bluuebunny/crossref_metadata_2025 huggingface dataset:
Correction: da Silva et al. Increased Sulphur Amino Acids Consumption as OH-Methionine or DL-Methionine Improves Growth Performance and Carcass Traits of Growing-Finishing Pigs Fed under Hot Conditions. Animals 2022, 12, 2159
Authors: da Silva Caio Abércio , Dias Cleandro Pazinato , Callegari Marco Aurélio , de Souza Kelly Lais , Barbi José Henrique , Fagundes Naiara Simarro , Batonon-Alavo Dolores I. , Foppa Luciana
Published: March 2023

In the original publication [...]
Applications of Artificial Intelligence in Orthodontics—An Overview and Perspective Based on the Current State of the Art
Authors: Kunz Felix , Stellzig-Eisenhauer Angelika , Boldt Julian
Published: March 2023

Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.
A Nonuniformity Correction Method Based on 1D Guided Filtering and Linear Fitting for High-Resolution Infrared Scan Images
Authors: Li Bohan , Chen Weicong , Zhang Yong
Published: March 2023

During imaging, each infrared focal plane linear array scan detector detection unit determines a row of pixels in the image output. This sensor’s nonuniformity appears as horizontal stripes. Correcting nonuniformity in high-resolution images without destroying delicate details is challenging. In this paper, a single-frame-based nonuniformity correction algorithm is proposed. A portion of a single-frame picture is intercepted initially. The 1D column guided filter is applied to smooth the captured image in the vertical direction. Then, the smooth image and high-frequency component with horizontal stripes and texture information are obtained. The subsequent step is to use the smooth portion of the image as the guided image and the high-frequency portion of the image as the input, so that the estimated nonuniformity noise of the image may be extracted using a 1D row guided filter. The segment of the corrected image is then obtained by subtracting the estimated nonuniformity noise from the segment of the raw image. The correction coefficients could be obtained by performing a linear regression fit on the pre- and post-guided filtering image segments. With the correction coefficients, the entire image could be corrected. Based on qualitative and quantitative analysis, the proposed algorithm outperforms other current advanced algorithms in terms of nonuniformity correction and real-time performance.
Assessment of Air Pollution in Different Areas (Urban, Suburban, and Rural) in Slovenia from 2017 to 2021
Authors: Ivanovski Maja , Alatič Kris , Urbancl Danijela , Simonič Marjana , Goričanec Darko , Vončina Rudi
Published: March 2023

Air pollution can have a significant effect on human health. The present work is focused on the investigation of daily, monthly, and annual concentration levels of five typical air pollutants (SO2, NO2, NOX, PM10, and PM2.5) in the Republic of Slovenia (RS) from January 2017 to December 2021. The study was conducted at five different monitoring stations of the following kind: traffic (A), industrial (D), and background (B, C, E). The obtained results showed a decline in the average concentrations for all the studied air pollutants through the years, respectively. The daily average SO2 concentrations were the lowest in the year 2021 at location B, which is classified as background location, while the highest were detected in the year 2018 at location E, which is also classified as background location. The average daily concentrations of NO2 and NOX were the highest at location A in the year 2017, whereas the lowest were detected in the year 2010 and 2021. It is believed that those results are a consequence of measures set by the Slovenian government during the COVID-19 pandemic. The PM10 and PM2.5 daily average concentrations were the highest at location A in 2017, while the lowest were observed in the year 2019 at location C. Meteorological parameters (temperature, wind speed, and relative humidity) were studied in addition. In general, the high temperatures in ambient air are responsible for the intense concentrations of air pollutants. It was found in the study results for temperature, wind speed, and relative humidity that no significant difference was shown between studied years.
Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction
Authors: Zhang Qi , Shao Min
Published: March 2023

