Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures.

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

Antimicrobial behavior of nanocoated orthodontic micro-implants: An in vitro study.

OBJECTIVE: The need to overcome the failure of orthodontic micro-implants which might reach to 30% has led to the development of different methods, one of which is nanoparticle deposition. AIM OF STUDY: To evaluate the anti-microbial efficiency of TiO2 and ZnO nanoparticles (NP) when used as a coating for orthodontic micro-implants. METHODS: Thirty titanium alloy micro-implants were used in the presented study. They were divided into three groups according to the coating method and the coating materials used: the control group without surface coating; the titanium dioxide (TiO2)-coated group, in which direct current (DC) spattering was used to coat the micro-implants with a TiO2 layer; and the TiO2 and zinc oxide (TiO2ZnO)-coated group, in which the micro-implants were coated with a TiO2 layer via direct current (DC) spattering and a zinc oxide (ZnO) layer via laser vacuum. The micro-implant surfaces were characterized using scanning electron microscopy (SEM) and an energy-dispersive spectrometer (EDS). The antibacterial susceptibility was assessed using gram-positive and gram-negative bacteria. RESULTS: SEM and EDS tests confirmed the coating of the micro-implants in the TiO2- and TiO2ZnO-coated groups. The micro-implants in the TiO2- and TiO2ZnO-coated groups demonstrated higher antibacterial ability than the control group. CONCLUSION: This study demonstrated the significance of improving the surface of orthodontic micro-implants by coating them with TiO2 and ZnO nanoparticles to improve osseointegration and prevent biofilm formation.

Mandibular Third Molar Impaction and Bone Change Distal to the Second Molar: A Panoramic Radiographic Study.

Background: The mandibular third molar is the most frequently impacted tooth. An impacted mandibular third molar (IMTM) can have negative consequences on the adjacent mandibular second molar (MSM), such as bone loss. An IMTM can be identified using orthopantomography (OPG). Our objective is to compare changes in bone level distal to the mandibular second molar (MSM) in patients with an extracted IMTM versus non-extracted IMTM using OPG. Methods: In this retrospective case-control study, 160 orthopantomograms (OPGs) of 80 patients who attended Dental Hospital of the University of Barcelona (HOUB) were randomly selected. Participants were stratified into a study group and control group. Results: Males and females experienced bone gain in the study group and bone loss in the control group. However, the difference in bone-level change was not statistically significant regarding gender in the study group. Within the study group, the age group of 29-39 years demonstrated significant (p-value = 0.042) bone gain after extraction compared to other age groups. However, the control group demonstrated bone loss in all age groups in which the difference is not statistically significant (p-value 0.794). Conclusions: Bone improvements distal to the MSM were observed after the extraction of an IMTM compared to when an IMTM was not extracted.

Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region.

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.

Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques.

Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR-SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR-SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR-SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm.

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America.

The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.

Gradient Monitored Reinforcement Learning.

This article presents a novel neural network training approach for faster convergence and better generalization abilities in deep reinforcement learning (RL). Particularly, we focus on the enhancement of training and evaluation performance in RL algorithms by systematically reducing gradient's variance and, thereby, providing a more targeted learning process. The proposed method, which we term gradient monitoring (GM), is a method to steer the learning in the weight parameters of a neural network based on the dynamic development and feedback from the training process itself. We propose different variants of the GM method that we prove to increase the underlying performance of the model. One of the proposed variants, momentum with GM (M-WGM), allows for a continuous adjustment of the quantum of backpropagated gradients in the network based on certain learning parameters. We further enhance the method with the adaptive M-WGM (AM-WGM) method, which allows for automatic adjustment between focused learning of certain weights versus more dispersed learning depending on the feedback from the rewards collected. As a by-product, it also allows for automatic derivation of the required deep network sizes during training as the method automatically freezes trained weights. The method is applied to two discrete (real-world multirobot coordination problems and Atari games) and one continuous control task (MuJoCo) using advantage actor-critic (A2C) and proximal policy optimization (PPO), respectively. The results obtained particularly underline the applicability and performance improvements of the methods in terms of generalization capability.

Data-driven models for atmospheric air temperature forecasting at a continental climate region.

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

Compaction Curves and Strength of Clayey Soil Modified with Micro and Nano Silica.

