bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Insight into an Alternative Therapeutic Approach to Restore Redox Homeostasis and Functional Mitochondria in Neurodegenerative Diseases
Authors: Hyun Dong-Hoon , Lee Jaewang
Published: December 2021

Neurodegenerative diseases are accompanied by oxidative stress and mitochondrial dysfunction, leading to a progressive loss of neuronal cells, formation of protein aggregates, and a decrease in cognitive or motor functions. Mitochondrial dysfunction occurs at the early stage of neurodegenerative diseases. Protein aggregates containing oxidatively damaged biomolecules and other misfolded proteins and neuroinflammation have been identified in animal models and patients with neurodegenerative diseases. A variety of neurodegenerative diseases commonly exhibits decreased activity of antioxidant enzymes, lower amounts of antioxidants, and altered cellular signalling. Although several molecules have been approved clinically, there is no known cure for neurodegenerative diseases, though some drugs are focused on improving mitochondrial function. Mitochondrial dysfunction is caused by oxidative damage and impaired cellular signalling, including that of peroxisome proliferator-activated receptor gamma coactivator 1α. Mitochondrial function can also be modulated by mitochondrial biogenesis and the mitochondrial fusion/fission cycle. Mitochondrial biogenesis is regulated mainly by sirtuin 1, NAD+, AMP-activated protein kinase, mammalian target of rapamycin, and peroxisome proliferator-activated receptor γ. Altered mitochondrial dynamics, such as increased fission proteins and decreased fusion products, are shown in neurodegenerative diseases. Due to the restrictions of a target-based approach, a phenotype-based approach has been performed to find novel proteins or pathways. Alternatively, plasma membrane redox enzymes improve mitochondrial function without the further production of reactive oxygen species. In addition, inducers of antioxidant response elements can be useful to induce a series of detoxifying enzymes. Thus, redox homeostasis and metabolic regulation can be important therapeutic targets for delaying the progression of neurodegenerative diseases.
Oxidative Stress and X-ray Exposure Levels-Dependent Survival and Metabolic Changes in Murine HSPCs
Authors: Karabulutoglu Melis , Finnon Rosemary , Cruz-Garcia Lourdes , Hill Mark A. , Badie Christophe
Published: December 2021

Haematopoietic bone marrow cells are amongst the most sensitive to ionizing radiation (IR), initially resulting in cell death or genotoxicity that may later lead to leukaemia development, most frequently Acute Myeloid Leukaemia (AML). The target cells for radiation-induced Acute Myeloid Leukaemia (rAML) are believed to lie in the haematopoietic stem and progenitor cell (HSPC) compartment. Using the inbred strain CBA/Ca as a murine model of rAML, progress has been made in understanding the underlying mechanisms, characterisation of target cell population and responses to IR. Complex regulatory systems maintain haematopoietic homeostasis which may act to modulate the risk of rAML. However, little is currently known about the role of metabolic factors and diet in these regulatory systems and modification of the risk of AML development. This study characterises cellular proliferative and clonogenic potential as well as metabolic changes within murine HSPCs under oxidative stress and X-ray exposure. Ambient oxygen (normoxia; 20.8% O2) levels were found to increase irradiated HSPC-stress, stimulating proliferative activity compared to low oxygen (3% O2) levels. IR exposure has a negative influence on the proliferative capability of HSPCs in a dose-dependent manner (0–2 Gy) and this is more pronounced under a normoxic state. One Gy x-irradiated HSPCs cultured under normoxic conditions displayed a significant increase in oxygen consumption compared to those cultured under low O2 conditions and to unirradiated HSPCs. Furthermore, mitochondrial analyses revealed a significant increase in mitochondrial DNA (mtDNA) content, mitochondrial mass and membrane potential in a dose-dependent manner under normoxic conditions. Our results demonstrate that both IR and normoxia act as stressors for HSPCs, leading to significant metabolic deregulation and mitochondrial dysfunctionality which may affect long term risks such as leukaemia.
Molecular Mechanistic Pathways Targeted by Natural Antioxidants in the Prevention and Treatment of Chronic Kidney Disease
Authors: Mohany Mohamed , Ahmed Mohammed M. , Al-Rejaie Salim S.
Published: December 2021

