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Artificial intelligence (AI) is revolutionizing the field of oral and dental healthcare by offering innovative tools and techniques for optimizing diagnosis, treatment planning, and patient management. This narrative review explores the current applications of AI in dentistry, focusing on its role in enhancing diagnostic accuracy and efficiency. AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression. By utilizing AI algorithms, dental professionals can detect issues like caries, periodontal disease and oral cancer at an earlier stage, thus improving patient outcomes. [ABSTRACT FROM AUTHOR]

The minor sudden stratospheric warming (SSW) event and the relevant planetary waves are investigated by analyzing ERA5 reanalysis data from July to December 2019. Frequency-wavenumber spectral analysis shows that the quasi-10-day and quasi-16-day waves dominate the stratosphere over the Southern Hemispheric polar region with the eastward-propagating wavenumber 1 during the SSW event. The corresponding amplitudes and phases of each wave mode have been fitted using the two-dimensional harmonic fitting method. The result suggests that quasi-16-day and quasi-10-day waves prior to the SSW event had an important effect on the occurrence of the SSW event. Furthermore, the Eliassen–Palm flux diagnosis shows that the quasi-16-day wave and quasi-10-day wave had poleward and equatorward-propagating components. The poleward-propagating component may have come from the tropical tropospheric convective activity. The equatorward component may have been excited by the atmospheric barotropic/baroclinic instability. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are used in an ever-increasing range of applications, thanks to their ability to monitor and transmit data related to ambient conditions in almost any area of interest. The optimization of coverage and the assurance of connectivity are fundamental for the efficiency and consistency of Wireless Sensor Networks. Optimal coverage guarantees that all points in the field of interest are monitored, while the assurance of the connectivity of the network nodes assures that the gathered data are reliably transferred among the nodes and the base station. In this research article, a novel algorithm based on Particle Swarm Optimization is proposed to ensure coverage and connectivity in Wireless Sensor Networks. The objective function is derived from energy function minimization methodologies commonly applied in bounded space circle packing problems. The performance of the novel algorithm is not only evaluated through both simulation and statistical tests that demonstrate the efficacy of the proposed methodology but also compared against that of relative algorithms. Finally, concluding remarks are drawn on the potential extensibility and actual use of the algorithm in real-world scenarios. [ABSTRACT FROM AUTHOR]

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To assess the pollutant emissions and dispersion of aircraft during the landing and takeoff (LTO) cycle, and address air pollution surrounding the airport resulting from flight operations, this study evaluated emissions throughout the LTO phase based on Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources was employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability. Specifically, a change in wind direction from 0° to 45° caused a shift of approximately 1000 m in the contaminant's center. An increase in wind speed from 3 m/s to 5 m/s led to a decrease in concentration by about 15%. Furthermore, a transition in atmospheric stability from category 'a' (very unstable) to 'f' (very stable) resulted in a two-order-of-magnitude increase in contaminant concentrations. [ABSTRACT FROM AUTHOR]

Launched on 22 November 2013, Swarm is the fourth in a series of pioneering Earth Explorer missions and also the European Space Agency's (ESA's) first constellation to advance our understanding of the Earth's magnetic field and the near-Earth electromagnetic environment. Swarm provides an ideal platform in the topside ionosphere for observing ultra-low-frequency (ULF) waves, as well as equatorial spread-F (ESF) events or plasma bubbles, and, thus, offers an excellent opportunity for space weather studies. For this purpose, a specialized time–frequency analysis (TFA) toolbox has been developed for deriving continuous pulsations (Pc), namely Pc1 (0.2–5 Hz) and Pc3 (22–100 mHz), as well as ionospheric plasma irregularity distribution maps. In this methodological paper, we focus on the ULF pulsation and ESF activity observed by Swarm satellites during a time interval centered around the occurrence of the 24 August 2016 Central Italy M6 earthquake. Due to the Swarm orbit's proximity to the earthquake epicenter, i.e., a few hours before the earthquake occurred, data from the mission may offer a variety of interesting observations around the time of the earthquake event. These observations could be associated with the occurrence of this geophysical event. Most notably, we observed an electron density perturbation occurring 6 h prior to the earthquake. This perturbation was detected when the satellites were flying above Italy. [ABSTRACT FROM AUTHOR]

Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart's magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart's magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology. [ABSTRACT FROM AUTHOR]

In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes. [ABSTRACT FROM AUTHOR]

The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use. [ABSTRACT FROM AUTHOR]

Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033. [ABSTRACT FROM AUTHOR]

This paper examines the response of the ionosphere to the impact of two moderate geomagnetic storms observed on January 17 and 26–27, 2013, under conditions of strong sudden stratospheric warming. The study uses data from ground-based ionosonde measurements at the Alma-Ata ionospheric station (43.25 N, 76.92 E) combined with optical observation data (The Spectral Airglow Temperature Imager (SATI)). Ionosonde data showed that the geomagnetic storms under consideration do not generate ionospheric storms but demonstrate some unusual types of diurnal foF2 variations with large (up to 60%) deviations in foF2 from median values observed during the night/morning periods on 13–15 and 20–23 January, which do not have any relation to solar or geomagnetic activity. Wave-like disturbances in ΔfoF2, Δh'F, and daily averaged foF2 values with a quasi-period of 5–8 days and peak-to-peak amplitude from about 1 MHz to 2 MHz (~from 20% to ~40%) and ~40 km are observed during the period 9–28 January, after registration of the occurrence of the major SSW event on 6–7 January. The observed variations in the OH emission rate are found to be quite similar to those observed in the ionospheric parameters that assume a community of processes in the stratosphere/mesosphere/ionosphere system. The study shows that the F region of the ionosphere is influenced by processes in the lower ionosphere, in this case by processes associated with sudden stratospheric warming SSW-2013, which led to modification of the structure of the ionosphere and compensation of processes associated with the development of the ionospheric storms. [ABSTRACT FROM AUTHOR]