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these observations convey accurate atmospheric information to the initial condition through data assimilation (DA) schemes. The massive data volume has pushed the community to develop novel approaches to reduce the number of assimilated channels while retaining as much information content as possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations of channel-selection schemes are (1) the deficiency in retaining the observational information content and (2) the higher cross-channel correlation in the observational error (R) matrix. This paper introduces a hyperspectral IR observation DA scheme in the principal component (PC) space. Four-month performance comparison case studies using the Weather Research and Forecasting model (WRF) as a forecast module between PC-score assimilation and the selected-channel assimilation experiment show that the PC-score assimilation scheme can reduce the initial condition’s root-mean-squared error for temperature and water vapor compared to the channel-selection scheme and thus improve the forecasting of precipitation and high-impact weather. Case studies using the Unified Forecast System Short-Range Weather (UFS-SRW) application as forecast module also indicate that the positive impact can be retained among different NWP models.
Application of Radar Radial Velocity Data Assimilation in the Forecasts of Typhoon Linfa Based on Different Horizontal Length Scale Factors
Authors: Bian Huimin , Min Jinzhong , Shen Feifei
Published: March 2023

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved.
A Study on the Wide Range of Relative Humidity in Cirrus Clouds Using Large-Ensemble Parcel Model Simulations
Authors: Zhao Miao , Shi Xiangjun
Published: March 2023

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RHi). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RHi are investigated through in situ observations and parcel model simulations. Vertical motion is an active external force that changes the RHi, and ice crystal deposition/sublimation plays a role in mitigating the change in the RHi. They are the two most important mechanisms involved in controlling the RHi fluctuation during cirrus evolution and could well explain the wide range of RHi in wave-related cirrus clouds. Furthermore, a comparison of statistical cloud characteristics from both observations and simulations shows that a very low value (e.g., 0.001) for the water vapor ice deposition coefficient is highly unlikely.
A Prompt Decarbonization Pathway for Shipping: Green Hydrogen, Ammonia, and Methanol Production and Utilization in Marine Engines
Authors: Shi Jie , Zhu Yuanqing , Feng Yongming , Yang Jun , Xia Chong
Published: March 2023

The shipping industry has reached a higher level of maturity in terms of its knowledge and awareness of decarbonization challenges. Carbon-free or carbon-neutralized green fuel, such as green hydrogen, green ammonia, and green methanol, are being widely discussed. However, little attention has paid to the green fuel pathway from renewable energy to shipping. This paper, therefore, provides a review of the production methods for green power (green hydrogen, green ammonia, and green methanol) and analyzes the potential of green fuel for application to shipping. The review shows that the potential production methods for green hydrogen, green ammonia, and green methanol for the shipping industry are (1) hydrogen production from seawater electrolysis using green power; (2) ammonia production from green hydrogen + Haber–Bosch process; and (3) methanol production from CO2 using green power. While the future of green fuel is bright, in the short term, the costs are expected to be higher than conventional fuel. Our recommendations are therefore as follows: improve green power production technology to reduce the production cost; develop electrochemical fuel production technology to increase the efficiency of green fuel production; and explore new technology. Strengthening the research and development of renewable energy and green fuel production technology and expanding fuel production capacity to ensure an adequate supply of low- and zero-emission marine fuel are important factors to achieve carbon reduction in shipping.
Statistical PM2.5 Prediction in an Urban Area Using Vertical Meteorological Factors
Authors: Saiohai Jutapas , Bualert Surat , Thongyen Thunyapat , Duangmal Kittichai , Choomanee Parkpoom , Szymanski Wladyslaw W.
Published: March 2023

A key concern related to particulate air pollution is the development of an early warning system that can predict local PM2.5 levels and excessive PM2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show great potential for this purpose. The objective of this study was to compare the performance of multiple linear regression (MLR) and multilayer perceptron (MLP) in predicting PM2.5 levels. The software was trained to predict PM2.5 levels up to 7 days in advance using data from long-term measurements of vertical meteorological factors taken at five heights above ground level (AGL)—10, 30, 50, 75, and 110 m—and PM2.5 concentrations measured 30 m AGL. The data used were collected between 2015 and 2020 at the Microclimate and Air Pollutants Monitoring Tower station at Kasetsart University, Bangkok, Thailand. The results showed that the correlation coefficients of PM2.5 predicted and observed using MLR and MLP were in the range of 0.69–0.86 and 0.64–0.82, respectively, for 1–3 days ahead. Both models showed satisfactory agreement with the measured data, and MLR performed better than MLP at PM2.5 prediction. In conclusion, this study demonstrates that the proposed approach can be used as a component of an early warning system in cities, contributing to sustainable air quality management in urban areas.
Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method
Authors: Zhang Lijun , Ji Tuo , Yu Shihao , Liu Guanchen
Published: March 2023

