bluuebunny/crossref_metadata_2025 huggingface dataset:
In-Situ Functionalization of Cotton Fabric by TiO2: The Influence of Application Routes
Authors: Rashid Mohammad Mamunur , Zorc Matija , Simončič Barbara , Jerman Ivan , Tomšič Brigita
Published: November 2022

The desirable chemical, physical, electronic, and optical properties of TiO2, as well as its high availability, non-toxicity, and low price, make it very popular in the modern functional textile industry. Here, TiO2 from titanium tetraisopropoxide (TTIP) precursors at concentrations of 2, 4, and 6% and commercial TiO2 nanoparticles (NPs) in dispersion form were applied to cotton textiles using low-temperature application methods (i.e., sol–gel pad–dry–cure, pad–hydrothermal, and exhaustion–hydrothermal methods) to provide a systematic study of the influence of low-temperature application processes and TIIP concentration and on the overall properties of TiO2-functionalized textile materials. The treated cotton fabric samples were characterized using scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDS), Fourier transform infrared spectroscopy (FT-IR), and X-ray diffraction spectroscopy (XRD) to determine their surface morphology, chemical composition, and crystal structure, while the optical properties of the synthesized TiO2 were determined using the absorption method and Tauc plotting. Afterwards, corresponding UV protection properties and photocatalytic self-cleaning activity were evaluated. In contrast to commercial TiO2, a relatively thin TiO2 deposition with an amorphous structure and a blue-shifted band gap between 3.18 and 3.28 eV was formed when applied at low temperatures. A sol with a TIIP concentrations of 2 and 4% applied using the exhaustion–hydrothermal and sol–gel dry-cure method, respectively, proved to be optimal. Both applied sol concentrations provided good UV protection and excellent photocatalytic performance, which exceeded that of commercial TiO2, even though the Ti contents in the samples were two- to three-times lower and the synthesized TiO2 exhibited an amorphous structure.
Production of Biofuel Additives Using Catalytic Bioglycerol Etherification: Kinetic Modelling and Reactive Distillation Design
Authors: Al-Rabiah Abdulrahman A. , Al Darwish Rayan K. , Alqahtani Abdullah E. , Chaves Diego Morais , da Silva Márcio J.
Published: November 2022

Glycerol is an unavoidable by-product of the biodiesel production process. The conversion of glycerol into valuable biofuel additives is essential in the fuel industry. The goal of this work is to develop a reactive distillation-based process for the production of biofuel additives by bio-glycerol etherification. In this study, a kinetic model using a lumping approach for glycerol etherification with tert-butyl alcohol (TBA) over Sn (II) phosphomolybdate (Sn1.5PMo12O40) catalyst was developed. Aspen Plus was used to validate the kinetic model by simulating the glycerol etherification with TBA in a batch reactor. The model predictions were in good agreement with the experimental data. A reactive distillation-based process to produce glycerol ethers was developed, and heat integration was conducted to reduce energy consumption. The energy requirements of the integrated process and the CO2 emissions were decreased by 17% and 14%, respectively. An economic evaluation was performed to study the profitability of the process for an annual capacity of 33,000 metric tons of glycerol ethers. It was found that the process is economically attractive, with a return on investment of 29.40% and a payback period of 2.2 years. The reactive distillation-based process is green and promising for producing biofuel additives that are sustainable and environmentally friendly.
Deliberate Surface Treatment of Zirconium Dioxide with Abrasive Brushing Tools
Authors: Hoyer Anton , Uhlmann Eckart
Published: November 2022

Brushing with bonded abrasives is a flexible finishing process used to reduce the roughness of technical surfaces. Although industrially widespread, especially for the finishing of metallic surfaces, insufficient knowledge of the motion, the material removal, and the wear behavior of the abrasive filaments complicates predictions of the work result. In particular, the reliable finishing of ceramics with bonded diamond grains proves difficult due to increased material removal rates, quickly leading to undesirable changes in the workpiece geometry. Based on technological investigations with abrasive brushing tools, this article provides insights into the surface finishing of zirconium dioxide with a focus on finding compromises between reduction in the surface roughness and alteration of the workpiece shape.
Novel Hydrazone Chromophore Sensor for Metallochromic Determination of Cadmium Ions
Authors: El-Nagar Islam , Youssef Ahmed M. , Abd El-Hakim A. A. , Kenawy El-Refaie , Mandour Hamada S. A. , Khattab Tawfik A.
Published: November 2022

