bluuebunny/crossref_metadata_2025 huggingface dataset:
Structure Optimization and Control Design of Electronic Oxygen Regulator
Authors: Jiang Dongsheng , Liu Yue , Yang Haowen , Fang Xingxing , Qian Binbin , Li Hui
Published: April 2023

The oxygen regulator is the core component of the aircraft life support system, which adjusts the flow and pressure of the breathing gas according to the pilot’s breathing needs. In response to the problem that structural parameters are difficult to adjust and prone to jitter when the indirect oxygen regulator system is stable, a direct oxygen regulator is designed using a stepper motor to drive a lung-type flapper, replacing the diaphragm lever-type structure of the indirect oxygen regulator. Due to the nonlinearity and time-varying nature of the dynamic characteristics of oxygen regulators, a single-neuron PID control strategy based on online identification of RBF neural networks is proposed to improve the PID control performance. The RBF neural network is used to identify the Jacobian information of the controlled object, and the single-neuron PID controller completes the online adjustment of the controller parameters to realize the intelligent control of the system. Simulation experimental studies are conducted to verify the performance of the direct oxygen regulator. The result analysis verifies the excellence of the single-neuron PID control strategy based on online recognition of the RBF neural network to improve the system performance.
Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
Authors: Cherkasova Olga , Vrazhnov Denis , Knyazkova Anastasia , Konnikova Maria , Stupak Evgeny , Glotov Vadim , Stupak Vyacheslav , Nikolaev Nazar , Paulish Andrey , Peng Yan , Kistenev Yury , Shkurinov Alexander
Published: April 2023

Gliomas, one of the most severe malignant tumors of the central nervous system, have a high mortality rate and an increased risk of recurrence. Therefore, early glioma diagnosis and the control of treatment have great significance. The blood plasma samples of glioma patients, patients with skull craniectomy defects, and healthy donors were studied using terahertz time-domain spectroscopy (THz-TDS). An analysis of experimental THz data was performed by machine learning (ML). The ML pipeline included (i) THz spectra smoothing using the Savitzky–Golay filter, (ii) dimension reduction with principal component analysis and t-distribution stochastic neighborhood embedding methods; (iii) data separability analyzed using Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The ML models’ performance was evaluated by a k-fold cross validation technique using ROC-AUC, sensitivity, and specificity metrics. It was shown that tree-based ensemble methods work more accurately than SVM. RF and XGBoost provided a better differentiation of the group of patients with glioma from healthy donors and patients with skull craniectomy defects. THz-TDS combined with ML was shown to make it possible to separate the blood plasma of patients before and after tumor removal surgery (AUC = 0.92). Thus, the applicability of THz-TDS and ML for the diagnosis of glioma and treatment monitoring has been shown.
LED Light Quality Affected Bioactive Compounds, Antioxidant Potential, and Nutritional Value of Red and White Cabbage Microgreens
Authors: Podsędek Anna , Frąszczak Barbara , Sosnowska Dorota , Kajszczak Dominika , Szymczak Kamil , Bonikowski Radosław
Published: April 2023

Microgreens are environmentally friendly and have health benefits in addition to their basic nutritional contents. The effect of white (W), white–blue (W + B), and white–red (W + R) light on the bioactive compounds, nutrient composition, and antioxidant potential of red and white cabbage microgreens were investigated using light-emitting diodes (LEDs). The results showed that protein, fat, ash, chlorophylls, and carotenoids were the highest in microgreens under W light, while phenolic compounds were highest in microgreens under W + B light. Supplementation with white light, as well as red or blue light, resulted in higher levels of sugars and total fiber in both white and red microgreens. Twenty-six and thirty-three phenolic compounds were identified in white and red cabbage microgreens, respectively. The identified phenolics belonged to three classes, including phenolic acids, flavonols, and anthocyanins. The antioxidant potential of both cabbage microgreens was determined by four methods (ABTS, DPPH, ORAC, and FRAP). It was found that the highest antioxidant potential was observed in microgreens grown under the W + B light combination. On the other hand, the W + R light combination increased the content of β-sitosterol and campesterol. The results may be helpful in the selection of the type of LED lighting that determines the high nutritional and health-promoting potential of white and red cabbage microgreens.
A Novel Active Noise Control Method Based on Variational Mode Decomposition and Gradient Boosting Decision Tree
Authors: Liang Xiaobei , Yao Jinyong , Luo Lei , Zhang Weifang , Wang Yanrong
Published: April 2023