Chronic kidney disease (CKD) is the progressive loss of renal function and the leading cause of end-stage renal disease (ESRD). Despite optimal therapy, many patients progress to ESRD and require dialysis or transplantation. The pathogenesis of CKD involves inflammation, kidney fibrosis, and blunted renal cellular antioxidant capacity. In this review, we have focused on in vitro and in vivo experimental and clinical studies undertaken to investigate the mechanistic pathways by which these compounds exert their effects against the progression of CKD, particularly diabetic nephropathy and kidney fibrosis. The accumulated and collected data from preclinical and clinical studies revealed that these plants/bioactive compounds could activate autophagy, increase mitochondrial bioenergetics and prevent mitochondrial dysfunction, act as modulators of signaling pathways involved in inflammation, oxidative stress, and renal fibrosis. The main pathways targeted by these compounds include the canonical nuclear factor kappa B (NF-κB), canonical transforming growth factor-beta (TGF-β), autophagy, and Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid factor 2-related factor 2 (Nrf2)/antioxidant response element (ARE). This review presented an updated overview of the potential benefits of these antioxidants and new strategies to treat or reduce CKD progression, although the limitations related to the traditional formulation, lack of standardization, side effects, and safety.
In Vitro and In Vivo Studies of Anti-Lung Cancer Activity of Artemesia judaica L. Crude Extract Combined with LC-MS/MS Metabolic Profiling, Docking Simulation and HPLC-DAD Quantification
Authors: Goda Marwa S. , Nafie Mohamed S. , Awad Basma M. , Abdel-Kader Maged S. , Ibrahim Amany K. , Badr Jihan M. , Eltamany Enas E.
Published: December 2021

Artemisia judaica L. (Family: Asteraceae) exhibited antioxidant, anti-inflammatory, and antiapoptotic effects. The in vitro cytotoxic activity of A. judaica ethanolic extract was screened against a panel of cancer cell lines. The results revealed its cytotoxic activity against a lung cancer (A549) cell line with a promising IC50 of 14.2 μg/mL compared to doxorubicin as a standard. This was confirmed through the downregulation of antiapoptotic genes, the upregulation of proapoptotic genes, and the cell cycle arrest at the G2/M phase. Further in vivo study showed that a solid tumor mass was significantly reduced, with a tumor inhibition ratio of 54% relative to doxorubicin therapy in a Xenograft model. From a chemical point of view, various classes of natural products have been identified by liquid chromatography combined with tandem mass spectrometry (LC-MS/MS). The docking study of the detected metabolites approved their cytotoxic activity through their virtual binding affinity towards the cyclin-dependent kinase 2 (CDK-2) and epidermal growth factor receptor (EGFR) active sites. Finally, A. judaica is a fruitful source of polyphenols that are well-known for their antioxidant and cytotoxic activities. As such, the previously reported polyphenols with anti-lung cancer activity were quantified by high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD). Rutin, quercetin, kaempferol, and apigenin were detected at concentrations of 6 mg/gm, 0.4 mg/gm, 0.36 mg/gm, and 3.9 mg/gm of plant dry extract, respectively. It is worth noting that kaempferol and rutin are reported for the first time. Herein, A. judaica L. may serve as an adjuvant therapy or a promising source of leading structures in drug discovery for lung cancer treatment.
Fabrication of the Cu2ZnSnS4 Thin Film Solar Cell via a Photo-Sintering Technique
Authors: Cao Vu Minh Han , Bae Jaesung , Shim Joongpyo , Hong Byungyou , Jee Hongsub , Lee Jaehyeong
Published: December 2021

Alternative photo-sintering techniques for thermal annealing processes are used to improve the morphology, layer properties, and enhance solar cell performance. The fast, nontoxic, low cost, and environmentally friendly characteristics of Cu2ZnSnS4 have led to its consideration as an alternative potential absorber layer in copper indium gallium diselenide thin film solar cells. This work investigates the photo-sintering process for the absorber layer of Cu2ZnSnS4 solar cells. A Cu2ZnSnS4 layer was grown by hot-injection and screen-printing techniques, and the characteristics of the photo-sintered Cu2ZnSnS4 layer were evaluated by X-ray Diffraction, Raman spectroscopy, Energy dispersive X-ray analysis, Ultraviolet-visible spectroscopy, and field emission scanning electron microscopes. Overall, the optimal composition was Cu-poor and Zn-rich, without a secondary phase, estimated optical band-gap energy of approximately 1.6 eV, and enhanced morphology and kesterite crystallization. Using an intensity pulse light technique to the CZTS layer, fabrication of the solar cell device demonstrated successfully, and the efficiency of 1.01% was achieved at 2.96 J/cm2.
A Perturbation Approach for Lateral Excited Vibrations of a Beam-like Viscoelastic Microstructure Using the Nonlocal Theory
Authors: Li Cheng , Zhu Chengxiu , Sui Suihan , Yan Jianwei
Published: December 2021

In this paper, we investigate the lateral vibration of fully clamped beam-like microstructures subjected to an external transverse harmonic excitation. Eringen’s nonlocal theory is applied, and the viscoelasticity of materials is considered. Hence, the small-scale effect and viscoelastic properties are adopted in the higher-order mathematical model. The classical stress and classical bending moments in mechanics of materials are unavailable when modeling a microstructure, and, accordingly, they are substituted for the corresponding effective nonlocal quantities proposed in the nonlocal stress theory. Owing to an axial elongation, the nonlinear partial differential equation that governs the lateral motion of beam-like viscoelastic microstructures is derived using a geometric, kinematical, and dynamic analysis. In the next step, the ordinary differential equations are obtained, and the time-dependent lateral displacement is determined via a perturbation method. The effects of external excitation amplitude on excited vibration are presented, and the relations between the nonlocal parameter, viscoelastic damping, detuning parameter, and the forced amplitude are discussed. Some dynamic phenomena in the excited vibration are revealed, and these have reference significance to the dynamic design and optimization of beam-like viscoelastic microstructures.
Investigating the Threshold Conditions of Air Breakdown with Mode-Locked Q-Switched Laser Pulses, and the Temporal Dynamics of Induced Plasma with Self-Scattering Phenomenon
Authors: Yen Kai-Ting , Wu Chih-Hung , Wang Pin-Hsun , Tuan Pi-Hui , Su Kuan-Wei
Published: December 2021

