Defined Threshold Value Research Articles

Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NOx and VOC emissions using satellite observation

To investigate changes in the ozone (O3) chemical production regime over the contiguous United States (CONUS) with accurate knowledge of concentrations of its precursors, we applied an inverse modeling technique with Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO2) and total formaldehyde (HCHO) retrieval products in the summers of 2011, 2014, and 2017, years in which United States National Emission Inventory were based. The inclusion of dynamic chemical lateral boundary conditions and lightning-induced nitric oxide emissions significantly account for the contribution of background sources in the free troposphere. Satellite-constrained nitrogen oxide (NOx) and non-methane volatile organic compounds (NMVOCs) emissions mitigate the discrepancy between satellite and modeled columns: the inversion suggested 2.33–2.84 (1.07–1.34) times higher NOx over the CONUS (over urban regions) and 0.28–0.81 times fewer NMVOCs emissions over the southeastern United States. The model-derived HCHO/NO2 column ratio shows gradual spatial changes in the O3 production regime near urban cores relative to previously defined threshold values representing NOx and VOC sensitive conditions. We also found apparent shifts from the NOx-saturated regime to the transition regime (or the transition regime to the NOx-limited regime) over the major cities in the western United States. In contrast, rural areas, especially in the east-southeastern United States, exhibit a decreased HCHO/NO2 column ratio by −1.30 ± 1.71 with a reduction in HCHO column primarily driven by meteorology, becoming sensitive to VOC emissions. Results show that incorporating satellite observations into numerical modeling could help policymakers implement appropriate emission control policies for O3 pollution.

Adaptive Remanufacturing – Decision Model for the Intelligent Maintenance of Production Resources

The life cycle extension of capital goods holds considerable ecological and economic potential in terms of improved resource efficiency in view of increasing resource scarcity and rising environmental awareness. The innovative approach of Adaptive Remanufacturing (AdR) allows to raise these potentials. The objective of this article is the detailed introduction of the structure and functioning of the AdR decision model. AdR describes a proactive, intelligent maintenance strategy based on structured decisions about the optimal time and scope of maintenance and remanufacturing measures. The basis of assessment are the remanufacturing-relevant components of a capital good. To protect these components from critical wear and tear, component conditions are monitored, and sensor measurement data are transferred into the decision model as dynamic input. Based on condition monitoring and defined threshold values per sensor, critical states of the monitored components are predicted. These forecasts trigger the decision process to identify and suggest suitable measures and the optimal point in time for their application based on a catalogue of measures, which serves as static input for the decision model. From this catalogue, feasible measures are preselected according to their technical suitability for specific components and wear mechanisms. To determine the optimal measure for a given component and wear, measures are assessed in terms of benefits regarding the state of a component as well as economic and ecological efforts. The decision model is implemented in a web-based software application. As a main result, it provides key figures regarding short- and long-term expenses, the CO2 consumption, and the effective duration per measure. Moreover, a prioritised measure recommendation is issued. The AdR decision model thus enables the substantiated selection of the optimal maintenance or remanufacturing measure per component.

Air quality monitoring system for IOT based smart city applications

It is critical to keep essential air pollutants within the World Health Organization's (WHO) prescribed levels in order to counteract the rising number of people killed by the accompanying health concerns. This is particularly true in enclosed spaces, wherein poor ventilation can amplify the impacts of pollutants. Knowing the amount of contaminants in the air would be a good starting point for taking measures to mitigate. This paper proposes a domestic air pollution monitoring system based on the Internet of Things framework. Two CO2 and PM2.5 sensor that are essential for air pollution monitoring with adjusted air quality forecasting are being sent to the cloud via a LoRaWAN protocol compatible gateways that connects the sensor to the server side of the network. The designed Web-based user interface dashboards enable the users to query the system and retrieve data as well as analytical data. Baseline techniques are designed and deployed to make it easier to configure triggers for each sensor node and transmit notifications when a measured parameter surpasses a defined threshold value.