For the detection of Cd(II) in aquatic media, a novel dicyanomethylene dihydrofuran hydrazone(DCDHFH)-based colorimetric chemosensor was developed. DCDHFH was prepared by an azo-coupling process involving the diazonium chloride of 2, 4-dichloroaniline and a dicyanomethylene dihydrofuran heterocyclic moiety bearing an active methyl group. The DCDHFH chromophore showed strong solvatochromism depending on solvent polarity due to electronic delocalization. The pH sensory effects of the DCDHFH chromophore were also explored. DCDHFH could be used to identify Cd(II) in the presence of other competitive metals, as indicated by variations in color and absorbance spectra. In the presence of cadmium ions, the synthesized DCDHFH probe with hydrazone recognition moiety exhibited a significant sensitivity and selectivity to cadmium ions at the ppm concentration level (10–250 ppm). A DCDHFH-immobilized paper test strip was also prepared and effectively used for the detection of cadmium in aqueous media at various concentrations. According to CIE Lab’s criteria, colorimetric strength (K/S), and the UV–Vis absorbance spectra, the cadmium detection abilities of the DCDHFH-immobilized paper strips were evaluated. The optimal pH range for the determination of Cd(II) was monitored in the area of 5.5–6.3, with a fast chromogenic change from yellow to red relying on the Cd(II) concentration. The deposition of dicyanomethylene dihydrofuran hydrazone onto the paper strip’s surface was studied by scanning electron microscopy (SEM).
Bimaxillary Distalization with Skeletal Anchorage for Management of Severe Skeletal Class III Open Bite Malocclusion
Authors: Alshehri Abdulrahman
Published: November 2022

Adult patients with severe vertical growth pattern, skeletal class III malocclusion and open bite anteriorly are difficult orthodontic cases to treat. An orthognathic surgical treatment plan may benefit adult patients with such types of complex malocclusions, however in certain cases, the patient’s medical history may contraindicate the surgical treatment plan. A male patient aged 17 years presented with a prognathic mandible, Angle’s class III malocclusion on a class III skeletal base with proclination in upper incisors, retroclination of lower incisors, and reduced facial convexity. Patient gave history of asthma and complex cardiac diseases including arrhythmia, irregular heartbeat, and pacemaker. This case report describes a non-surgical approach of distalization of mandibular and maxillary arches performed with the help of miniplates to achieve an improvement in the facial balance without surgery.
Kangaroo Mother Care in Vietnam: A National Survey of a Middle-Income Country
Authors: Cavallin Francesco , Trevisanuto Daniele , Tiep Tran Viet , Diep Nguyen Thi Ngoc , Hao Vuong Thi , Ngan Doan Thi , Thuy Nguyen Thi , Hoi Nguyen Thi Xuan , Moccia Luciano
Published: November 2022

Background: Kangaroo mother care (KMC) is a low-cost intervention that is indicated to be a highly effective practice for which adoption and implementation are lacking. We investigated the current provision of KMC in Vietnam and explored differences among levels of healthcare facility. Methods: A survey form was sent to 187 hospitals in Vietnam, representing the three levels (central, provincial and district) of public hospital-based maternity services. Results: Overall response rate was 74% (138/187 hospitals). Routine KMC implementation was estimated in 49% of the hospitals. Where KMC was implemented or was being introduced, half of the hospitals had a written protocol and a KMC-dedicated room, and held educational courses on KMC. KMC was mainly performed by the mother. Skin-to-skin contact was mostly performed for <12 h/day (55%), exclusive breastfeeding at discharge was very frequent (89%) and early discharge was considered in half of the hospitals (54%), while follow-up was not performed in 29% of the hospitals. Participants considered follow-up after discharge as the main barrier to KMC implementation, and indicated education (of both parents and health caregivers) and environment upgrades (KMC-dedicated room and equipment) as the most important facilitators. Conclusions: Our survey estimated a limited implementation of KMC in Vietnamese maternity hospitals, with marked variations across the different levels of maternity services. Areas of improvements include increasing the duration of skin-to-skin contact, arranging dedicated spaces for KMC, involving the relatives (especially at district level), extending the availability of a written protocol, improving the eligibility process, and implementing early discharge and follow-up monitoring.
Impact of Philosophy for Children and Its Challenges: A Systematic Review
Authors: Ab Wahab Mohd Kaziman , Zulkifli Hafizhah , Abdul Razak Khadijah
Published: November 2022

Philosophy for children (P4C) has been implemented worldwide. P4C has been researched empirically in order to evaluate its effectiveness and address the current lack of a systemic literature review of research on P4C. Therefore, this SLR study aims to identify how P4C positively affects aspects other than students’ thinking and the challenges that teachers and students face in implementing the program. The methodology and writing method used was PRISMA (preferred reporting items for systematic review and meta-analysis). Articles and materials related to the topic were located primarily using two databases, Web of Science and Scopus. Using thematic analysis, this SLR derived five main themes, namely (1) higher-order thinking skills (HOTS), (2) safe environments, (3) civilized students, (4) democracy in discussion, and (5) the culture of thinking in the classroom. There are also challenges faced by teachers and students.
The Impact of Training Time on Understanding the Treatment of Adolescent Idiopathic Scoliosis According to SOSORT International Guidelines: An Online Survey Study
Authors: Linhares Natália Albim , Barbosa Juliene Corrêa , Penha Patrícia Jundi , Andrade Rodrigo Mantelatto , Ribeiro Ana Paula , Magalhães Mauricio Oliveira
Published: November 2022

