fractional order BVD model for a better understanding of the interactions occurring at the QCM sensor surface.
Microcontroller-Based PUF for Identity Authentication and Tamper Resistance of Blockchain-Compliant IoT Devices
Authors: Vinko Davor , Miličević Kruno , Lukić Ivica , Köhler Mirko
Published: July 2023

Blockchain-based applications necessitate the authentication of connected devices if they are employed as blockchain oracles. Alongside identity authentication, it is crucial to ensure resistance against tampering, including safeguarding against unauthorized alterations and protection against device counterfeiting or cloning. However, attaining these functionalities becomes more challenging when dealing with resource-constrained devices like low-cost IoT devices. The resources of IoT devices depend on the capabilities of the microcontroller they are built around. Low-cost devices utilize microcontrollers with limited computational power, small memory capacity, and lack advanced features such as a dedicated secure cryptographic chip. This paper proposes a method employing a Physical Unclonable Function (PUF) to authenticate identity and tamper resistance in IoT devices. The suggested PUF relies on a microcontroller’s internal pull-up resistor values and, in conjunction with the microcontroller’s built-in analog comparator, can also be utilized for device self-checking. A main contribution of this paper is the proposed PUF method which calculates the PUF value as the average value of many single PUF measurements, resulting in a significant increase in accuracy. The proposed PUF has been implemented successfully in a low-cost microcontroller device. Test results demonstrate that the device, specifically the microcontroller chip, can be identified with high accuracy (99.98%), and the proposed PUF method exhibits resistance against probing attempts.
Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals
Authors: Shi Di , Gidion Gunnar , Reindl Leonhard M. , Rupitsch Stefan J.
Published: July 2023

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients’ breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.
Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
Authors: Alwagdani Maram Saleh , Jaha Emad Sami
Published: July 2023

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.
End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles
Authors: Gu Junyi , Lind Artjom , Chhetri Tek Raj , Bellone Mauro , Sell Raivo
Published: July 2023

Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. However, there are two major research gaps in existing works: (i) they lack fusion (and synchronization) of multi-sensors, camera, LiDAR and radar; and (ii) generic scalable, and user-friendly end-to-end implementation. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as camera, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors, namely, camera, LiDAR, and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications and suitable for quick and large-scale practical deployment.
Humidity Sensor Composed of Laser-Induced Graphene Electrode and Graphene Oxide for Monitoring Respiration and Skin Moisture
Authors: Fei Xianxiang , Huang Junyi , Shi Wenqing
Published: July 2023

Respiratory rate and skin humidity are important physiological signals and have become an important basis for disease diagnosis, and they can be monitored by humidity sensors. However, it is difficult to employ high-quality humidity sensors on a broad scale due to their high cost and complex fabrication. Here, we propose a reliable, convenient, and efficient method to mass-produce humidity sensors. A capacitive humidity sensor is obtained by ablating a polyimide (PI) film with a picosecond laser to produce an interdigital electrode (IDE), followed by drop-casting graphene oxide (GO) as a moisture-sensitive material on the electrode. The sensor has long-time stability, a wide relative humidity (RH) detection range from 10% to 90%, and high sensitivity (3862 pF/%RH). In comparison to previous methods, the technology avoids the complex procedures and expensive costs of conventional interdigital electrode preparation. Furthermore, we discuss the effects of the electrode gap size and the amount of graphene oxide on humidity sensor performance, analyze the humidity sensing mechanism by impedance spectrum, and finally perform the monitoring of human respiratory rate and skin humidity change in a non-contact manner.
An Innovative Approach for Removing Stripe Noise in Infrared Images
Authors: Zhao Xiaohang , Li Mingxuan , Nie Ting , Han Chengshan , Huang Liang
Published: July 2023

The non-uniformity of infrared detectors’ readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm’s effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm’s superiority and efficacy.
Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions
Authors: Bhave Anushka , van Delden Josephine , Gloor Peter A. , Renold Fritz K.
Published: July 2023

This paper presents novel preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. We have developed a real-time software to calculate the physical synchronicity of team members by tracking the difference in arm, leg, and head movements using Lightweight OpenPose. We employ facial expression recognition to evaluate the musicians’ collective emotions. Through correlation and regression analysis, we establish that higher levels of synchronized body and head movements correspond to lower levels of disgust, anger, sadness, and higher levels of joy among the musicians. Furthermore, we utilize 1-D CNNs to predict the collective emotions of the musicians. The model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.
A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis
Authors: Moon Kee S. , Lee Sung Q
Published: July 2023

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients’ health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level.
Intelligent Resource Allocation for V2V Communication with Spectrum–Energy Efficiency Maximization
Authors: Xu Chunning , Wang Shumo , Song Ping , Li Ke , Song Tiecheng
Published: July 2023

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum–energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.
Modelling and Analysis of Expansion Joints’ Effect on Dynamic Performance of Railway Rigid Overhead System
Authors: Feng Xiaohe , Hu Zeyao , Gao Shibin , Duan Fuchuan , Chu Wenping , Song Yang
Published: July 2023

This study focuses on developing a comprehensive model of a rigid overhead system, which includes essential components such as the suspension structure, positioning clamp, and expansion joint. The modelling approach utilizes finite element theory and beam elements to accurately represent the displacement, stiffness, and mass characteristics of the system. The models also incorporate the suspension structure and positioning line clamp, which play crucial roles in suspending and positioning the busbar. Various suspension structures and positioning line clamps are evaluated based on their dynamic characteristics. The expansion joint, responsible for connecting different anchor sections of the rigid overhead system, undergoes a detailed analysis. Different assembly scenarios, including ideal and deflected assembly conditions, are considered. To simulate the dynamic behaviour of the expansion joint, additional beams are introduced into the system model. The primary finding of the analysis is that the maximum stresses observed in the constructed expansion joint model, under different temperature conditions and normal/deflected assembly conditions, remain within the permissible stress limits of the material. This indicates a high level of safety. However, certain areas exhibit stress concentration, particularly at the sliding block B and sliding rod A positions. This stress concentration is primarily attributed to the unique assembly form of the expansion joint. To improve stress distribution and enhance service reliability, the analysis suggests optimizing the installation deflection angle and geometric design of the expansion joint. Furthermore, the concentrated mass at the expansion joint significantly impacts the current collection quality of the pantograph-overhead system. Mitigating this negative impact can be achieved by reducing the mass of the expansion joint.
Laser Sensing and Chemometric Analysis for Rapid Detection of Oregano Fraud
Authors: Fiorani Luca , Lai Antonia , Puiu Adriana , Artuso Florinda , Ciceroni Claudio , Giardina Isabella , Pollastrone Fabio
Published: July 2023

World health is increasingly threatened by the growing number of spice-related food hazards. Further development of reliable methods for rapid, non-targeted identification of counterfeit ingredients within the supply chain is needed. ENEA has developed a portable, user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser and multivariate calibration. Following a study on the authenticity of saffron, the instrument was challenged with a more elusive adulterant, olive leaves in oregano. The results show that the reported method of laser sensing and chemometric analysis was able to detect adulterants at mass ratios of at least 20% in less than five minutes.