Activation of oxidative stress, comorbidity and survival of end-stage renal disease patients treated with hemodialysis

Abstract. The aim of the study was to analyze the characteristics of serum concentrations of oxidative stress (OS) markers depending on the quantitative assessment of comorbidity and taking into account the most informative indicators of OS, to prospectively assess changes in comorbid status, death rates and individual comorbid conditions in patients with end-stage renal disease (ESRD) treated with hemodialysis (HD).
 Methods. The cohort prospective open-label study included 156 patients with ESRD, treated with HD. The study was conducted in two stages. In the first – the structure and quantitative assessment of comorbid diseases, determination of serum concentrations of oxidative stress (OS) markers and their analysis depending on the comorbid status were studied. On the second – taking into account the defined threshold values (Cut-off) of the most informative markers of the OS, an assessment of changes in comorbid status, frequency of comorbid conditions and fatal events done. A modified polymorbidity index (MPI) was calculated to assess comorbid status. The concentration of OS serum markers was determined by spectrophotometric method. Statistical analysis was performed by using "MedCalc", version 19.3 (Ostend, Belgium).
 Results. Serum concentrations of all prooxidant markers were significantly higher and antioxidant markers were significantly lower in the HD patients with high comorbid status compared to those of patients with low comorbidity (p<0.0001). Correlation analysis between MIP and the studied OS markers showed that the largest correlation (rho=0.874) was established with the serum concentration of malondialdehyde (MDAs). During the observation period in the group of patients with a concentration of MDAs > 668.72 μmol/ml found a significant, compared with the group with a biomarker content ≤ 668.72 μmol/ml, an increase in the proportion of patients with chronic obstructive pulmonary disease (COPD) (by 81.84 % vs 28.48 %; p<0.0001), cardiovascular diseases (CVD) (by 56.0 % vs 36.4 %; p=0.019) and cerebrovascular (CEVD) diseases (by 73.33 % vs 30.42 %; p<0.0001). The proportion of patients with fractures in the group of patients with MDAs > 668.72 μmol/l increased fourfold (p=0.0140). The increase in MIP is 34.11 % vs 17.1 % (p<0.0001), five-year cumulative survival – 45.3 % vs 63.3 % (p=0.0312; HR – 2,1527, 95% CI: 1,2458 –3,7199), five-year CV survival – 61,6 % vs 80.8 % (p=0.0094; HR – 2.7955, 95% CI: 1.3664 – 5.7191) in groups with MDAs > 668.72 and ≤ 668.72 μmol/ml, respectively.
 Conclusions. In patients with ESRD, treated with HD, serum concentrations of MDAs > 668.72 μmol/l is a biochemical determinant of a significant increase, in the medium term, the number of comorbid conditions, deaths, fatal CV and CEVD events, the proportion of patients with COPD, fractures, CVD and CEVD, reduction of cumulative and CV survival.