The lack of knowledge by health professionals in the management of scoliosis can lead to an erroneous diagnosis. The aim of the current study was to understand the influence of length of professional experience on the knowledge of Brazilian physical therapists regarding international guidelines for the treatment of adolescent idiopathic scoliosis. The study was conducted using an online questionnaire consisting of 23 questions, divided into 8 questions on sociodemographic data and 15 questions based on information provided by the guidelines in the SOSORT 2016. In total, 506 physiotherapists were interviewed, and they comprised the study sample. The results showed that professionals who have been working for more than 6 years in the field have greater knowledge of AIS, seek to become more professional, and with that, have an understanding of AIS that is more aligned with the SOSORT. Length of experience made a difference when considering knowledge of the treatment of idiopathic scoliosis. The present study showed that a time since graduation of 6 years or more was satisfactory.
Analysis of Quadratic Correlation between Dryness Indices and Wine Grape Yield to Estimate Future Climate Impacts in Hungary
Authors: Lakatos László , Mika János
Published: November 2022

In many regions, water availability influences grape yield fluctuations more than thermal conditions. This study analyzes dryness indices calculated from observed and simulated RCM data to establish statistical relationships with observed yield data, considered an indicator of food safety. Five dryness indices were analyzed: the number of days without rain, the maximum number of consecutive dry days, climatic water balance, dryness index, and vineyard water indicator. These indices were analyzed for three periods: 1986–2005 (recent past), 2016–2035 (near future) and 2081–2100 (distant future). After this analysis, quadratic regression connections were established between the indices and available wine grape yields in the 22 wine regions of Hungary for 2005–2021 without information on grape varieties and for 2017–2021 with data on grape varieties. Linear agro-technological trends were extracted from these wine grape yield series, whereas the residuals exhibited significant quadratic regression in slightly over 50% of the indices and regions, according to the F-test for the 17 year series. For the short series, these proportions are 29 and 27% for the selected seven white and seven red wine grapes. According to the most significant quadratic regressions, combined with the projected dryness indices, we can expect less average yields with higher interannual variability in the future.
A Colorimetric Detection of Noradrenaline in Wastewater Using Citrate-Capped Colloidal Gold Nanoparticles Probe
Authors: Ally Numeerah , Hendricks Nokwanda , Gumbi Bhekumuzi
Published: November 2022

This study reports a simple, fast, and low-cost detection of noradrenaline (NA) in wastewater using citrate-capped colloidal gold nanoparticles (AuNPs). The addition of NA to citrate-capped colloidal AuNPs generates a colour modulation that the bare eye can detect due to the aggregation of the colloidal AuNPs. The relationship between the NA concentration and colloidal AuNPs aggregation was further monitored by ultraviolet–visible light (UV–vis) spectroscopy in an aqueous solution. The method displayed a linear range of 0–500 μM with R2 = 0.99 and an LOD and LOQ of 42.2 and 140.5 μM. Application in an environmental sample collected from the Darville Wastewater Treatment Plant shows that this work provided a cost-effective and spectrophotometric method that could be used for monitoring contamination in wastewater.
Visualization of an Ultra-Short THz Beams with a Radiation Propagation Analysis of the Novel Israeli Free Electron Laser
Authors: Gerasimov Michael , Perutski Boris , Dyunin Egor , Gerasimov Jacob , Friedman Aharon
Published: November 2022

Tera Hertz radiation is currently the most researched and useful area in almost all fields of science and industry. The additional challenge is expressed in the form of radiation, pulses of femto-seconds in length are supposed to pass through a transmission line (TL) most efficiently, at a wide range of frequencies. These are complex beams, which make up the electromagnetic (EM) field, represented in the frequency domain in terms of cavity eigenmodes. A simulation allows to describe of the phase-amplitude and spectral characteristics of multimode radiation free-electron laser (FEL) operating in various operational parameters. The analysis is performed through the transmission of optical rays accurately, with each ray being characterized by amplitude, position, and angle in 3D space. A light field representation of a complex EM field is obtained via Wigner Distribution Function, which allows to describe of the dynamics of field evolution in future propagation by a ray tracing (RT) method. The final diagnostics will determine the design of the TL to be assembled in an innovative accelerator under construction at the Schlesinger Family Center for Compact Accelerators, Radiation Sources, and Applications.
Composite Spatial Manipulation Framework for Redirected Walking
Authors: Alsaeedi Nassr , Zündorf Albert
Published: November 2022