The deterioration of the health state of lithium-ion batteries will lead to the degradation of the battery performance, the reduction of the maximum available capacity, the continuous shortening of the service life, the reduction of the driving range of electric vehicles, and even the occurrence of safety accidents in electric vehicles driving. To solve the problem that the traditional battery management system is difficult to accurately manage and predict its health condition, this paper proposes the mechanism and influencing factors of battery degradation. The battery capacity is selected as the characterization of the state of health (SOH), and the long short-term memory (LSTM) model of battery capacity is constructed. The intrinsic pattern of capacity degradation is detected and extracted from the perspective of time series. Experimental results from NASA and CALCE battery life datasets show that the prediction approach based on the LSTM model can accurately predict the available capacity and the remaining useful life (RUL) of the lithium-ion battery.
Dehydrogenation of Alkali Metal Aluminum Hydrides MAlH4 (M = Li, Na, K, and Cs): Insight from First-Principles Calculations
Authors: Zhou Rui , Mo Xiaohua , Huang Yong , Hu Chunyan , Zuo Xiaoli , Ma Yu , Wei Qi , Jiang Weiqing
Published: March 2023

Complex aluminum hydrides with high hydrogen capacity are among the most promising solid-state hydrogen storage materials. The present study determines the thermal stability, hydrogen dissociation energy, and electronic structures of alkali metal aluminum hydrides, MAlH4 (M = Li, Na, K, and Cs), using first-principles density functional theory calculations in an attempt to gain insight into the dehydrogenation mechanism of these hydrides. The results show that the hydrogen dissociation energy (Ed-H2) of MAlH4 (M = Li, Na, K, and Cs) correlates with the Pauling electronegativity of cation M (χP); that is, the Ed-H2 (average value) decreases, i.e., 1.211 eV (LiAlH4) < 1.281 eV (NaAlH4) < 1.291 eV (KAlH4) < 1.361 eV (CsAlH4), with the increasing χP value, i.e., 0.98 (Li) > 0.93 (Na) > 0.82 (K) > 0.79 (Cs). The main reason for this finding is that alkali alanate MAlH4 at higher cation electronegativity is thermally less stable and held by weaker Al-H covalent and H-H ionic interactions. Our work contributes to the design of alkali metal aluminum hydrides with a favorable dehydrogenation, which is useful for on-board hydrogen storage.
Analysis of the Numerical Solutions of the Elder Problem Using Big Data and Machine Learning
Authors: Khotyachuk Roman , Johannsen Klaus
Published: March 2023

In this study, the numerical solutions to the Elder problem are analyzed using Big Data technologies and data-driven approaches. The steady-state solutions to the Elder problem are investigated with regard to Rayleigh numbers (Ra), grid sizes, perturbations, and other parameters of the system studied. The complexity analysis is carried out for the datasets containing different solutions to the Elder problem, and the time of the highest complexity of numerical solutions is estimated. An approach to the identification of transient fingers and the visualization of large ensembles of solutions is proposed. Predictive models are developed to forecast steady states based on early-time observations. These models are classified into three possible types depending on the features (predictors) used in a model. The numerical results of the prediction accuracy are given, including the estimated confidence intervals for the accuracy, and the estimated time of 95% predictability. Different solutions, their averages, principal components, and other parameters are visualized.
Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends
Authors: Chiarion Giovanni , Sparacino Laura , Antonacci Yuri , Faes Luca , Mesin Luca
Published: March 2023

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.
Assessment of Graphite, Graphene, and Hydrophilic-Treated Graphene Electrodes to Improve Power Generation and Wastewater Treatment in Microbial Fuel Cells
Authors: Borja-Maldonado Fátima , López Zavala Miguel Ángel
Published: March 2023