An accurate wind shear model is an important prerequisite in extrapolating the wind resource from lower heights to the increasing hub height of wind turbines. Based on the 1-year dataset (collected in 2014) consisting of 15-minute intervals collected at heights of 2, 10, 50, 100, and 150 m on an anemometer tower in northern China, the present study focuses on the time-varying relationship between the wind shear coefficient (WSC) and atmospheric stability and proposes a wind shear model considering atmospheric stability. Through the relationship between Monin–Obukhov (M-O) length and gradient Richardson number, the M-O length is directly calculated by wind data, and the WSC is calculated by combining the Panofsky and Dutton (PD) models, which enhances the engineering practicability of the model. Then, the performance of the model is quantified and compared with two alternative methods: the use of annual average WSC and the use of stability change WSC extrapolation. The analysis demonstrates that the proposed model outperforms the other approaches in terms of normal root mean square error (NRMSE) and normal bias (NB). More specifically, this method reduces the NRMSE and NB by 24–29% and 76–95%, respectively. Meanwhile, it reaches the highest extrapolation accuracy under unstable and stable atmospheric conditions. The results are verified using the Weibull distribution. [ABSTRACT FROM AUTHOR]

This study investigates the day-to-day variability of equatorial plasma bubbles (EPBs) over the Atlantic–American region and their connections to atmospheric planetary waves during the sudden stratospheric warming (SSW) event of 2021. The investigation is conducted on the basis of the GOLD (Global Observations of the Limb and Disk) observations, the ICON (Ionospheric Connection Explorer) neutral wind dataset, ionosonde measurements, and simulations from the WACCM-X (Whole Atmosphere Community Climate Model with thermosphere–ionosphere eXtension). We found that the intensity of EPBs was notably reduced by 35% during the SSW compared with the non-SSW period. Furthermore, GOLD observations and ionosonde data show that significant quasi-6-day oscillation (Q6DO) was observed in both the intensity of EPBs and the localized growth rate of Rayleigh–Taylor (R-T) instability during the 2021 SSW event. The analysis of WACCM-X simulations and ICON neutral winds reveals that the Q6DO pattern coincided with an amplification of the quasi-6-day wave (Q6DW) in WACCM-X simulations and noticeable ∼6-day periodicity in ICON zonal winds. The combination of these multi-instrument observations and numerical simulations demonstrates that certain planetary waves like the Q6DW can significantly influence the day-to-day variability of EPBs, especially during the SSW period, through modulating the strength of prereversal enhancement and the growth rate of R-T instability via the wind-driven dynamo. These findings provide novel insights into the connection between atmospheric planetary waves and ionospheric EPBs. [ABSTRACT FROM AUTHOR]

The main scientific result of this paper is the demonstration of the fact that tidal effects induced by the Moon affect the Schumann resonance amplitudes measured at magnetometers located at different geographical locations of the Global Coherence Monitoring System. Each magnetometer is paired with the closest monitoring station of the global tidal wave measurement network. This paper introduces the Schumann Resonance Complexity Index (SRCI), computed by using the calibrated H-rank algorithm on the local magnetic field data recorded by each magnetometer of the Global Coherence Monitoring System. Tidal wave data recorded at each monitoring station are also used to compute the Tidal Wave Complexity Index (TWCI). Eliminating diel cycles from the SRCI data yields significant statistical correlations between the SRCI and TWCI data, proving the hypothesis of tidal effects on the global network of magnetometers. [ABSTRACT FROM AUTHOR]

Novel technologies such as single-cell RNA and single-nucleus RNA sequencing have shed new light on the complexity of different microglia populations in physiological and pathological states. The transcriptomic profiling of these populations has led to the subclassification of specific disease-associated microglia and microglia clusters in neurodegenerative diseases. A common profile includes the downregulation of homeostasis and the upregulation of inflammatory markers. Furthermore, there is concordance in few clusters between murine and human samples. Apolipoprotein E, which has long been considered a high-risk factor for late-onset Alzheimer's disease, is strongly regulated in both these murine and human clusters. Transforming growth factor-β plays an essential role during the development and maturation of microglia. In a pathological state, it attenuates their activation and is involved in numerous cell regulatory processes. Transforming growth factor-β also has an influence on the deposition of amyloid-beta, as it is involved in the regulation of key proteins and molecules. Taken together, this review highlights the complex interaction of apolipoprotein E, the triggering receptor on myeloid cells 2, and transforming growth factor-β as part of a regulatory axis in microglia at the onset and over the course of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Laryngeal squamous cell carcinoma (LSCC) constitutes a noteworthy subset of head and neck cancers, contributing to about 4.5% of all malignancies. Its clinical behavior and characteristics exhibit variations contingent upon the specific anatomical site affected, with the glottis, supraglottis, and subglottis emerging as the most prevalent locations. Notably, squamous cell carcinoma represents a predominant histological type, accounting for 85% to 95% of all laryngeal cancers. The gender disparity is evident, with a higher incidence among males, exhibiting a ratio of 3.9:1. Moreover, disparities among racial groups are observed, as African American patients tend to manifest the condition at a younger age, coupled with lower overall survival rates compared to their Caucasian, Hispanic, and Asian counterparts. The primary etiological factors implicated in the onset of laryngeal cancer are tobacco and alcohol consumption, with a direct correlation to the intensity and duration of usage. Importantly, the risk diminishes gradually following cessation, necessitating a substantial period of at least 15 years for a return to baseline rates. Given the diverse nature of laryngeal SCC, treatment modalities are tailored based on the specific site and stage of the disease. Therapeutic interventions, such as radiotherapy, transoral laser microsurgery, open horizontal partial laryngectomy, or total laryngectomy, are employed with the overarching goal of preserving organ function. This study delves into the intricate realm of laryngeal SCC, specifically exploring the involvement of heat shock proteins (HSPs) in disease progression. This research meticulously examines the expression levels of Hsp10, Hsp27, Hsp60, and Hsp90 in dysplastic and benign tissue samples extracted from the right vocal cord, utilizing immunohistochemistry analysis. The focal point of the investigation revolves around unraveling the intricate role of these molecular chaperones in tissue differentiation mechanisms and cellular homeostasis, particularly within the inflammatory milieu characteristic of the tumor phenotype. The findings from this study serve as a robust histopathological foundation, paving the way for more in-depth analyses of the underlying mechanisms governing the contribution of the four chaperones to the development of squamous cell carcinoma in the larynx. Additionally, the data gleaned from this research hint at the potential of these four chaperones as valuable biomarkers, not only for diagnostic purposes but also for prognostication and ongoing patient monitoring. As our understanding of the molecular intricacies deepens, the prospect of targeted therapeutic interventions and personalized treatment strategies for laryngeal SCC becomes increasingly promising. [ABSTRACT FROM AUTHOR]