In this study, we present a composite spatial manipulation framework for the redirected walking technique. The proposed framework focuses on utilizing two different approaches simultaneously to manipulate the user’s position and orientation in the physical space, aiming to substantially improve their redirection in a confined physical space and reduce the special requirements for the RDW technique. Each approach utilizes different perceptual processes. The first is a discrete spatial manipulation approach that introduces translation and/or rotation gains to the user’s virtual perspective in the immersive virtual environment (IVE) during temporal events such as eyeblinks. The second approach is the continuous spatial manipulation approach, which continuously introduces (with each frame) translation and/or rotation gains below the user’s perception threshold to their virtual perspective in the IVE. Two simulation experiments were conducted to investigate the feasibility of adopting the composite spatial manipulation framework for RDW without considering the user’s walking behavior or the impact of the proposed approach on user performance in the immersive virtual environment. In the second simulation experiment we aimed to investigate the performance of the proposed approach while considering the user’s walking behavior and performance in the IVE. Finally, a user experiment was conducted to validate the proposed framework and its impact on the user’s performance in the IVE. The findings revealed a significant improvement in the redirection performance of the proposed controller when it was compared to the classical RDW controller. Additionally, there was significant improvement in the user’s performance when the composite RDW controller was utilized.
Physical-Mechanical Properties of Chartwell® Coupling Agent-Treated Calcium Carbonate and Silica-Reinforced Hybrid Natural Rubber Composites
Authors: Ribeiro Gabriel Deltrejo , Hiranobe Carlos Toshiyuki , da Silva José Francisco Resende , Torres Giovanni Barrera , Paim Leonardo Lataro , Job Aldo Eloizo , Cabrera Flávio Camargo , dos Santos Renivaldo José
Published: November 2022

In this work investigated the possibility of applying a superficial treatment to ultra-fine calcium carbonate aiming to improve its interaction with the polymer chains of natural rubber so it does not act just as a filler. Commercial processes commonly use 40 phr of Silica as reinforcement filler. Here, we have evaluated the partial replacement of Silica by two types of calcium carbonate into hybrid natural rubber composites, untreated ultra-fine calcium carbonate and with ultra-fine calcium carbonate treated with 2% Chartwell C-515.71HR®. We added calcium carbonate fillers to the composite mixtures (as replacements for commercial silica treated with silane) and studied their influence on the vulcanization process. According to our findings, between 25% and 75% of the silica can be replaced with treated calcium carbonate, and up to 30 parts of CaCO3 can be combined with 100 parts of NR without compromising the properties of the polymer matrix (NR), which generates economic advantages for this industry. Treated calcium carbonate was able to link the inorganic and organic parts of the composite due to its bifunctionality; hence, it can be used as a filler to partially replace silica in hybrid NR composites.
Two-Dimensional Distributed Transmission-Line Models for Broadband Full-Tensor Anisotropic Acoustic Metamaterials Based on Transformation Acoustics
Authors: Nagayama Tsutomu , Toshima Akihiro , Fukushima Seiji , Watanabe Toshio
Published: November 2022

We propose the design method for broadband acoustic metamaterials based on the concept of transformation acoustics. Two-dimensional distributed transmission-line (TL) models for full-tensor anisotropic electromagnetic metamaterials are applied to full-tensor anisotropic acoustic metamaterials and the design formulas are shown to uniquely determine the structural parameters of the unit cells. Two-dimensional acoustic waveguide unit cell structures for realizing the TL models are proposed and an acoustic carpet cloak and an acoustic illusion medium are designed according to the introduced theory. The complex sound pressure distributions in the acoustic waveguides of the unit cells are calculated by full-wave simulations to verify the validity of the proposed method, and the broadband operations of the designed carpet cloak and illusion medium are confirmed from the results.
Revamping Optimization of a Pressure Piping System Using Artificial Neural Networks
Authors: Caponetto Riccardo , Fargione Giovanna , Giudice Fabio , Schiavo Marco
Published: November 2022

The paper proposes a new methodology for revamping design and optimization of a process piping system. Starting from ASME B31.3 Process Piping prescriptions for stress analysis, a nonlinear model is built to express the relationship between stress distribution generated by expansion and sustained loads (pressure, weight) and the geometry and routing of the pipeline, focusing on geometric parameters of expansion loops. The number of design variables affecting stress distribution over the pipe, together with the constraints to be respected, would make it hard to formulate an optimization procedure based on deterministic methods. This problem is overcome by applying a Feed Forward Neural Network, backpropagation trained, which makes it possible to interpolate a non-linear and multidimensional relation over a domain enclosed within the boundaries of a training set. Prediction of code stresses is obtained through the fitting of an artificial neural network for each examined loadcases. Network parameters are tuned offline, starting from a set of data obtained by finite element numerical simulation. As a result, an optimal geometry for expansion loops is found, allowing to revamp pipe routing by halving loops number and keeping code stress within the allowable limits.
Load-Settlement Curve and Subgrade Reaction of Strip Footing on Bi-Layered Soil Using Constitutive FEM-AI Coupled Techniques
Authors: Ebid Ahmed M. , Onyelowe Kennedy C. , Salah Mohamed
Published: November 2022

