Anomaly detection is an important data analysis task. It is used to identify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect ...abnormalities in many different domains including financial fraud detection, computer network intrusion, human behavioural analysis, gene expression analysis and many more. Recently, in the financial sector, there has been renewed interest in research on detection of fraudulent activities. There has been a lot of work in the area of clustering based unsupervised anomaly detection in the financial domain. This paper presents an in-depth survey of various clustering based anomaly detection technique and compares them from different perspectives. In addition, we discuss the lack of real world data and how synthetic data has been used to validate current detection techniques.
•Recently, in the financial sector, there has been renewed interest in research on detection of fraudulent activities.•This paper presents an in-depth survey of various clustering based anomaly detection techniques and compares them from different perspectives.•In addition, we discuss the lack of real world data and how synthetic data has been used to validate current detection techniques.
Expert’s knowledge base systems are not effective as a decision-making aid for physicians in providing accurate diagnosis and treatment of heart diseases due to vagueness in information and ...impreciseness and uncertainty in decision making. For this reason, automatic diagnostic fuzzy systems are very time demanding to improve the diagnostic accuracy. In this paper, we have developed an automatic fuzzy diagnostic system based on genetic algorithm (GA) and a modified dynamic multi-swarm particle swarm optimization (MDMS-PSO) for prognosticating the risk level of heart disease. Our proposed fuzzy diagnostic system (FS) works as follows: i) Preprocess the data sets ii) Effective attributes are selected through statistical methods such as Correlation coefficient, R-Squared and Weighted Least Squared (WLS) method, iii) Weighted fuzzy rules are formed on the basis of selected attributes using GA, iv) MDMS-PSO is employed for the optimization of membership functions (MFs) of FS, v) Build the ensemble FS from the generated fuzzy knowledge base by fusing the different local FSs. Finally, to ascertain the efficiency of the adaptive FS, the applicability of the FS is appraised with quantitative, qualitative and comparative analysis on the publicly available different real-life data sets. From the empirical analysis, we see that this hybrid model can manage the knowledge vagueness and decision-making uncertainty precisely and it has yielded better accuracy on the different publicly available heart disease data sets than other existing methods so that it justifies its adaptability with different data sets.
Display omitted
Transdermal delivery of drugs is more challenging for drugs that are insoluble or sparingly soluble in water and most organic solvents. To overcome this problem, ionic liquid ...(IL)-mediated ternary systems have been suggested as potential drug carriers. Here, we report potent ternary (IL–EtOH–IPM) systems consisting of biocompatible ILs, ethanol (EtOH), and isopropyl myristate (IPM) that can dissolve a significant amount of the sparingly soluble drug acyclovir (ACV). The ternary systems were optically transparent and thermodynamically stable with a wide range of IL pertinence. An in vitro drug permeation study showed that the ILs in the ternary systems dramatically enhanced ACV permeation into and across the skin. Fourier Transform Infrared spectroscopy of the stratum corneum (sc) after treatment with ternary systems showed that the skin barrier function was reduced by disturbance of the regularly ordered arrangement of corneocytes and modification of the surface properties of the sc during permeation. Histological analysis, and skin irritation studies using a reconstructed human epidermis model showed the safety profile of the ternary system, and there were no significant changes in the structures of the sc, epidermis, and dermis. Therefore, ternary systems containing biocompatible ILs are promising for transdermal delivery of insoluble or sparingly soluble drugs.
Cadmium (Cd) is a widespread environmental contaminant, and its increasing concentrations in rice poses significant risks to human health. Globally, rice is a staple food for millions of people, and ...consequently, effective strategies to reduce Cd accumulation in rice are needed. This study investigates the effect of soil pH (Soil 1: 4.6; Soil 2: 6.6) and iron (Fe) application (at 0, 1.0 and 2.0 g/kg) on Fe plaque formation, Cd sequestration in Fe plaques and Cd bioaccumulation in different parts of the rice plant for three different Cd-graded paddy soils (0, 1.0 and 3.0 mg/kg, respectively) using two Australian rice cultivars under glasshouse conditions. Results show that grain and straw yield declined as Cd toxicity increased, and the toxic effects of Cd were lower in the Quest cultivar than in the Langi cultivar. With applications of Cd at 1.0 mg/kg and 3.0 mg/kg, Cd concentrations in rice grown in Soil 1 were 1.09 mg/kg and 1.37 mg/kg, respectively, while those in rice grown in Soil 2 were 0.38 mg/kg and 0.52 mg/kg, respectively. Soil pH significantly affected the bioaccumulation of Cd in different parts of the rice plant. At both levels of Cd application, Cd concentration was highest in the root, followed by the stem, leaf, husk and grain. Cd was more concentrated in Fe plaques formed by the application of Fe than in rice plant tissues. The Quest cultivar had a higher ability to produce Fe plaques and a 1.3- and 1.4-times higher Cd concentration compared with the Langi cultivar in Soils 1 and 2, respectively.
Display omitted
•Grain and straw yield of rice plant was declined with soil Cd up to 36% and 26%.•Grain Cd concentration was 2.8-fold higher in rice grown in soil pH 4.6 than soil pH 6.6.•Fe plaque formation was higher in Quest rice in soil pHs of 4.6 and 6.6 than Langi.•Iron plaque formation depends on soil pH, rice cultivars and addition of Fe.