Rainfall, or more generally the precipitation process (flux), is a clear example of chaotic variables resulting from a highly nonlinear dynamical system, the atmosphere, which is represented by a set of physical equations such as the Navier–Stokes equations, energy balances, and the hydrological cycle, among others. As a generalization of the Euclidean (ordinary) measurements, chaotic solutions of these equations are characterized by fractal indices, that is, non-integer values that represent the complexity of variables like the rainfall. However, observed precipitation is measured as an aggregate variable over time; thus, a physical analysis of observed fluxes is very limited. Consequently, this review aims to go through the different approaches used to identify and analyze the complexity of observed precipitation, taking advantage of its geometry footprint. To address the review, it ranges from classical perspectives of fractal-based techniques to new perspectives at temporal and spatial scales as well as for the classification of climatic features, including the monofractal dimension, multifractal approaches, Hurst exponent, Shannon entropy, and time-scaling in intensity–duration–frequency curves. [ABSTRACT FROM AUTHOR]

Sporadic E (Es) layers are thin layers of enhanced electron density that commonly appear at altitudes of 90–130 km, often impacting radio communications and navigation systems. The wind shear theory posits that the vertical ion drift, influenced by atmospheric neutral winds and the magnetic field, serves as a significant dynamic driver for the formation and movement of Es layers. In current studies, both the heights of ion vertical velocity null (IVN) and the maximum vertical ion convergence (VICmax) have been proposed as the potential height of Es layer occurrence. In this study, utilizing the neutral atmospheric wind data derived from the WACCM-X (The Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension), we computed and compared these two parameters with the observed Es layer heights recorded by the FORMOSAT-3/COSMIC (FORMOsa SATellite-3/Constellation Observing System for Meteorology, Ionosphere, and Climate) radio occultation (RO) observations. The comparative analysis suggests that IVN is a more likely node for Es layer occurrence than VICmax. Subsequently, we examined the height–time distributions of IVN and Es layers, as well as their respective descent rates at different latitudes. These results demonstrated a notable agreement in height variations between IVN and Es layers. The collective results presented in this paper provide strong support that the ion vertical velocity null plays a crucial role in determining the height of Es layers. [ABSTRACT FROM AUTHOR]

In this study, the plausible role of trimethylamine N-oxide (TMAO), a microbiota metabolite, was investigated as a link between peripheral inflammation and the inflammation of the central nervous system using different cell lines. TMAO treatment favored the differentiation of adipocytes from preadipocytes (3T3-L1 cell line). In macrophages (RAW 264.7 cell line), which infiltrate adipose tissue in obesity, TMAO increased the expression of pro-inflammatory cytokines. The treatment with 200 μM of TMAO seemed to disrupt the blood–brain barrier as it induced a significant decrease in the expression of occludin in hCMECs. TMAO also increased the expression of pro-inflammatory cytokines in primary neuronal cultures, induced a pro-inflammatory state in primary microglial cultures, and promoted phagocytosis. Data obtained from this project suggest that microbial dysbiosis and increased TMAO secretion could be a key link between peripheral and central inflammation. Thus, TMAO-decreasing compounds may be a promising therapeutic strategy for neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The Microwave Limb Sounder (MLS) onboard the satellite Aura measures the temperature at 01:44 LST (after midnight) and at 13:44 LST after noon in the equatorial middle atmosphere. The signatures of the migrating solar diurnal tide (DW1) show up in the difference between the night-time and the daytime temperature profiles. We find a good agreement between the equatorial DW1 proxy of the Aura/MLS observations and the migrating diurnal tide estimated by the Global Scale Wave Model (GSWM) in March. The equatorial DW1 proxy is shown for the time interval from 2004 to 2021 reaching a temporal resolution of 1 day. The amplitude modulations of the DW1 proxy are correlated at several altitudes. There are indications of a semi-annual and annual oscillation (SAO and AO) of the DW1 proxy. The composite of 17 events of major sudden stratospheric warmings (SSWs) shows that the equatorial, mesospheric DW1 proxy is reduced by about 10% during the first week after the SSW event. The nodes and bellies of the equatorial DW1 proxy are shifted downward by about 1–2 km in the first week after the SSW. The 14 day-oscillation of the DW1 proxy in the equatorial mesosphere is enhanced from 25 days before the SSW onset to 5 days after the SSW onset. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding. [ABSTRACT FROM AUTHOR]

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented. [ABSTRACT FROM AUTHOR]

Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors. [ABSTRACT FROM AUTHOR]

Pregnancy with SARS-CoV-2 infection can raise the risk of many complications, including severe COVID-19 and maternal–fetal adverse outcomes. Additionally, endothelial damage occurs as a result of direct SARS-CoV-2 infection, as well as immune system, cardiovascular, and thrombo-inflammatory reactions. In this narrative review, we focus on endothelial dysfunction (ED) in pregnancy, associated with obstetric complications, such as preeclampsia, fetal growth retardation, gestational diabetes, etc., and SARS-CoV-2 infection in pregnant women that can cause ED itself and overlap with other pregnancy complications. We also discuss some shared mechanisms of SARS-CoV-2 pathophysiology and ED. [ABSTRACT FROM AUTHOR]