Some Clayey soils are generally categorized as weak soils, and structures lying on such soils have been exposed to severe damage. Therefore, the central thesis of this paper is the impact of a waste material known as a silica fume as nano and micro material on soil's behaviour. To evaluate the effects of those additives on Atterberg limits, compaction characteristics and unconfined compressive strength, clayey soil samples have been transformed using micro and nano silica fume (by-product materials). In the current investigation, silica fume is used at four different percentages: 0, 2, 4, and 7%. The results show that the plasticity index of soil decreases with the addition of micro silica and increases with the addition of nano-silica. Increasing nano silica percentage improves the dry density of the compacted soil and reduces the optimum moisture content. An opposite behavior is observed with adding micro silica to compacted soil. Finally, 4% of silica fume is found to be the optimum dosage to improve the unconfined compressive strength of the treated soil with both additives. As a result, treating the weak clay soil with micro and/or nano-silica fume has the potential to be impactful.

Spilled stones following pyelolithotomy: An unusual mimic of peritoneal carcinomatosis.

While spillage of intraperitoneal gallstones has been reported frequently in the literature, spilled renal stones after urologic intervention is rare. The dropped renal stones may mimic peritoneal carcinomatosis (PC) on imaging, causing concern and potentially leading to unnecessary diagnostic workup. Additionally, these dropped stones may cause surrounding inflammation, potentially leading to the formation of adhesions or an intra-abdominal abscess. Calcifications along the peritoneal lining are generally interpreted as peritoneal carcinomatosis until proven otherwise. However, this case highlights the importance of a detailed history and comparison with prior imaging. We describe a rare case of intraperitoneal spilled renal stones after pyelolithotomy initially mistaken for PC, in addition to a review of diagnostic pitfalls and radiologic mimics of PC.

Asymptomatic pneumoperitoneum in the setting of pneumatosis intestinalis: a benign entity or surgical emergency?

Pneumatosis intestinalis is a potential cause of asymptomatic pneumoperitoneum without peritonitis. The disease can be managed conservatively and presents a clinical scenario where pneumoperitoneum does not necessitate surgical management. This case illustrates the importance of acknowledging the condition and its variable presentation, allowing for increased awareness and avoidance of invasive procedures when not indicated.

Breast Arterial Calcifications: Reporting Preferences and Impact on Screening for Coronary Artery Disease.

OBJECTIVE: Breast arterial calcifications (BAC) have been shown to correlate with measures of coronary artery disease risk stratification, although reporting of BAC is optional by BI-RADS guidelines. The purpose of this study is to determine referring provider preferences in BAC reporting on mammography reports and if such reporting has any impact on patient management. METHODS: This study was approved by the local institutional review board. A voluntary eight-question survey regarding the preferences and outcomes of BAC reporting on mammography was distributed to 1085 primary care physicians, obstetrics and gynecologists, medical oncologists, and breast and general surgeons in our health system via a secure online platform. Data analysis including Pearson chi-square was performed with a P-value of <0.05 for significance. RESULTS: A response rate of 19.1% (207/1085) was attained, with 21/207 (10.1%) of respondents indicating they do not routinely order mammograms excluded from further analysis. A total of 62.4% (116/186) of ordering physicians indicated a preference for reporting of BAC in both the body and impression of the radiology report, with 82.3% (153/186) of respondents placing importance on the quantity of atherosclerotic calcifications. Most participants (148/186, 79.6%) reported that the presence of BAC would prompt further investigation for coronary artery disease and associated risk factors. CONCLUSION: The majority of responding physicians indicated a preference for detailed reporting of BAC and that such reporting would impact patient care. Understanding referring provider preferences regarding ancillary findings of BAC will allow for improved communication and value in mammography.

Idiopathic Mandibular Condyle Resorption.

Idiopathic mandibular condylar resorption is a rare condition in which the mandibular condyle of the temporomandibular joint (TMJ) becomes resorbed and thus reduces in size and volume. This leads to TMJ dysfunction that commonly requires surgical correction; however, more conservative interventions can also be utilized. We present a case of idiopathic mandibular condyle resorption in a 17-year-old female presenting with TMJ pain and clicking with mastication. A definitive diagnosis of this condition ultimately requires imaging studies, a reliable option being magnetic resonance imaging (MRI), which will reveal erosion of the mandibular condylar process (often bilaterally) with diminished mass and volume leading to the known sequelae of symptoms.

Atypical Mixed Variant of Pericallosal Lipoma With Splenial Dysgenesis.

Pericallosal lipomas are rare benign intracranial masses that arise during embryonic development, typically categorized into tubulonodular and curvilinear subtypes. A mixed variant of both tubulonodular and curvilinear subtypes is very rare. Patients with pericallosal lipomas may be asymptomatic or may have different presentations, such as headaches. Conservative medical management is the mainstay of therapy for those without epileptic seizures or associated vascular malformations. We present a case of a mixed variant pericallosal lipoma in a patient with chronic headaches that were diagnosed using head computed tomography (CT) and brain magnetic resonance imaging (MRI).

