AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images.

This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002-0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05-0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models' efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage.

Remote Heart Rate Estimation Based on Transformer with Multi-Skip Connection Decoder: Method and Evaluation in the Wild.

Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos. Our method is based on the skin color variation caused by the change in blood volume in its surface. The presented heart rate estimation framework consists of three main steps: (1) the segmentation of the facial region of interest (ROI) based on the landmarks obtained by 3DDFA; (2) the extraction of the spatial and global features; and (3) the estimation of the heart rate value from the obtained features based on the proposed method. This paper investigates which feature extractor performs better by captioning the change in skin color related to the heart rate as well as the optimal number of frames needed to achieve better accuracy. Experiments were conducted using two publicly available datasets (LGI-PPGI and Vision for Vitals) and our own in-the-wild dataset (12 videos collected by four drivers). The experiments showed that our approach achieved better results than the previously published methods, making it the new state of the art on these datasets.

High gain differentiator based neuro-adaptive arbitrary order sliding mode control design for MPE of standalone wind power system.

In this paper, we introduce a novel Maximum Power Point Tracking (MPPT) controller for standalone Wind Energy Conversion Systems (WECS) with Permanent Magnet Synchronous Generators (PMSG). The primary novelty of our controller lies in its implementation of an Arbitrary Order Sliding Mode Control (AOSMC) to effectively overcome the challenges caused by the measurement noise in the system. The considered model is transformed into a control-convenient input-output form. Additionally, we enhance the control methodology by simultaneously incorporating Feedforward Neural Networks (FFNN) and a high-gain differentiator (HGO), further improving the system performance. The FFNN estimates critical nonlinear functions, such as the drift term and input channel, whereas the HGO estimates higher derivatives of the system outputs, which are subsequently fed back to the control inputs. HGO reduces sensor noise sensitivity, rendering the control law more practical. To validate the proposed novel control technique, we conduct comprehensive simulation experiments compared against established literature results in a MATLAB environment, confirming its exceptional effectiveness in maximizing power extraction in standalone wind energy applications.

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study.

Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver's state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver's maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver's stable state when aggressive external events or maneuvers occur.

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete.

The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models' cast and printed flexural strength values were also correlated. The model's performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

A molecular simulation study on amine-functionalized silica/polysulfone mixed matrix membrane for mixed gas separation.

Polysulfone (PSF) based mixed matrix membranes (MMMs) are one of the most broadly studied polymeric materials used for CO2/CH4 separation. The performance of existing PSF membranes encounters a bottleneck for widespread expansion in industrial applications due to the trade-off amongst permeability and selectivity. Membrane performance has been postulated to be enhanced via functionalization of filler at different weight percentages. Nonetheless, the preparation of functionalized MMMs without defects and its empirical study that exhibits improved CO2/CH4 separation performance is challenging at an experimental scale that needs prior knowledge of the compatibility between the filler and polymer. Molecular simulation approaches can be used to explore the effect of functionalization on MMM's gas transport properties at an atomic level without the challenges in the experimental study, however, they have received less scrutiny to date. In addition, most of the research has focused on pure gas studies while mixed gas transport properties that reflect real separation in functionalized silica/PSF MMMs are scarcely available. In this work, a molecular simulation computational framework has been developed to investigate the structural, physical properties and gas transport behavior of amine-functionalized silica/PSF-based MMMs. The effect of varying weight percentages (i.e., 15-30 wt.%) of amine-functionalized silica and gas concentrations (i.e., 30% CH4/CO2, 50% CH4/CO2, and 70% CH4/CO2) on physical and gas transport characteristics in amine-functionalized silica/PSF MMMs at 308.15 K and 1 atm has been investigated. Functionalization of silica nanoparticles was found to increase the diffusion and solubility coefficients, leading to an increase in the percentage enhancement of permeability and selectivity for amine-functionalized silica/PSF MMM by 566% and 56%, respectively, compared to silica/PSF-based MMMs at optimal weight percentage of 20 wt.%. The model's permeability differed by 7.1% under mixed gas conditions. The findings of this study could help to improve real CO2/CH4 separation in the future design and concept of functionalized MMMs using molecular simulation and empirical modeling strategies.