Literature reviews are valuable for summarizing and evaluating the available evidence in various medical fields, including nephrology. However, identifying and exploring the potential sources requires focus and time devoted to literature searching for clinicians and researchers. ChatGPT is a novel artificial intelligence (AI) large language model (LLM) renowned for its exceptional ability to generate human-like responses across various tasks. However, whether ChatGPT can effectively assist medical professionals in identifying relevant literature is unclear. Therefore, this study aimed to assess the effectiveness of ChatGPT in identifying references to literature reviews in nephrology. We keyed the prompt "Please provide the references in Vancouver style and their links in recent literature on... name of the topic" into ChatGPT-3.5 (03/23 Version). We selected all the results provided by ChatGPT and assessed them for existence, relevance, and author/link correctness. We recorded each resource's citations, authors, title, journal name, publication year, digital object identifier (DOI), and link. The relevance and correctness of each resource were verified by searching on Google Scholar. Of the total 610 references in the nephrology literature, only 378 (62%) of the references provided by ChatGPT existed, while 31% were fabricated, and 7% of citations were incomplete references. Notably, only 122 (20%) of references were authentic. Additionally, 256 (68%) of the links in the references were found to be incorrect, and the DOI was inaccurate in 206 (54%) of the references. Moreover, among those with a link provided, the link was correct in only 20% of cases, and 3% of the references were irrelevant. Notably, an analysis of specific topics in electrolyte, hemodialysis, and kidney stones found that >60% of the references were inaccurate or misleading, with less reliable authorship and links provided by ChatGPT. Based on our findings, the use of ChatGPT as a sole resource for identifying references to literature reviews in nephrology is not recommended. Future studies could explore ways to improve AI language models' performance in identifying relevant nephrology literature. [ABSTRACT FROM AUTHOR]

Using a network of meteor radar observations, observational evidence of polar-to-tropical mesospheric coupling during the 2018 major sudden stratosphere warming (SSW) event in the northern hemisphere is presented. In the tropical lower mesosphere, a maximum zonal wind reversal (−24 m/s) is noted and compared with that identified in the extra-tropical regions. Moreover, a time delay in the wind reversal between the tropical/polar stations and the mid-latitudes is detected. A wide spectrum of waves with periods of 2 to 16 days and 30–60 days were observed. The wind reversal in the mesosphere is due to the propagation of dominant intra-seasonal oscillations (ISOs) of 30–60 days and the presence and superposition of 8-day period planetary waves (PWs). The ISO phase propagation is observed from high to low latitudes (60° N to 20° N) in contrast to the 8-day PW phase propagation, indicating the change in the meridional propagation of winds during SSW, hence the change in the meridional circulation. The superposition of dominant ISOs and weak 8-day PWs could be responsible for the delay of the wind reversal in the tropical mesosphere. Therefore, this study has strong implications for understanding the reversed (polar to tropical) mesospheric meridional circulation by considering the ISOs during SSW. [ABSTRACT FROM AUTHOR]

Biopharmaceutical companies are critical in developing vaccines, treatments, and diagnostics for COVID-19. Thus, understanding the contagion effects of their stock market can have important economic implications, especially in the context of global financial markets. Due to the COVID-19 pandemic, biopharmaceutical companies' stock markets may have experienced sudden volatility and risk changes, which may have had spillover effects on other sectors and markets. Policymakers can take pre-emptive measures to stabilize financial markets. Analyzing the contagion effects makes it even more relevant to analyze the stock market response of four leading pharmaceutical companies that either developed vaccines against COVID-19 or drugs that help to fight the virus, namely, Pfizer, AbbVie Inc., Sanofi, and Bristol Myers Squibb. The analysis considers two periods, before and during the COVID-19 crisis, and considers the influence of the market volatility and technological market index. In order to capture the contagion effects, DCC-GARCH models have been applied, which estimate time-varying correlation coefficients using a multivariate GARCH framework, allowing for the modeling of time-varying volatility and correlations in financial returns. The results reveal the impact of market volatility on the returns of all four pharmaceutical companies. Additionally, a contagion effect between all four companies, the technological market, and market volatility was observed during the COVID-19 period. [ABSTRACT FROM AUTHOR]

The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term. [ABSTRACT FROM AUTHOR]

For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridized forms are widely used for deterministic solar irradiance forecasting. The implementation of probabilistic solar irradiance forecasting, which is gaining prominence in grid management since it offers information on the likelihood of different outcomes, is another task we carry out using quantile regression. The novelty of this paper lies in the combination of a hybrid deep learning model (CNN-LSTM) with quantile regression for the computation of prediction intervals at different confidence levels. The training of the different machine learning algorithms is performed over a year's worth of sky images and meteorological data from the years 2019 to 2020. The data were measured at the University of French Polynesia (17.5770° S, 149.6092° W), on the island of Tahiti, which has a tropical climate. Overall, the hybrid model (CNN-LSTM) is the best performing and most accurate in terms of deterministic and probabilistic metrics. In addition, it was found that the CNN, LSTM, and ANN show good results against persistence. [ABSTRACT FROM AUTHOR]

In recent decades, cardiac computed tomography (CT) has emerged as a powerful non-invasive tool for risk stratification, as well as the detection and characterization of coronary artery disease (CAD), which remains the main cause of morbidity and mortality in the world. Advances in technology have favored the increasing use of cardiac CT by allowing better performance with lower radiation doses. Coronary artery calcium, as assessed by non-contrast CT, is considered to be the best marker of subclinical atherosclerosis, and its use is recommended for the refinement of risk assessment in low-to-intermediate risk individuals. In addition, coronary CT angiography (CCTA) has become a gate-keeper to invasive coronary angiography (ICA) and revascularization in patients with acute chest pain by allowing the assessment not only of the extent of lumen stenosis, but also of its hemodynamic significance if combined with the measurement of fractional flow reserve or perfusion imaging. Moreover, CCTA provides a unique incremental value over functional testing and ICA by imaging the vessel wall, thus allowing the assessment of plaque burden, composition, and instability features, in addition to perivascular adipose tissue attenuation, which is a marker of vascular inflammation. There exists the potential to identify the non-obstructive lesions at high risk of progression to plaque rupture by combining all of these measures. [ABSTRACT FROM AUTHOR]

The liquid–liquid phase separation (LLPS) of biomolecules induces condensed assemblies called liquid droplets or membrane-less organelles. In contrast to organelles with lipid membrane barriers, the liquid droplets induced by LLPS do not have distinct barriers (lipid bilayer). Biomolecular LLPS in cells has attracted considerable attention in broad research fields from cellular biology to soft matter physics. The physical and chemical properties of LLPS exert a variety of functions in living cells: activating and deactivating biomolecules involving enzymes; controlling the localization, condensation, and concentration of biomolecules; the filtration and purification of biomolecules; and sensing environmental factors for fast, adaptive, and reversible responses. The versatility of LLPS plays an essential role in various biological processes, such as controlling the central dogma and the onset mechanism of pathological diseases. Moreover, biomolecular LLPS could be critical for developing new biotechnologies such as the condensation, purification, and activation of a series of biomolecules. In this review article, we introduce some fundamental aspects and recent progress of biomolecular LLPS in living cells and test tubes. Then, we discuss applications of biomolecular LLPS toward biotechnologies. [ABSTRACT FROM AUTHOR]