In this study, graphite, graphene, and hydrophilic-treated graphene electrodes were evaluated in a dual-chamber microbial fuel cell (DC-MFC). Free-oxygen conditions were promoted in anodic and cathodic chambers. Hydrochloric acid at 0.1 M and pH 1.1 was used as a catholyte, in addition to deionized water in the cathodic chamber. Domestic wastewater was used as a substrate, and a DuPontTM Nafion 117 membrane was used as a proton exchange membrane. The maximum power density of 32.07 mW·m−2 was obtained using hydrophilic-treated graphene electrodes and hydrochloric acid as catholyte. This power density was 1.4-fold and 32-fold greater than that of graphene (22.15 mW·m−2) and graphite (1.02 mW·m−2), respectively, under the same operational conditions. In addition, the maximum organic matter removal efficiencies of 69.8% and 75.5% were obtained using hydrophilic-treated graphene electrodes, for hydrochloric acid catholyte and deionized water, respectively. Therefore, the results suggest that the use of hydrophilic-treated graphene functioning as electrodes in DC-MFCs, and hydrochloric acid as a catholyte, favored power density when domestic wastewater is degraded. This opens up new possibilities for improving DC-MFC performance through the selection of suitable new electrode materials and catholytes.
Nanoscale Chemical Surface Analyses of Recycled Powder for Direct Metal Powder Bed Fusion Ti-6Al-4V Root Analog Dental Implant: An X-ray Photoelectron Spectroscopy Study
Authors: Matsko Anastasia , Shaker Nader , Fernandes Ana Carla B. C. J. , Haimeur Asmaa , França Rodrigo
Published: March 2023

Over the past couple of decades, additive manufacturing and the use of root-analogue-printed titanium dental implants have been developed. Not all powder particles are sintered into the final product during the additive manufacturing process. Reuse of the remaining powder could reduce the overall implant manufacturing cost. However, Ti-6Al-4V powder particles are affected by heat, mechanical factors, and oxidization during the powder bed fusion manufacturing process. Degradation of the powder may harm the final surface composition and decrease the biocompatibility and survival of the implant. The uncertainty of the recycled powder properties prevents implant fabrication facilities from reusing the powder. This study investigates the chemical composition of controlled, clean, and recycled titanium alloy powder and root-analogue implants (RAI) manufactured from these powders at three different depths. The change in titanium’s quantity, oxidization state, and chemical composition in powder and RAI implants have been demonstrated and analyzed. While not identical, the surface chemical composition of the recycled powder implant and the implant manufactured from unused powder are similar. The results also indicate the presence of TiO2 on all surfaces. Many studies confirmed that titanium dioxide on the implant’s surface correlates with better osteointegration, reduced bacterial infection, and increased corrosion resistance. Considering economic and environmental aspects, surface chemical composition comparison of clean and reused powder is crucial for the future manufacturing of cost-effective and biocompatible implants.
The Role of a Lung Vascular Endothelium Enriched Gene TMEM100
Authors: Pan Jiakai , Liu Bin , Dai Zhiyu
Published: March 2023

Transmembrane protein 100 (TMEM100) is a crucial factor in the development and maintenance of the vascular system. The protein is involved in several processes such as angiogenesis, vascular morphogenesis, and integrity. Furthermore, TMEM100 is a downstream target of the BMP9/10 and BMPR2/ALK1 signaling pathways, which are key regulators of vascular development. Our recent studies have shown that TMEM100 is a lung endothelium enriched gene and plays a significant role in lung vascular repair and regeneration. The importance of TMEM100 in endothelial cells’ regeneration was demonstrated when Tmem100 was specifically deleted in endothelial cells, causing an impairment in their regenerative ability. However, the role of TMEM100 in various conditions and diseases is still largely unknown, making it an interesting area of research. This review summarizes the current knowledge of TMEM100, including its expression pattern, function, molecular signaling, and clinical implications, which could be valuable in the development of novel therapies for the treatment of cardiovascular and pulmonary diseases.
Assessment of Thyroid Stiffness and Viscosity in Autoimmune Thyroiditis Using Novel Ultrasound-Based Techniques
Authors: Stoian Dana , Borlea Andreea , Sporea Ioan , Popa Alexandru , Moisa-Luca Luciana , Popescu Alina
Published: March 2023