Diversified noise sources pose great challenges in the engineering of an ANC (active noise control) system design. To solve this problem, this paper proposes an ANC method based on VMD (variational mode decomposition) and Ensemble Learning. VMD is used to extract IMFs (Intrinsic Model Functions) of different types of noise and obtain the approximate entropy of each IMF. Clustering analysis on the output of VMD is conducted based on the PCA (principal component analysis) dimension reduction method and k-means++ method to get classification results for different noises. On the basis of the clustering results, different GBDT (gradient boosting decision tree) regressors are constructed for different noise types, in order to create a high-performance ANC system for multiple noise sources. To verify the effectiveness of the proposed method, this paper designed four simulation schemes for the ANC: obstacle-free rectangular enclosed space, rectangular enclosed space with obstacle, obstacle-free trapezoidal enclosed space and trapezoidal enclosed space with obstacle. When machine gun noise is used as an example, noise attenuation by the proposed method in four simulation schemes is −23.27 dB, −21.6 dB, −19.08 dB and −15.48 dB respectively.
Employing FGP-3D, a Fully Gated and Anchored Methodology, to Identify Skeleton-Based Action Recognition
Authors: Islam M Shujah , Algosaibi Abdullah , Rafaqat Warda , Bakhat Khush
Published: April 2023

Recent years have seen an explosion in interest in and development of action recognition based on skeletal data. Contemporary methods using fully gated units can successfully extract characteristics from human skeletons by relying on the human topology that has been predefined. Despite advancements, fully gated unit-based techniques have trouble generalizing to other domains, particularly when dealing with various human topological structures. In this context, we introduce FGP-3D, a novel skeleton-based action recognition technique that can generalize across datasets while being effective at learning spatiotemporal features from human skeleton sequences. This is accomplished via a multi-head attention technique to learn an ideal dependence feature matrix from the uniform distribution. We next re-evaluate state-of-the-art techniques as well as the suggested novel descriptor FGP-3D in order to examine the cross-domain generalizability of skeleton-based action recognition in real-world video skeleton statistics. After being applied to commonly used action categorization datasets, experimental results demonstrate that the proposed FGP-3D, with pre-training, generalizes well and outperforms the state-of-the-art.
Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
Authors: Almaleh Abdulaziz , Almushabb Reem , Ogran Rahaf
Published: April 2023

Behavioral malware analysis is a powerful technique used against zero-day and obfuscated malware. Additionally referred to as dynamic malware analysis, this approach employs various methods to achieve enhanced detection. One such method involves using machine learning and deep learning algorithms to learn from the behavior of malware. However, the task of weight initialization in neural networks remains an active area of research. In this paper, we present a novel hybrid model that utilizes both machine learning and deep learning algorithms to detect malware across various categories. The proposed model achieves this by recognizing the malicious functions performed by the malware, which can be inferred from its API call sequences. Failure to detect these malware instances can result in severe cyberattacks, which pose a significant threat to the confidentiality, privacy, and availability of systems. We rely on a secondary dataset containing API call sequences, and we apply logistic regression to obtain the initial weight that serves as input to the neural network. By utilizing this hybrid approach, our research aims to address the challenges associated with traditional weight initialization techniques and to improve the accuracy and efficiency of malware detection based on API calls. The integration of both machine learning and deep learning algorithms allows the proposed model to capitalize on the strengths of each approach, potentially leading to a more robust and versatile solution to malware detection. Moreover, our research contributes to the ongoing efforts in the field of neural networks, by offering a novel perspective on weight initialization techniques and their impact on the performance of neural networks in the context of behavioral malware analysis. Experimental results using a balanced dataset showed 83% accuracy and a 0.44 loss, which outperformed the baseline model in terms of the minimum loss. The imbalanced dataset’s accuracy was 98%, and the loss was 0.10, which exceeded the state-of-the-art model’s accuracy. This demonstrates how well the suggested model can handle malware classification.
Multi-Location Assortment Optimization with Drone and Human Courier Joint Delivery
Authors: Wu Mengting , Pei Zhi
Published: April 2023

This paper studies the constrained multi-location assortment optimization problem under joint delivery by drone and human courier. To maximize the revenue of the retailer, product assortment and last-mile delivery methods are configured simultaneously. To handle the joint delivery problem with product assortment, a mixed multinomial logit model is established to depict the customer purchase behavior. We also take into account the load capacity of drones and the delivery distance limit of drones and human couriers. To the best of our knowledge, the present paper is the first to study the assortment optimization problem considering joint delivery by drone and human courier. The problem is first formulated as a mixed-integer linear programming (MILP) model, which is then transformed into a conic quadratic mixed-integer model with McCormick inequalities (Conic + MC). In the numerical study, it is demonstrated that Conic + MC outperforms MILP as Conic + MC finds the optimal solution within 300 s, while MILP needs more than 600 s. Furthermore, several management insights are revealed for retailers regarding assortment planning when facing different kinds of product revenue structures, and advice is provided for online ordering platforms in terms of setting delivery limits.
Missing the Present: Nostalgia and the Archival Impulse in Gentrification Photography
Authors: Price Zeena
Published: April 2023