Energy, exergy, economic, environment, exergo-environment based assessment of amine-based hybrid solvents for natural gas sweetening.

Natural gas is the cleanest form of fossil fuel that needs to be purified from CO2 and H2S to diminish harmful emissions and provide feasible processing. The conventional chemical and physical solvents used for this purpose have many drawbacks, including corrosion, solvent loss, high energy requirement, and the formation of toxic compounds, which ultimately disrupt the process and affect the environment. Hybrid solvents have lately been researched to cater to these liabilities and enhance process economics. This study screened eight solvents based on CO2 selectivity viscosity, absorption enthalpy, corrosivity, working capacity, specific heat, and vapor pressure. From the screened solvents, ten cases of hybrid solvents are simulated and optimized on Aspen HYSYS®. Furthermore, 5Es (Energy, Exergy, Economic, Environmental, and Exergy-environmental) analyses were performed on optimized cases, and results were compared with the base case, MEA (30 wt%). The hybrid blend of Sulfolane and MDEA with weight percentages of 6% and 24%, respectively, showed the highest energy savings of 20% concerning the base case. In addition, it offered 93% savings in exergy destruction and 17.26% in the total operating cost of the process. It is also promising to the environment due to reduced entropy sent to the ecosystem and controlled CO2 emissions. Therefore, the blend of Sulfolane and MDEA is proposed to Supersede the conventional solvent MEA for the natural gas sweetening process.

Modelling and optimization study to improve the filtration performance of fibrous filter.

Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure drop, and sometime type of aerosol (liquid or solid) plays a crucial role in the performance of the fibrous filter. To avoid overdesigning of the filter along with better performance, optimum set of parameters are to be decided before the manufacturing process. In the current effort, the desirability approach and along with the "Response Surface Methodology (RSM)" were considered to optimize filtration efficiency and pressure drop simultaneously. In this perspective, the impact of Filtration velocity (v), Basis weight (φ), Particle diameter (dp), and Packing fraction (α) on filtration efficiency (η) and pressure drop (Pd) was studied. Based on the outcome, the predicted values lie within experimental data through smart agreement. The maximum percentage (%) error was only 3% and 6% filtration efficiency (η) and pressure drop (Pd), which determine the effectiveness of this useful model. The most dominant factor which affects the filtration efficiency (η) was found to be the Basis weight (φ), followed by packing fraction. However, in the case of pressure drop, the most dominant factors were filtration speed followed by the pachining fraction. Moreover, artificial neural network (ANN) models are developed for the prediction of filtration efficiency and pressure drop. The model accuracy has been estimated by calculating "Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2)". Both models show promising results when compared with experimental data with the R2 value of 98.50-99.86. The optimized values of the maximum filtration efficiency and minimum pressure drop simultaneously were obtained for v = 5, φ = 59.60, dp = 52.23, α = 0.24 according to desirability approach.

3D Vehicle Detection and Segmentation Based on EfficientNetB3 and CenterNet Residual Blocks.

In this paper, we present a two stages solution to 3D vehicle detection and segmentation. The first stage depends on the combination of EfficientNetB3 architecture with multiparallel residual blocks (inspired by CenterNet architecture) for 3D localization and poses estimation for vehicles on the scene. The second stage takes the output of the first stage as input (cropped car images) to train EfficientNet B3 for the image recognition task. Using predefined 3D Models, we substitute each vehicle on the scene with its match using the rotation matrix and translation vector from the first stage to get the 3D detection bounding boxes and segmentation masks. We trained our models on an open-source dataset (ApolloCar3D). Our method outperforms all published solutions in terms of 6 degrees of freedom error (6 DoF err).

Exergy, advance exergy, and exergo-environmental based assessment of alkanol amine- and piperazine-based solvents for natural gas purification.