The estimation of viscosity by measuring the shear-wave dispersion (SWD) using ultrasound 2D shear-wave elastography (SWE) is becoming more and more popular. Recent research suggests that SWD can be used in addition to 2D-SWE (shear-wave speed) to diagnose diffuse liver disease. Viscosity was studied for the assessment of normal thyroid tissue. This study aims to evaluate the use of viscosity measurements in patients with chronic autoimmune thyroiditis using the SuperSonic MACH®30 ultrasound machine (Hologic SuperSonic Imagine, Aix-en-Provence, France) which provides the Vi PLUS mode for viscosity and the 2D SWE PLUS mode for shear-wave speed measurements. Valid measurements were obtained in 308 cases, 153 with chronic autoimmune thyroiditis (CAT) and 155 with no thyroid pathology (95.95% feasibility of the methods). The differences between the healthy group and the CAT group were statistically significant both for Vi PLUS (2.5 ± 0.4 vs. 2.8 ± 0.5, p < 0.0001) and for 2D-SWE PLUS (13.5 ± 3.3 vs. 23.1 ± 8.3, p < 0.0001). The diagnostic performance was poor for Vi PLUS alone (AUC = 0.69; cut-off > 2.5 Pa·s, se = 68.6%; sp = 64.52%) and good for 2D-SWE PLUS alone (AUC = 0.861; cut-off > 18.4 kPa, se = 69.9%; sp = 92.2%). Vi PLUS correlated with 2D-SWE PLUS, with the presence of CAT, the thyroid volume, levothyroxine replacement therapy and age. Statistically significant differences were found between the CAT subgroup receiving thyroid replacement therapy and the subgroup without therapy: 24.74 ± 8.33 vs. 21.93 ± 8.12 kPa for 2D-SWE (p = 0.0380) and 3 ± 0.5 vs. 2.7 ± 0.4 Pa·s for Vi PLUS (p = 0.0193). Elastography-based methods improve the classic ultrasound evaluation: 2D-SWE PLUS performed somewhat better in distinguishing CAT from normal thyroid tissue, while Vi PLUS made a slightly better assessment regarding the functional status.
Natural Course of COVID-19 and Independent Predictors of Mortality
Authors: Orlando Luana , Bagnato Gianluca , Ioppolo Carmelo , Franzè Maria Stella , Perticone Maria , Versace Antonio Giovanni , Sciacqua Angela , Russo Vincenzo , Cicero Arrigo Francesco Giuseppe , De Gaetano Alberta , Dattilo Giuseppe , Fogacci Federica , Tringali Maria Concetta , Di Micco Pierpaolo , Squadrito Giovanni , Imbalzano Egidio
Published: March 2023

Background: During the SARS-CoV-2 pandemic, several biomarkers were shown to be helpful in determining the prognosis of COVID-19 patients. The aim of our study was to evaluate the prognostic value of N-terminal pro-Brain Natriuretic Peptide (NT-pro-BNP) in a cohort of patients with COVID-19. Methods: One-hundred and seven patients admitted to the Covid Hospital of Messina University between June 2022 and January 2023 were enrolled in our study. The demographic, clinical, biochemical, instrumental, and therapeutic parameters were recorded. The primary outcome was in-hospital mortality. A comparison between patients who recovered and were discharged and those who died during the hospitalization was performed. The independent parameters associated with in-hospital death were assessed by multivariable analysis and a stepwise regression logistic model. Results: A total of 27 events with an in-hospital mortality rate of 25.2% occurred during our study. Those who died during hospitalization were older, with lower GCS and PaO2/FiO2 ratio, elevated D-dimer values, INR, creatinine values and shorter PT (prothrombin time). They had an increased frequency of diagnosis of heart failure (p < 0.0001) and higher NT-pro-BNP values. A multivariate logistic regression analysis showed that higher NT-pro-BNP values and lower PT and PaO2/FiO2 at admission were independent predictors of mortality during hospitalization. Conclusions: This study shows that NT-pro-BNP levels, PT, and PaO2/FiO2 ratio are independently associated with in-hospital mortality in subjects with COVID-19 pneumonia. Further longitudinal studies are warranted to confirm the results of this study.
A Single Arm Clinical Study on the Effects of Continuous Erythropoietin Receptor Activator Treatment in Non-Dialysis Patients with Chronic Heart Failure and Renal Anemia
Authors: Sezai Akira , Sekino Hisakuni , Taoka Makoto , Osaka Shunji , Tanaka Masashi
Published: March 2023