Неинвазивная оценка структурных изменений печени при ее диффузных заболеваниях

Актуальность. К важнейшим факторам, которые влияют на прогноз, тактику и результаты лечения хронических диффузных заболеваний печени (ХДЗП), в частности неалкогольной жировой болезни печени (НАЖБП) и хронического гепатита, ассоциированного с вирусом С (ХГС), относятся скорость прогрессирования фиброзной транс­формации печени и площадь фиброзной ткани. Распространенность фиброза является предиктором портальной гипертензии и таких летальных осложнений, как кровотечение из варикозно расширенных вен пищевода, выраженная печеночная недостаточность и гепатоцеллюлярная карцинома. Материалы и методы. Обследованы 66 пациентов с ХДЗП. Все больные были разделены на две группы. I группу составили пациенты с ХГС и циррозом печени, ассоциированным с вирусом гепатита С, во II группу вошли пациенты с НАЖБП. В зависимости от выраженности фиброза пациенты каждой группы были разделены на три подгруппы: без фиброза (F0), с умеренным (FI + FII) и выраженным фиброзом (FIII + FIV). Вирусная этиология болезни была подтверждена с помощью иммуноферментного анализа и ПЦР-диагностики. I группу составили 24 (57,1 %) мужчины и 18 (42,9 %) женщин, средний возраст пациентов был (45,8 ± 2,2) года. Диагноз НАЖБП подтверждали с помощью объективного обследования (наличие избыточного веса тела, висцерального распределения жировой ткани), выявления инсулинорезистентности и гиперлипидемии. II группу составили 8 мужчин (33,3 %) и 16 женщин (66,7 %), средний возраст (47,3 ± 3,1) года. У всех больных диагноз был подтвержден морфологически. Выполняли ультразвуковое исследование печени в В-режиме, а также сдвиговолновую эластографию (SWE). Результаты. В 13 (19,7 %) случаях (у 7 больных ХГС и у 6 пациентов с НАЖБП) фиброзные изменения печени отсутствовали. В 43,9 % случаев (у 19 больных ХГС и у 10 пациентов с НАЖБП) диагностирована умеренная стадия фиброза печени, что соответствовало 1 и 2 стадии фиброза по шкале METAVIR. В 36,4 % случаев (у 16 больных ХГС и у 8 пациентов с НАЖБП) диагностирована стадия выраженного фиброза печени, что соответствовало 3 и 4 стадии фиброза по шкале METAVIR. По данным ROC-анализа, пороговое значение SWE, выше которого диагностировали умеренную стадию фиброза печени при ХГС, составляло 6,63 кПа (с чувствительностью 94,7 % и специфичностью 85,7 %), при оценке выраженного фиброза — более 8,81 кПа (с чувствительностью 93,7 % и специфичностью 84,2 %). При НАЖБП пороговое значение для умеренного фиброза — 5,56 кПа (AUC = 0,867 (95% ДИ 0,606–0,982; р < 0,001)), для выраженного — более 7,87 кПа (с чувствительностью 87,5 % и специфичностью 90,0 %). Выводы. Умеренный фиброз по данным SWE у больных ХГС соответствует значению 6,63 кПа и 5,56 кПа — для НАЖБП; выраженный фиброз диагностируют при показателях более 8,81 и 7,87 кПа для ХГС и НАЖБП соответственно. Метод SWE обладает высокой чувствительностью и специфичностью для определения степени фиброза печени у пациентов с ХГС и НАЖБП и может быть рекомендован для использования в клинической практике. Внедрение SWE с использованием определенных пороговых значений показателей жесткости позволяет оптимизировать неинвазивную диагностику структурных изменений печени у пациентов с ХГС и НАЖБП.

Effect of configuration-dependent multi-body forces on interconversion kinetics of a chiral tetramer model

We describe a reformulation of the four-site molecular model for chiral phenomena introduced by Latinwo et al. ["Molecular model for chirality phenomena," J. Chem. Phys. 145, 154503 (2016)]. The reformulation includes an additional eight-body force that arises from an explicit configuration-dependent term in the potential energy function, resulting in a coarse-grained energy-conserving force field for molecular dynamics simulations of chirality phenomena. In this model, the coarse-grained interaction energy between two tetramers depends on their respective chiralities and is controlled by a parameter λ, where λ < 0 favors local configurations involving tetramers of opposite chirality and λ > 0 gives energetic preference to configurations involving tetramers of the same chirality. We compute the autocorrelation function for a quantitative chirality metric and demonstrate that the multi-body force modifies the interconversion kinetics such that λ ≠ 0 increases the effective barrier for enantiomer inversion. Our simulations reveal that for λ > 0 and temperatures below a sharply defined threshold value, this effect is dramatic, giving rise to spontaneous chiral symmetry breaking and locking molecules into their chiral identity.

Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning

Diabetic retinopathy (DR) has become one of the major causes of blindness. Due to the increased prevalence of diabetes worldwide, diabetic patients exhibit high probabilities of developing DR. There is a need to develop a labor-less computer-aided diagnosis system to support the clinical diagnosis. Here, we attempted to develop simple methods for severity grading and lesion detection from retinal fundus images. We developed a severity grading system for DR by transfer learning with a recent convolutional neural network called EfficientNet-B3 and the publicly available Kaggle Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 training dataset, which includes artificial noise. After removing the blurred and duplicated images from the dataset using a numerical threshold, the trained model achieved specificity and sensitivity values ≳ 0.98 in the identification of DR retinas. For severity grading, the classification accuracy values of 0.84, 0.95, and 0.98 were recorded for the 1st, 2nd, and 3rd predicted labels, respectively. The utility of EfficientNets-B3 for the severity grading of DR as well as the detailed retinal areas referred were confirmed via visual explanation methods of convolutional neural networks. Lesion extraction was performed by applying an empirically defined threshold value to the enhanced retinal images. Although the extraction of blood vessels and detection of red lesions occurred simultaneously, the red and white lesions, including both soft and hard exudates, were clearly extracted. The detected lesion areas were further confirmed with ground truth using the DIARETDB1 database images with general accuracy. The simple and easily applicable methods proposed in this study will aid in the detection and severity grading of DR, which might help in the selection of appropriate treatment strategies for DR.

Simulating rhizodeposition patterns around growing and exuding root systems

Abstract In this study, we developed a novel model approach to compute the spatio-temporal distribution patterns of rhizodeposits around growing root systems in three dimensions. This model approach allows us to study the evolution of rhizodeposition patterns around complex three-dimensional root systems. Root systems were generated using the root architecture model CPlantBox. The concentration of rhizodeposits at a given location in the soil domain was computed analytically. To simulate the spread of rhizodeposits in the soil, we considered rhizodeposit release from the roots, rhizodeposit diffusion into the soil, rhizodeposit sorption to soil particles and rhizodeposit degradation by microorganisms. To demonstrate the capabilities of our new model approach, we performed simulations for the two example rhizodeposits mucilage and citrate and the example root system Vicia faba. The rhizodeposition model was parameterized using values from the literature. Our simulations showed that the rhizosphere soil volume with rhizodeposit concentrations above a defined threshold value (i.e. the rhizodeposit hotspot volume) exhibited a maximum at intermediate root growth rates. Root branching allowed the rhizospheres of individual roots to overlap, resulting in a greater volume of rhizodeposit hotspots. This was particularly important in the case of citrate, where overlap of rhizodeposition zones accounted for more than half of the total rhizodeposit hotspot volumes. Coupling a root architecture model with a rhizodeposition model allowed us to get a better understanding of the influence of root architecture as well as rhizodeposit properties on the evolution of the spatio-temporal distribution patterns of rhizodeposits around growing root systems.

Multi-label software bug categorisation based on fuzzy similarity

The efficiency of the software depends on timely detection of bugs. For better quality and low-cost development bug fixing time should be minimised. Categorisation of software bugs helps to understand the root cause of software bugs and to improve triaging. As the software development approach is modular and multi-skilled, it is possible that one software bug can affect multiple modules, and multiple developers can fix newly reported bugs. Hence, a multi-label categorisation of software bugs is needed. Fuzzy similarity techniques can be helpful in understanding the belongingness of software bugs in multiple categories. In this paper a multi-label fuzzy similarity based categorisation technique is presented for effective categorisation of software bugs. Fuzzy similarity between a pair of bugs is computed and, based on a user defined threshold value, the bugs are categorised. Experiments are performed on software bug data sets, and the performance of the proposed classifier is evaluated.