This study presents a hybrid Artificial Intelligence-Finite Element Method (AI-FEM) predictive model to estimate the modulus of a subgrade reaction of a strip footing rested on a bi-layered profile. A parametric study was carried out using 2D Plaxis FEM models for strip footings with width (B) and rested on a bi-layered profile with top layer thickness (h) and bottom layer thickness (H). The soil was modeled using the well-known Mohr-Coulomb’s constitutive law. The extracted load-settlement curve from each FEM model is approximated to hyperbolic function and its factors (a, b) were determined. The subgrade reaction value (Ks) is the (stress/settlement), hence (1/Ks = a·Δ + b). Both inputs and outputs of the parametric study were collected in a single database containing the geometrical factors (B, h & H), soil properties of the top and bottom layers (c, φ & γ) and the extracted hyperbolic factors (a, b). Finally, three AI techniques—Genetic Programming (GP), Evolutionary Polynomial Regression (EPR) and Artificial Neural Networks (ANN)—were implemented to develop three predictive models to estimate the values of (a, b) using the collected database. The three developed models showed different accuracy values of (50%, 65% and 80%) for (GP, EPR and ANN), respectively. The innovation of the developed model is its ability to capture the degradation of a subgrade reaction by increasing the stress (or the settlement) according to the hyperbolic formula.
Common, Less Common, and Unexpected Complications after Bariatric Surgery: A Pictorial Essay
Authors: Iacobellis Francesca , Dell’Aversano Orabona Giuseppina , Brillantino Antonio , Di Serafino Marco , Rengo Alessandro , Crivelli Paola , Romano Luigia , Scaglione Mariano
Published: November 2022

Bariatric surgery has demonstrated a higher rate of success than other nonsurgical treatments in selected patients with obesity; however, like all medical procedures, postoperative complications may occur, ranging between 2 and 10% and, although rare, they can be life threatening. Complications may be unspecific (any surgery-related complications) or specific (linked to the specific surgical procedure) and can be distinguished as common, less common, and unexpected. According to the onset, they may be acute, when occurring in the first 30 days after surgery, or chronic, with a presentation after 30 days from the procedure. The aim of this pictorial essay is to review the radiological aspects of surgical techniques usually performed and the possible complications, in order to make radiologists more confident with the postsurgical anatomy and with the normal and abnormal imaging findings.
Dynamic Changes in Liver Stiffness in Patients with Chronic Hepatitis B Undergoing Antiviral Therapy
Authors: Lazar Alin , Sporea Ioan , Popa Alexandru , Lupusoru Raluca , Gherhardt Diana , Mare Ruxandra , Apostu Alexandru , Hnatiuc Madalina , Șirli Roxana
Published: November 2022

This is a retrospective single-center study that included 87 subjects. All subjects had chronic hepatitis B or HBV cirrhosis and underwent nucleos(t)ide analogs (NUC) treatment for more than one year. The study aimed to evaluate the dynamic changes in liver stiffness (LS) measured by transient elastography (TE) during a median interval of 64 months. Patients were assessed prior to starting therapy and followed up annually. Liver stiffness measurements (LSM) were performed annually, and ten valid LSMs were obtained in each session. Reliable LSMs were defined as the median value of 10 measurements with Interquartile range/median (IQR/M) ≤ 30%. A significant decrease in liver stiffness values (p < 0.001) was observed during follow-up. In patients with liver cirrhosis, the LSMs decreased significantly after only one year, 24.6 ± 4.3 kPa vs. 13.5 ± 4.2 kPa (p = 0.007), whereas the decrease in non-cirrhotic patients was not significant, 7.31 ± 3.62 vs. 6.80 ± 2.41 (p = 0.27). Liver stiffness decrease was more significant in patients with initially higher transaminases. Undetectable viral load was achieved in 73.5% of patients in year one, 82.7% in year two, and 90.8% in year three of treatment. In conclusion, our study reveals a decrease in liver stiffness by TE in patients with chronic hepatitis B when undergoing anti-HBV therapy in the first two years. It can be used as a method for follow-up in patients undergoing NUC therapy.
Dedicated CCTA Followed by High-Pitch Scanning versus TRO-CT for Contrast Media and Radiation Dose Reduction: A Retrospective Study
Authors: Wang Kun , Wang Xiaodong , Zheng Shaoqiang , Li Cheng , Jin Liang , Li Ming
Published: November 2022