A Q-switched Nd:YAG laser with mode-locked modulations is utilized to explore the laser-induced air breakdown. The various modulation depths of the mode-locking within the Q-switched pulse can be utilized to investigate the threshold conditions. With the GHz high-speed detectors to accurately measure the temporal pulse shape pulse by pulse, it is verified that the air breakdown threshold is crucially determined by the peak-power density instead of the energy density from the statistic results, especially for mode-locked Q-switched lasers. The stability of the system for laser-induced breakdown can be evaluated by threshold width through fitting the statistical result. Otherwise, by measuring the temporal characteristics of the excitation pulse and the induced plasma, it is further found that the plasma radiation displays a few-nanoseconds time delay to the excitation pulse and shows a decaying tail to be 10 times longer than the plasma build-up time. Moreover, the incident laser pulse is observed to be self-scattered by the air breakdown, and a rapidly modulated scattering rate is found with a slight delay time to the excitation mode-locked subpulse modulations.
Deep Learning-Based Context-Aware Recommender System Considering Contextual Features
Authors: Jeong Soo-Yeon , Kim Young-Kuk
Published: December 2021

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.
Natural Hydrocarbon Samples Classification by Topological Analysis Method
Authors: Fedotov Andrey , Grishin Pavel , Ivonin Dmitriy , Chernyavskiy Mikhail , Grachev Eugene
Published: December 2021

Nowadays material science involves powerful 3D imaging techniques such as X-ray computed tomography that generates high-resolution images of different structures. These methods are widely used to reveal information about the internal structure of geological cores; therefore, there is a need to develop modern approaches for quantitative analysis of the obtained images, their comparison, and classification. Topological persistence is a useful technique for characterizing the internal structure of 3D images. We show how persistent data analysis provides a useful tool for the classification of porous media structure from 3D images of hydrocarbon reservoirs obtained using computed tomography. We propose a methodology of 3D structure classification based on geometry-topology analysis via persistent homology.
Noise Source Identification in Training Facilities and Gyms
Authors: Wróbel Jakub , Pietrusiak Damian
Published: December 2021

This paper deals with noise problems in industrial sites adapted for commercial training venues. The room acoustics of such an object were analyzed in the scope of the reverberation time and potential acoustic adaptation measures are indicated. Identification and classification of noise sources in training facilities and gyms was carried out based on the acoustic measurements. The influence of rubber padding on impact and noise reduction was investigated in the case of chosen noise-intensive exercise activities performed in a previously described acoustic environment. Potential noise reduction measures are proposed in the form of excitation reduction, vibration isolation, and room acoustics adaptation.
High Crystalline Quality Conductive Polypyrrole Film Prepared by Interface Chemical Oxidation Polymerization Method
Authors: Wang Yuefei , Song Renjing , Li Li , Fu Rongpeng , Liu Zhiguo , Li Bingsheng
Published: December 2021

The authors report that polypyrrole (PPy) films with large area and high crystalline quality have been achieved using an interfacial chemical oxidation method. By dissolving different reactants in two immiscible solvents, the PPy is synthetized at the interface region of the two solutions. The PPy films have sharp XRD diffraction peaks, indicating that the molecular chains in the film are arranged in a high degree of order and that they reflect high crystalline quality. High crystal quality is also conducive to improving electrical conductivity. The conductivity of the as prepared PPy film is about 0.3 S/cm, and the carrier mobility is about 5 cm2/(Vs). In addition, the biggest advantage of this method is that the prepared PPy film has a large area and is easy to transfer to other substrates. This will confidently broaden the application of PPy in the future.
Gigantic Coaxial Line for Experimental Studies of the Interaction of Nanosecond Electromagnetic Pulses with an Ionized Gas Medium
Authors: Gushchin Mikhail , Palitsin Alexey , Strikovskiy Askold , Zudin Ilya , Korobkov Sergey , Loskutov Konstantin , Gromov Alexander , Goykhman Mikhail , Rodin Yuri , Korchagin Vyacheslav , Kornishin Sergey , Kotov Alexey , Kuzin Alexey , Terekhin Vladimir
Published: December 2021