Multi-label software bug categorisation based on fuzzy similarity

The efficiency of the software depends on timely detection of bugs. For better quality and low-cost development bug fixing time should be minimised. Categorisation of software bugs helps to understand the root cause of software bugs and to improve triaging. As the software development approach is modular and multi-skilled, it is possible that one software bug can affect multiple modules, and multiple developers can fix newly reported bugs. Hence, a multi-label categorisation of software bugs is needed. Fuzzy similarity techniques can be helpful in understanding the belongingness of software bugs in multiple categories. In this paper a multi-label fuzzy similarity based categorisation technique is presented for effective categorisation of software bugs. Fuzzy similarity between a pair of bugs is computed and, based on a user defined threshold value, the bugs are categorised. Experiments are performed on software bug data sets, and the performance of the proposed classifier is evaluated.

Alpha Spectrometry of Radon Short-Lived Progeny in Drinking Water and Assessment of the Public Effective Dose: Results from the Cilento Area, Province of Salerno, Southern Italy.

Radon is a naturally occurring radioactive gas present in the hydrosphere, lithosphere and atmosphere abundantly. Its ionizing radiation provides the largest human internal exposure by inhalation and ingestion to natural sources, constituting a serious health hazard. The contribution to total exposure is mainly due to inhalation, as ingestion by food or drinking water is typically very small. However, because of public health concerns, the contributions from all these sources are limited by regulations and remedial action should be taken in the event that the defined threshold values are overcome. In this paper, the first campaign of measurements to control the radon activity concentration in drinking water from public water supplies in the province of Salerno, south Italy, is described. The results represent a main reference for the area, as it was never investigated before. The purpose of this survey was to contribute to data compilation concerning the presence of radon-222 in groundwater in the Campania region and to determine the associated risk for different age groups. The maximum radon activity concentrations and the related total annual public effective dose turned out to be lower than the threshold values (100 Bq/l and 0.1 mSv/y, respectively) indicated by international guidelines and the national regulation, showing that the health risks for public consumption can be considered negligible.

The role of public debt on economic growth with capital loss

AbstractThis paper studies some models in which the “engine of growth” is public debt and capital accumulation. Public debt is assumed to be driven by domestic debt and external debt. The study shows that debt will support economic growth if the initial level of productivity is greater than the cost of investment. In an economy with and without the loss of capital, the article shows the existence of a unique optimal share of investment in physical and human capital and/or new technology. Moreover, if the productivity of the first period is lower than that of a defined threshold value then there exists an optimal level of debt. In addition, if the productivity of the first period is greater than or equal to a defined threshold value, the bigger the debt, the better economies become. Furthermore, this study shows that in an economy with capital loss there is a hidden overhead equivalent to an artificially‐augmented interest rate.

Integrated petrophysical evaluation of the Lower Middle Bakken Member in the Viewfield Pool, southeastern Saskatchewan, Canada

The Viewfield Pool of the Lower Middle Bakken Member, the largest light oil pool in Saskatchewan, Canada has been described as an unconventional resource play. Reservoir evaluation in this pool has been challenging because of low permeability, complex lithology and reservoir heterogeneity. To address the difficulties, this study employed an integrated approach to derive reservoir parameters and provided a detailed petrophysical characterization of the Viewfield Middle Bakken play, which led to a division of Unit A, the Lower Middle Bakken Member into two subunits: A1 for the lower interval and A2 for the upper interval. Petrophysical models were developed based on thorough analyses of core measurements, conventional well logs and available advanced nuclear magnetic resonance (NMR) well log data. The proposed models were applied to determine reservoir properties including shale content, porosity, permeability and water saturation for the two subunits in 163 vertical wells among which three have NMR logs. The results reveal that subunits A1 and A2 differ significantly in petrophysical properties and reservoir quality. A2, the very fine-grained dolomitic sandstone subunit has larger pore size, higher porosity and permeability, lower shale content and water saturation, and thus represents the preferred interval for oil production across the Viewfield Bakken Pool area and has greater resource potential than the siltstone subunit A1. The mapped petrophysical properties show spatial trends that align well with the previously recognized regional geological features. Favourable potential high productivity zones can be outlined by the areas of intersection of all essential reservoir parameters above defined threshold values.

