Solubility measurement,Hansen solubility parameters and solution thermodynamics of gemfibrozil in different pharmaceutically used solvents

Gemfibrozil (GEM) is cholesterol-lowering agent which is being proposed as poorly water soluble drug (PWSD). Temperature based solubility values of GEM are not yet available in literature or any pharmacopoeia/monograph. Hence, the present studies were carried out to determine the solubility of PWSD GEM (as mole fraction) in various pharmaceutically used solvents such as water (H₂O), methanol (MeOH), ethanol (EtOH), isopropanol (IPA), 1-butanol (1-BuOH), 2-butanol (2-BuOH), ethylene glycol (EG), propylene glycol (PG), polyethylene glycol-400 (PEG-400), ethyl acetate (EA), dimethyl sulfoxide (DMSO) and Transcutol^® (THP) at the temperatures ranging from T?=?298.2 K–318.2?K under atmospheric pressure P?=?0.1?MPa. Equilibrium/experimental solubilities of GEM were recorded by applying a saturation shake flask methodology and regressed using ‘van’t Hoff and Apelblat models’. Hansen solubility parameters for GEM and various pharmaceutically used solvents were estimated using HSPiP software. The solid states of GEM (both in pure and equilibrated states) were studied by ‘Differential Scanning Calorimetry’ which confirmed no transformation of GEM after equilibrium. Experimental solubilities of GEM in mole fraction were observed maximum in THP (1.81?×?10^?1) followed by DMSO, PEG-400, EA, 1-BuOH, 2-BuOH, IPA, EtOH, PG, MeOH, EG and H₂O (3.24?×?10^?6) at T?=?318.2 K and similar tendencies were also recorded at T?=?298.2 K, T?=?303.2 K, T?=?308.2 K and T?=?313.2 K. ‘Apparent thermodynamic analysis’ on experimental solubilities furnished ‘endothermic and entropy-driven dissolution’ of GEM in each pharmaceutically used solvent.     

Multilink Solution for Seamless Transition Between Multicast and Unicast Areas in 5G Core Network

Wireless Personal Communications - The exponential growth in technology observed over the past decade has introduced newer ways to exploit network and cyber-physical system-related vulnerabilities....     

Microwave near-field nondestructive detection and characterization of disbonds in concrete structures using fuzzy logic techniques

This paper presents a near-field microwave nondestructive testing technique for disbond/crack detection and evaluation in a concrete structure backed by an infinite half space of any material. A model describing the interaction of waves radiated out from an open-ended rectangular waveguide, in the near-field, with any layered medium will be utilized. The theoretical model calculates the effective reflection coefficient of the structure, at the aperture of the waveguide, as a function of the frequency of operation, the thickness and dielectric properties of the layers of the structures, including the standoff distance. The frequency of operation and standoff distance (the measurement parameters) can be optimized to achieve maximum sensitivity to the presence of the disbond. The presence of a disbond in a structure is viewed as an additional layer and will change the properties of the effective reflection coefficient (phase and magnitude). This change will depend on the thickness and location of the disbond. This fact will be used to investigate the potential of utilizing multiple frequency measurements to obtain disbond location and thickness information. A fuzzy logic model relating the phase of reflection coefficient, frequency of operation, and standoff distance to the disbond thickness and depth was generated and utilized.     

Effect of Enzymatic Hydrolysis on the Antioxidant Activity of Red and Green Seaweeds and Characterization of the Active Extracts

In the present study, the effect of enzymatic hydrolysis on the antioxidant activity of three red seaweeds (Chondria cornuta, Chondria dasyphylla, and Murrayella periclados) and two green seaweeds (Cladophora sp. and Ulva lactuca) from the Kuwait coast were evaluated. The seaweeds were hydrolyzed with five carbohydrases and three proteases, and the resulting extracts were tested for antioxidant activity. The total phenolic content and yield of the extracts varied greatly depending upon the species and enzyme used for hydrolysis. Of the 40 enzymatic extracts screened for antioxidant activity, the Viscozyme and Alcalase extracts of M. periclados, Neutrase and Ultraflo extracts of Cladophora sp., and Neutrase extracts of C. cornuta had high antioxidant activity compared to other enzymatic extracts in various in vitro assays. Fractionation of the extracts revealed that the radical scavenging and reducing power of the extracts were mainly due to phenolic fractions. In contrast, the iron-chelating ability was mainly due to protein fractions. The level of prevention of lipid oxidation in the liposome model system varied for different fractions of extracts and did not correlate with total phenolic content and other antioxidant properties. The results of the study show that, by hydrolyzing seaweeds with specific enzymes, customized seaweed extracts with specific bioactivity could be obtained.     