If gentrification is a violent form of “un-homing” (Elliot-Cooper et al., p. 494), then it is no surprise to witness an intensification of photographic practice in gentrifying areas; photography is, after all, fundamentally a place-making practice. Taking “home” to include the wider neighborhood and urban environment (Blunt and Sheringham 2019), this paper argues that the concept of anticipatory nostalgia is a useful way of understanding the recent wave of black and white photography in gentrifying areas. As well as signifying a sense of loss, anticipatory nostalgia, defined as missing the present before it has gone (Batcho and Shikh 2016), can also be seen as an aesthetic strategy of documenting places before they are lost to gentrification. Using the works of Colby Deal (Beautiful, Still), Jules Renault (Suspended in Time), and Lorenzo Grifantini (W10) as case studies, this paper argues that this type of photography, which explicitly utilizes an archival aesthetic, invites spectators to interrogate the intimate ties between home, memory, and identity. While melancholic, these images serve as a call to action and a form of speculation about the future—rejecting the shiny, computer-generated aesthetics of gentrification for a humanized, often gritty, and authentic version of home.
Main Emission Sources and Health Risks of Polycyclic Aromatic Hydrocarbons and Nitro-Polycyclic Aromatic Hydrocarbons at Three Typical Sites in Hanoi
Authors: Zhang Hao , Pham Chau-Thuy , Chen Bin , Zhang Xuan , Wang Yan , Bai Pengchu , Zhang Lulu , Nagao Seiya , Toriba Akira , Nghiem Trung-Dung , Tang Ning
Published: April 2023

Particulate matter-bound polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) were first systematically studied in downtown (XT), suburban (GL) and rural (DA) sites in winter and summer in Hanoi, Vietnam, from 2019 to 2022. The mean concentrations of PAHs and NPAHs ranged from 0.76 ng m−3 to 50.2 ng m−3 and 6.07 pg m−3 to 1.95 ng m−3, respectively. The concentrations of PAHs and NPAHs in winter were higher than in summer, except for NPAHs in XT. We found the benzo[a]pyrene (BaP)/benzo[ghi]perylene (BgPe) ratio could effectively identify biomass burning in this study, in which a higher [BaP]/[BgPe] value indicates a greater effect of biomass burning on PAHs and NPAHs. The results indicated that atmospheric PAHs and NPAHs were mainly affected by motor vehicles (especially the unique motorcycles in Southeast Asia) in the summer in Hanoi. In winter, all sites were affected by the burning of rice straw to varying degrees, especially DA. The incremental lifetime cancer risk (ILCR) in Hanoi was first determined through ingestion, inhalation and dermal absorption. The results showed that residents in Hanoi faced high health risks, while females experienced higher health risks than males. The ingestion and dermal pathways indicated higher exposure risks than the usually considered inhalation pathway.
Brake Particle PN and PM Emissions of a Hybrid Light Duty Vehicle Measured on the Chassis Dynamometer
Authors: Dimopoulos Eggenschwiler Panayotis , Schreiber Daniel , Habersatter Joel
Published: April 2023

Brake particle emissions number (PN) and mass (PM) of a light-duty hybrid-electric vehicle have been assessed under realistic driving patterns on a chassis dynamometer. Therefore, the front-right disc brake was enclosed in a specifically designed casing featuring controlled high scavenging air ventilation. The WLTC cycle was chosen for most measurements. Different scavenging flow rates have been tested assessing their influence on the measured particles as well as on the temperature of the braking friction partners. Particle transport efficiencies have been assessed revealing scavenging flow rates with losses below 10%. During the performed cycle, most brake particle emissions occurred during braking. There were also isolated emission peaks during periods with no brakes in use, especially during vehicle accelerations. Sequential WLTC cycles showed a continuous decrease in the measured PN and PM emissions; however, size-number and size-mass distributions have been very similar. The measured PN emission factors (>23 nm) at the right front wheel over the WLTC cycle lie at 5.0 × 1010 1/km, whereas the PM emission factor lies at 3.71 mg/km for PM < 12 µm and 1.58 mg/km for PM < 2.5 µm. These values need to roughly triple in order to obtain the brake particle emission of all four brakes and wheels of the entire vehicle. Thus, the brake PN emissions factors have been in the same order of magnitude as the tailpipe PN of a Euro 6 light-duty vehicle equipped with a particle filter. Finally, differences between brake particle emissions in hybrid and all-electric operating modes have been assessed by a series of specific measurements, demonstrating the potential of all-electric vehicle operation in reducing brake particles by a factor of two.
Revisiting the Influence of ENSO on the Arctic Stratosphere in CMIP5 and CMIP6 Models
Authors: Hu Jinggao , Shen Yifan , Deng Jiechun , Jia Yanpei , Wang Zixu , Li Anqi
Published: April 2023