Purification of Natural gas is vital for utilizing it as a source of energy harvesting for the world. Amine-based chemical absorption technique is the most utilized in the gas field for the purification of gas that ensures the purity of the sweet gas stream with the elimination of carbon dioxide. However, it is considered an energy-intensive process to deal with considerable energy loss and environmental damage to the ecosystem. Five cases have been developed in this study based on various blends comprising mono and tertiary amines in combination with piperazine with a focus on the use of Aqueous Monodiethanolamine (Aq. MDEA), Aqueous Monoethanolamine (Aq. MEA) and piperazine (Pz) for the CO2 sequestration from the sour natural gas extracted from the remote location located in the province of Baluchistan in Pakistan. The use of exergy, advanced exergy, and exergo environment for optimizing and selecting a suitable solvent combination that may result in an effective separation process has been proposed. Five cases have been developed based on various blends such as mono and tertiary amines combined with piperazine. From the results of all the studied scenarios, Case IV, based on the combination of Aqueous monoethanolamine and piperazine, provides reduced exergy destruction of 2551.7 KW. It was observed that the maximum removal of CO2 around 99.87 wt% is achieved in case IV. In addition, advance exergy analysis also highlights that case-IV has a venue of 25% exergy destruction avoidable, which would further enhance its performance. Nevertheless, still, case-IV has 75% exergy destruction unavoidable. The environmental factors show that Case-IV has a reduced exergy destruction factor of 0.96, a highly environmentally benign choice as a solvent with a high value of 1.03, and case-IV has the higher operational stability and higher exergy efficiency with an exergy stability value of 0.40. Therefore, monoethanolamine combined with piperazine to be an effective and efficient solvent blend that could be an energy-effective approach for sweetening the natural gas.

Prevalence of Keratoconus in a Population-Based Study in Syria.

Aim: To determine the prevalence and associations of keratoconus (KC) in a university student population in Syria. Methods: A prospective multicentre cross-sectional cohort study was conducted at two universities in Syria. Student volunteers were recruited from Tishreen University (Latakia governorate) and Damascus University (Damascus governorate). All participants underwent a comprehensive ocular examination. Placido/Scheimpflug-based corneal imaging using the Sirius (CSO, Florence. Italy), and a questionnaire to evaluate the baseline characteristics and medical history, as well as to highlight possible risk factors of KC. Univariate and bivariate analyses were performed. Results: The estimated prevalence of KC among all subjects was 1.43% (n = 12). A strong association between eye rubbing and keratoconus was found (OR 9.33, 95% CI 2.94-29.63, P < 0.001). Damascus University participants had a higher prevalence of KC than Tishreen University. However, the difference was not statistically significant. Conclusion: The prevalence of keratoconus in this Syrian student population was 1.43%. The results of this study demonstrate a high prevalence of keratoconus in the study population. Early detection of keratoconus through screening may yield benefits in preventing devastating sequelae of KC in populations with a high prevalence.

Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis.

The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures.

Chemical profile, anti 5-lipoxygenase and cyclooxegenase inhibitory effects of ginger (Zingiber officinale) rhizome, callus and callus treated with elicitors / Perfil químico, anti-5-lipoxigenase e efeitos inibitórios da ciclooxegenase do rizoma de gengibre (Zingiber officinale), do calo e calos tratados com elicitores

The present study investigated the chemical profiles and evaluated the inhibitory effect against 5-Lipoxygenase (5-Lox) activity for extracts of ginger rhizome, callus, and callus treated with the elicitors; yeast extract (100, 300 and 500 mg/L), glycine (100, 200 and 300 mg/L) and salicylic acid (100 and 200 mg/L). Oils and chloroform: methanol (CM) extracts were prepared by maceration in petroleum ether and CM (1:1, v/v), respectively. Chemical profiles were determined by gas chromatography/mass spectrometry (GC/MS) analysis. Oil of the callus recorded higher 5-Lox inhibitory effect (IC50 58.33±4.66 µg/mL) than the oil of rhizome (IC50168.34±15.64 µg/mL) and comparable to that of the positive control; Nordihydroguaiaretic acid (IC50 61.25±1.02 µg/mL). The chemical profile of the callus oil contained large amounts of fatty acids, mainly the unsaturated fatty acid oleic acid (31.11%) and saturated fatty acid palmitic acid (28.56%). Elicitors modified the chemical profile of the callus and ameliorated the anti-5-Lox activity of CM extract of the callus. CM extracts of callus treated with 100 and 300 mg/L yeast extract and 50 mg/L salicylic acid significantly suppressed (P ≤ 0.05) the 5-Lox activity by 33.16%, 25.46% and 16%, respectively as compared to the CM extract of untreated callus. In conclusion, ginger callus could be considered as a valuable dietary supplement in the treatment of various inflammatory disorders.