We aimed to compare dedicated coronary computed tomography angiography (CCTA) followed by high-pitch scanning and triple-rule-out computed tomography angiography (TRO-CTA) in terms of radiation dose, contrast media (CM) use, and image quality. Patients with acute chest pain were retrospectively enrolled and assigned to group A (n = 55; scanned with dedicated CCTA followed by high-pitch scanning) or group B (n = 45; with TRO-CTA). Patient characteristics, radiation dose, CM use, and quantitative parameters (CT value, image noise, signal-to-noise ratio, contrast-to-noise ratio, and image quality score) of pulmonary arteries (PAs), thoracic aortae (TAs), and coronary arteries (CAs) were compared. The total effective dose was significantly lower in group A (6.25 ± 2.94 mSv) than B (8.93 ± 4.08 mSv; p < 0.001). CM volume was significantly lower in group A (75.7 ± 8.9 mL) than B (95.0 ± 0 mL; p < 0.001). PA and TA image quality were significantly better in group B, whereas that of CA was significantly better in group A. Qualitative image scores of PA and TA scans rated by radiologists were similar, whereas that of CA scans was significantly higher in group A than B (p < 0.001). Dedicated CCTA followed by high-pitch scanning demonstrated lower radiation doses and CM volume without debasing qualities of PA, TA, and CA scans than did TRO-CTA.
Effects of Escherichia coli and Proteus mirabilis on the Growth and Aggregation of Calcium Oxalate Crystal under Microaerobic Conditions
Authors: Saelee Krittaya , Lulitanond Aroonlug , Sae-ung Nattaya , Prasongwatana Vitoon , Boonsiri Patcharee , Tavichakorntrakool Ratree
Published: November 2022

Escherichia coli and Proteus mirabilis are common single- and polymicrobial urinary tract infections which can survive under various oxygen levels, including inside of stone matrices. Therefore, we aimed to investigate and compare the calcium oxalate monohydrate (COM) lithogenic activities including COM crystal growth and aggregation under microaerobic conditions of E. coli and P. mirabilis isolated from the same stone matrix. The crystal growth was analyzed as the delta crystal area while the crystal aggregation was analyzed as the number of crystal aggregates. The results showed that compared to blank control, E. coli, P. mirabilis and the co-culture of E. coli and P. mirabilis were able to significantly promote COM crystal growth under microaerobic conditions. Interestingly, the delta crystal area in the co-culture under microaerobic conditions was larger than that of E. coli alone and P. mirabilis alone. In addition, only P. mirabilis alone and the co-culture were able to significantly increase COM aggregates. This study demonstrated that single- and co-culture of E. coli and P. mirabilis could promote COM crystal growth and aggregation under microaerobic conditions. The co-culture of E. coli and P. mirabilis may provide the combination effect on COM crystal interactions. The bacterial surfaces and the important effects on bacteria–crystal interactions should be further evaluated.
BUViTNet: Breast Ultrasound Detection via Vision Transformers
Authors: Ayana Gelan , Choe Se-woon
Published: November 2022

Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew’s correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance.
Generalized Landauer Bound for Information Processing: Proof and Applications
Authors: Anderson Neal G.
Published: November 2022

A generalized form of Landauer’s bound on the dissipative cost of classical information processing in quantum-mechanical systems is proved using a new approach. This approach sidesteps some prominent objections to standard proofs of Landauer’s bound—broadly interpreted here as a nonzero lower bound on the amount of energy that is irreversibly transferred from a physical system to its environment for each bit of information that is lost from the system—while establishing a far more general result. Specializations of our generalized Landauer bound for ideal and non-ideal information processing operations, including but not limited to the simplified forms for erasure and logical operations most familiar from the literature, are presented and discussed. These bounds, taken together, enable reconsideration of the links between logical reversibility, physical reversibility, and conditioning of operations in contexts that include but are far more general than the thermodynamic model systems that are most widely invoked in discussions of Landauer’s Principle. Because of the strategy used to prove the generalized bounds and these specializations, this work may help to illuminate and resolve some longstanding controversies related to dissipation in computation.
Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network
Authors: Xu Ming , Gao Jinfeng , Zhang Zhong , Wang Heshan
Published: November 2022

Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial practice, the running state of bearings is monitored by collecting data from multiple sensors, for instance, the drive end, the fan end, and the base. Given the complexity of the operating conditions and the limited number of bearing-fault samples, obtaining complementary fault features using the traditional fault-diagnosis method, which uses statistical characteristic in time or frequency, is difficult and relies heavily on prior knowledge. In addition, intelligent bearing-fault diagnosis based on a convolutional neural network (CNN) has several deficiencies, such as single-scale fixed convolutional kernels, excessive dependence on experts’ experience, and a limited capacity for learning a small training dataset. Considering these drawbacks, a novel intelligent bearing-fault-diagnosis method based on signal-to-RGB image mapping (STRIM) and multichannel multiscale CNN (MCMS-CNN) is proposed. First, the signals from three different sensors are converted into RGB images by the STRIM method to achieve feature fusion. To extract RGB image features effectively, the proposed MCMS-CNN is established, which can automatically learn complementary and abundant features at different scales. By increasing the width and decreasing the depth of the network, the overfitting caused by the complex network for a small dataset is eliminated, and the fault classification capability is guaranteed simultaneously. The performance of the method is verified through the Case Western Reserve University’s (CWRU) bearing dataset. Compared with different DL approaches, the proposed approach can effectively realize fault diagnosis and substantially outperform other methods.
Extreme Electron Acceleration with Fixed Radiation Energy
Authors: Good Michael R. R. , Singha Chiranjeeb , Zarikas Vasilios
Published: November 2022