The Arctic stratospheric response to El Niño–Southern Oscillation (ENSO) is assessed using the historical simulations provided by the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively). CMIP6 models can well reproduce the ENSO signals in the Arctic stratosphere and have an ameliorated performance compared to CMIP5 models. Specifically, El Niño is associated with an intensified Pacific–North American pattern that leads to a considerable enhancement of planetary wavenumber 1 but a small reduction of planetary wavenumber 2, and thus, a warm and weakened stratospheric polar vortex. The case for La Niña is nearly the opposite, with a cool and strengthened stratospheric polar vortex. In CMIP6, the ENSO-related stratospheric signal matures in the February–March–April season and increases with ENSO magnitude, regardless of the ENSO phase. However, the stratospheric response to strong El Niño (La Niña) is weaker (stronger) than that which should be achieved if the response changes linearly with the amplitude of El Niño (La Niña). An asymmetric time evolution of stratospheric signals exists between strong El Niño and La Niña events. The stratospheric response caused by strong El Niño is weaker from late winter to early spring but stronger in middle and late spring compared to that caused by strong La Niña. By contrast, the Arctic stratospheric signal in moderate El Niño events is larger than that in moderate La Niña. Compared to ENSO-neutral winters, stratospheric sudden warming occurs more (less) frequently in El Niño (La Niña), as simulated by CMIP6 high-top models.
A Metamodel-Based Optimization of Physical Parameters of High Resolution NWP ICON-LAM over Southern Italy
Authors: Cinquegrana Davide , Zollo Alessandra Lucia , Montesarchio Myriam , Bucchignani Edoardo
Published: April 2023

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window.
NAO Seasonal Forecast Using a Multivariate Air–Sea Coupled Deep Learning Model Combined with Causal Discovery
Authors: Mu Bin , Jiang Xin , Yuan Shijin , Cui Yuehan , Qin Bo
Published: April 2023

The North Atlantic Oscillation (NAO) is a major climatic phenomenon in the Northern Hemisphere, but the underlying air–sea interaction and physical mechanisms remain elusive. Despite successful short-term forecasts using physics-based numerical models, longer-term forecasts of NAO continue to pose a challenge. In this study, we employ advanced data-driven causal discovery techniques to explore the causality between multiple ocean–atmosphere processes and NAO. We identify the best NAO predictors based on this causality analysis and develop NAO-MCD, a multivariate air–sea coupled model that incorporates causal discovery to provide 1–6 month lead seasonal forecasts of NAO. Our results demonstrate that the selected predictors are strongly associated with NAO development, enabling accurate forecasts of NAO. NAO-MCD significantly outperforms conventional numerical models and provides reliable seasonal forecasts of NAO, particularly for winter events. Moreover, our model extends the range of accurate forecasts, surpassing state-of-the-art performance at 2- to 6-month lead-time NAO forecasts, substantially outperforming conventional numerical models.
RETRACTED: Wei et al. Climate Adaptability Analysis on the Shape of Outpatient Buildings for Different Climate Zones in China Based on Low-Energy Target. Atmosphere 2022, 13, 2121
Authors: Wei Youman , Wang Siyan , Dang Hongwei , Liu Peng
Published: April 2023

The journal retracts the article entitled “Climate Adaptability Analysis on the Shape of Outpatient Buildings for Different Climate Zones in China Based on Low-Energy Target” by Wei et al [...]
Genesis of New Particle Formation Events in a Semi-Urban Location in Eastern Himalayan Foothills
Authors: Das Barlin , Pathak Binita , Chutia Lakhima , Subba Tamanna , Bhuyan Pradip Kumar
Published: April 2023

New particle formation (NPF) events identified using scanning mobility particle sizer (SMPS) measurements, their subsequent growth and other characteristics over Dibrugarh, a semi-urban location in the eastern Himalayan foothills (EHF), during November–December 2016 are presented. The mean total number concentration of ultrafine aerosols was found to be high during morning and evening rush hours. The NPF occurrence frequency was found to be 14%. The temporal evolution of the hourly average aerosol number size distribution revealed that the nucleation burst occurred at a lower size spectrum, supporting the existence of NPF burst events. It continued to grow through coagulation loss and condensation sink with an average growth rate of 17.16 ± 12.29 nm/hr. The satellite-based observations showed a high concentration of the NPF precursors NO2, SO2, and HCHO during the NPF days. The backward air mass trajectories confirmed that the sources of emissions were confined within an area of radius ~100 km surrounding the observation site. These locally generated precursors and their associated photochemistry could be a probable reason for NPF occurrence at the study site.
Density-Distance Outlier Detection Algorithm Based on Natural Neighborhood
Authors: Zhang Jiaxuan , Yang Youlong
Published: April 2023

Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing algorithms are mainly divided into density-based algorithms and distance-based algorithms. However, both approaches have some drawbacks. The former struggles to handle low-density modes, while the latter cannot detect local outliers. Moreover, the outlier detection algorithm is very sensitive to parameter settings. This paper proposes a new two-parameter outlier detection (TPOD) algorithm. The method proposed in this paper does not need to manually define the number of neighbors, and the introduction of relative distance can also solve the problem of low density and further accurately detect outliers. This is a combinatorial optimization problem. Firstly, the number of natural neighbors is iteratively calculated, and then the local density of the target object is calculated by adaptive kernel density estimation. Secondly, the relative distance of the target points is computed through natural neighbors. Finally, these two parameters are combined to obtain the outlier factor. This eliminates the influence of parameters that require users to determine the number of outliers themselves, namely, the top-n effect. Two synthetic datasets and 17 real datasets were used to test the effectiveness of this method; a comparison with another five algorithms is also provided. The AUC value and F1 score on multiple datasets are higher than other algorithms, indicating that outliers can be found accurately, which proves that the algorithm is effective.
Joint Discrete Approximation of Analytic Functions by Shifts of the Riemann Zeta Function Twisted by Gram Points II
Authors: Laurinčikas Antanas
Published: April 2023

In this paper, a theorem is obtained on the approximation in short intervals of a collection of analytic functions by shifts (ζ(s+itkα1),…,ζ(s+itkαr)) of the Riemann zeta function. Here, {tk:k∈N} is the sequence of Gram numbers, and α1,…,αr are different positive numbers not exceeding 1. It is proved that the above set of shifts in the interval [N,N+M], here M=o(N) as N→∞, has a positive lower density. For the proof, a joint limit theorem in short intervals for weakly convergent probability measures is applied.
Statistical Analysis of Inverse Lindley Data Using Adaptive Type-II Progressively Hybrid Censoring with Applications
Authors: Alotaibi Refah , Nassar Mazen , Elshahhat Ahmed
Published: April 2023

This paper deals with the statistical inference of the unknown parameter and some life parameters of inverse Lindley distribution under the assumption that the data are adaptive Type-II progressively censored. The maximum likelihood method is considered to acquire the point and interval estimates of the distribution parameter, reliability, and hazard rate functions. The approximate confidence intervals are also addressed. The delta method is taken into consideration to approximate the variances of the estimators of the reliability and hazard rate functions to get the required intervals. Based on the assumption of gamma prior, we further consider Bayesian estimation of the different parameters. The Bayes estimates are obtained by considering squared error and general entropy loss functions. The Bayes estimates and highest posterior density credible intervals are obtained by employing the Markov chain Monte Carlo procedure. An exhaustive numerical study is conducted to compare the offered estimates with regard to their root means squared error, relative absolute biases, confidence lengths, and coverage probabilities. To explain the suggested methods, two applications are investigated. The numerical findings show that the Bayes estimates perform better than those obtained based on the maximum likelihood method. The Bayesian estimations using the asymmetric loss function give more efficient estimates than the symmetric loss. Finally, the inverse Lindley distribution is recommended to be used as a suitable model to fit airborne communication transceiver and wooden toys data sets when compared with some competitive models including inverse Weibull, inverse gamma and alpha power inverted exponential.
Longitudinal Data Analysis Based on Bayesian Semiparametric Method
Authors: Jiao Guimei , Liang Jiajuan , Wang Fanjuan , Chen Xiaoli , Chen Shaokang , Li Hao , Jin Jing , Cai Jiali , Zhang Fangjie
Published: April 2023

A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters. It results in easy implementation of the MCMC algorithm for estimation of model parameters and demonstrates fast convergence. The proposed model framework associated with the MCMC algorithm is validated by four covariance structures and a real-life dataset. A simple Monte Carlo study of the model under four covariance structures and an analysis of the real dataset show that the new model framework and its associated Bayesian posterior inferential method through the MCMC algorithm perform fairly well in the sense of easy implementation, fast convergence, and smaller root mean square errors compared with the same model without the specified autoregression structure.
On an Indefinite Metric on a Four-Dimensional Riemannian Manifold
Authors: Razpopov Dimitar , Dzhelepov Georgi , Dokuzova Iva
Published: April 2023