Total quality management (TQM) has become a reliable quality and customer satisfaction approach that can meet or exceed customer satisfaction. Previous studies have not really tied total quality management in the aspects of SERVQUAL as the major influence on customer satisfaction in the Ghanaian telecommunication sector, thus the need for this research. As customer satisfaction continues to rise, telecommunication customers expect service quality to improve to meet their satisfaction. The quality of service determines the customer satisfaction level in a service industry, such as telecom. Effective total quality management would result in better service quality. SERVQUAL is arguably the model to assess the effectiveness of customer satisfaction in the service industry. The aim of this study is to assess the influence of total quality management on customer satisfaction in the telecom industry from a TQM–SERVQUAL perspective. The study relied on questionnaires to gather data about the TQM services on customer satisfaction. A self-completion questionnaire was administered to customers and employees of MTN Ghana and Vodafone Ghana. Both companies operate with the same procedures and equipment, hence the need to use the same apparatus for all contacted customers and employees. There were 800 respondents from both companies' employees and customers, although 850 questionnaires were administered. Descriptive statistical analysis was used through the questionnaire. The investigators also ran preliminary tests such as reliability, validity, and multicollinearity tests to ascertain if the received data were reasonable enough for the research and would fit the expected model. The data collected were analyzed and the inference was drawn in the study. The key finding states that total quality management had a significant impact on service quality and customer satisfaction. It is also concluded that service quality has a positive influence on customer satisfaction. Moreover, service quality has been detected as a major variable of TQM in achieving customer satisfaction in telecommunication companies in Ghana. [ABSTRACT FROM AUTHOR]

Globally, over the next few decades, more than 2.5 billion people will suffer from hearing impairment, including profound hearing loss, and millions could potentially benefit from a cochlea implant. To date, several studies have focused on tissue trauma caused by cochlea implantation. The direct immune reaction in the inner ear after an implantation has not been well studied. Recently, therapeutic hypothermia has been found to positively influence the inflammatory reaction caused by electrode insertion trauma. The present study aimed to evaluate the hypothermic effect on the structure, numbers, function and reactivity of macrophages and microglial cells. Therefore, the distribution and activated forms of macrophages in the cochlea were evaluated in an electrode insertion trauma cochlea culture model in normothermic and mild hypothermic conditions. In 10-day-old mouse cochleae, artificial electrode insertion trauma was inflicted, and then they were cultured for 24 h at 37 °C and 32 °C. The influence of mild hypothermia on macrophages was evaluated using immunostaining of cryosections using antibodies against IBA1, F4/80, CD45 and CD163. A clear influence of mild hypothermia on the distribution of activated and non-activated forms of macrophages and monocytes in the inner ear was observed. Furthermore, these cells were located in the mesenchymal tissue in and around the cochlea, and the activated forms were found in and around the spiral ganglion tissue at 37 °C. Our findings suggest that mild hypothermic treatment has a beneficial effect on immune system activation after electrode insertion trauma. [ABSTRACT FROM AUTHOR]

The International Global Navigation Satellite Systems Service (IGS) ionospheric total electron content (TEC) data are used to study the periodic perturbation in the ionosphere during the 2019 Antarctic sudden stratospheric warming (SSW) event, a rare Southern Hemisphere minor SSW event in the last 40 years. A 14.5 day periodic signal with a zonal wavenumber of 0 is observed in the mesosphere and the lower thermosphere (MLT) region and the ionosphere during this SSW period, which could be related to the lunar tide. The 14.5 day periodic disturbance in the IGS TEC exhibits local time dependence and latitudinal variation, with the maximum amplitude appearing between 1000 and 1600 LT in the equatorial ionization anomaly (EIA) crest regions. Additionally, the 14.5 day periodic oscillation shows an obvious longitudinal variability, with the weakest amplitude appearing in the longitudinal region of 30° W–60° E. [ABSTRACT FROM AUTHOR]

This study provides a narrative review of diterpenoid alkaloids (DAs), a family of extremely important natural products found predominantly in some species of Aconitum and Delphinium (Ranunculaceae). DAs have long been a focus of research attention due to their numerous intricate structures and diverse biological activities, especially in the central nervous system (CNS). These alkaloids originate through the amination reaction of tetra or pentacyclic diterpenoids, which are classified into three categories and 46 types based on the number of carbon atoms in the backbone structure and structural differences. The main chemical characteristics of DAs are their heterocyclic systems containing β-aminoethanol, methylamine, or ethylamine functionality. Although the role of tertiary nitrogen in ring A and the polycyclic complex structure are of great importance in drug-receptor affinity, in silico studies have emphasized the role of certain sidechains in C13, C14, and C8. DAs showed antiepileptic effects in preclinical studies mostly through Na+ channels. Aconitine (1) and 3-acetyl aconitine (2) can desensitize Na+ channels after persistent activation. Lappaconitine (3), N-deacetyllapaconitine (4), 6-benzoylheteratisine (5), and 1-benzoylnapelline (6) deactivate these channels. Methyllycaconitine (16), mainly found in Delphinium species, possesses an extreme affinity for the binding sites of α7 nicotinic acetylcholine receptors (nAChR) and contributes to a wide range of neurologic functions and the release of neurotransmitters. Several DAs such as bulleyaconitine A (17), (3), and mesaconitine (8) from Aconitum species have a drastic analgesic effect. Among them, compound 17 has been used in China for decades. Their effect is explained by increasing the release of dynorphin A, activating the inhibitory noradrenergic neurons in the β-adrenergic system, and preventing the transmission of pain messages by inactivating the Na+ channels that have been stressed. Acetylcholinesterase inhibitory, neuroprotective, antidepressant, and anxiolytic activities are other CNS effects that have been investigated for certain DAs. However, despite various CNS effects, recent advances in developing new drugs from DAs were insignificant due to their neurotoxicity. [ABSTRACT FROM AUTHOR]

