A New Monte Carlo Based Algorithm for the Gaussian Process Classification Problem

Gaussian process is a very promising novel technology that has been applied to both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification problem, because we encounter intractable integrals. In this paper we develop a new derivation that transforms the problem into that of evaluating the ratio of multivariate Gaussian orthant integrals. Moreover, we develop a new Monte Carlo procedure that evaluates these integrals. It is based on some aspects of bootstrap sampling and acceptancerejection. The proposed approach has beneficial properties compared to the existing Markov Chain Monte Carlo approach, such as simplicity, reliability, and speed

Impact of vitamin D deficiency on maternal and birth outcomes in the Saudi population: a cross-sectional study

STROBE Statement. (DOC 92.5 kb

Brain Changes in Long-Term Zen Meditators Using Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging: A Controlled Study

Introduction: This work aimed to determine whether 1H magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are correlated with years of meditation and psychological variables in long-term Zen meditators compared to healthy non-meditator controls. Materials and Methods: Design. Controlled, cross-sectional study. Sample. Meditators were recruited from a Zen Buddhist monastery. The control group was recruited from hospital staff. Meditators were administered questionnaires on anxiety, depression, cognitive impairment and mindfulness. 1H-MRS (1.5 T) of the brain was carried out by exploring four areas: both thalami, both hippocampi, the posterior superior parietal lobule (PSPL) and posterior cingulate gyrus. Predefined areas of the brain were measured for diffusivity (ADC) and fractional anisotropy (FA) by MR-DTI. Results: Myo-inositol (mI) was increased in the posterior cingulate gyrus and Glutamate (Glu), N-acetyl-aspartate (NAA) and N-acetyl-aspartate/Creatine (NAA/Cr) was reduced in the left thalamus in meditators. We found a significant positive correlation between mI in the posterior cingulate and years of meditation (r = 0.518; p = .019). We also found significant negative correlations between Glu (r =20.452; p = .045), NAA (r =20.617; p = .003) and NAA/Cr (r =20.448; P = .047) in the left thalamus and years of meditation. Meditators showed a lower Apparent Diffusion Coefficient (ADC) in the left posterior parietal white matter than did controls, and the ADC was negatively correlated with years of meditation (r =20.4850, p = .0066). Conclusions: The results are consistent with the view that mI, Glu and NAA are the most important altered metabolites. This study provides evidence of subtle abnormalities in neuronal function in regions of the white matter in meditators

ANTI-MICROBIAL EVALUATION OF NEW THIAZOLO PYRIMIDINE DERIVATIVES

Twelve new heterocyclic compounds containing thiazolo pyrimidine moiety were thus synthesized.. Compound 2 was synthesized by reaction of α-amino naphthol 1 with potassium isothiocyanate. Reaction of compound 2 with sodium nitrite and hydrochloric acid afforded the corresponding diazonuim salt 3, which converted to 2-hydrazino thiazolo derivatives 4. Compound 2 carried out to synthesize imidazolo thiazolo derivative 5, 6 and thiazolo pyrimidino derivative 7, 8 and 9 respectively. Compound 4 was carried out to synthesize of compound 10. Compound 10 was carried out to synthesize of triazolo thiazolo and thiadiazolo derivatives 11 and 12 respectively. The structures of all newly synthesized compounds were elucidated by elemental analysis, IR, 1H-NMR and mass spectral data. The assignments of the new products were tested for Anti-microbial activities. Results are suggesting thiazolo pyrimidine derivatives emerge as valuable compounds with great potential to be used as antibacterial and antifungal agents, and as promising candidates for further efficiency evaluation. The detailed synthesis, spectroscopic data and Anti-microbial activities are reported

Real-Time Pose Estimation for Autonomous Vehicles Using Probabilistic Landmark Maps and Sensor Fusion

This study introduces a robust and accurate method for estimating autonomous vehicle position, facilitating safe navigation in urban and highway settings. The proposed technique employs a probabilistic particle filter framework, which, unlike approaches constrained by Gaussian assumptions, represents probability densities as samples, enabling more flexible position estimation. A key innovation lies in integrating a finely tuned Unscented Kalman Filter (UKF) to fuse radar and lidar data specifically for robust detection of pole-like static landmarks, whose positions and associated uncertainties are probabilistically modeled within an offline reference map. The particle filter leverages Bayesian filtering, associating UKF-derived landmark observations with this probabilistic map to refine the vehicle's pose. Broad simulation tests validate the method's effectiveness, achieving a mean localization error of approximately 11 cm in both longitudinal and lateral directions. Furthermore, the system demonstrates robustness, maintaining localization accuracy below 30 cm even with landmark position uncertainties up to 2 meters, and confirms real-time capability exceeding 100 Hz. These findings establish the approach as a reliable and precise solution for autonomous vehicle localization across various scenarios

Real-Time Autonomous Vehicle Navigation via Rule-Based Waypoint Selection and Spline-Guided MPC