Our research focuses on the tangent space of a point on a four-dimensional Riemannian manifold. Besides having a positive definite metric, the manifold is endowed with an additional tensor structure of type (1,1), whose fourth power is minus the identity. The additional structure is skew-circulant and compatible with the metric, such that an isometry is induced in every tangent space on the manifold. Both structures define an indefinite metric. With the help of the indefinite metric, we determine circles in different two-planes in the tangent space on the manifold. We also calculate the length and area of the circles. On a smooth closed curve, such as a circle, we define a vector force field. Further, we obtain the circulation of the vector force field along the curve, as well as the flux of the curl of this vector force field across the curve. Finally, we find a relation between these two values, which is an analog of the well-known Green’s formula in the Euclidean space.
Second Hankel Determinant for a New Subclass of Bi-Univalent Functions Related to the Hohlov Operator
Authors: Liu Likai , Zhai Jie , Liu Jin-Lin
Published: April 2023

A new subclass of bi-univalent functions associated with the Hohlov operator is introduced. Certain properties such as the coefficient bounds, Fekete-Szegö inequality and the second Hankel determinant for functions in the subclass are obtained. In particular, several known results are generalized.
Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans
Authors: Motta Pedro Crosara , Cortez Paulo César , Silva Bruno R. S. , Yang Guang , Albuquerque Victor Hugo C. de
Published: April 2023

Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.
Prediction of Contaminated Areas Using Ultraviolet Fluorescence Markers for Medical Simulation: A Mobile Phone Application Approach
Authors: Chiu Po-Wei , Hsu Chien-Te , Huang Shao-Peng , Chiou Wu-Yao , Lin Chih-Hao
Published: April 2023

The use of ultraviolet fluorescence markers in medical simulations has become popular in recent years, especially during the COVID-19 pandemic. Healthcare workers use ultraviolet fluorescence markers to replace pathogens or secretions, and then calculate the regions of contamination. Health providers can use bioimage processing software to calculate the area and quantity of fluorescent dyes. However, traditional image processing software has its limitations and lacks real-time capabilities, making it more suitable for laboratory use than for clinical settings. In this study, mobile phones were used to measure areas contaminated during medical treatment. During the research process, a mobile phone camera was used to photograph the contaminated regions at an orthogonal angle. The fluorescence marker-contaminated area and photographed image area were proportionally related. The areas of contaminated regions can be calculated using this relationship. We used Android Studio software to write a mobile application to convert photos and recreate the true contaminated area. In this application, color photographs are converted into grayscale, and then into black and white binary photographs using binarization. After this process, the fluorescence-contaminated area is calculated easily. The results of our study showed that within a limited distance (50–100 cm) and with controlled ambient light, the error in the calculated contamination area was 6%. This study provides a low-cost, easy, and ready-to-use tool for healthcare workers to estimate the area of fluorescent dye regions during medical simulations. This tool can promote medical education and training on infectious disease preparation.
CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments
Authors: Faragó Paul , Ștefănigă Sebastian-Aurelian , Cordoș Claudia-Georgiana , Mihăilă Laura-Ioana , Hintea Sorin , Peștean Ana-Sorina , Beyer Michel , Perju-Dumbravă Lăcrămioara , Ileșan Robert Radu
Published: April 2023

Parkinson’s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson’s disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.
Effect of Uniaxial Compression Frequency on Osteogenic Cell Responses in Dynamic 3D Cultures
Authors: Kontogianni Georgia-Ioanna , Loukelis Konstantinos , Bonatti Amedeo Franco , Batoni Elisa , De Maria Carmelo , Naseem Raasti , Dalgarno Kenneth , Vozzi Giovanni , MacManus David B. , Mondal Subrata , Dunne Nicholas , Vitale-Brovarone Chiara , Chatzinikolaidou Maria
Published: April 2023

The application of mechanical stimulation on bone tissue engineering constructs aims to mimic the native dynamic nature of bone. Although many attempts have been made to evaluate the effect of applied mechanical stimuli on osteogenic differentiation, the conditions that govern this process have not yet been fully explored. In this study, pre-osteoblastic cells were seeded on PLLA/PCL/PHBV (90/5/5 wt.%) polymeric blend scaffolds. The constructs were subjected every day to cyclic uniaxial compression for 40 min at a displacement of 400 μm, using three frequency values, 0.5, 1, and 1.5 Hz, for up to 21 days, and their osteogenic response was compared to that of static cultures. Finite element simulation was performed to validate the scaffold design and the loading direction, and to assure that cells inside the scaffolds would be subjected to significant levels of strain during stimulation. None of the applied loading conditions negatively affected the cell viability. The alkaline phosphatase activity data indicated significantly higher values at all dynamic conditions compared to the static ones at day 7, with the highest response being observed at 0.5 Hz. Collagen and calcium production were significantly increased compared to static controls. These results indicate that all of the examined frequencies substantially promoted the osteogenic capacity.
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification
Authors: Quintana Gonzalo Iñaki , Li Zhijin , Vancamberg Laurence , Mougeot Mathilde , Desolneux Agnès , Muller Serge
Published: April 2023

Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by the choice of patch size, as there is no unique patch size that is adapted to all lesion sizes. In addition, the impact of input image resolution on performance is not yet fully understood. In this work, we study the impact of patch size and image resolution on the classifier performance for 2D mammograms. To leverage the advantages of different patch sizes and resolutions, a multi patch-size classifier and a multi-resolution classifier are proposed. These new architectures perform multi-scale classification by combining different patch sizes and input image resolutions. The AUC is increased by 3% on the public CBIS-DDSM dataset and by 5% on an internal dataset. Compared with a baseline single patch size and single resolution classifier, our multi-scale classifier reaches an AUC of 0.809 and 0.722 in each dataset.
Radiation Necrosis Following Stereotactic Radiosurgery or Fractionated Stereotactic Radiotherapy with High Biologically Effective Doses for Large Brain Metastases
Authors: Johannwerner Leonie , Werner Elisa M. , Blanck Oliver , Janssen Stefan , Cremers Florian , Yu Nathan Y. , Rades Dirk
Published: April 2023

In Radiation Therapy Oncology Group 90-05, the maximum tolerated dose of single-fraction radiosurgery (SRS) for brain metastases of 21–30 mm was 18 Gy (biologically effective dose (BED) 45 Gy12). Since the patients in this study received prior brain irradiation, tolerable BED may be >45 Gy12 for de novo lesions. We investigated SRS and fractionated stereotactic radiotherapy (FSRT) with a higher BED for radiotherapy-naive lesions. Patients receiving SRS (19–20 Gy) and patients treated with FSRT (30–48 Gy in 3–12 fractions) with BED > 49 Gy12 for up to 4 brain metastases were compared for grade ≥ 2 radiation necrosis (RN). In the entire cohort (169 patients with 218 lesions), 1-year and 2-year RN rates were 8% after SRS vs. 2% and 13% after FSRT (p = 0.73) in per-patient analyses, and 7% after SRS vs. 7% and 10% after FSRT (p = 0.59) in per-lesion analyses. For lesions ≤ 20 mm (137 patients with 185 lesions), the RN rates were 4% (SRS) vs. 0% and 15%, respectively, (FSRT) (p = 0.60) in per-patient analyses, and 3% (SRS) vs. 0% and 11%, respectively, (FSRT) (p = 0.80) in per-lesion analyses. For lesions > 20 mm (32 patients with 33 lesions), the RN rates were 50% (SRS) vs. 9% (FSRT) (p = 0.012) in both per-patient and per-lesion analyses. In the SRS group, a lesion size > 20 mm was significantly associated with RN; in the FSRT group, lesion size had no impact on RN. Given the limitations of this study, FSRT with BED > 49 Gy12 was associated with low RN risk and may be safer than SRS for brain metastases > 20 mm.
Systemic Prenatal Stress Exposure through Corticosterone Application Adversely Affects Avian Embryonic Skin Development
Authors: Gellisch Morris , Bablok Martin , Divvela Satya Srirama Karthik , Morosan-Puopolo Gabriela , Brand-Saberi Beate
Published: April 2023

Prenatal stress exposure is considered a risk factor for developmental deficits and postnatal behavioral disorders. While the effect of glucocorticoid-associated prenatal stress exposure has been comprehensively studied in many organ systems, there is a lack of in-depth embryological investigations regarding the effects of stress on the integumentary system. To approach this, we employed the avian embryo as a model organism and investigated the effects of systemic pathologically-elevated glucocorticoid exposure on the development of the integumentary system. After standardized corticosterone injections on embryonic day 6, we compared the stress-exposed embryos with a control cohort, using histological and immunohistochemical analyses as well as in situ hybridization. The overarching developmental deficits observed in the stress-exposed embryos were reflected through downregulation of both vimentin as well as fibronectin. In addition, a deficient composition in the different skin layers became apparent, which could be linked to a reduced expression of Dermo-1 along with significantly reduced proliferation rates. An impairment of skin appendage formation could be demonstrated by diminished expression of Sonic hedgehog. These results contribute to a more profound understanding of prenatal stress causing severe deficits in the integumentary system of developing organisms.
Translational Aspects of Cardiovascular Biology: From Bench to Bedside
Authors: Santulli Gaetano
Published: April 2023