The effect of Urban Heat Island mitigation strategies on outdoor human thermal comfort in the city of Baghdad

Todays, most Iraqi cities suffer from extremely hot-dry climate for long periods throughout the year. However, most urban patterns that exist inside these cities are not suitable for this harsh conditions and lead to an increase in the value of the Urban Heat Island (UHI) index. Consequently, this will increase outdoor human thermal discomfort as well as energy consumption and air pollution in cities. This study attempts to evaluate the effect of UHI mitigation strategies on outdoor human thermal comfort in three different common types of urban patterns in the biggest and most populated city in Iraq, Baghdad. Three different mitigation strategies are used here – vegetation, cool materials, and urban geometry – to build 18 different scenarios. Three-dimensional numerical software ENVI-met 4.2 is utilised to analyse and assess the studied parameters. The input data for simulations process are based on two meteorological stations in Baghdad: Iraqi Meteorological Organization & Seismology, and Iraqi Agrometeorological Network. All measurements are taken in a pedestrian walkway. The results of different scenarios are compared based on their effect on human thermal comfort. Outdoor thermal comfort is assessed according to Predicted Mean Vote index, as mentioned in ISO 7730 standard. This study provides a better understanding of the role of UHI mitigation strategies on human thermal comfort in the outdoor spaces of Baghdad's residential neighbourhoods. This can help generate guidelines of urban design and planning practices for better thermal performance in hot and dry cities.     

Distribution Policies for Datalog

Theory of Computing Systems - Modern data management systems extensively use parallelism to speed up query processing over massive volumes of data. This trend has inspired a rich line of research...     

BER Performance Using Linear Phase Orthogonal Binary Codes for Multi-users Mobile Communication

Wireless Personal Communications - Nowadays, modern wireless mobile communication systems attempt to increase the number of subscribers simultaneously, starting from the third to fifth generations....     

Efficient Back-off Mechanism for Multimedia Support in 802.11e EDCA Wireless Ad-Hoc Networks

This paper presents an effective back-off mechanism to improve quality of service (QoS) for multimedia applications over the IEEE 802.11e enhanced distribution channel access (EDCA) standard. It can be done with proposed algorithm called dynamic fast adaptation for contention-based EDCA (DFA-EDCA) mechanism. The main concept of proposed DFA-EDCA mechanism is to use the exponential functions to adaptively tune the back-off parameters in IEEE 802.11e EDCA according to the changes on a network load within a short period. In this proposed mechanism an intra-access category (intra-AC) differentiation mechanism is provided to increase its back-off time randomly and achieve discrimination of same priority traffic on different stations. The performance evaluations have been conducted by using Network Simulator (NS-2). The simulation results show that the proposed DFA-EDCA mechanism has greatly outperformed the previous mechanisms such as non-linear dynamic adaptation scheme of the minimum contention window (CW_min-HA), dynamic adaptation algorithm of the maximum contention window (CW_max-adaptation), adaptive enhanced distributed coordination function and the conventional EDCA in terms of goodput, gain of goodput, packet delay, collision rate and channel utilization ratio (CUR). It has significantly reduced both packet delay and collision rate simultaneously together with an obviously increment in both goodput and CUR, which lead to the improvement in QoS for multimedia applications.     

Quantized lower bounds on grid-based localization algorithm for wireless sensor networks

In this paper, we introduce a Quantized Cramer Rao Bound (Q-CRB) method, which adapts the use of the CRB to handle grid-based localization algorithms with certain constraints, such as localization boundaries. In addition, we derive a threshold granularity level which identifies where the CRB can be appropriately applied to this type of algorithm. Moreover, the derived threshold value allows the users of grid-based LSE techniques to probably avoid some unnecessary complexities associated with using high grid resolutions. To examine the feasibility of the new proposed bound, the grid-based least square estimation (LSE) technique was implemented. The Q-CRB was used to evaluate the performance of the LSE method under extensive simulation scenarios. The results show that the Q-CRB provided a tight bound in the sense that the Q-CRB can characterize the behaviour of location errors of the LSE technique at various system parameters, e.g. granularity levels, measurement accuracies, and in the presence or absence of localization boundaries.     

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T-spherical fuzzy-weighted zero-inconsistency method

Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high-risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi-criteria decision-making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre-possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T-spherical fuzzy-weighted zero-inconsistency (CT-SFWZIC) method. CT-SFWZIC was developed based on a new fuzzy set (i.e., complex T-spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T-spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10-fold cross-validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively.