bluuebunny/crossref_metadata_2025 huggingface dataset:
UV-Accelerated Synthesis of Gold Nanoparticle–Pluronic Nanocomposites for X-ray Computed Tomography Contrast Enhancement
Authors: Gomes Deizilene S. B. , Paterno Leonardo G. , Santos Aline B. S. , Barbosa Debora P. P. , Holtz Beatriz M. , Souza Maysa R. , Moraes-Souza Rafaianne Q. , Garay Aisel V. , de Andrade Laise R. , Sartoratto Patricia P. C. , Mertz Damien , Volpato Gustavo T. , Freitas Sonia M. , Soler Maria A. G.
Published: May 2023

Eco-friendly chemical methods using FDA-approved Pluronic F127 (PLU) block copolymer have garnered much attention for simultaneously forming and stabilizing Au nanoparticles (AuNPs). Given the remarkable properties of AuNPs for usage in various fields, especially in biomedicine, we performed a systematic study to synthesize AuNP-PLU nanocomposites under optimized conditions using UV irradiation for accelerating the reaction. The use of UV irradiation at 254 nm resulted in several advantages over the control method conducted under ambient light (control). The AuNP-PLU-UV nanocomposite was produced six times faster, lasting 10 min, and exhibited lower size dispersion than the control. A set of experimental techniques was applied to determine the structure and morphology of the produced nanocomposites as affected by the UV irradiation. The MTT assay was conducted to estimate IC50 values of AuNP-PLU-UV in NIH 3T3 mouse embryonic fibroblasts, and the results suggest that the sample is more compatible with cells than control samples. Afterward, in vivo maternal and fetal toxicity assays were performed in rats to evaluate the effect of AuNP-PLU-UV formulation during pregnancy. Under the tested conditions, the treatment was found to be safe for the mother and fetus. As a proof of concept or application, the synthesized Au:PLU were tested as contrast agents with an X-ray computed tomography scan (X-ray CT).
Coffee Staining and Simulated Brushing Induced Color Changes and Surface Roughness of 3D-Printed Orthodontic Retainer Material
Authors: Bangalore Durgesh , Alshehri Abdullah M. , Alsadon Omar , Alaqeel Samer M. , Alageel Omar , Alsarani Majed M. , Almansour Haitham , AlShahrani Obaid
Published: May 2023

This in vitro study evaluated the influence of combined coffee staining and simulated brushing-induced color changes and surface roughness on 3D-printed orthodontic retainers. Specimens measuring 10 × 10 × 0.75 mm3 were obtained either by conventional vacuum forming or 3D printing at four print angulations (0°, 15°, 30°, and 45°) (n = 10). The prepared specimens were immersed in a coffee beverage and then mechanically brushed using a simulating device. The specimen’s color difference (ΔE) and surface roughness (Ra) were quantified using a spectrophotometer and a non-contact profilometer, respectively. The highest and lowest mean ΔE values were recorded for the 3D-printed-45° (4.68 ± 2.07) and conventional (2.18 ± 0.87) groups, respectively. The overall mean comparison of ΔE between the conventional and 3D-printed groups was statistically significant (p < 0.01). After simulated brushing, all groups showed a statistically significant increase in the Ra values (p < 0.01). The highest Ra was in the 3D-printed-45° (1.009 ± 0.13 µm) and conventional (0.743 ± 0.12 µm) groups, respectively. The overall ΔE of 3D-printed orthodontic retainers was not comparable to conventional VFRs. Among the different angulations used to print the retainers, 15° angulations were the most efficient in terms of color changes and surface roughness and were comparable to conventional VFRs.
Denaturation and Digestion Increase the Antioxidant Capacity of Proteins
Authors: Kut Kacper , Bartosz Grzegorz , Sadowska-Bartosz Izabela
Published: May 2023

It has been estimated and demonstrated that the antioxidant capacity of proteins is increased as a result of digestion in the gastrointestinal tract, which can be contributed by denaturation and digestion. This study aimed to evaluate the effect of denaturation and proteolytic digestion on the antioxidant activity of bovine serum albumin (BSA) and chicken egg white proteins in model systems. Denaturation with an anionic detergent (sodium dodecyl sulfate) and digestion with papain and trypsin increased the antioxidant activity/capacity of the proteins, apparently due to the increased exposure of amino acid residues responsible for the antioxidant activity of proteins (tyrosine, tryptophan, cysteine, histidine, arginine, and cystine in the ABTS● decolorization assay; cysteine, tryptophan, tyrosine, and cystine in the FRAP assay). As the increase in the protein antioxidant activity/capacity was limited in extent, it does not invalidate the use of the antioxidant capacity of proteins to be consumed as a rough measure of their antioxidant capacity after modifications in the gastrointestinal tract.
DEM Simulation of a Rotary Drum with Inclined Flights Using the Response Surface Methodology
Authors: Zhu Xu , Xie Lixin , Xu Shichang , Zhang Wen
Published: May 2023

Conventional flighted rotary drums usually have flights parallel to the rotating axis, which cannot facilitate the axial motion of the materials in the drum. Here, a new type of horizontal rotary drum with inclined flights and beads was designed. Inclined flights are used to facilitate the axial movement of beads and material, while beads are used as fillers to increase the gas-liquid contact area and to crush the solid materials. We simulated the drum and studied the axial motion of fillers using the discrete element method (DEM). To improve the mass and heat transfer performance, we optimized the distribution of beads in the active phase. The effects of the rotational speed, joint angle, and inlet flow rate in the drum were investigated systematically. The individual effects were evaluated in terms of the mass of particles in the active phase (MAP) and passive phase (MPP), the percentage of the active phase occupied by the particles (OAR), and the axial speed (AS). The response surface methodology (RSM) was used to investigate the significant effects of the interaction between the parameters. The maximum MAP value can be obtained by the following parameters: a rotational speed of 37 rpm, joint angle of 139°, and inlet flow rate of 7.83 kg/s. The interaction between rotational speed and inlet flow rate is the most significant for MAP. The joint angle and inlet flow rate have a significant interactive effect on AS. Besides, the rotational speed, joint angle and inlet flow rate show an interactive effect on OAR and AS. Based on the optimization results, the effect of the inclined angle on the axial motion of beads was also evaluated. The axial motion of the beads occurs mainly in the active phase. Compared to the drum without inclined flights, the drum with inclined flights has an enhanced axial speed increased by 26%. This study will be helpful for the design and optimization of drums with inclined flights.
Influence of Internal Structural Parameters on the Inner Flow and Outer Spray Characteristics for Feedback-Free Fluidic Oscillator
Authors: Wu Zhijun , Leng Pengfei , Gao Yu , Zhao Wenbo , Hu Zongjie , Lu Xiangxiang , Shi Rongzhu , Xie Wei , Li Liguang
Published: May 2023

Feedback-free fluidic oscillators, relying on the interaction of jets to oscillate, have a wide range of operation frequencies and no moving parts, which is promising for future applications. In this paper, the sweep angle, oscillation frequency, and volume flow rate of the feedback-free fluidic oscillator with a cavity width of 4.6 mm were measured experimentally, and the sweeping mechanism was analyzed based on the internal flow simulation. The influence of the impact angle, cavity exit width, and shoulder radius on the internal flow and external spray characteristics was analyzed by the numerical method. The results show that the sweep angle and oscillation frequency are affected throughout the increase in impact angle from 80° to 120°. However, only when the SR is greater than 0.88 mm does the internal flow and spray state change significantly. The volume flow rate is mainly affected by the cavity exit width and increases linearly from 5.3 mL/s to 9.3 mL/s as the width increases from 0.45 mm to 0.85 mm. It can be surmised that the sweep angle is approximately linear with the distance between the hit point and the impact cavity centerline.
Examining Transit-Time Ultrasonic Flowmeter Inaccuracies during Changing Gas Velocity Profiles
Authors: Alsaqoor Sameh , Piechota Piotr , Alahmer Ali , As’ad Samer , Beithu Nabil , Wędrychowicz Wiesław , Andruszkiewicz Artur , Kotomski Patryk
Published: May 2023

This study investigates the impact of changes in velocity profiles on the measurement inaccuracies of gas flow streams detected by an ultrasonic flowmeter. The cross-sectional velocity profile was influenced by the downhill flow rate, causing variations in the shape factor coefficient. The flowmeter processing equation should consider the factor of shape coefficient variations. Consideration for these variations can result in errors in the measurement of the flow stream. The processing equation assumes a single, constant value for the shape factor coefficient, which can lead to inaccuracies. This article covers the inaccuracies of the transit-time ultrasonic flowmeter caused by a change in the velocity profile of the flowing gas, such as air. A realistic flow system was established with measured flow rates ranging from 43 m3/h to 225 m3/h. The findings of this study can serve as a valuable reference for the design and implementation of more accurate and efficient flow measurement systems that can enhance process efficiency.
Special Issue on “Bioethanol Production Processes”
Authors: Moreno Antonio D. , Manzanares Paloma
Published: May 2023