A large-scale coaxial line filled with the plasma of RF discharge has been developed for laboratory modeling of the effects of the interaction of ultrashort electromagnetic pulses (EMPs) with the atmosphere and the ionosphere in the KROT facility. The oversized coaxial line ensures pulse transmission through an ionized medium in the TEM mode, which corresponds to the polarization of the transverse electromagnetic wave in free space, and in uniform isotropic plasma. The coaxial line has a length of 10 m and a diameter of 140 cm. The processes of propagation of the nanosecond and subnanosecond pulses in this line, in vacuum and with plasma, have been simulated numerically.
A Spectrum Correction Method Based on Optimizing Turbulence Intensity
Authors: Yi Wenwu , Lu Ziqi , Hao Junbo , Zhang Xinge , Chen Yan , Huang Zhihong
Published: December 2021

Based on the classical spectral representation method of simulating turbulent wind speed fluctuation, a harmonic superposition algorithm was introduced in detail to calculate the homogeneous turbulence wind field simulation in space. From the view of the validity of the numerical simulation results in MATLAB and the simulation efficiency, this paper discussed the reason for the bias existing between three types of turbulence intensity involved in the whole simulation process: simulated turbulence intensity, setting reference turbulence intensity, and theoretical turbulence intensity. Therefore, a novel spectral correction method of a standard deviation compensation coefficient was proposed. The simulation verification of the correction method was carried out based on the Kaimal spectrum recommended by IEC61400-1 by simulating the uniform turbulent wind field in one-dimensional space at the height of the hub of a 15 MW wind turbine and in two-dimensional space in the rotor swept area. The results showed that the spectral correction method proposed in this paper can effectively optimize the turbulence intensity of the simulated wind field, generate more effective simulation points, and significantly improve the simulation efficiency.
Antibacterial and Biodegradable Electrospun Filtering Membranes for Facemasks: An Attempt to Reduce Disposable Masks Use
Authors: Costa Sofia M. , Pacheco Luísa , Antunes Wilson , Vieira Ricardo , Bem Nuno , Teixeira Pilar , Fangueiro Raul , Ferreira Diana P.
Published: December 2021

Due to the prevalence of the COVID-19 pandemic, the demand for disposable facemasks has become a global issue. Unfortunately, the use of these products has negative effects on the environment, and therefore, the use of biodegradable materials is a powerful strategy to overcome this challenge. Aligned with this concept, in this work, biodegradable facemasks were developed using poly(ε-caprolactone) (PCL) polymer and cotton natural fibers. The filter layer was produced using an electrospinning technique, since electrospun membranes present remarkable characteristics for air filtration. The electrospun membranes were functionalized with different nanoparticles (NPs), including silver (Ag), titanium dioxide (TiO2) and magnesium oxide (MgO), in order to include new properties, namely antibacterial effect. The developed membranes were characterized by FESEM, EDS, ATR-FTIR, GSDR and TGA, which confirmed the successful impregnation of NPs onto PCL membranes. The antibacterial effect and filtration efficiency were assessed, with the PCL/MgO NPs membrane presenting better results, showing inhibition zone diameters of 25.3 and 13.5 mm against Gram-positive and Gram-negative bacteria, respectively, and filtration efficiency of 99.4%. Three facemask prototypes were developed, and their filtration efficiency, air permeability and thermal comfort were evaluated. Overall, this study demonstrates the potential of PCL/NPs electrospun membranes to act as an active and biodegradable filter layer in facemasks.
Liquid CO2 Phase-Transition Rock Fracturing: A Novel Technology for Safe Rock Excavation
Authors: Sui Haoyue , Su Tianming , Hu Ruilin , Yang Ke , Cheng Yaxing
Published: December 2021

In order to determine the applicability of liquid CO2 phase-transition fracturing technology in rock mass excavations, the principles of CO2 phase-transition fracturing were analyzed, and field tests of liquid CO2 phase-transition fracturing were performed. An “Unmanned Aerial Vehicle (UAV) camera shooting + Microstructure Image Processing System (MIPS) analyzing” method was used to acquire the rock mass characteristics. Further, the Hilbert–Huang Transform (HHT) energy analysis principle was adopted to analyze the characteristics of fracturing vibration waves. The experimental results showed that during the process of fracturing, there were both dynamic actions of rock breakage due to excitation stress wave impacts, and quasi-static actions of rock breakage caused by gasification expansion wedges. In semi-infinite spaces, rock-breakage zones can mainly be divided into crushing zones, fracture zones, and vibration zones. At the same time, under ideal fracturing effects and large volumes, the fracturing granularity will be in accordance with the fractal laws. For example, the larger the fractal dimensions, the higher the proportion of small fragments, and vice versa. Moreover, the vibration waves of the liquid CO2 phase-transition fracturing have short durations, fast attenuation, and fewer high-frequency components. The dominant frequency band of energy will range between 0 and 20 Hz. The liquid CO2 phase-transition fracturing technology has been observed to overcome the shortcomings of traditional explosive blasting methods and can be applied to a variety of rock types. It is a safe and efficient method for rock-breaking excavations; therefore, the above technology effectively provides a new method for the follow-up of similar engineering practices.
Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression
Authors: Astolfi Davide , Pandit Ravi
Published: December 2021