Erythropoiesis-stimulating agents improve the NYHA functional class and decrease the hospital readmission rates for heart failure; however, little is known about the influence of continuous erythropoietin receptor activator (CERA) on the heart. Therefore, a prospective study was conducted to investigate the effects of CERA on cardiac and renal function and oxidative stress in chronic heart failure with renal anemia. Sixty patients with chronic heart failure and renal anemia were enrolled and received CERA for 12 months. The primary endpoints were hemoglobin (Hb) and hematocrit, and the secondary endpoints were: (1) atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP); (2) NYHA class; (3) echocardiography; (4) blood urea nitrogen, creatinine, cystatin C, and urinary albumin; (5) high-sensitivity C-reactive protein; (6) oxidized low-density lipoprotein (Ox-LDL); and (7) renin, angiotensin-II, and aldosterone. There was a significant difference in the Hb levels measured before and after CERA administration. The BNP, ANP, NYHA, left ventricular mass index, renal function, and Ox-LDL decreased significantly after CERA administration. This study shows that CERA improves anemia and reduces renal impairment, as well as cardiac and oxidative stress. The result of this study is useful for a study in which switching from CERA to a new renal anemia drug, hypoxia-inducible factor prolyl-hydroxylase inhibitor, is investigated.
Mental Stress Detection Using a Wearable In-Ear Plethysmography
Authors: Barki Hika , Chung Wan-Young
Published: March 2023

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual’s health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color–word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.
Functionalization of a Fully Integrated Electrophotonic Silicon Circuit for Biotin Sensing
Authors: Pérez-Diaz Oscar , Estrada-Wiese Denise , Aceves-Mijares Mariano , González-Fernández Alfredo A.
Published: March 2023

Electrophotonic (EPh) circuits are novel systems where photons and electrons can be controlled simultaneously in the same integrated circuit, attaining the development of innovative sensors for different applications. In this work, we present a complementary metal-oxide-semiconductor (CMOS)-compatible EPh circuit for biotin sensing, in which a silicon-based light source is monolithically integrated. The device is composed of an integrated light source, a waveguide, and a p–n photodiode, which are all fabricated in the same chip. The functionalization of the waveguide’s surface was investigated to biotinylate the EPh system for potential biosensing applications. The modified surfaces were characterized by AFM, optical microscopy, and Raman spectroscopy, as well as by photoluminescence measurements. The changes on the waveguide’s surface due to functionalization and biotinylation translated into different photocurrent intensities detected in the photodiode, demonstrating the potential uses of the EPh circuit as a biosensor.
Neurocognitive Psychiatric and Neuropsychological Alterations in Parkinson’s Disease: A Basic and Clinical Approach
Authors: González-Usigli Héctor Alberto , Ortiz Genaro Gabriel , Charles-Niño Claudia , Mireles-Ramírez Mario Alberto , Pacheco-Moisés Fermín Paul , Torres-Mendoza Blanca Miriam de Guadalupe , Hernández-Cruz José de Jesús , Delgado-Lara Daniela Lucero del Carmen , Ramírez-Jirano Luis Javier
Published: March 2023