O presente estudo teve como objetivo investigar os perfis químicos e avaliar o efeito inibitório da atividade da 5-Lipoxigenase (5-Lox) em extratos de rizoma, calo e calo de gengibre tratados com os eliciadores; extrato de levedura (100, 300 e 500 mg / L), glicina (100, 200 e 300 mg / L) e ácido salicílico (100 e 200 mg / L). Extratos de óleos e clorofórmio: metanol (CM) foram preparados por maceração em éter e CM (1: 1, v / v), respectivamente. Os perfis químicos foram determinados por análise de cromatografia gasosa / espectrometria de massa (GC / MS). O óleo do calo registrou maior efeito inibitório de 5-Lox (IC50 58,33 ± 4,66 µg / mL) do que o óleo de rizoma (IC50168,34 ± 15,64 µg / mL) e comparável ao do controle positivo; Ácido nordi-hidroguaiarético (IC50 61,25 ± 1,02 µg / mL). O perfil químico do óleo de calo continha grandes quantidades de ácidos graxos, principalmente o ácido graxo insaturado ácido oleico (31,11%) e ácido graxo saturado palmítico (28,56%). Os elicitores modificaram o perfil químico do calo e melhoraram a atividade anti-5-Lox do extrato de CM do calo. Extratos de CM de calos tratados com 100 e 300 mg / L de extrato de levedura e 50 mg / L de ácido salicílico suprimiram significativamente (P ≤ 0,05) a atividade de 5-Lox em 33,16%, 25,46% e 16%, respectivamente, em comparação com o extrato de CM de calo não tratado. Em conclusão, o calo de gengibre pode ser considerado um suplemento dietético valioso no tratamento de vários distúrbios inflamatórios.

Myeloid malignancies with translocation t(4;12)(q11-13;p13): molecular landscape, clonal hierarchy and clinical outcomes.

Translocation t(4;12)(q11-13;p13) is a recurrent but very rare chromosomal aberration in acute myeloid leukaemia (AML) resulting in the non-constant expression of a CHIC2/ETV6 fusion transcript. We report clinico-biological features, molecular characteristics and outcomes of 21 cases of t(4;12) including 19 AML and two myelodysplastic syndromes (MDS). Median age at the time of t(4;12) was 78 years (range, 56-88). Multilineage dysplasia was described in 10 of 19 (53%) AML cases and CD7 and/or CD56 expression in 90%. FISH analyses identified ETV6 and CHIC2 region rearrangements in respectively 18 of 18 and 15 of 17 studied cases. The t(4;12) was the sole cytogenetic abnormality in 48% of cases. The most frequent associated mutated genes were ASXL1 (n = 8/16, 50%), IDH1/2 (n = 7/16, 44%), SRSF2 (n = 5/16, 31%) and RUNX1 (n = 4/16, 25%). Interestingly, concurrent FISH and molecular analyses showed that t(4;12) can be, but not always, a founding oncogenic event. Median OS was 7.8 months for the entire cohort. In the 16 of 21 patients (76%) who received antitumoral treatment, overall response and first complete remission rates were 37% and 31%, respectively. Median progression-free survival in responders was 13.7 months. Finally, t(4;12) cases harboured many characteristics of AML with myelodysplasia-related changes (multilineage dysplasia, MDS-related cytogenetic abnormalities, frequent ASXL1 mutations) and a poor prognosis.

A Molecular Simulation Study of Silica/Polysulfone Mixed Matrix Membrane for Mixed Gas Separation.