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.
A Supervised Learning Method for Improving the Generalization of Speaker Verification Systems by Learning Metrics from a Mean Teacher
Authors: Kim Ju-Ho , Shim Hye-Jin , Jung Jee-Weon , Yu Ha-Jin
Published: December 2021

The majority of recent speaker verification tasks are studied under open-set evaluation scenarios considering real-world conditions. The characteristics of these tasks imply that the generalization towards unseen speakers is a critical capability. Thus, this study aims to improve the generalization of the system for the performance enhancement of speaker verification. To achieve this goal, we propose a novel supervised-learning-method-based speaker verification system using the mean teacher framework. The mean teacher network refers to the temporal averaging of deep neural network parameters, which can produce a more accurate, stable representations than fixed weights at the end of training and is conventionally used for semi-supervised learning. Leveraging the success of the mean teacher framework in many studies, the proposed supervised learning method exploits the mean teacher network as an auxiliary model for better training of the main model, the student network. By learning the reliable intermediate representations derived from the mean teacher network as well as one-hot speaker labels, the student network is encouraged to explore more discriminative embedding spaces. The experimental results demonstrate that the proposed method relatively reduces the equal error rate by 11.61%, compared to the baseline system.
Laser-Induced Period Surface Structures to Improve Solderability of Electrical Solder Pads
Authors: Rung Stefan , Hellmann Ralf
Published: December 2021

We report on structuring copper representing soldering pads of printed circuit boards by laser-induced periodic surface structures. Femtosecond laser radiation is used to generate low spatial frequency laser-induced surface structures, having a spatial period of 992 nm and a modulation depth of 120 nm, respectively. The slump of screen-printed solder paste is measured to compare the solder coverage on the pads after the solder process on a hot plate. A comparative study of the coverage of solder paste on a fresh polished pad, a pad stored for two weeks, and femtosecond laser-structured pads reveals the improved wettability of structured pads even after storage. In addition, leaded and lead-free solder pads are compared with the particular advantages of the solder-free pad when periodically laser structured. Our findings are attributed to two major effects: namely, the increase of the surface area and the improved surface chemical wettability. Overall, the application of laser-induced periodic surface structures helps to reduce the demand of lead-based solder in the electronic industry and provides a feasible method for a fast and spatial selective way of surface functionalization.
Kinetic Solar Envelope: Performance Assessment of a Shape Memory Alloy-Based Autoreactive Façade System for Urban Heat Island Mitigation in Athens, Greece
Authors: Koukelli Christina , Prieto Alejandro , Asut Serdar
Published: December 2021

The paper explores the potentials of shape memory alloys (SMAs) for the design of autoreactive façade systems without using additional external energy. The exploration is conducted and assessed through the design of a façade concept for the city of Athens in Greece, aiming to improve both the indoor and outdoor environment by means of a kinetic autoreactive system featuring a dual-seasonal function, with a focus on the building’s direct and indirect impact on the urban heat island (UHI) effect. The paper covers a demonstration of the methodology followed, using a feedback-loop logic informed by environmental and energy performance evaluation studies in Grasshopper to optimize the geometry and movement of the shading component. During the façade design process, a comprehensive and systematic computational toolset is being developed, targeted on the abovementioned performance evaluation studies. Through the development and assessment of the façade concept, the objective is to explore the potentials and limitations for the application of autoreactive envelopes in the façade design. At the same time, the aim is to exploit the possibilities and optimization potentials offered through the developed iterative computational workflows, by showcasing the methodology and interoperability logic of the digital tools used for the data interchange.
Enhancement of Schottky Junction Silicon Solar Cell with CdSe/ZnS Quantum Dots Decorated Metal Nanostructures
Authors: Nguyen Ha Trang , Tran Thanh Thao , Bhatt Vishwa , Kumar Manjeet , Song Jinwon , Yun Ju-Hyung
Published: December 2021

Recently, in the solar energy society, several key technologies have been reported to meet a grid parity, such as cost-efficient materials, simple processes, and designs. Among them, the assistive plasmonic of metal nanoparticles (MNPs) integrating with the downshifting on luminescent materials attracts much attention. Hereby, Si-based Schottky junction solar cells are fabricated and examined to enhance the performance. CdSe/ZnS quantum dots (QDs) with different gold nanoparticles (Au NPs) sizes were incorporated on a Si light absorbing layer. Due to the light scattering effect from plasmonic resonance, the sole Au NPs layer results in the overall enhancement of Si solar cell’s efficiency in the visible spectrum. However, the back-scattering and high reflectance of Au NPs lead to efficiency loss in the UV region. Therefore, the QDs layer acting as a luminescent downshifter is deployed for further efficiency enhancement. The QDs layer absorbs high-energy photons and re-emits lower energy photons in 528 nm of wavelength. Such a downshift layer can enhance the overall efficiency of Si solar cells due to poor intrinsic spectral response in the UV region. The optical properties of Au NPs and CdSe QDs, along with the electrical properties of solar cells in combination with Au/QD layers, are studied in depth. Moreover, the influence of Au NPs size on the solar cell performance has been investigated. Upon decreasing the diameters of Au NPs, the blueshift of absorbance has been observed, cooperating with QDs, which leads to the improvement of the quantum efficiency in the broadband of the solar spectrum.
The Impact of Rolling Stock Heterogeneity on Tram Stop Dwell Time
Authors: Majstorović Igor , Ahac Maja , Ahac Saša , Bašić Silvio
Published: December 2021