We examine the extreme situation of radiation from an electron that is asymptotically accelerated to the speed of light, resulting in finite emission energy. The analytic solution explicitly demonstrates the difference between radiation power loss and kinetic power loss (null).
Deep Learning and Entropy-Based Texture Features for Color Image Classification
Authors: Lhermitte Emma , Hilal Mirvana , Furlong Ryan , O’Brien Vincent , Humeau-Heurtier Anne
Published: November 2022

In the domain of computer vision, entropy—defined as a measure of irregularity—has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images. Moreover, the literature is missing comparative analyses of entropy-based and modern deep learning-based classification methods for RGB color images. In order to address this matter, we first propose a new entropy-based measure for RGB images based on a multivariate approach. This multivariate approach is a bi-dimensional extension of the methods that have been successfully applied to multivariate signals (unidimensional data). Then, we compare the classification results of this new approach with those obtained from several deep learning methods. The entropy-based method for RGB image classification that we propose leads to promising results. In future studies, the measure could be extended to study other color spaces as well.
Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Digital Twin
Authors: Zhang Rui , Zeng Zhiqiang , Li Yanfeng , Liu Jiahao , Wang Zhijian
Published: November 2022

Bearing is a key part of rotating machinery. Accurate prediction of bearing life can avoid serious failures. To address the current problem of low accuracy and poor predictability of bearing life prediction, a bearing life prediction method based on digital twins is proposed. Firstly, the vibration signals of rolling bearings are collected, and the time-domain and frequency-domain features of the actual data set are extracted to construct the feature matrix. Then unsupervised classification and feature selection are carried out by improving the self-organizing feature mapping method. Using sensitive features to construct a twin dataset framework and using the integrated learning CatBoost method to supplement the missing data sets, a complete digital twin dataset is formed. Secondly, important information is extracted through macro and micro attention mechanisms to achieve weight amplification. The life prediction of rolling bearing is realized by using fusion features. Finally, the proposed method is verified by experiments. The experimental results show that this method can predict the bearing life with a limited amount of measured data, which is superior to other prediction methods and can provide a new idea for the health prediction and management of mechanical components.
Competition between Variable–Supply and Fixed–Supply Currencies
Authors: Wang Guizhou , Hausken Kjell
Published: November 2022

For one variable–supply currency in isolation, one player’s Cobb–Douglas utility depends on the current supply divided by the initial supply, multiplied by the inverse of the accumulative inflation/deflation. With equal weight assigned to both factors, money printing outweighs inflation, and money withdrawal outweighs deflation. The study design is to analyze how competition between one variable–supply and one fixed–supply currency impacts the player’s choice of currency. Applying the 1959–2021 US M2 money supply data and the 1635–2021 US inflation data, the player’s utility increases over time when assigning high weight to money printing/withdrawal and increases less or decreases overall when assigning high weight to inflation/deflation. With different player support for the two currencies, depending on each currency’s backing, convenience, confidentiality, transaction efficiency, financial stability, and security, replicator dynamics is used to determine the player’s volume fraction of transactions in each currency. Low, high, increasing, and decreasing support of a currency are analyzed. Each fraction may increase, decrease, be inverse U–shaped, U–shaped, and approach low or high levels over time. For example, high weight assigned to money printing may cause the player to eventually prefer the variable–supply currency unless the player supports the fixed–supply currency highly and increasingly.
Infrared Weak and Small Target Detection Based on Top-Hat Filtering and Multi-Feature Fuzzy Decision-Making
Authors: Yang Degui , Bai Zhengyang , Zhang Junchao
Published: November 2022

Infrared weak and small target detection in a complex background has always been a research hotspot in the fields of area defense and long-range precision strikes. Among them, the single-frame infrared weak and small target detection technology is even more difficult to study due to factors such as lack of target motion information, complex background, and low signal-to-noise ratio. Aiming at the problem of a high false alarm rate in infrared weak and small target detection caused by the complex background edges and noise interference in infrared images, this paper proposes an infrared weak and small target detection algorithm based on top-hat filtering and multi-feature fuzzy decision-making. The algorithm first uses the multi-structural element top-hat operator to filter the original image and then obtains the suspected target area through adaptive threshold segmentation; secondly, it uses image feature algorithms, such as central pixel contrast, regional gradient, and directional gradient, to extract the feature information of the suspected target at multiple scales, and the fuzzy decision method is used for multi-feature fusion to achieve the final target detection. Finally, the performance of the proposed algorithm and several existing comparison algorithms are compared using the measured infrared sequence image data of five different scenarios. The results show that the proposed algorithm has obvious advantages in various performance indicators compared with the existing algorithms for infrared image sequences in different interference scenarios, especially for complex background types, and has a lower performance under the condition of ensuring the same detection rate and false alarm rate and in meeting the real-time requirements of the algorithm.
EFA-Trans: An Efficient and Flexible Acceleration Architecture for Transformers
Authors: Yang Xin , Su Tao
Published: November 2022