Polysulfone-based mixed matrix membranes (MMMs) incorporated with silica nanoparticles are a new generation material under ongoing research and development for gas separation. However, the attributes of a better-performing MMM cannot be precisely studied under experimental conditions. Thus, it requires an atomistic scale study to elucidate the separation performance of silica/polysulfone MMMs. As most of the research work and empirical models for gas transport properties have been limited to pure gas, a computational framework for molecular simulation is required to study the mixed gas transport properties in silica/polysulfone MMMs to reflect real membrane separation. In this work, Monte Carlo (MC) and molecular dynamics (MD) simulations were employed to study the solubility and diffusivity of CO2/CH4 with varying gas concentrations (i.e., 30% CO2/CH4, 50% CO2/CH4, and 70% CO2/CH4) and silica content (i.e., 15-30 wt.%). The accuracy of the simulated structures was validated with published literature, followed by the study of the gas transport properties at 308.15 K and 1 atm. Simulation results concluded an increase in the free volume with an increasing weight percentage of silica. It was also found that pure gas consistently exhibited higher gas transport properties when compared to mixed gas conditions. The results also showed a competitive gas transport performance for mixed gases, which is more apparent when CO2 increases. In this context, an increment in the permeation was observed for mixed gas with increasing gas concentrations (i.e., 70% CO2/CH4 > 50% CO2/CH4 > 30% CO2/CH4). The diffusivity, solubility, and permeability of the mixed gases were consistently increasing until 25 wt.%, followed by a decrease for 30 wt.% of silica. An empirical model based on a parallel resistance approach was developed by incorporating mathematical formulations for solubility and permeability. The model results were compared with simulation results to quantify the effect of mixed gas transport, which showed an 18% and 15% percentage error for the permeability and solubility, respectively, in comparison to the simulation data. This study provides a basis for future understanding of MMMs using molecular simulations and modeling techniques for mixed gas conditions that demonstrate real membrane separation.

Vitamin D exerts neuroprotection via SIRT1/nrf-2/ NF-kB signaling pathways against D-galactose-induced memory impairment in adult mice.

Vitamin D (Vt. D) is one of the vital hormone having multiple functions in various tissues, including brain. Several evidences reported that Vt. D plays a significant part in memory and cognition as its inadequate amount may accelerate cognitive impairment. This study shows for the first time the antioxidant potential of Vt. D against D-Galactose (D-gal) induced oxidative stress mediated Alzheimer disease (AD) pathology in male adult albino mice. The result reveals that the mice exposed to D-gal (120 mg/kg) for eight weeks have pre-and post-synaptic dysfunction and impaired memory investigated through Morris water maze and Y-maze tests. This is followed by the suppressed Nuclear factor erythroid 2-related factor 2 (NRF2), Heme Oxygenase-1 (HO-1) and elevated expressions of Nuclear Factor kappa B (NF-kB), Tumor Necrosis Factor alpha (TNF-α) and Interleukin 1 beta (IL-1ß) proteins in the brain homogenates evaluated through western blotting technique. On the other hand Vt. D (100 µg/kg) administration (three times a week for 4 weeks) activated Silent mating type information regulation 2 homolog 1 (SIRT1) and significantly improved both the neuronal synapse and memory, reduced oxidative stress by upregulating NRF-2 and HO-1 and downregulating NF-kB, TNF-α and IL-1ß proteins expression. Most importantly, Vt. D significantly abrogate the amyloidogenic pathway of amyloid beta (Aß) production against D-gal in the brains of adult male albino mice. These results reveal that Vt. D being an antioxidant agent plays a vital role in reducing the AD pathophysiology in D-gal induced animal model of aging, therefore act as a potential drug candidate in neurodegenerative diseases.

Preparation and Characterization of Semi-Flexible Substrates from Natural Fiber/Nickel Oxide/Polycaprolactone Composite for Microstrip Patch Antenna Circuitries for Microwave Applications.