The prediction of stop dwell time is a major issue in travel speed modeling, i.e., in the definition of travel time for high-frequency and high-ridership rail public transport. This is due to the numerous influential factors associated with stop dwell time, variable both in space and time, such as passenger flow, vehicle and stop design characteristics, and traffic organization. To investigate the impact of the heterogeneity of tram vehicles on stop dwell time, a survey was conducted regarding the tram network of the City of Zagreb. The dwell time at three consecutive island stops served by three different tram vehicle types was analyzed. The stops are located near the city center, in a separate tram corridor, at the far side of signalized intersections. Dwell time was determined and evaluated through the statistical analysis of observed, measured, and video-recorded data. The results show that at stops with up to 200 passengers per hour, the dwell time is around 15 s. For volumes of 20 passengers or less per tram, the dwell time is mostly affected by the tram door opening mechanism and opening/closing time. As the passenger volumes become higher, the number of doors per vehicle becomes more significant.
Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks
Authors: Sohail Muhammad , Ali Ghulam , Rashid Javed , Ahmad Israr , Almotiri Sultan H. , AlGhamdi Mohammed A. , Nagra Arfan A. , Masood Khalid
Published: December 2021

Multi-culture facial expression recognition remains challenging due to cross cultural variations in facial expressions representation, caused by facial structure variations and culture specific facial characteristics. In this research, a joint deep learning approach called racial identity aware deep convolution neural network is developed to recognize the multicultural facial expressions. In the proposed model, a pre-trained racial identity network learns the racial features. Then, the racial identity aware network and racial identity network jointly learn the racial identity aware facial expressions. By enforcing the marginal independence of facial expression and racial identity, the proposed joint learning approach is expected to be purer for the expression and be robust to facial structure and culture specific facial characteristics variations. For the reliability of the proposed joint learning technique, extensive experiments were performed with racial identity features and without racial identity features. Moreover, culture wise facial expression recognition was performed to analyze the effect of inter-culture variations in facial expression representation. A large scale multi-culture dataset is developed by combining the four facial expression datasets including JAFFE, TFEID, CK+ and RaFD. It contains facial expression images of Japanese, Taiwanese, American, Caucasian and Moroccan cultures. We achieved 96% accuracy with racial identity features and 93% accuracy without racial identity features.
A Prototype of an Electronic Pegboard Test to Measure Hand-Time Dexterity with Impaired Hand Functionality
Authors: Al-Naami Bassam , Al-Naimat Feras , Almalty Abdul-Majeed Raja M. , Visconti Paolo , Al-Hinnawi Abdel-Razzak
Published: December 2021

This paper proposes an electronic prototype of the Grooved Pegboard Test (GPT), which is normally used to test the presence of hand dexterity. The prototype imitates the geometrical dimensions of an on-the-market GPT device, but it is electronic, not manual like the one available now for users. The suggested electronic GPT device makes automated time calculation between placing the first and the last peg in their designated locations, instead of manually observing a stopwatch normally used during the GPT. The electronic GPT prototype consists of a fabricated wooden box, electronics (switches and microcontroller), and liquid crystal display (LCD). A set of 40 normal volunteers, 20 females and 20 males, tested the designed prototype. A set of six volunteers with chronic medical conditions also participated in evaluating the proposed model. The results on normal volunteers showed that the proposed electronic GPT device yielded time calculations that match the population mean value of similar calculations by the GPT device. The one-sample t-test showed no significant difference in calculations between the new electronic GPT and the manual GPT device. The p-value was much higher than 0.05, indicating the possible use of the suggested electronic GPT device.
Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods
Authors: Podolská Kateřina
Published: December 2021

The aim of this paper is to statistically examine whether there are different patterns in daily numbers of deaths during the quiet periods of solar activity, in contrast to the periods of the strong solar storms. We considered three periods of solar storms (storm of 14 July 2000 Bastille Day Event, storm of 28 October 2003 Halloween Solar Storms, and storm of 17 March 2015 St. Patrick’s Day event) and three periods of continuous very low solar activity (13 September–24 October 1996, 21 July–20 August 2008, and 31 July–31 August 2009) during the Solar Cycles No. 23 and No. 24. In particular, we focus on diseases of the nervous system (group VI from ICD-10) and diseases of the circulatory system (group IX from ICD-10) separately for both sexes and two age groups (under 39 and 40+). We demonstrate that in the resulting graphical models there was a connection between the daily number of deaths and all indices of solar and geomagnetic activity in periods of low solar activity in contrast to periods of strong solar storms in some monitored groups according to age, sex, and group of diagnosis.
Magic Wavelengths for 1S–nS and 2S–nS Transitions in Hydrogenlike Systems
Authors: Adhikari Chandra M. , Canales Jonathan C. , Arthanayaka Thusitha P. W. , Jentschura Ulrich D.
Published: December 2021