The transportation sector is facing a profound challenge to utilize a greater proportion of sustainable substitutes in relation to oil-derived products [...]
Biowax Production from the Hydrotreatment of Refined Palm Oil (RPO)
Authors: Olarte Giovanny , Garzón Laura , Sarmiento José , López-Giraldo Luis Javier , Vivas-Báez July C.
Published: May 2023

In this study, conditions were determined to obtain a solid wax with a waxy ester content of more than 25% from the hydrotreating of palm oil. The experiments were conducted in a pilot-scale fixed-bed reactor. The influence of temperature, liquid hourly space velocity (LHSV), and pressure on the conversion of triglycerides were evaluated using a nickel molybdenum catalyst (NiMo/Al2O3). The variables were evaluated between 240 and 260 °C, 1 and 2 h−1 and 41 and 55 bar, respectively. Based on these results, the best conditions were T:240–260 °C; P: 90 bar; LHSV: 1.5 h−1; hydrogen/oil ratio 472 LN/L with a conversion around 60 wt%; and a selectivity towards waxy esters of 40 wt%. These conditions were then validated with a second catalyst (NiMoB/Al2O3), yielding a triglyceride conversion of about 60 wt% and a waxy ester concentration of around 30 wt%.
Ageing Studies of Pt- and Pd-Based Catalysts for the Combustion of Lean Methane Mixtures
Authors: Istratescu Georgeta M. , Hayes Robert E.
Published: May 2023

This paper presents results obtained for the thermal and hydrothermal ageing of seven commercial precious metals-based catalysts for the combustion of methane. Experiments are performed in a large excess of oxygen representing lean conditions. Temperatures used are those typically found in lean burn compression ignition engines. The precious metals used were platinum, palladium and rhodium, present either singly or in combination. The most active catalyst contains a platinum and palladium mixture, with palladium being dominant. This catalyst was also the least affected by both thermal and hydrothermal ageing. The second most active catalyst contained only palladium, but this catalyst also demonstrated more susceptibility to ageing. The least active catalyst contained only platinum, although this catalyst was also the least affected by hydrothermal ageing. The addition of rhodium to either palladium or platinum–palladium catalysts caused a more rapid loss in activity at higher temperatures, although the loss in activity at lower temperatures was similar in magnitude to those catalysts without rhodium. In some cases, cycling the reactor temperature between high and low restored some activity to the catalyst. In all cases, the catalyst activity was observed to be lower in the presence of water, after both thermal and hydrothermal ageing.
The Charisma of Fruits: From Greek Mythology to Genesis
Authors: Moubayed Anna-Maria
Published: May 2023

Concerned with the representations of fruits in Greco-Roman mythology and Genesis, this paper first explores the various meanings of charis and its conceptualization, and their embodiments. It then addresses object agency, before questioning the possible propriety of certain fruits in visual and textual narratives to emanate and/or appropriate charisma. To do so, the paper presents a discussion of the linguistic and conceptual mutability and malleability of the term ‘charis’ and its conceptualization into charisma, as well as its possible manifestations or translations in fruits, thus transforming the latter into (accidental) actors. Finally, this study provides an exploratory reflection on the ambiguity and metamorphic aspect of “charismatic” fruits in the context of myths and the Genesis narrative represented in the visual arts, and their translation into fairy tale narratives and modern advertising campaigns.
COVID-19 and the View from Africa
Authors: Davies Tim , Matengu Kenneth , Hall Judith E.
Published: May 2023

In Africa, refusal of COVID-19 and other vaccines is widespread for different reasons, including disbelief in the existence of the virus itself and faith in traditional remedies. In sub-Saharan countries, refusal is often made worse by opposition to vaccines by the religious establishments. This is a pressing problem, as Africa has the highest vaccine-avoidable mortality rate for children under the age of five in the world. Dialogue between those wishing to promote vaccines and those who resist them is essential if the situation is to be improved. This article argues that Western and other aid agencies seeking to promote vaccination programs need to develop a dialogue with resisters, and in this process to embrace and commend the ancient African philosophical tradition of Ubuntu, incorporating it into these programs as a way to overcome such entrenched resistance. The paper concludes with concrete recommendations for how to accomplish this goal.
Mapping Neo-Modern and Postmodern Qur’ānic Reformist Discourse in the Intellectual Legacy of Fazlur Rahman and Mohammed Arkoun
Authors: Ahmad Humaira
Published: May 2023