The outbreak of the coronavirus disease (COVID-19) pandemic has become a worldwide health catastrophe instigated by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Countries are ...battling to slow the spread of this virus by testing and treating patients, along with other measures such as prohibiting large gatherings, maintaining social distance, and frequent, thorough hand washing, as no vaccines or medicines are available that could effectively treat infected people for different types of SARS-CoV-2 variants. However, the testing procedure to detect this virus is lengthy. This study proposes a surface plasmon resonance-based biosensor for fast detection of SARS-CoV-2. The sensor employs a multilayered configuration consisting of TiO2–Ag–MoSe2 graphene with a BK7 prism. Antigen–antibody interaction was considered the principle for this virus detection. Immobilized CR3022 antibody molecules for detecting SARS-CoV-2 antigens (S-glycoprotein) are used for this sensor. It was found that the proposed sensor’s sensitivity (194°/RIU), quality factor (54.0390 RIU−1), and detection accuracy (0.2702) outperformed those of other single and multilayered structures. This study could be used as a theoretical base and primary step in constructing an actual sensor.
In the conventional permanent magnet linear generators (PMLGs) used for oceanic wave energy conversion system, demagnetization could cause everlasting degradation in electrical power generation. This ...paper presents a new design that can be applied to various PMLGs to avoid demagnetization. To check the effectiveness of the proposed technique, a PMLG is considered, which allows both the fixed and variable length of airgaps for analysis. The finite element analysis is used by using the software package ANSYS/Ansoft to simulate the testing PMLG for two conditions: with and without using the proposed technique. Different parameters and characteristics of the PMLG under both conditions are presented in detail. Both the simulation and test results show that the proposed design is able to avoid the demagnetization problem successfully.
Polypharmacy, defined as the concurrent use of multiple medications, is a growing concern globally. This study aimed to identify the significant factors that predict the perceived burden of ...medication and health-related quality of life.
Adults, aged 18 years and above who have used at least two regular medicines, were invited to complete the study questionnaires between June and October 2019. Multiple linear regression analysis was conducted to identify significant predictors for perceived burden of medication and health-related quality of life.
A total of 119 participants completed this study. The average age of the participants was 63 years (SD±16 years). Factors significantly predicting perceived burden of medication were participants' current health condition (p = 0.001), overall burden of treatment (p<0.001) and being hypertensive (p = 0.037). Similarly, participants' current health condition (p<0.001) and overall burden of treatment (p = 0.086) were significant predictors for perceived health-related quality of life.
This study revealed that hypertensive participants in poor health tended to experience higher perceived burden of medication, which in turn was found to be correlated with lower perceived health-related quality of life.
Since injection administration for diabetes is invasive, it is important to develop an effective transdermal method for insulin. However, transdermal delivery remains challenging owing to the strong ...barrier function of the stratum corneum (SC) of the skin. Here, we developed ionic liquid (IL)-in-oil microemulsion formulations (MEFs) for transdermal insulin delivery using choline–fatty acids (ChlFAs)comprising three different FAs (C18:0, C18:1, and C18:2)as biocompatible surface-active ILs (SAILs). The MEFs were successfully developed using ChlFAs as surfactants, sorbitan monolaurate (Span-20) as a cosurfactant, choline propionate IL as an internal polar phase, and isopropyl myristate as a continuous oil phase. Ternary phase behavior, dynamic light scattering, and transmission electron microscopy studies revealed that MEFs were thermodynamically stable with nanoparticle size. The MEFs significantly enhanced the transdermal permeation of insulin via the intercellular route by compromising the tight lamellar structure of SC lipids through a fluidity-enhancing mechanism. In vivo transdermal administration of low insulin doses (50 IU/kg) to diabetic mice showed that MEFs reduced blood glucose levels (BGLs) significantly compared with a commercial surfactant-based formulation by increasing the bioavailability of insulin in the systemic circulation and sustained the insulin level for a much longer period (half-life > 24 h) than subcutaneous injection (half-life 1.32 h). When ChlC18:2 SAIL-based MEF was transdermally administered, it reduced the BGL by 56% of its initial value. The MEFs were biocompatible and nontoxic (cell viability > 90%). They remained stable at room temperature for 3 months and their biological activity was retained for 4 months at 4 °C. We believe SAIL-based MEFs will alter current approaches to insulin therapy and may be a potential transdermal nanocarrier for protein and peptide delivery.
Turmeric, a globally cultivated spice, holds significance in medicine, and cosmetics, and is also a very popular ingredient in South Asian cuisine. A study involving 53 turmeric genotypes evaluated ...for rhizome yield and related traits at Spices Research Center, Bogura, Bangladesh over three years (2019-22). A randomized complete block design was followed with two replications. ANOVA revealed significant trait variations among genotypes. Genotype T0015 emerged as the highest yielder at 28.04 t/ha. High heritability (0.58-0.99) and genetic advance characterized plant height (PH), mother rhizome weight (WMR), primary and secondary finger weights (WPF and WSF), and yield per plant (YPP) across seasons. Genetic gain (GG) was prominent in these traits. Genotypic and phenotypic coefficient variations (GCV and PCV) (6.24-89.46 and 8.18-90.88, respectively) across three years highlighted mother rhizome weight's importance followed by numbers of primary finger (NPF), and WPF. Positive and significant correlations, especially with PH, WMR, WPF, and YPP, emphasized their relevance to fresh yield (FY). Multiple linear regression identified PH, number of mother rhizome (NMR) and WMR as key contributors, explaining 37-79% of FY variability. Cluster analysis grouped genotypes into five clusters with maximum distance observed between clusters II and III. The geometric adaptability index (GAI) assessed adaptability and superiority, revealing nine genotypes outperforming the best existing cultivar. Genotype T0117 as the top performer based on GAI, followed by T0103 and T0094. Mean rank analysis favoured T0121 as the best performer, succeeded by T0117, T0082 and T0106. The top ten genotypes (T0015, T0061, T0082, T0085, T0094, T0103, T0106, T0117, T0121 and T0129) were identified as superior based on yield and overall ranking, warranting further evaluation. These findings may induce a window for improving turmeric research and ultimately play a role in enhancing its cultivation and productivity.
Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the ...transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.
PDF