Larval Indices of Vector Mosquitoes as Predictors of Dengue Epidemics: An Approach to Manage Dengue Outbreaks Based on Entomological Parameters in the Districts of Colombo and Kandy, Sri Lanka.

Background Early detection of dengue epidemics is a vital aspect in control programmes. Predictions based on larval indices of disease vectors are widely used in dengue control, with defined threshold values. However, there is no set threshold in Sri Lanka at the national or regional levels for Aedes larval indices. Therefore, the current study aimed at developing threshold values for vector indices in two dengue high-risk districts in Sri Lanka. Methods Monthly vector indices (House Index [HI], Container Index [CI], Breteau Index for Aedes aegypti [BIagp], and Ae. albopictus [BIalb]), of ten selected dengue high-risk Medical Officer of Health (MOH) areas located in Colombo and Kandy districts, were collected from January 2010 to June 2019, along with monthly reported dengue cases. Receiver Operating Characteristic (ROC) curve analysis in SPSS (version 23) was used to assess the discriminative power of the larval indices in identifying dengue epidemics and to develop thresholds for the dengue epidemic management. Results Only HI and BIagp denoted significant associations with dengue epidemics at lag periods of one and two months. Based on Ae. aegypti, average threshold values were defined for Colombo as Low Risk (2.4 ≤ BIagp < 3.8), Moderate Risk (3.8 ≤ BIagp < 5), High Risk (BIagp ≥ 5), along with BIagp 2.9 ≤ BIagp < 4.2 (Low Risk), 4.2 ≤ BIagp < 5.3 (Moderate Risk), and BIagp ≥ 5.3 (High Risk) for Kandy. Further, 5.5 ≤ HI < 8.9, 8.9 ≤ HI < 11.9, and HI ≥ 11.9 were defined as Low Risk, Moderate Risk, and High Risk average thresholds for HI in Colombo, while 6.9 ≤ HI < 9.1 (Low Risk), 8.9 ≥ HI < 11.8 (Moderate Risk), and HI ≥ 11.8 (High Risk) were defined for Kandy. Conclusions The defined threshold values for Ae. aegypti and HI could be recommended as indicators for early detection of dengue epidemics and to drive vector management activities, with the objective of managing dengue epidemics with optimal usage of financial, technical, and human resources in Sri Lanka.

Chemistry reduction of complex CO2 chemical kinetics: application to a gliding arc plasma

A gliding arc (GA) plasma has great potential for CO2 conversion into value-added chemicals, because of its high energy efficiency. To improve the application, a 2D/3D fluid model is needed to investigate the CO2 conversion mechanisms in the actual discharge geometry. Therefore, the complex CO2 chemical kinetics description must be reduced due to the huge computational cost associated with 2D/3D models. This paper presents a chemistry reduction method for CO2 plasmas, based on the so-called directed relation graph method. Depending on the defined threshold values, some marginal species are identified. By means of a sensitivity analysis, we can further reduce the chemistry set by removing one by one the marginal species. Based on the so-called flux-sensitivity coupling, we obtain a reduced CO2 kinetics model, consisting of 36 or 15 species (depending on whether the 21 asymmetric mode vibrational states of CO2 are explicitly included or lumped into one group), which is applied to a GA discharge. The results are compared with those predicted with the full chemistry set, and very good agreement is reached. Moreover, the range of validity of the reduced CO2 chemistry set is checked, telling us that this reduced set is suitable for low power GA discharges. Finally, the time and spatial evolution of the CO2 plasma characteristics are presented, based on a 2D model with the reduced kinetics.