Accurate stratospheric temperature observations are crucial for weather forecasts and climate change studies. This paper discusses a precise measurement method for the stratospheric temperature profile using narrow bands with ultra-high spectral resolution from nadir-viewing satellites. First, the CO2 absorption band around 15 μm is selected as the major sounding source by the calculation and analysis of the temperature Jacobian and the atmospheric molecular spectra. Next, the influence of spectral resolution, spectral range and instrumental noise on the sounding capability is analyzed, and the sounding feasibility of the single spectral band and multiple spectral bands is discussed under the condition that the spaceborne long-wave infrared space heterodyne spectrometer (SHS) is selected as suggested sounder onboard the satellite. Finally, the optimal joint-sounding scheme of narrow bands is proposed. The temperature retrieval and validation show that the joint-sounding of two discontinuous narrow bands can realize the high precision measurement of the stratospheric temperature profile for the given spectral resolution, spectral range, and instrumental noise. When the sounder adopts two narrow bands (the regions of 666.87–676.44 cm−1 and 683.58–693.15 cm−1) and a spectral resolution of 0.03 cm−1, the retrieval accuracy (RMSE) is about 0.9 K over a pressure range of 200 to 0.7 hPa (11.5–50 km). This study will provide technical preparation for high-precision and low-cost satellite sounder design for stratospheric temperature observations. [ABSTRACT FROM AUTHOR]

Quince (Cydonia oblonga Miller) is a plant that is commonly cultivated around the world, known for centuries for its valuable nutritional and healing properties. Although quince fruit are extremely aromatic, due to their high hardness and sour, astringent, and bitter taste, they are not suitable for direct consumption in an unprocessed form. However, they are an important raw material in fruit processing, e.g., in the production of jams, jellies, and juices. Quince fruits fall under the category of temperate fruits, so their shelf life can be predicted. Considering that technological processing affects not only the organoleptic properties and shelf life but also the functional properties of fruits, the aim of this research was to determine the impact of various types of technological treatments on the physicochemical and bioactive properties of quince fruit. In fresh, boiled, and fried fruits and in freshly squeezed quince fruit juice, basic parameters, such as the content of dry matter, moisture, soluble solids (°Brix), pH, total acidity, water activity, and color parameters (L*a*b*) were determined. The content of key bioactive ingredients, i.e., tannins, carotenoids, flavonoids, phenolic acids, and total polyphenols, was also determined, as well as the antioxidant activity of raw and technologically processed (cooked, fried, and squeezed) quince fruits. The conducted research showed that fresh quince fruit and processed quince products can be a very good source of bioactive ingredients in the diet, such as tannins (3.64 ± 0.06 mg/100 g in fresh fruit; from 2.22 ± 0.02 mg/100 g to 5.59 ± 0.15 g/100 g in products), carotenoids (44.98 ± 0.18 mg/100 g in fresh fruit; from 141.88 ± 0.62 mg/100 g to 166.12 ± 0.62 mg/100 g in products), and polyphenolic compounds (246.98 ± 6.76 mg GAE/100 g in fresh fruit; from 364.53 ± 3.76 mg/100 g to 674.21 ± 4.49 mg/100 g in products). Quince fruit and quince products are also characterized by high antioxidant properties (452.41 ± 6.50 µM TEAC/100 g in fresh fruit; 520.78 ± 8.56 µM TEAC/100 g to 916.16 ± 6.55 µM TEAC/100 g in products). The choice of appropriate technological processing for the quince fruit may allow producers to obtain high-quality fruit preserves and act a starting point for the development of functional products with the addition of quince fruit in its various forms, with high health-promoting values and a wide range of applications in both the food and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer has a poor prognosis and is non-responsive to first-line therapies; hence, new therapeutic strategies are needed. Enhanced store-operated Ca2+ entry (SOCE) has been widely described as a contributing factor to tumorigenic behavior in several tumor types, particularly in breast cancer cells. SOCE-associated regulatory factor (SARAF) acts as an inhibitor of the SOCE response and, therefore, can be a potential antitumor factor. Herein, we generated a C-terminal SARAF fragment to evaluate the effect of overexpression of this peptide on the malignancy of triple-negative breast cancer cell lines. Using both in vitro and in vivo approaches, we showed that overexpression of the C-terminal SARAF fragment reduced proliferation, cell migration, and the invasion of murine and human breast cancer cells by decreasing the SOCE response. Our data suggest that regulating the activity of the SOCE response via SARAF activity might constitute the basis for further alternative therapeutic strategies for triple-negative breast cancer. [ABSTRACT FROM AUTHOR]

Power generation estimation is one of the key steps in wind farm micro-sitting, and its accuracy is related to the wake decay constant in the wake model. Considering the influence of wind resource distribution in different regions, this study carried out interval optimization for the wake decay constant of offshore wind farms in the Yellow Sea region of China. Given the very small length of the sea surface roughness, atmospheric stability is a critical factor influencing the wake extent and recovery speed of offshore wind farms. WAsP 10 (Wind Atlas Analysis and Application software) simulates the wake of various scenarios, and the selection range of wake attenuation constant is investigated by combining the cases of two offshore wind farms in the Yellow Sea region. The study found that the higher the atmospheric stability, the larger the wake and the lower the wake attenuation coefficient. The Yellow Sea wind farm's wake error system is between 0.03 and 0.04, and the forecast error can be controlled to within 3%. When simulating the wind farm at Yellow Sea offshore for improving power generation and economic evaluation, it is critical to select the correct value range of wake decay constant. [ABSTRACT FROM AUTHOR]

