Synthesis of visible light driven TiO2 coated carbon nanospheres for degradation of dyes

Herein, we report the successful synthesis of visible light driven metal doped TiO2 coated carbon nanospheres (CNS) via a facile hydrothermal approach. The synthesized materials were characterized by standard analytical techniques, such as XRD, SEM-EDS-mapping, TEM, FTIR, PL, Raman and UV-Vis absorption spectroscopy. The effect of dopants on the band gap energy, crystallite size and photocatalytic properties of the TiO2 coated CNS was investigated systematically. The incorporation of dopants in TiO2 matrix found to significantly extend the absorption edge toward visible region and efficient separation of charge carriers on excitation. The photodegradation of two different organic dyes were investigated to evaluate the activity of the photocatalyst under different conditions such as dopant percentage, catalyst dose, different quenchers and calcination temperature. The best photocatalytic activity was observed with 3.0% Ce, doped TiO2 coated CNS with 1.5gL-1 concentration calcined at 400°C. We also performed the antibacterial activity of pure and doped-TiO2 coated CNS against pathogenic gram negative and gram positive bacteria. The doped-TiO2 coated CNS exhibited excellent antibacterial activity against both bacteria

Nanoplasmonics beyond Ohm's law

In tiny metallic nanostructures, quantum confinement and nonlocal response change the collective plasmonic behavior with important consequences for e.g. field-enhancement and extinction cross sections. We report on our most recent developments of a real-space formulation of an equation-of-motion that goes beyond the common local-response approximation and use of Ohm's law as the central constitutive equation. The electron gas is treated within a semi-classical hydrodynamic model with the emergence of a new intrinsic length scale. We briefly review the new governing wave equations and give examples of applying the nonlocal framework to calculation of extinction cross sections and field enhancement in isolated particles, dimers, and corrugated surfaces.Comment: Invited paper for TaCoNa-Photonics 2012 (www.tacona-photonics.org), to appear in AIP Conf. Pro

Incoherent Transport through Molecules on Silicon in the vicinity of a Dangling Bond

We theoretically study the effect of a localized unpaired dangling bond (DB) on occupied molecular orbital conduction through a styrene molecule bonded to a n++ H:Si(001)-(2x1) surface. For molecules relatively far from the DB, we find good agreement with the reported experiment using a model that accounts for the electrostatic contribution of the DB, provided we include some dephasing due to low lying phonon modes. However, for molecules within 10 angstrom to the DB, we have to include electronic contribution as well along with higher dephasing to explain the transport features.Comment: 9 pages, 5 figure

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Neurosymbolic Reinforcement Learning and Planning: A Survey

The area of Neurosymbolic Artificial Intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing sub-fields such as Neurosymbolic Deep Learning (Neurosymbolic DL) and Neurosymbolic Reinforcement Learning (Neurosymbolic RL). Compared to traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement Learning(RL), a long-standing Artificial Intelligence(AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this paper is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies:Learning for Reasoning, Reasoning for Learning and Learning-Reasoning. These categories are further divided into sub-categories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. Additionally, we identify research opportunities and challenges in various applications within this dynamic field.Comment: 16 pages, 9 figures, IEEE Transactions on Artificial Intelligenc

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

General practitioners\u27 knowledge and approach to chronic kidney disease in Karachi, Pakistan

Due to lack of adequate number of formally trained nephrologists, many patients with chronic kidney disease (CKD) are seen by general practitioners (GPs). This study was designed to assess the knowledge of the GPs regarding identification of CKD and its risk factors, and evaluation and management of risk factors as well as complications of CKD. We conducted a cross-sectional survey of 232 randomly selected GPs from Karachi during 2011. Data were collected on a structured questionnaire based on the kidney disease outcomes and quality initiative recommendations on screening, diagnosis, and management of CKD. A total of 235 GPs were approached, and 232 consented to participate. Mean age was 38.5 ± 11.26 years; 56.5% were men. Most of the GPs knew the traditional risk factors for CKD, i.e., diabetes (88.4%) and hypertension (80%), but were less aware of other risk factors. Only 38% GPs were aware of estimated glomerular filtration rate in evaluation of patients with CKD. Only 61.6% GPs recognized CKD as a risk factor for cardiovascular disease. About 40% and 29% GPs knew the correct goal systolic and diastolic blood pressure, respectively. In all, 41% GPs did not know when to refer the patient to a nephrologist. Our survey identified specific gaps in knowledge and approach of GPs regarding diagnosis and management of CKD. Educational efforts are needed to increase awareness of clinical practice guidelines and recommendations for patients with CKD among GPs, which may improve management and clinical outcomes of this population

Noncovalent Interactions by QMC: Speedup by One-Particle Basis-Set Size Reduction

While it is empirically accepted that the fixed-node diffusion Monte-Carlo (FN-DMC) depends only weakly on the size of the one-particle basis sets used to expand its guiding functions, limits of this observation are not settled yet. Our recent work indicates that under the FN error cancellation conditions, augmented triple zeta basis sets are sufficient to achieve a benchmark level of 0.1 kcal/mol in a number of small noncovalent complexes. Here we report on a possibility of truncation of the one-particle basis sets used in FN-DMC guiding functions that has no visible effect on the accuracy of the production FN-DMC energy differences. The proposed scheme leads to no significant increase in the local energy variance, indicating that the total CPU cost of large-scale benchmark noncovalent interaction energy FN-DMC calculations may be reduced.Comment: ACS book chapter, accepte

Robust normalization protocols for multiplexed fluorescence bioimage analysis

study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner (Nat Biotechnol 24:1270–8, 2006; Proc Natl Acad Sci 110:11982–7, 2013). Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples

Nanoparticle-Assisted Water-Flooding in Berea Sandstones

The use of nanoparticles to improve reservoir characterization or to enhance oil recovery (EOR) has recently received intensive interest; however, there are still many unresolved questions. This work reports a systematic study of the effect of rutile TiO2 nanoparticle-assisted brine flooding. Rutile ellipsoid TiO2 nanoparticles were synthesized and stabilized by trisodium citrate dihydrate for brine flooding of water-wet Berea sandstone cores. Careful characterization of the rock samples and nanomaterials before and after the flooding was conducted, and the relative contributions to the modified flooding results from the stabilizer and the nanoparticles of different concentrations were examined. The oil recovery performance was evaluated both at the breakthrough (BT) point and at the end of flooding (∼3.2 pore volumes). Nanoparticle migration behavior was also investigated in order to understand the potential mechanisms for oil recovery. The results showed that both nanoparticle transport rate and EOR effect were strongly dependent on the particle concentration. The oil recovery efficiency at the BT point was found to increase at low nanoparticle concentrations but decrease at higher values. A maximum 33% increase of the recovery factor was observed at the BT point for a TiO2 concentration of 20 ppm, but higher nanoparticle concentrations usually had higher ultimate recovery factors. The presence of an oil phase was found to accelerate the particle migration though the core. The discussion of various mechanisms suggested that the improvement in the mobility ratio, possible wettability change, and log-jamming effect were responsible for the observed phenomena