We study the magic wavelength for two-photon 1S–nS transitions in a hydrogen and deuterium atom, as well as 2S–nS transitions, where the lower level is the metastable 2S state. At the magic wavelength, the dynamic Stark shifts of the ground and the excited state of the transition coincide, so that the transition frequency is independent of the intensity of the trapping laser field. Experimentally feasible magic wavelengths of transitions with small slopes in the atomic polarizabilities are determined; these are the most stable magic wavelengths against variations of the laser frequency. We provide data for the magic wavelengths for the 1S–nS and 2S–nS transitions in hydrogen and deuterium, with n=2,⋯,8. We also analyze the stability of the elimination of the ac Stark shift at the magic wavelength against tiny variations of the trapping laser frequency from the magic value.
The Snoring Index Identifies Risk of Non-Alcoholic Fatty Liver Disease in Patients with Obstructive Sleep Apnea Syndrome
Authors: Bahr Katharina , Simon Perikles , Leggewie Barbara , Gouveris Haralampos , Schattenberg Jörn
Published: December 2021

Background: The aim of this observational cohort study was to explore the severity of liver disease in patients with suspected obstructive sleep apnea in Germany. Methods: Patients undergoing polysomnography or home sleep apnea testing (HSAT) as an evaluation for the presence of OSA were screened using vibration-controlled transient elastography (VCTE) and continuous attenuation parameter (CAP) with a Fibroscan ® Mini 430. Clinical and laboratory data were collected following the overnight exam. Results: In total, 78 patients (28 female (35.9%), mean age 54.2 years) with OSA defined by an apnea-hypopnea-index >5 events/hour were included between OCT 2020 and APR 2021. Patients exhibited a high metabolic risk profile with 17% known diabetes mellitus type 2 (T2D), 62% arterial hypertension, 14% hyperlipidemia and 36% BMI > 30 kg/m2. The prevalence of steatosis defined by a CAP > 280 dB/m was 54%. The prevalence of at least significant fibrosis was 16% (E > 9.0 kPa). Interestingly, patients with a snoring index above the median of 278/h showed significantly higher CAP-values (p = 0.0002). In addition, the proportion of oxygen saturations below 90% (t90) correlated with CAP-values (p = 0.02), as well as metabolic risk factors including increased waist circumference (p = 0.005) and body mass index (BMI) (p = 0.035). On the other hand, the apnea-hypopnea-index (AHI) as a marker of OSA severity did not correlate with VCTE, CAP or laboratory parameters. Conclusion: Patients with moderate to severe OSA have a high prevalence of hepatic steatosis. The snoring index is an easy-to-use clinical tool to identify patients at risk for relevant liver disease within the larger group of patients with OSA.
ADAMTS Proteins and Vascular Remodeling in Aortic Aneurysms
Authors: Mougin Zakaria , Huguet Herrero Julia , Boileau Catherine , Le Goff Carine
Published: December 2021

Extracellular matrix (ECM) in the vascular wall is a highly dynamic structure composed of a set of different molecules such as elastins, collagens, fibronectin (Fn), laminins, proteoglycans, and polysaccharides. ECM undergoes remodeling processes to regulate vascular smooth muscle and endothelial cells’ proliferation, differentiation, and adhesion. Abnormalities affecting the ECM can lead to alteration in cellular behavior and from this, this can conduce to the development of pathologies. Metalloproteases play a key role in maintaining the homeostasis of ECM by mediating the cleavage of different ECM components. There are different types of metalloproteases: matrix metalloproteinases (MMPs), disintegrin and metalloproteinases (ADAMs), and ADAMs with thrombospondin motifs (ADAMTSs). ADAMTSs have been found to participate in cardiovascular physiology and diseases and specifically in aortic aneurysms. This review aims to decipher the potential role of ADAMTS proteins in the physiopathologic development of Thoracic Aortic Aneurysms (TAA) and Abdominal Aortic Aneurysms (AAA). This review will focus on what is known on the ADAMTS family involved in human aneurysms from human tissues to mouse models. The recent findings on THSD4 (encoding ADAMTSL6) mutations in TAA give a new insight on the involvement of the ADAMTS family in TAA.
Safety of Multiple Vaccinations and Durability of Vaccine-Induced Antibodies in an Italian Military Cohort 5 Years after Immunization
Authors: Ferlito Claudia , Visco Vincenzo , Biselli Roberto , Cattaruzza Maria Sofia , Carreras Giulia , Salerno Gerardo , Lista Florigio , Capobianchi Maria Rosaria , Castilletti Concetta , Lapa Daniele , Antonelli Guido , Gentile Massimo , Sorice Maurizio , Riitano Gloria , Lucania Giuseppe , Riccieri Valeria , Mainiero Fabrizio , Angeloni Antonio , Lucarelli Marco , Ferraguti Giampiero , Autore Alberto , Lastilla Marco , Salemi Simonetta , Biondo Michela Ileen , Picchianti-Diamanti Andrea , Caporuscio Sara , Teloni Raffaela , Mariotti Sabrina , Nisini Roberto , D’Amelio Raffaele
Published: December 2021