The main histopathological hallmarks of Parkinson’s disease (PD) are the degeneration of the dopaminergic neurons of the substantia nigra pars compacta and the loss of neuromelanin as a consequence of decreased dopamine synthesis. The destruction of the striatal dopaminergic pathway and blocking of striatal dopamine receptors cause motor deficits in humans and experimental animal models induced by some environmental agents. In addition, neuropsychiatric symptoms such as mood and anxiety disorders, hallucinations, psychosis, cognitive impairment, and dementia are common in PD. These alterations may precede the appearance of motor symptoms and are correlated with neurochemical and structural changes in the brain. This paper reviews the most crucial pathophysiology of neuropsychiatric alterations in PD. It is worth noting that PD patients have global task learning deficits, and cognitive functions are compromised in a way is associated with hypoactivation within the striatum, anterior cingulate cortex, and inferior frontal sulcus regions. An appropriate and extensive neuropsychological screening battery in PD must accurately assess at least five cognitive domains with some tests for each cognitive domain. This neuropsychological screening should consider the pathophysiological and clinical heterogeneity of cognitive dysfunction in PD.
Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
Authors: Delgado-Gallegos Juan Luis , Avilés-Rodriguez Gener , Padilla-Rivas Gerardo R. , De los Ángeles Cosío-León María , Franco-Villareal Héctor , Nieto-Hipólito Juan Iván , de Dios Sánchez López Juan , Zuñiga-Violante Erika , Islas Jose Francisco , Romo-Cardenas Gerardo Salvador
Published: March 2023

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.
Current Advances in Papillary Craniopharyngioma: State-Of-The-Art Therapies and Overview of the Literature
Authors: Jannelli Gianpaolo , Calvanese Francesco , Paun Luca , Raverot Gerald , Jouanneau Emmanuel
Published: March 2023

Craniopharyngiomas are commonly classified as low-grade tumors, although they may harbor a malignant behavior due to their high rate of recurrence and long-term morbidity. Craniopharyngiomas are classically distinguished into two histological types (adamantinomatous and papillary), which have been recently considered by the WHO classification of CNS tumors as two independent entities, due to different epidemiological, radiological, histopathological, and genetic patterns. With regard to papillary craniopharyngioma, a BRAF V600 mutation is detected in 95% of cases. This genetic feature is opening new frontiers in the treatment of these tumors using an adjuvant or, in selected cases, a neo-adjuvant approach. In this article, we present an overview of the more recent literature, focusing on the specificities and the role of oncological treatment in the management of papillary craniopharyngiomas. Based on our research and experience, we strongly suggest a multimodal approach combining clinical, endocrinological, radiological, histological, and oncological findings in both preoperative workup and postoperative follow up to define a roadmap integrating every aspect of this challenging condition.
The Moderating Effect of Flexible Work Option on Structural Empowerment and Generation Z Contextual Performance
Authors: Taibah Daliah , Ho Theresa C. F.
Published: March 2023

Leading and managing Generation Z can be a daunting task due to the distinct expectations, behaviours, and preferences they bring with them compared to prior generations. As such, when managing Gen Z workers, it is essential that leaders are aware of these variations to effectively manage their teams. Hence, this research endeavours to investigate the role of flexible work options on strengthening the relationship between structural empowerment and contextual work performance among Generation Z. This study employed a quantitative approach via an online questionnaire distributed to full- or part-time employed Gen Z workers in Jeddah, Saudi Arabia working within the wholesale and retail sectors. The purpose of this study is, therefore, to determine the impact of the moderating effect of flexible work options on the relationship between structural empowerment and Gen Z employee work performance. The findings suggest that H1, the moderating effect of the flexible work option on the opportunity and contextual work performance relationship, is not supported, while H2 and H3, the moderating effect of the Flexible work option on the relationships of support as well as information and contextual work performance, are both supported. However, it has a negative effect on the relationship between access to support and Gen Z contextual work performance and a positive effect on the relationship between access to information and Gen Z contextual work performance. The study’s findings highlight the necessary structural empowerment for enhancing Generation Z’s contextual work performance, offering useful information to management, policy makers, and the business as a whole.
Heuristic Approach for Net-Zero Energy Residential Buildings in Arid Region Using Dual Renewable Energy Sources
Authors: Ismaeil Esam M. H. , Sobaih Abu Elnasr E.
Published: March 2023