Cardiovascular disease is the leading cause of death worldwide, and the search for novel mechanisms and therapeutics is desperately needed [...]
Update on Diabetic Kidney Disease (DKD): Focus on Non-Albuminuric DKD and Cardiovascular Risk
Authors: Scilletta Sabrina , Di Marco Maurizio , Miano Nicoletta , Filippello Agnese , Di Mauro Stefania , Scamporrino Alessandra , Musmeci Marco , Coppolino Giuseppe , Di Giacomo Barbagallo Francesco , Bosco Giosiana , Scicali Roberto , Piro Salvatore , Purrello Francesco , Di Pino Antonino
Published: April 2023

The classic description of diabetic kidney disease (DKD) involves progressive stages of glomerular hyperfiltration, microalbuminuria, proteinuria, and a decline in the estimated glomerular filtration rate (eGFR), leading to dialysis. In recent years, this concept has been increasingly challenged as evidence suggests that DKD presents more heterogeneously. Large studies have revealed that eGFR decline may also occur independently from the development of albuminuria. This concept led to the identification of a new DKD phenotype: non-albuminuric DKD (eGFR < 60 mL/min/1.73 m2, absence of albuminuria), whose pathogenesis is still unknown. However, various hypotheses have been formulated, the most likely of which is the acute kidney injury-to-chronic kidney disease (CKD) transition, with prevalent tubular, rather than glomerular, damage (typically described in albuminuric DKD). Moreover, it is still debated which phenotype is associated with a higher cardiovascular risk, due to contrasting results available in the literature. Finally, much evidence has accumulated on the various classes of drugs with beneficial effects on DKD; however, there is a lack of studies analyzing the different effects of drugs on the various phenotypes of DKD. For this reason, there are still no specific guidelines for therapy in one phenotype rather than the other, generically referring to diabetic patients with CKD.
Ependyma in Neurodegenerative Diseases, Radiation-Induced Brain Injury and as a Therapeutic Target for Neurotrophic Factors
Authors: Ma Xin-Yu , Yang Ting-Ting , Liu Lian , Peng Xiao-Chun , Qian Feng , Tang Feng-Ru
Published: April 2023

The neuron loss caused by the progressive damage to the nervous system is proposed to be the main pathogenesis of neurodegenerative diseases. Ependyma is a layer of ciliated ependymal cells that participates in the formation of the brain-cerebrospinal fluid barrier (BCB). It functions to promotes the circulation of cerebrospinal fluid (CSF) and the material exchange between CSF and brain interstitial fluid. Radiation-induced brain injury (RIBI) shows obvious impairments of the blood–brain barrier (BBB). In the neuroinflammatory processes after acute brain injury, a large amount of complement proteins and infiltrated immune cells are circulated in the CSF to resist brain damage and promote substance exchange through the BCB. However, as the protective barrier lining the brain ventricles, the ependyma is extremely vulnerable to cytotoxic and cytolytic immune responses. When the ependyma is damaged, the integrity of BCB is destroyed, and the CSF flow and material exchange is affected, leading to brain microenvironment imbalance, which plays a vital role in the pathogenesis of neurodegenerative diseases. Epidermal growth factor (EGF) and other neurotrophic factors promote the differentiation and maturation of ependymal cells to maintain the integrity of the ependyma and the activity of ependymal cilia, and may have therapeutic potential in restoring the homeostasis of the brain microenvironment after RIBI or during the pathogenesis of neurodegenerative diseases.
Extracellular Targets to Reduce Excessive Scarring in Response to Tissue Injury
Authors: Fertala Jolanta , Wang Mark L. , Rivlin Michael , Beredjiklian Pedro K. , Abboud Joseph , Arnold William V. , Fertala Andrzej
Published: April 2023

Excessive scar formation is a hallmark of localized and systemic fibrotic disorders. Despite extensive studies to define valid anti-fibrotic targets and develop effective therapeutics, progressive fibrosis remains a significant medical problem. Regardless of the injury type or location of wounded tissue, excessive production and accumulation of collagen-rich extracellular matrix is the common denominator of all fibrotic disorders. A long-standing dogma was that anti-fibrotic approaches should focus on overall intracellular processes that drive fibrotic scarring. Because of the poor outcomes of these approaches, scientific efforts now focus on regulating the extracellular components of fibrotic tissues. Crucial extracellular players include cellular receptors of matrix components, macromolecules that form the matrix architecture, auxiliary proteins that facilitate the formation of stiff scar tissue, matricellular proteins, and extracellular vesicles that modulate matrix homeostasis. This review summarizes studies targeting the extracellular aspects of fibrotic tissue synthesis, presents the rationale for these studies, and discusses the progress and limitations of current extracellular approaches to limit fibrotic healing.