This paper presents a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm aimed at improving autonomous highway navigation. LSPP uniquely combines localized quintic splines with a speed-profile optimizer to generate smooth, dynamically feasible trajectories that prioritize obstacle avoidance, passenger comfort, and strict adherence to road constraints such as lane boundaries. By leveraging real-time data from the vehicle’s sensor fusion module, LSPP accurately interprets the positions of nearby vehicles and obstacles, producing safe paths that are passed to the Model Predictive Control (MPC) module for precise execution. Simulations show LSPP reduces lateral jerk by 30% and computation time by 25% compared to Bézier-based methods, confirming enhanced comfort and efficiency. Extensive testing across diverse highway scenarios further demonstrates LSPP’s superior performance in trajectory smoothness, lane-keeping, and responsiveness over traditional approaches, validating it as a compelling solution for safe, comfortable, and efficient autonomous highway driving

Treatability Study of Car Wash Wastewater Using Upgraded Physical Technique with Sustainable Flocculant

Grease, oil, hydrocarbon residues, heavy metals, and surfactants are all present in car wash wastewater (CWW), which all can have detrimental effects on the environment and human health. This study was designed to assess CWW treatment using an upgraded physical technique combined with a range of conventional and more sustainable coagulants. Physical treatment effectively lowered the oil and grease (O&G) and chemical oxygen demand (COD) of the CWW by 79 ± 15% and 97 ± 1.6%, respectively. Additional treatment was provided using chemical coagulation–flocculation– settling. In jar test studies, humic acid (HA) and alum were found to provide significantly higher turbidity removal, 79.2 ± 3.1% and 69.8 ± 8.0%, respectively, than anionic polyacrylamide (APA), 7.9 ± 5.6% under influent turbidity values from 89 to 1000 NTU. Overall physical/chemical treatment of CWW yielded 97.3 ± 0.8% COD removal, and 99.2 ± 0.4% O&G removal using HA and alum. Due to the numerous problems created when using synthetic coagulants, naturally occurring coagulants that have no impact on human health, such as HA, are highly desirable options. The findings of this study show that treating CWW provides several advantages for sustainable development, health and well-being, and raising public knowledge and support for water reuse

New insights into the impact of neuro-inflammation in rheumatoid arthritis.

Rheumatoid arthritis (RA) is considered to be, in many respects, an archetypal autoimmune disease that causes activation of pro-inflammatory pathways resulting in joint and systemic inflammation. RA remains a major clinical problem with the development of several new therapies targeted at cytokine inhibition in recent years. In RA, biologic therapies targeted at inhibition of tumor necrosis factor alpha (TNFα) have been shown to reduce joint inflammation, limit erosive change, reduce disability and improve quality of life. The cytokine TNFα has a central role in systemic RA inflammation and has also been shown to have pro-inflammatory effects in the brain. Emerging data suggests there is an important bidirectional communication between the brain and immune system in inflammatory conditions like RA. Recent work has shown how TNF inhibitor therapy in people with RA is protective for Alzheimer's disease. Functional MRI studies to measure brain activation in people with RA to stimulus by finger joint compression, have also shown that those who responded to TNF inhibition showed a significantly greater activation volume in thalamic, limbic, and associative areas of the brain than non-responders. Infections are the main risk of therapies with biologic drugs and infections have been shown to be related to disease flares in RA. Recent basic science data has also emerged suggesting that bacterial components including lipopolysaccharide induce pain by directly activating sensory neurons that modulate inflammation, a previously unsuspected role for the nervous system in host-pathogen interactions. In this review, we discuss the current evidence for neuro-inflammation as an important factor that impacts on disease persistence and pain in RA

Computer-Based Stereoscopic Parts Recognition for Robotic Applications

Most of robotic handling and assembly operations are based on sensors such as range and touch sensors. In certain circumstances, such as in the presence of ionizing radiation where most customary sensors will degrade over time due to radiation exposure, these sensors won\u27t function properly. Utilizing two or more cameras (stereo vision) located outside the target zone and analyzing their images to identify location and dimensions of parts within the robot workspace is an alternative for using sensors. Object Recognition is affected by the light condition which oftentimes causes the gray-scale or red, green, and blue values to have a relatively small dynamic range. With this small dynamic range, edge detection algorithms fail to detect the proper edges and therefore cause improper image segmentation. To tackle this problem, a transformation on the (r,g,b) values of the pixels is introduced and applied prior to the edge detection and segmentation process. A stereoscopic computer vision system with multiple cameras is then used to compute the distance of the object from the origin of a global Euclidean coordinate system with high resolution. As an application of computer vision, a classifier for testing remote solar panels for cleanness condition, and performing cleaning when necessary, is introduced. A classification algorithm consisting of: the classification vector, the metric used, the training of the classifier, the testing of the classifier, and the classifier is put into play for everyday use. A smart cleaning robot is being designed based on this system to perform the cleaning autonomously when necessary. Another application of computer vision is inspecting the degree of air pollution. A real time classification algorithm that uses a quantization algorithm based on prior calibration is applied to evaluate the quality of air. The intelligent system, based on this algorithm, classifies the air using a numeric system from 1 to 10 which is then transformed to a qualitative scale