Renewal and Reform have been the most discussed and dominant themes of Muslim intelligentsia, as they lived through the subjection of the greater part of the Muslim world by the Western colonial powers during the 18th and 19th centuries. The intellectual discourse on reform by the early Muslim reformers pivoted to the adoption of Western science and values and to the struggle of developing a new ilm al kalām (theology) complementary to modern science and western ideologies. The subsequent reformers, however, were more critical of Western ideas of civilization. Fazlur Rahman and Mohammed Arkoun belonged to the later wave of Muslim reformist movement of the 20th century and are the most well-known trail blazers of this reformist discourse, which centered on the Qur’ān. This article provides insight into the reform strategies of Dr. Fazlur Rahman and Mohammed Arkoun by mapping out key concepts in their discourses and their influence on later generations of reformers. Fazlur Rahman identified the stagnant intellectual legacy as the sole cause of the downfall of Muslim Civilization, caused by the absence of Ijtihād (independent legal reasoning). In his opinion, blind imitation based on precedence and consensus has only created a new hierarchy of traditional ulama (religious scholar) whose retrogressive mindset monopolized the interpretation of the Qur’ān. Making the Qur’ān as the center point for reform, he advocated its rereading based on the comprehension of the élan (spirit) of the Qur’ān. Similarly, Mohammed Arkoun, being trained in postmodern literary theory, adopted post structural methods for re-reading the Qur’ānic text. Arkoun’s critique and approach is interwoven with complex terminologies. He advocated desacralizing the text and the radical rethinking of Islam as a cultural and religious system. This appraisal promotes a philosophical perspective in combination with an anthropological and historical approach. Both these reformers have their own set of advocates and detractors. Undeniably, however, as this paper argues, Arkoun’s approach of understanding the Qur’ān can disturb the conventional prevalent belief system.
Personality Functioning in Current Epilepsy Patients and People Recovered from Epilepsy
Authors: Kang Weixi , Malvaso Antonio
Published: May 2023

The aim of the current study is to examine the effect of epilepsy recovery on personality traits by analyzing data from a large representative cohort from the United Kingdom. This study used data from Understanding Society: the UK Household Longitudinal Study (UKHLS). A predictive normative modelling approach along with one-sample t-tests were used to analyze the personality trait differences between 190 current epilepsy patients (40% male) with a mean age of 43.95 ± 15.68 years and 102 previous epilepsy patients (45.1% male) with a mean age of 47.50 ± 15.27 years. People who recovered from epilepsy had lower Neuroticism but higher Openness, Conscientiousness, and Extraversion scores than people who did not recover from epilepsy after controlling for demographic covariates, which can be potentially explained by the psychophysiological pathways. Health professionals can make better post-discharge plans based on personality traits as a result of epilepsy recovery.
Assessing the Causality Relationship between the Geopolitical Risk Index and the Agricultural Commodity Markets
Authors: Micallef Joseph , Grima Simon , Spiteri Jonathan , Rupeika-Apoga Ramona
Published: May 2023

The aim of this study was to investigate the Granger causality between geopolitical risk (GPR) sub-indices in order to examine the implications of geopolitical risk on ten agricultural commodities classified as softs or grains. The Granger causality test was used to determine the causal relationship between the daily GPR sub-indices and the future prices of ten essential agricultural commodities from 31 March 2000 to 31 March 2022. We discovered that the GPR Threat and Act sub-indices Granger-caused changes in the wheat and oat commodity prices. These findings were also connected to the ongoing Russian–Ukrainian conflict, which has had an impact on agricultural commodity prices because both countries are major agricultural producers. The empirical results also showed how the GPR Threat sub-index Granger-affected the future prices of soybean oil, coffee, wheat, and oats. On the other hand, the GPR Act sub-index only Granger-affected the future price of oats. The findings of this study should provide useful information to both policymakers and governments to help them acknowledge the importance of geopolitical risk when setting their national policies related to food security.
Pricing Kernels and Risk Premia implied in Bitcoin Options
Authors: Winkel Julian , Härdle Wolfgang Karl
Published: May 2023

Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class.
Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy
Authors: Orusa Tommaso , Viani Annalisa , Moyo Boineelo , Cammareri Duke , Borgogno-Mondino Enrico
Published: May 2023

Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was focused on the Aosta Valley Region in NW Italy. To assess population exposure to these patterns, the following datasets have been considered: (1) HDX Meta population dataset refined and updated in order to map population distribution and its features; (2) Landsat collection (missions 4 to 9) from 1984 to 2022 obtained and calibrated in Google Earth Engine to model LST trends. A pixel-based analysis was performed considering Aosta Valley settlements and relative population distribution according to the Meta population dataset. From Landsat data, LST trends were modelled. The LST gains computed were used to produce risk exposure maps considering the population distribution and structure (such as ages, gender, etc.). To check the consistency and quality of the HDX population dataset, MAE was computed considering the ISTAT population dataset at the municipality level. Exposure-risk maps were finally realized adopting two different approaches. The first one considers only LST gain maximum by performing an ISODATA unsupervised classification clustering in which the separability of each class obtained and was checked by computing the Jeffries–Matusita (J-M) distances. The second one was to map the rising temperature exposure by developing and performing a risk geo-analysis. In this last case the input parameters considered were defined after performing a multivariate regression in which LST maximum was correlated and tested considering (a) Fractional Vegetation Cover (FVC), (b) Quote, (c) Slope, (d) Aspect, (e) Potential Incoming Solar Radiation (mean sunlight duration in the meteorological summer season), and (f) LST gain mean. Results show a steeper increase in LST maximum trend, especially in the bottom valley municipalities, and especially in new built-up areas, where more than 60% of the Aosta Valley population and domestic animals live and where a high exposure has been detected and mapped with both approaches performed. Maps produced may help the local planners and the civil protection services to face global warming from a One Health perspective.
Uncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods
Authors: Hartmann Jan , Alkhatib Hamza
Published: May 2023

Terrestrial laser scanners (TLSs) are a standard method for 3D point cloud acquisition due to their high data rates and resolutions. In certain applications, such as deformation analysis, modelling uncertainties in the 3D point cloud is crucial. This study models the systematic deviations in laser scan distance measurements as a function of various influencing factors using machine-learning methods. A reference point cloud is recorded using a laser tracker (Leica AT 960) and a handheld scanner (Leica LAS-XL) to investigate the uncertainties of the Z+F Imager 5016 in laboratory conditions. From 49 TLS scans, a wide range of data are obtained, covering various influencing factors. The processes of data preparation, feature engineering, validation, regression, prediction, and result analysis are presented. The results of traditional machine-learning methods (multiple linear and nonlinear regression) are compared with eXtreme gradient boosted trees (XGBoost). Thereby, it is demonstrated that it is possible to model the systemic deviations of the distance measurement with a coefficient of determination of 0.73, making it possible to calibrate the distance measurement to improve the laser scan measurement. An independent TLS scan is used to demonstrate the calibration results.
Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
Authors: Peng Junxiang , Zeiner Niklas , Parsons David , Féret Jean-Baptiste , Söderström Mats , Morel Julien
Published: May 2023

Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance.
Maritime Infrared Target Detection Using a Dual-Mode Background Model
Authors: Zhou Anran , Xie Weixin , Pei Jihong
Published: May 2023

With the rapid development of marine business, the intelligent detection of ship targets has become the key to marine safety. However, it is difficult to accurately detect maritime infrared targets due to severe sea clutter interference in strong wind waves or dim sea scenes. To adapt to diverse marine environments, a dual-mode sea background model is proposed for target detection. According to the global contrast of the image, the scene is divided into the sea surface with violent changes and the sea surface with stable changes. In the first stage, the preliminary background model suitable for steadily changing scenes is proposed. The pixel-level foreground mask is generated through the background block filter and the posterior probability criterion. Moreover, the learning rate parameter is adjusted using the detection results of two adjacent frames. In the second stage, the background model suitable for highly fluctuating scenes is proposed. Moreover, the local correlation feature is used to enhance the local contrast of the frame. The experimental results for the different scenes show that the proposed method has a better detection performance than the other comparison algorithms.
Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction
Authors: Yang Shuting , Chen Hao , Chen Wen
Published: May 2023

Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the correspondence between matching points. To address the above problems, a novel generalized stereo matching method based on the iterative optimization of hierarchical graph structure consistency cost is proposed for urban 3D scene reconstruction. First, the self-similarity of images is used to construct k-nearest neighbor graphs. The left-view and right-view graph structures are mapped to the same neighborhood, and the graph structure consistency (GSC) cost is proposed to evaluate the similarity of the graph structures. Then, cross-scale cost aggregation is used to adaptively weight and combine multi-scale GSC costs. Next, object-based iterative optimization is proposed to optimize outliers in pixel-wise matching and mismatches in disparity discontinuity regions. The visibility term and the disparity discontinuity term are iterated to continuously detect occlusions and optimize the boundary disparity. Finally, fractal net evolution is used to optimize the disparity map. This paper verifies the effectiveness of the proposed method on a public US3D dataset and a self-made dataset, and compares it with state-of-the-art stereo matching methods.
Spatial Variation of PM2.5 Indoors and Outdoors: Results from 261 Regulatory Monitors Compared to 14,000 Low-Cost Monitors in Three Western States over 4.7 Years
Authors: Wallace Lance , Zhao Tongke
Published: May 2023

Spatial variation of indoor and outdoor PM2.5 within three states for a five-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study (Wallace et al., 2022 Indoor Air 32:13105) investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids the use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name “PM2.5_alt”. This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 is not homogeneous even at distances < 100 m. There is good agreement between PurpleAir outdoor monitors located <100 m apart and collocated Federal Equivalent Methods (FEM).
Gain Enhancement and Cross-Polarization Suppression of Cavity-Backed Antennas Using a Flared Ground Cavity and Iris
Authors: Liu Yanxia , Isleifson Dustin , Shafai Lotfollah
Published: May 2023

Herein, we present new design principles for gain enhancement and cross-polarization suppression in dual-polarized cavity-backed antennas and demonstrate the capability in an octagonal cavity-backed open prism antenna (OCROP). In our approach, the gain is enhanced through an optimal flaring procedure and a novel metallic iris is used to control the electromagnetic fields and thereby reduce the cross-polarization. Previously, we investigated a dual-polarized OCROP antenna configuration and were able to simultaneously achieve 50% impedance bandwidth, 40% cross-polarization bandwidth (≤25 dB), and 10.2 dBi peak gain. In this study, we investigated gain enhancement by flaring an upper section of the ground cavity sidewalls, while maintaining a constant cavity height. Two cases were investigated: (1) the flare angle was modified, while the ratio of the non-flared to flared sidewall heights was kept constant, and (2) the ratio of the non-flared to flared sidewall heights was varied. In case 1, we established that, while increasing the flare angle results in a gain increase, there is a limit, as cross-polarization at the upper operating frequencies increases. In case 2, we were able to reduce the aperture phase error and achieve a higher peak gain of 12.8 dBi. To address the increased cross-polarization at the high frequency end when a large flare was used, we added a metallic iris at the junction of non-flared and flared sidewalls. We showed that increasing the iris width generally decreases the cross-polarization at high frequencies, without compromising the gain and impedance bandwidth. At an optimal width, it provides a nearly constant, low cross-polarization (below −25.8 dB) and a peak gain of 13.3 dBi, across the entire 50.7% impedance bandwidth of the antenna. We fabricated and successfully tested a prototype to verify the design and simulation approach. These results prove that incorporating an aperture flare with a metallic iris can significantly improve the gain and cross-polarization performance of cavity-backed antennas.
Characterization of Deep Learning-Based Speech-Enhancement Techniques in Online Audio Processing Applications
Authors: Rascon Caleb
Published: May 2023

Deep learning-based speech-enhancement techniques have recently been an area of growing interest, since their impressive performance can potentially benefit a wide variety of digital voice communication systems. However, such performance has been evaluated mostly in offline audio-processing scenarios (i.e., feeding the model, in one go, a complete audio recording, which may extend several seconds). It is of significant interest to evaluate and characterize the current state-of-the-art in applications that process audio online (i.e., feeding the model a sequence of segments of audio data, concatenating the results at the output end). Although evaluations and comparisons between speech-enhancement techniques have been carried out before, as far as the author knows, the work presented here is the first that evaluates the performance of such techniques in relation to their online applicability. This means that this work measures how the output signal-to-interference ratio (as a separation metric), the response time, and memory usage (as online metrics) are impacted by the input length (the size of audio segments), in addition to the amount of noise, amount and number of interferences, and amount of reverberation. Three popular models were evaluated, given their availability on public repositories and online viability, MetricGAN+, Spectral Feature Mapping with Mimic Loss, and Demucs-Denoiser. The characterization was carried out using a systematic evaluation protocol based on the Speechbrain framework. Several intuitions are presented and discussed, and some recommendations for future work are proposed.
Electrochemical Detection of ompA Gene of C. sakazakii Based on Glucose-Oxidase-Mimicking Nanotags of Gold-Nanoparticles-Doped Copper Metal-organic Frameworks
Authors: Zhang Hongyan , Xu Guiqing , Chen Yuming , Li Xu , Wang Shaopeng , Jiang Feihao , Zhan Pengyang , Lu Chuanfu , Cao Xiaodong , Ye Yongkang , Tao Yunlai
Published: May 2023