The paper aims to analyze the contagion effect coming from the developed stock markets of the US and Germany to the emerging CEE stock markets of Romania, Czech Republic, Hungary, and Poland using daily data for the period April 2005–April 2021. The paper also captures the level of integration of these emerging stock markets by analyzing the volatility spillover phenomenon. The quantification of the contagion effect coming from the developed to the emerging stock markets consisted of an empirical analysis based on the DCC-GARCH (Dynamic Conditional Correlation) model. Through this multivariate model, the time-varying conditional correlations were analyzed, both in periods of normal economic development and in times of economic instability, when there was a significant increase in the correlation coefficients between developed and emerging stock market indices. Furthermore, the level of connectedness between these markets has been analyzed using the volatility spillover index developed by Diebold and Yilmaz. The empirical results surprised the high level of integration of the analyzed stock markets in Central and Eastern Europe, with the intensity of volatility transmission between these markets increasing significantly during times of crisis. All stock market indices analyzed show periods during which they transmit net volatility and periods during which they receive net volatility, indicating a bidirectional volatility spillover phenomenon. Mostly, the BET, PX, and WIG indices are net transmitters of volatilities, whereas the BUX index is net recipient, except during the COVID-19 crisis, when it transmitted net volatility to the other three indices. Finally, using a Markov switching-regime VAR approach with two regimes, we explored the contagion effect between emerging CEE and developed stock markets during the COVID-19 pandemic. The empirical results proved a shift around the outbreak of the health crisis, after which the high volatility regime dominates the CEE markets. The contagion effects from developed stock markets to emerging CEE markets significantly increased during the first stage of the health crisis. [ABSTRACT FROM AUTHOR]

In this intellectual work, the clinical and educational aspects of dentistry were confronted with practical applications of artificial intelligence (AI). The aim was to provide an up-to-date overview of the upcoming changes and a brief analysis of the influential advancements in the use of AI in dental education since 2020. In addition, this review provides a guide for a dental curriculum update for undergraduate and postgraduate education in the context of advances in AI applications and their impact on dentistry. Unsurprisingly, most dental educators have limited knowledge and skills to assess AI applications, as they were not trained to do so. Also, AI technology has evolved exponentially in recent years. Factual reliability and opportunities with OpenAI Inc.'s ChatGPT are considered critical inflection points in the era of generative AI. Updating curricula at dental institutions is inevitable as advanced deep-learning approaches take over the clinical areas of dentistry and reshape diagnostics, treatment planning, management, and telemedicine screening. With recent advances in AI language models, communication with patients will change, and the foundations of dental education, including essay, thesis, or scientific paper writing, will need to adapt. However, there is a growing concern about its ethical and legal implications, and further consensus is needed for the safe and responsible implementation of AI in dental education. [ABSTRACT FROM AUTHOR]

There are many studies that focus on extracting harmonics from both DC and AC sides of grid-interfaced photovoltaic (PV) systems. Based on these studies, the paper introduces an efficient method depending on hybrid DC voltage, and an active and reactive power (DC-V PQ) control scheme in a single-stage three-phase grid-interfaced PV system. The proposed scheme is designed to regulate DC voltage to minimize power loss and energy share between the network reconfiguration and the utility grid. Moreover, the technique is more effective at dealing with uncertainty and has higher reliability under various operating scenarios. These operations are the insertion of linear load 1, nonlinear load, and linear load 2. Moreover, a novel objective function (OF) is developed to improve the dynamic response of the system. OF is coupled with a particle swarm optimization (PSO) algorithm and a gradient optimization (GBO) algorithm. The analysis and the comparative study prove the superiority of GBO with counterfeits algorithm. [ABSTRACT FROM AUTHOR]

The increasing penetration of Distributed Generators (D.G.) into the existing power system has brought some real challenges regarding the transient response of electrical systems. The injection of D.G.s and abrupt load changes may cause massive power, current, and voltage overshoots/undershoots, which consequently affects the equilibrium of the existing power system and deteriorate the performance of the connected electrical appliances. A robust and intelligent control strategy is of utmost importance to cope with these issues and enhance the penetration level of D.G.s into the existing power system. This paper presents a Modified Particle Swarm Optimization (MPSO) algorithm-based intelligent controller for attaining a desired power-sharing ratio between the M.G. and the main grid with an optimal transient response in a grid-tied Microgrid (M.G.) system. The proposed MPSO algorithm includes an additional parameter named best neighbor particles (rbest) in the velocity updating equation to convey additional information to every individual particle about all its neighbor particles, consequently leading to the increased exploration capability of the algorithm. The MPSO algorithm optimizes P.I. parameters for transient and steady-state response improvement of the studied M.G. system. The main dynamic response evaluation parameters are the overshoot and settling time for active and reactive power during the D.G. connection and load change. Furthermore, the performance of the proposed controller is compared with the PI-PSO-based MG controller, which validates the effectiveness of the proposed M.G. control scheme in maintaining the required active and reactive power under different operating conditions with minimum possible overshoot and settling time. [ABSTRACT FROM AUTHOR]

Follistatin (FST) as a gonadal protein is central to the establishment and maintenance of pregnancy. Trophoblasts' migration and invasion into the endometrium are critical events in placental development. This study aimed to elucidate the role of FST in the migration and invasion of placental trophoblasts of mice. We found that FST increased the vitality and proliferation of primary cultured trophoblasts of embryonic day 8.5 (E8.5) mice and promoted wound healing of trophoblasts. Moreover, FST significantly induced migration of trophoblasts in a microfluidic device and increased the number of invasive trophoblasts by Matrigel-coated transwell invasion assay. Being treated with FST, the adhesion of trophoblasts was inhibited, but intracellular calcium flux of trophoblasts was increased. Western blotting results showed that FST had no significant effects on the level of p-Smad3 or the ratio of p-Smad3/Smad3 in trophoblasts. Interestingly, FST elevated the level of p-JNK; the ratio of p-JNK/JNK; and expression of migration-related proteins N-cadherin, vimentin, ezrin and MMP2 in trophoblasts. Additionally, the migration of trophoblasts and expression of N-cadherin, vimentin, and MMP2 in trophoblasts induced by FST were attenuated by JNK inhibitor AS601245. These findings suggest that the elevated FST in pregnancy may act as a chemokine to induce trophoblast migration and invasion through the enhanced JNK signaling to maintain trophoblast function and promote placental development. [ABSTRACT FROM AUTHOR]