The Spectrum of Sleep Disorders Among Children: A Cross-sectional Study at a South Indian Tertiary Care Hospital.

Introduction Sleep problems during infancy and early childhood are fairly common and rarely recognized in pediatric practice. These are mostly related to the initiation and maintenance of night-time sleep. Understanding sleep patterns and disorders associated with sleep is challenging, especially in the pediatric age group. This study was done to estimate the magnitude of sleep disorders in children and to evaluate the associated risk factors. Methods This cross-sectional study was carried out among 450 children visiting the pediatric outpatient department of Sri Ramachandra Institute of Higher Education and Research, Chennai, India between November 2018 and June 2019. Children with chronic illnesses and a history of physical or mental trauma in the past six months were excluded. The Sleep Disturbance Scale for Children (SDSC) was used to gather information regarding sleep disorders. Results It was observed that a majority of the participants (72.2%) slept 9-11 hours per day. Among 46.2% of the participants, the time lag between bedtime and sleep time was less than 15 minutes. Overall, sleep problems were present in 34% of the participants. History of sleep problems in infancy, absence of siblings, and parental presence while sleeping emerged as statistically significant risk factors for childhood sleep disorders (p: <0.05). Conclusion We believe our study provides a basis for exploring the pattern and problems associated with sleep behavior among children. There is a need for setting up routine screening measures in pediatric outpatient departments to facilitate early detection of sleep disorders in order to avoid complications.

Nonadherence to antihypertensive medications is related to pill burden in apparent treatment-resistant hypertensive individuals.

OBJECTIVE: Nonadherence to medication is present in at least 50% of patients with apparent treatment-resistant hypertension. We examined the factors associated with nonadherence as detected by a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based urine antihypertensive drug assay. METHODS: All urine antihypertensive test results, carried out for uncontrolled hypertension (BP persistently >140/90 mmHg) between January 2015 and December 2016 at a single toxicology laboratory were analysed. Drugs detected were compared with the antihypertensive drugs prescribed. Patients were classified as adherent (all drugs detected), partially nonadherent (at least one prescribed drug detected) or completely nonadherent (no drugs detected). Demographic and clinical parameters were compared between the adherent and nonadherent groups. Binary logistic regression analysis was performed to determine association between nonadherence and demographic and clinical factors. RESULTS: Data on 300 patients from nine hypertension centres across the United Kingdom were analysed. The median age was 59 years, 47% women, 71% Caucasian, median clinic BP was 176/95 mmHg and the median number of antihypertensive drugs prescribed was four. One hundred and sixty-six (55%) were nonadherent to prescribed medication with 20% of these being completely nonadherent. Nonadherence to antihypertensive medication was independently associated with younger age, female sex, number of antihypertensive drugs prescribed, total number of all medications prescribed (total pill burden) and prescription of a calcium channel blocker. CONCLUSION: This LC-MS/MS urine analysis-based study suggests the majority of patients with apparent treatment-resistant hypertension are nonadherent to prescribed treatment. Factors that are associated with nonadherence, particularly pill burden, should be taken into account while treating these patients.

Isothermal titration calorimetry and surface plasmon resonance analysis using the dynamic approach.

Biophysical techniques such as isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) are routinely used to ascertain the global binding mechanisms of protein-protein or protein-ligand interaction. Recently, Dumas etal, have explicitly modelled the instrument response of the ligand dilution and analysed the ITC thermogram to obtain kinetic rate constants. Adopting a similar approach, we have integrated the dynamic instrument response with the binding mechanism to simulate the ITC profiles of equivalent and independent binding sites, equivalent and sequential binding sites and aggregating systems. The results were benchmarked against the standard commercial software Origin-ITC. Further, the experimental ITC chromatograms of 2'-CMP + RNASE and BH3I-1 + hBCLXL interactions were analysed and shown to be comparable with that of the conventional analysis. Dynamic approach was applied to simulate the SPR profiles of a two-state model, and could reproduce the experimental profile accurately.

Medication adherence and treatment-resistant hypertension: a review.

Nonadherence is a common reason for treatment failure and treatment resistance. No matter how it is defined, it is a major issue in the management of chronic illnesses. There are numerous methods to assess adherence, each with its own strengths and weaknesses; however, no single method is considered the best. Nonadherence is common in patients with hypertension, and it is present in a large proportion of patients with uncontrolled blood pressure taking three or more antihypertensive agents. Availability of procedure-based treatment options for these patients has shed further light on this important issue with development of new methods to assess adherence. There is, however, no consensus on the management of nonadherence, which reflects the complex interplay of factors responsible for it.