The present work developed an electrochemical genosensor for the detection of virulence outer membrane protein A (ompA, tDNA) gene of Cronobacter sakazakii (C. sakazakii) by exploiting the excellent glucose-oxidase-mimicking activity of copper Metal-organic frameworks (Cu-MOF) doped with gold nanoparticle (AuNPs). The signal nanotags of signal probes (sDNA) that biofunctionalized AuNPs@Cu-MOF (sDNA-AuNPs@Cu-MOF) were designed using an Au-S bond. The biosensor was prepared by immobilization capture probes (cDNA) onto an electrodeposited AuNPs-modified glassy carbon electrode (GCE). AuNPs@Cu-MOF was introduced onto the surface of the GCE via a hybridization reaction between cDNA and tDNA, as well as tDNA and sDNA. Due to the enhanced oxidase-mimicking activity of AuNPs@Cu-MOF to glucose, the biosensor gave a linear range of 1.0 × 10−15 to 1.0 × 10−9 mol L−1 to tDNA with a detection limit (LOD) of 0.42 fmol L−1 under optimized conditions using differential pulse voltammetry measurement (DPV). It can be applied in the direct detection of ompA gene segments in total DNA extracts from C. sakazakii with a broad linear range of 5.4−5.4 × 105 CFU mL−1 and a LOD of 0.35 CFU mL−1. The biosensor showed good selectivity, fabricating reproducibility and storage stability, and can be used for the detection of ompA gene segments in real samples with recovery between 87.5% and 107.3%.
Positioning of Unmanned Underwater Vehicle Based on Autonomous Tracking Buoy
Authors: Li Yuhan , Ruan Ruizhi , Zhou Zupeng , Sun Anqing , Luo Xiaonan
Published: May 2023

This paper presents a novel method for the dynamic positioning of an unmanned underwater vehicle (UUV) with unknown trajectories based on an autonomous tracking buoy (PUVV-ATB) that indirectly positions the UUV using ultra-short baseline measurements. The method employs a spatial location geometric model and divides the positioning process into four steps, including data preprocessing to detect geometric errors and apply mean filtering, direction capture, position tracking, and position synchronization. To achieve these steps, a new adaptive tracking control algorithm is proposed that does not require trajectory prediction and is applied to the last three steps. The algorithm is deployed to the buoy for tracking simulation and sea trial experiments, and the results are compared with those of a model predictive control algorithm. The autonomous tracking buoy based on the adaptive tracking control algorithm runs more stably and can better complete the precise tracking task for the UUV with a positioning error of less than 10 cm. This method breaks the premise of trajectory prediction based on traditional tracking control algorithms, providing a new direction for further research on UUV localization. Furthermore, the conclusion of this paper has important reference value for other research and application fields related to UUV.
Oscillator Selection Strategies to Optimize a Physically Unclonable Function for IoT Systems Security
Authors: Aparicio-Téllez Raúl , Garcia-Bosque Miguel , Díez-Señorans Guillermo , Celma Santiago
Published: May 2023

Physically unclonable functions avoid storing secret information in non-volatile memories and only generate a key when it is necessary for an application, rising as a promising solution for the authentication of resource-constrained IoT devices. However, the need to minimize the predictability of physically unclonable functions is evident. The main purpose of this work is to determine the optimal way to construct a physically unclonable function. To do this, a ring oscillator physically unclonable function based on comparing oscillators in pairs has been implemented in an FPGA. This analysis shows that the frequencies of the oscillators greatly vary depending on their position in the FPGA, especially between oscillators implemented in different types of slices. Furthermore, the influence of the chosen locations of the ring oscillators on the quality of the physically unclonable function has been analyzed and we propose five strategies to select the locations of the oscillators. Among the strategies proposed, two of them stand out for their high uniqueness, reproducibility, and identifiability, so they can be used for authentication purposes. Finally, we have analyzed the reproducibility for the best strategy facing voltage and temperature variations, showing that it remains stable in the studied range.
Fault Root Cause Tracking of the Mechanical Components of CNC Lathes Based on Information Transmission
Authors: Zhang Yingzhi , Guo Guiming , Liu Jialin
Published: May 2023

This study proposes a new method for the immediate fault warning and fault root tracing of CNC lathes. Here, the information acquisition scheme was formulated based on the analysis of the coupling relationship between the mechanical parts of CNC lathes. Once the collected status signals were de-noised and coarse-grained, transfer entropy theory was introduced to calculate the net entropy of information transfer between the mechanical parts, after which the information transfer model was constructed. The sliding window method was used to determine the probability threshold interval of the net information transfer entropy between the lathe mechanical parts under different processing modes. Therefore, the transition critical point was determined according to the information entropy, and the fault development process was clarified. By analyzing the information transfer changes between the parts, fault early warning and fault root tracking on the CNC lathe were realized. The proposed method realizes the digitalization and intelligentization of fault diagnosis and has the advantages of timely and efficient diagnosis. Finally, the effectiveness of the proposed method is verified by a numerical control lathe tool processing experiment.
Brain Connectivity Analysis in Distinct Footwear Conditions during Infinity Walk Using fNIRS
Authors: Khan Haroon , Pinto-Orellana Marco Antonio , Mirtaheri Peyman
Published: May 2023

Gait and balance are an intricate interplay between the brain, nervous system, sensory organs, and musculoskeletal system. They are greatly influenced by the type of footwear, walking patterns, and surface. This exploratory study examines the effects of the Infinity Walk, pronation, and footwear conditions on brain effective connectivity patterns. A continuous-wave functional near-infrared spectroscopy device collected data from five healthy participants. A highly computationally efficient connectivity model based on the Grange causal relationship between the channels was applied to data to find the effective relationship between inter- and intra-hemispheric brain connectivity. Brain regions of interest (ROI) were less connected during the barefoot condition than during other complex walks. Conversely, the highest interconnectedness between ROI was observed while wearing flat insoles and medially wedged sandals, which is a relatively difficult type of footwear to walk in. No statistically significant (p-value <0.05) effect on connectivity patterns was observed during the corrected pronated posture. The regions designated as motoric, sensorimotor, and temporal became increasingly connected with difficult walking patterns and footwear conditions. The Infinity Walk causes effective bidirectional connections between ROI across all conditions and both hemispheres. Due to its repetitive pattern, the Infinity Walk is a good test method, particularly for neuro-rehabilitation and motoric learning experiments.
Interactions of Nanomaterials with Gut Microbiota and Their Applications in Cancer Therapy
Authors: Li Xiaohui , Wei Huan , Qi Jiachen , Ma Ke , Luo Yucheng , Weng Lixing
Published: May 2023

Cancer treatment is a challenge by its incredible complexity. As a key driver and player of cancer, gut microbiota influences the efficacy of cancer treatment. Modalities to manipulate gut microbiota have been reported to enhance antitumor efficacy in some cases. Nanomaterials (NMs) have been comprehensively applied in cancer diagnosis, imaging, and theranostics due to their unique and excellent properties, and their effectiveness is also influenced by gut microbiota. Nanotechnology is capable of targeting and manipulating gut microbiota, which offers massive opportunities to potentiate cancer treatment. Given the complexity of gut microbiota–host interactions, understanding NMs–gut interactions and NMs–gut microbiota interactions are important for applying nanotechnologies towards manipulating gut microbiota in cancer prevention and treatment. In this review, we provide an overview of NMs–gut interactions and NMs–gut microbiota interactions and highlight the influences of gut microbiota on the diagnosis and treatment effects of NMs, further illustrating the potential of nanotechnologies in cancer therapy. Investigation of the influences of NMs on cancer from the perspective of gut microbiota will boost the prospect of nanotechnology intervention of gut microbiota for cancer therapy.
Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks
Authors: Fatani Abdulaziz , Dahou Abdelghani , Abd Elaziz Mohamed , Al-qaness Mohammed A. A. , Lu Songfeng , Alfadhli Saad Ali , Alresheedi Shayem Saleh
Published: May 2023

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.
Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data
Authors: Ge Lin , Han Qi , Tong Xiaojun , Wang Yizhen
Published: May 2023

In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT2 angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT2 angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.