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In this study various surface layer parameters, fluxes, and eddy diffusivity profiles have been estimated by making use of routine meteorological data for both unstable and stable conditions. Several empirical relationships for estimating turbulence parameters are tested with data. The estimation schemes are discussed at length for both stability conditions. Routine meteorological observations have been used for the computations of net radiation, sensible heat flux, friction velocity, scaling temperature in the surface layer, Monin-Obukhov length, and eddy diffusivity profiles. These parameters are important and are used for the estimation of input parameters for air quality models for predicting ground-level concentrations. Special emphasis has been given to stable conditions by using recent formulations for turbulence parameters and their verification with data. Two recent schemes have been compared. About 153 h of data have been selected from the field experiments sponsored by the Electric Power Research Institute around the Kincaid Power Plant in 1980-81 for the validation of net radiation, friction velocity, and Monin-Obukhov length. Several statistical techniques have been used to assess the performance of the schemes. Based on the statistical measures, the chosen parameterization schemes are judged to perform well.

The aqueous root extract of the Delphinium denudatum was evaluated for acute oral toxicity and antinociceptive effects in mice. Oral doses up to 14,000 mg/kg did not induce death, some CNS depression was observed and the LD50 was found at 16,100 mg/kg. At the four dose levels used (200-1600 mg/kg, p.o.), D. denudatum extract demonstrated a dose-dependent antinociceptive effect in the thermal and chemical models of analgesia. Pretreatment with naloxone did not alter the analgesic effect of D. denudatum extract suggesting that some mechanism other than opioid is involved in their action. [ABSTRACT FROM AUTHOR]

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]

In crude oil and oil cuttings, there are some sulfurous, nitrogenous, oxygenated compounds, metals, and unsaturated compounds. They can have destructive effects on equipment, catalysts, and the quality of final products. Due to the importance of the problem, one of the most important operations in any refinery is the purification operation, the purpose of which is to eliminate or reduce such harmful compounds, especially sulfur compounds. The most common industrial method for its removal is hydrogen desulfurization (hydrodesulfurization). Of course, this method also has disadvantages and it is necessary to use catalysts that, in addition to sulfur removal, improve other fuel properties. This study investigated the approach mechanism of dibenzothiophene (DBT) to chemically active sites of carbon nanotubes and dehydrogenates them. After simulating and optimizing the structure of dibenzothiophene and carbon nanotube (8–8) has been calculated and evaluated the structural, electrical, and thermodynamic properties of the dehydrogenation reaction by the Density Functional Theory (DFT) method. Carbon nanotube as a nano-catalyst has a different location to interact with dibenzothiophene examined all locations (passing from the central axis). The results show dibenzothiophene is more likely to approach and interact and desulfurize on the end of the carbon nanotube and release H2S gas. In the step of electron exchange, the gap energy reaches (Eg = 5.08 eV) at the end of the nanotube, and is compared to the gap energy on the outer wall of the nanotube (Eg = 5.55 eV). Therefore, different locations of carbon nanotubes are effective in absorbing and converting sulfur compounds. [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]

Purpose: The purpose of this paper is to examine the existence of the day-of-the-week effect in the Indian stock market. Design/methodology/approach: Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1), Exponential GARCH (EGARCH) (1, 1) and Threshold GARCH (TGARCH) (1, 1) models are employed to examine the day-of-the-week effect in the Indian stock market for the period of 28 years from 3rd July, 1990 to 31st March, 2022. Findings: The empirical results derived from the GARCH models indicate the existence of day-of-the-week effects on stock returns and volatility of the Indian stock market. The study reveals that all the days of the week are positive and significant in National Stock Exchange (NSE)-Nifty market returns. The findings confirm the persistence of ARCH and GARCH effects in the daily return series. Moreover, the asymmetric GARCH models show that the daily stock returns exhibit significant asymmetric (leverage) effects. Practical implications: The results of this study established that the Indian stock market is not efficient and there exists an opportunity to the traders for predicting the future prices and earning abnormal profits in the Indian stock market. The findings of the study are important for traders, investors and portfolio managers to earn abnormal returns by cross-border diversification. Originality/value: First, to the best of the authors' knowledge, this paper is the first to study the day-of-the-week effect in Indian stock market considering the most recent and longer time period (1990–2022). Second, unlike previous research, this study used GARCH models (GARCH, EGARCH and TGARCH) to capture the volatility clustering in the data. [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]

This study investigates the occurrence and characteristics of low‐level jets (LLJs) up to 200 m above sea level in the North Sea area, specifically in the southernmost part of the basin, at the entrance of the English Channel. Using a short‐range Doppler lidar installed in Dunkerque Port, on the northern coast of France, wind profiles were recorded for 4 years and analyzed statistically. LLJs were detected on more than 11,000 of the 10‐min average wind profiles (5% of time), with similar jet core height and core speed distributions as in other sites in the southern North Sea area, and a similar annual cycle. However, there were differences in the core directions and the daily cycle, with afternoon northeastly jets being the most frequent in Dunkerque, whereas southwesterly nocturnal jets were dominant in the other North Sea sites. This suggests that wind channeling in the Dover Strait is likely a major factor for LLJ formation in Dunkerque region. The study also examined the conditions of LLJ occurrence using European Centre for Medium‐Range Weather Forecast (ECMWF) reanalysis v5 and ultrasonic anemometer data, but the different types of LLJs sharing the same core direction and occurrence conditions made it difficult to quantify specific jet formation mechanisms. This is the first experimental study on the North Sea shore, and at the entrance of the English Channel, providing valuable insights into the LLJ behavior in the region. [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]