The topic of transformers is rapidly emerging as one of the most important key primitives in neural networks. Unfortunately, most hardware designs for transformers are deficient, either hardly considering the configurability of the design or failing to realize the complete inference process of transformers. Specifically, few studies have paid attention to the compatibility of different computing paradigms. Thus, this paper presents EFA-Trans, a highly efficient and flexible hardware accelerator architecture for transformers. To reach high performance, we propose a configurable matrix computing array and leverage on-chip memories optimizations. In addition, with the design of nonlinear modules and fine-grained scheduling, our architecture can perform complete transformer inference. EFA-Trans is also compatible with dense and sparse patterns, which further expands its application scenarios. Moreover, a performance analytic model is abstracted to guide the determination of architecture parameter sets. Finally, our designs are developed by RTL and evaluated on Xilinx ZCU102. Experimental results demonstrate that EFA-Trans provides 23.74× and 7.58× improvement in energy efficiency compared with CPU and GPU, respectively. It also shows DSP efficiency is between 3.59× and 21.07× higher than others, outperforming existing advanced works.
A Method for High-Value Driving Demonstration Data Generation Based on One-Dimensional Deep Convolutional Generative Adversarial Networks
Authors: Wu Yukun , Wu Xuncheng , Qiu Siyuan , Xiang Wenbin
Published: November 2022

As a promising sequential decision-making algorithm, deep reinforcement learning (RL) has been applied in many fields. However, the related methods often demand a large amount of time before they can achieve acceptable performance. While learning from demonstration has greatly improved reinforcement learning efficiency, it poses some challenges. In the past, it has required collecting demonstration data from controllers (either human or controller). However, demonstration data are not always available in some sparse reward tasks. Most importantly, there exist unknown differences between agents and human experts in observing the environment. This means that not all of the human expert’s demonstration data conform to a Markov decision process (MDP). In this paper, a method of reinforcement learning from generated data (RLfGD) is presented, and consists of a generative model and a learning model. The generative model introduces a method to generate the demonstration data with a one-dimensional deep convolutional generative adversarial network. The learning model applies the demonstration data to the reinforcement learning process to greatly improve the effectiveness of training. Two complex traffic scenarios were tested to evaluate the proposed algorithm. The experimental results demonstrate that RLfGD is capable of obtaining higher scores more quickly than DDQN in both of two complex traffic scenarios. The performance of reinforcement learning algorithms can be greatly improved with this approach to sparse reward problems.
Gated Multi-Attention Feedback Network for Medical Image Super-Resolution
Authors: Shang Jianrun , Zhang Xue , Zhang Guisheng , Song Wenhao , Chen Jinyong , Li Qilei , Gao Mingliang
Published: November 2022

Medical imaging technology plays a crucial role in the diagnosis and treatment of diseases. However, the captured medical images are often in a low resolution (LR) due to the limited imaging condition. Super-resolution (SR) technology is a feasible solution to enhance the resolution of a medical image without increasing the hardware cost. However, the existing SR methods often ignore high-frequency details, which results in blurred edges and an unsatisfying visual perception. In this paper, a gated multi-attention feedback network (GAMA) is proposed for medical image SR. Specifically, a gated multi-feedback network is employed as the backbone to extract hierarchical features. Meanwhile, a layer attention feature extraction (LAFE) module is introduced to refine the feature map. In addition, a channel-space attention reconstruction (CSAR) module is built to enhance the representational ability of the semantic feature map. Furthermore, a gradient variance loss is tailored as the regularization in guiding the model learning to regularize the model in generating a faithful high-resolution image with rich textures and sharp edges. The experiments verify the effectiveness of the proposed GAMA compared with the state-of-the-art approaches.
Research on Improved DenseNets Pig Cough Sound Recognition Model Based on SENets
Authors: Song Hang , Zhao Bin , Hu Jun , Sun Haonan , Zhou Zheng
Published: November 2022

In order to real-time monitor the health status of pigs in the process of breeding and to achieve the purpose of early warning of swine respiratory diseases, the SE-DenseNet-121 recognition model was established to recognize pig cough sounds. The 13-dimensional MFCC, ΔMFCC and Δ2MFCC were transverse spliced to obtain six groups of parameters that could reflect the static, dynamic and mixed characteristics of pig sound signals respectively, and the DenseNet-121 recognition model was used to compare the performance of the six sets of parameters to obtain the optimal set of parameters. The DenseNet-121 recognition model was improved by using the SENets attention module to enhance the recognition model’s ability to extract effective features from the pig sound signals. The results showed that the optimal set of parameters was the 26-dimensional MFCC + ΔMFCC, and the rate of recognition accuracy, recall, precision and F1 score of the SE-DenseNet-121 recognition model for pig cough sounds were 93.8%, 98.6%, 97% and 97.8%, respectively. The above results can be used to develop a pig cough sound recognition system for early warning of pig respiratory diseases.
Geothermal Energy and Structural Geology
Authors: Mazzoli Stefano
Published: November 2022

Structural geology has a long tradition of applications and developments in the field of energy resources [...]