We previously examined the safety and immunogenicity of multiple vaccines administered to a military cohort, divided into two groups, the first composed of students at military schools, thus operating inside the national borders for at least 3 years, and the other formed of soldiers periodically engaged in a 9-month-long mission abroad (Lebanon). In the current study, we analyzed 112 individuals of this cohort, 50 pertaining to the first group and 62 to the second group, in order to examine the possible late appearance of side effects and to calculate the half-life of the induced antibodies. Moreover, the possible involvement of B-cell polyclonal activation as a pathogenetic mechanism for long term antibody persistence has even been explored. No late side effects, as far as autoimmunity and/or lymphoproliferation appearance, have been noticed. The long duration of the vaccine induced anti-HAV antibodies has been confirmed, whereas the antibodies induced by tetravalent meningococcal polysaccharide vaccine have been found to persist above the threshold for putative protection for a longer time, and anti-tetanus, diphtheria, and polio 1 and 3 for a shorter time than previously estimated. No signs of polyclonal B-cell activation have been found, as a possible mechanism to understand the long antibody persistence.
Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells
Authors: Liu Yining , Zhao Min
Published: December 2021

Background: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mechanisms of neuropeptides and their receptors at the single-cell level remain unclear. We conducted a systematic single-cell transcriptome data integration analysis to clarify the similarities and variations of neuropeptide-mediated cell communication between various malignancies. Methods: Based on the single-cell expression information in 72 cancer datasets across 24 cancer types, we characterized actively expressed neuropeptides and receptors as having log values of the quantitative transcripts per million ≥ 1. Then, we created the putative cell-to-cell communication network for each dataset by using the known interaction of those actively expressed neuropeptides and receptors. To focus on the stable cell communication events, we identified neuropeptide and downstream receptors whose interactions were detected in more than half of all conceivable cell-cell interactions (square of the total cell population) in a dataset. Results: Focusing on those actively expressed neuropeptides and receptors, we built over 76 million cell-to-cell communications across 70 cancer datasets. Then the stable cell communication analyses were applied to each dataset, and about 14 million stable cell-to-cell communications could be detected based on 16 neuropeptides and 23 receptors. Further functional analysis indicates these 39 genes could regulate blood pressure and are significantly associated with patients’ survival among over ten thousand The Cancer Genome Atlas (TCGA)pan-cancer samples. By zooming in lung cancer-specific clinical features, we discovered the 39 genes appeared to be enriched in the patients with smoking. In skin cancer, they may differ in the patients with the distinct histological subtype and molecular drivers. Conclusions: At the single-cell level, stable cell communications across cancer types demonstrated some common and distinct neuropeptide-receptor patterns, which could be helpful in determining the status of neuropeptide-based cell communication and developing a peptide-based therapy strategy.
Clinical Relevance of Cerebrospinal Fluid Antibody Titers in Anti-N-Methyl-d-Aspartate Receptor Encephalitis
Authors: Cai Meng-Ting , Zheng Yang , Wang Sa , Lai Qi-Lun , Fang Gao-Li , Shen Chun-Hong , Xu Yong-Feng , Zhang Yin-Xi , Ding Mei-Ping
Published: December 2021

Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis. To date, there has been no study on the relationship between antibody (Ab) titers and clinical phenotype. This study aims to clarify the relationship between cerebrospinal fluid Ab titers and clinical manifestations of anti-NMDAR encephalitis at onset. Seventy-six consecutive patients with a definite diagnosis were enrolled. The relationship between Ab titers and different onset symptoms including psychiatric symptoms, seizures, and memory deficits were analyzed. We further investigated the correlation between Ab titers and clinical severity as assessed by the modified Rankin scale (mRS) and the clinical assessment scale for autoimmune encephalitis (CASE), respectively. The Ab titers had a median value of 1:10 (range 1:1–1:100). There was no significant difference in titers among various clinical factors including gender and combination of tumor and other diseases (each p > 0.05). Patients presenting with psychiatric symptoms at onset had higher titers than those with seizures (p = 0.008) and memory deficits (p = 0.003). The mRS scores revealed a significant but weak correlation with Ab titers (r = 0.243, p = 0.034), while CASE scores did not correlate with the titers (p = 0.125). Our findings indicated that the Ab titers were associated with the type of onset symptoms, with a higher level of patients with psychiatric symptoms. Regarding the clinical severity, the titers showed a weak correlation with the mRS, but no correlation with the CASE.