The study intended to utilizing waste organic fiber for low-cost semi-flexible substrate fabrication to develop microstrip patch antennas for low band communication applications. All the semi-flexible substrates (12.2 wt. % OPEFF/87.8 wt. % PCL, 12.2 wt. % NiO/87.8 wt. % PCL, and 25 wt. % OPEFF/25 wt. % NiO/50 wt. % PCL) were fabricated by oil palm empty fruit fiber (OPEFF) mixed with nickel oxide (NiO) nanoparticles reinforced with polycaprolactone (PCL) as a matrix using a Thermo Haake blending machine. The morphology and crystalized structure of the substrates were tested using Fourier transform infrared (FTIR) spectrometry, X-ray diffraction (X-RD) technique, and scanning electron microscopy (SEM), respectively. The thermal stability behavior of the substrates was analyzed using thermogravimetric analysis (TGA) and differential thermogravimetric (DTG) thermogram. The dielectric properties were characterized by an open-ended coaxial probe (OEC) connected with Agilent N5230A PNA-L Network Analyzer included the 85070E2 dielectric software at frequency range of 8 to 12 GHz. The experimental results showed that NiO/OPEFF/PCL composites exhibit controllable permittivity dielectric constant εr'(f) between 1.89 and 4.2 (Farad/meter, (F/m)), with loss factor εr''(f) between 0.08 and 0.62 F/m, and loss tangent (tan δ) between 0.05 and 0.18. Return losses measurement of the three patch antennas OPEFF/PCL, NiO/PCL, and OPEFF/NiO/PCL are -11.93, -14.2 and -16.3 dB respectively. Finally, the commercial software package, Computer Simulation Technology Microwave Studio (CSTMWS), was used to investigate the antenna performance by simulate S-parameters based on the measured dielectric parameters. A negligible difference is found between the measured and simulated results. Finally, the results obtained encourage the possibility of using natural fibers and nickel oxide in preparation of the substrates utilize at microwave applications.

Investigation of Heat Transfer and Pressure Drop in Microchannel Heat Sink Using Al2O3 and ZrO2 Nanofluids.

A new micro heat exchanger was analyzed using numerical formulation of conjugate heat transfer for single-phase fluid flow across copper microchannels. The flow across bent channels harnesses asymmetric laminar flow and dean vortices phenomena for heat transfer enhancement. The single-channel analysis was performed to select the bent channel aspect ratio by varying width and height between 35-300 µm for Reynolds number and base temperature magnitude range of 100-1000 and 320-370 K, respectively. The bent channel results demonstrate dean vortices phenomenon at the bend for Reynolds number of 500 and above. Thermal performance factor analysis shows an increase of 18% in comparison to straight channels of 200 µm width and height. Alumina nanoparticles at 1% and 3% concentration enhance the Nusselt number by an average of 10.4% and 23.7%, respectively, whereas zirconia enhances Nusselt number by 16% and 33.9% for same concentrations. On the other hand, thermal performance factor analysis shows a significant increase in pressure drop at high Reynolds number with 3% particle concentration. Using zirconia for nanofluid, Nusselt number of the bent multi-channel model is improved by an average of 18% for a 3% particle concentration as compared to bent channel with deionized water.

Correction of hypoplastic mandible in non-syndromic temporomandibular joint ankylosis patients with distraction osteogenesis in Mayo Hospital Lahore: A descriptive study.

OBJECTIVE: To evaluate the efficacy of simultaneous gap arthroplasty and distraction osteogenesis in the treatment of non-syndromic temporomandibular joint ankylosis patients with hypoplastic mandible. METHODS: The descriptive prospective study was conducted at the Department of Oral and Maxillofacial Surgery, King Edward Medical University / Mayo Hospital, Lahore, Pakistan, from January to December, 2016, and comprised patients having temporomandibular joint ankylosis with hypoplastic mandible. Interpositional gap arthoplasty, osteotomy and application of distractor were done in all the patients. Preoperative and post operative data was collected and analysed us in gSPSS12 . RESULTS: Of the 63 patients, 29(46%) were males with a mean age of 18.48±2.32 years and 34(54%) were females with a mean age of 16.41±3.21 years. Overall mean age was 17.37±3 years (range: 9- 24 years). Of the total, 20(32%) patients were given a single distractor on ramus per side, while 43(68%) were given double distractors (ramus + body) per side. Lengthening was achieved in all (100%) patients both in ramus and body. Only 1(1.6%) patient presented with a complication during the distraction period, and the case was successfully managed surgically. CONCLUSIONS: Distraction osteogenesis is a promising treatment option for the correction of mandibular deformities as a result of temporomandibular joint ankylosis.