Plants are an entity essential to the function of the biosphere as well as human health. In the context of human health, this research investigated the effect of Lasia spinosa (Lour) leaf methanolic extracts (LSML) on antioxidative enzymes and gene expression as well as biochemical and histological markers in a streptozotocin (STZ)-induced diabetes model. Fructose-fed streptozotocin (STZ)-induced diabetic animals were subjected to a four-week intervention followed by the assessment of the animal's blood and tissues for enzymatic, biochemical, histological, and genetic changes. LSML-treated groups were shown to decrease plasma glucose levels and improve body and organ weights compared to the untreated group in a dose-dependent manner. At the doses of 125 and 250 mg/kg b.w., LSML were able to normalize serum, hepatic, and renal biochemical parameters and restore the pancreas, kidney, liver, and spleen tissue architectures to their native state. A considerable increase (p < 0.01) of liver antioxidant enzymes CAT, SOD, GSH, and a decrease of MDA level in LSML-treated groups were found at higher doses. The improved mRNA expression level of antioxidant genes CAT, SOD2, PON1, and PFK1 was also found at the doses of 125 mg/kg and 250 mg/kg BW when compared to untreated control groups. The results demonstrate that LSML impacts the upregulation of antioxidative gene expressions, thus improving the diabetic complications in animal models which need to be affirmed by compound-based antioxidative actions for therapeutic development. [ABSTRACT FROM AUTHOR]

This paper aims to empirically test the impact of entrepreneurial competencies on the performances of SMEs in Bangladesh. The data are obtained from 115 entrepreneurs by a structured questionnaire. The partial least square structural equation modeling (PLS-SEM) method is applied to find the causal relationship between exogenous and endogenous variables. The results reveal that the organizing and leading, learning, relationship, and commitment competencies of entrepreneurs have a meaningful impact on SME performance. On the contrary, strategic and opportunity competencies have no effect on the performance of SMEs. The contribution of this study is to find out the relevant entrepreneurial competencies and to empirically test their impacts on the performance of SMEs in a developing country setting. This study gives insights into the policymakers regarding the entrepreneurship training and development program. Finally, the competency measurement of this study provides a unique scope for designing such training and development programs for entrepreneurs. [ABSTRACT FROM AUTHOR]

The rising uncertainty of the global markets has caused the price fluctuation of the equipment and materials in PT&D (Power Transmission and Distribution) projects to be more complicated. As the cost of equipment and materials accounts for a large proportion of the total investment of the PT&D projects, this study investigates the co-movement of the equipment and materials price and metal commodities' price with the use of wavelet coherency and partial wavelet coherency analysis under a time-frequency framework. Our results indicate that (i) the co-movement manifests mainly in the short term and long term; (ii) there is no clear lead-lag relationship in the co-movement; (iii) the short-term co-movement is activated by the continuous rising of metal commodity price; (iv) the long-term co-movement is largely driven by the overall market price movement, but this effect is weakened when the metal is of high importance in producing the equipment. Our study shed light on the planning and investment in the PT&D projects for both the purchase and production sides. [ABSTRACT FROM AUTHOR]

Although the characteristics of the traveling 10-day waves (10DWs) above the middle stratosphere have been well explored, little research has been performed on the counterpart in the troposphere and lower stratosphere (TLS). In the present study, we use radiosonde observations and MERRA-2 data from 2020 to characterize traveling 10DWs in mid-latitudes in the TLS. Single-site observations in both hemispheres show that strong 10DW activities are always accompanied by strong eastward jets (10–13 km). MERRA-2 data indicate that in the troposphere, the eastward-propagating modes with larger wavenumbers, i.e., E3, E4, E5 and E6 are dominant, while in the lower stratosphere, the eastward- and westward-propagating modes with small zonal wavenumbers, e.g., 1 and 2, are dominant. Further research on E3, E4, E5 and E6 modes in the troposphere of both hemispheres shows that all the wave activities are positively correlated to the background zonal wind. The refractive index squared reveals that a strong eastward jet is suitable for these four modes to propagate. However, just above the jet, the eastward wind decreases with altitude, and a thick evanescence region emerges above 15 km. E3, E4, E5 and E6 10DWs cannot propagate upward across the tropopause; as such, this can explain why these four modes are weak or even indiscernible in the stratosphere and above. In the troposphere, E5 10DW at 32° S was the most dominant mode in 2020. A case study of the anomalously strong E5 10DW activity on 12 May 2020 indicates that the wave amplification resulted from the upward and equatorward transmission of wave energy flows. Moreover, the tropopause and equatorial region can prevent the propagations of wave energy flows of E5 10DW. The upward-propagating wave energy is deposited near the tropopause, resulting in significant short-term variability in the tropopause height. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is a chronic neuroinflammatory and demyelinating disease of the central nervous system (CNS), characterised by the infiltration of peripheral immune cells, multifocal white-matter lesions, and neurodegeneration. In recent years, microglia have emerged as key contributors to MS pathology, acting as scavengers of toxic myelin/cell debris and modulating the inflammatory microenvironment to promote myelin repair. In this review, we explore the role of two neuropeptides, pituitary adenylate cyclase-activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP), as important regulators of microglial functioning during demyelination, myelin phagocytosis, and remyelination, emphasising the potential of these neuropeptides as therapeutic targets for the treatment of MS. [ABSTRACT FROM AUTHOR]

Variability in the ionosphere during the 2020–2021 sudden stratospheric warming (SSW) is investigated using a combination of Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) observations and the Whole Atmosphere Community Climate Model with thermosphere–ionosphere eXtension (WACCM-X) simulations. The unprecedented spatial–temporal sampling of the low latitude ionosphere afforded by COSMIC-2 enables investigating the short-term (<5 days) variability in the ionosphere during the SSW event. The COSMIC-2 observations reveal a reduction in the diurnal and zonal mean ionosphere total electron content (ITEC) and reduced amplitude of the diurnal variation in the ionosphere during the SSW. Enhanced ITEC amplitudes of the semidiurnal solar and lunar migrating tides and the westward propagating semidiurnal tide with zonal wavenumber 3 are also observed. The WACCM-X simulations demonstrate that these variations are driven by variability in the stratosphere–mesosphere during the 2020–2021 SSW event. The results show the impact of the 2020–2021 SSW on the mean state, diurnal, and semidiurnal variations in the ionosphere, as well as the capabilities of the COSMIC-2 mission to observe short-term variability in the ionosphere that is driven by meteorological variability in the lower atmosphere. [ABSTRACT FROM AUTHOR]