Optimizing a net-zero energy (NZE) residential building using what renewable energy resources are available in desert environments and budgeted within the limits of a governmental construction project is proving to be increasingly challenging for many countries, including the Kingdom of Saudi Arabia (KSA). Buildings in such regions encounter significantly high annual energy consumption rates, especially in the cooling capacity across a project’s life cycle, which in turn impacts the investment value. Therefore, this study presents a heuristic approach that aimed to examine the feasibility of NZE residential buildings in the KSA using an arid campus case study within the period of 2021–2022 based on the dual renewable energy sources of a geothermal heat pump (GHP), which served as a cooling system, and photovoltaic thermal collectors (PVT) serving as a power generation system. This study adopted a numerical technical assessment in the case study, using HAP software to analyze heating/cooling systems, and PVsyst V7.1.0 software for the variable simulation of solar photovoltaic power systems. This heuristic approach, through two assessment stages, achieved significant outcomes for a sustainable bottom-line, and provide a practical approach for achieving an NZE residential building in the King Faisal University (KFU) case study, as well as a reduction in energy consumption as well as the maintenance cost, which has a positive consequence on the payback period. Our study’s results have implications for both sustainable and green buildings with similar characteristics to those we investigated, and our results could be used to develop installation guidelines for renewable energy systems. Furthermore, our results can provide decision makers with a basis for retrofitting existing buildings to enhance their energy efficiency, increase investment value, as well as prevent the indiscriminate installation of renewable energy sources to merely increase the renewable energy installation rate.
Analytical Assessment of the Structural Behavior of a Specific Composite Floor System at Elevated Temperatures Using a Newly Developed Hybrid Intelligence Method
Authors: Han Shaoyong , Zhu Zhun , Mortazavi Mina , El-Sherbeeny Ahmed M. , Mehrabi Peyman
Published: March 2023

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures.
Aesthetic Possibilities of Building Thermal Control through Colored Envelopes
Authors: Cánovas-Saura Antonio , Cabrera-Lozoya Andrés , Padilla Javier
Published: March 2023

The possibilities of obtaining efficient building thermal control by means of colored envelopes while maintaining broad aesthetic options have been evaluated. The use of colored bilayers, where the inner layer presents strong solar absorbent or reflectance properties, while the outer layer provides visible coloration, represents a feasible way. A representative set of twenty bilayers spanning a broad color palette has been studied in two configurations, static and dynamic. The dynamic configuration allows for the switching of the inner layer between absorptive and reflective states. Simulations have been carried out in a total of nineteen locations around the world, representing the main habitable climates according to Köppen–Geiger climate classification. Comparative results between climate zones where this approach may have a significant impact have been obtained, and identification of feasible climate zones has been carried out. The use of the simpler static configuration is recommended for tropical climates, whereas the dynamic configuration could be effective in moderate climate locations (within arid or temperate climate classification). Possible savings for cold climates are negligible.
Lateral Distortional Buckling Resistance Predictions of Composite Alveolar Beams: A Review
Authors: de Oliveira Vinicius Moura , Rossi Alexandre , Ferreira Felipe Piana Vendramell , de Carvalho Adriano Silva , Martins Carlos Humberto
Published: March 2023

Few studies have investigated the structural behavior of steel-concrete composite alveolar beams in hogging bending regions. Their resistance can be reached by lateral distortional buckling (LDB), coupling LDB and local failure modes, or limit states of cracking or crushing in the concrete slab. This case is characteristic of continuous or cantilever elements. Another critical issue is that the design and calculation recommendations only address the LDB verification on steel-concrete composite beams without web openings, thus disregarding the interaction between the buckling modes. Furthermore, it is necessary to use adaptations of these formulations for beams with web openings. This review paper aims to evaluate the different approaches for standard code adaptations to verify the LDB resistance of the beams in question and to highlight the investigations that addressed this issue. The addressed adaptations consist of different approaches which determine the cross-section geometric properties in the central region of the openings, the so-called double T section, in the region of the web posts (solid section), and the averages between the solid section and double T section. The accuracy of the formulations in question is verified against experimental results from the literature. Furthermore, discussions and suggestions for further studies are presented.