An Efficient Smart Weighted and Neighborhood-enabled Load Balancing Scheme for Constraint Oriented Networks

In wireless sensor networks (WNSs), uniform load or traffic distribution strategy is one of the main challenging issues, which is tightly coupled with the resource-limited networks. To address this problem, various mechanisms have been developed and presented in the literature. However, these approaches were either application specific that is designed for a specific application area such as smart building or overlay complex. Therefore, a simplified and energy efficient load-balancing scheme is always needed for the resource-limited networks. In this paper, an efficient and neighborhood-enabled load-balancing scheme is presented to resolve the aforementioned issues specifically with available resources. For this purpose, the proposed scheme bounds every member node to collect various information about neighboring nodes i.e., those nodes resides in its communication range. Moreover, if residual energy Er of sensor node is less than the defined threshold value then it shares this information with neighboring nodes. In the proposed neighborhood-enabled load balancing scheme, every sensor node Ci prefers to route packets through the optimal paths particularly those paths where probability of critical nodes is negligible i.e., path where critical node(s) are not deployed. Simulation results showed that the proposed neighborhood-enabled load-balancing scheme is better than existing approaches in terms of network lifetime (both individual node and whole WSNs), throughput, and average packet delivery ratio and end-to-end delay performance metrics.

Strukturert pasientforløp ved urinsyregikt

Pharmacological treatment and follow-up of gout is often inadequate.The patients risk repeated, painful arthritis flares and some develop chronic tophaceous gout and joint damage. Since 1 March 2017, a structured care pathway has been established for patients with gout at the Department of Rheumatology, Haukeland University Hospital. Patients were placed on preventive medication, the majority on allopurinol, and were regularly monitored. Patient education is key to the care pathway. The treatment goal is to lower the concentration of serum urate to a level below a defined threshold value (360 µmol/l for non-tophaceous gout or 300 µmol/l for tophaceous gout). Patient data were collected on a continuous basis and recorded in a research database. The care pathway is assessed after 18months. A total of 103patients have been included, of whom 93 (90%) are men, with an average age of 63 years and large variations in duration of the disease (min.-max. 0-36 years). Eight patients left the care pathway during the project. The average level of serum urate at inclusion was 446 µmol/l (min.-max. 144-751 µmol/l). The average maximum dose of allopurinol was 333mg (min.-max. 100-700mg). Survival analysis showed that three months after the start of urate-lowering therapy, 49% of the patients had achieved their analytical treatment goal. Altogether 83% achieved the goal within six months. The majority (70%) were free of flares after achieving the treatment goal. A standardised care pathway for gout enables adaptation of preventive treatment through regular monitoring. The care pathway helps the vast majority of patients to achieve treatment goals, which is crucial to avoid arthritis flares.

Real Time Cyberbullying Detection

Automated approaches for detecting cyberbullying on online platforms has remained a primary research concern over past years. Cyber bullying is defined as the use of electronic communication to bully a person, typically by sending messages of intimidating or threatening nature. The victims especially teenagers suffer from loss of confidence, depression, sleep disorder. The research on automated cyberbullying approach is mainly focused on data driven methods. Such methods work on a database of static texts, usually collected from online platforms and are not feasible for dynamic nature of a real-life social networking scenarios. The aim of our research is to develop a cyberbullying detection system using Fuzzy Logic. Three types of bullying emotions are considered in this research work namely aggression, abuse and threat. In the proposed approach chat between two users is continuously monitored and emotion present in each message is determined. Based on the emotion each user’s behavior is categorized as decent or bullying. If the detected bullying nature is higher than a defined threshold value the account of user is ceased and reported automatically. The proposed approach is tested with a chat application developed in Microsoft .Net Framework and approach can detect cyber bullying in good time. The proposed approach, if implemented with social networking platforms can serve as a useful aid for preventing online harassment. The developed algorithm can also be applied in surveillance and human behavioral analysis.

Wavelet-Ann Based Fault Location Identification in Micro Gridinter Connected Transmission System

This paper presents a novel protection scheme for the protection of transmission system with microgrid (MG) having of wind energy, solar PV energy and fuel cell sources. MGs provide environmental, economical benefits for the end consumers, power usages and society. However, transmission line and MGs poses major technical challenges. Protection system must respond both MG and utility grid failures. Technical challenges of MG protection are to respond to main and MG faults. A MG model is designed and it is connected to a transmission line. Later, for detection and classification of faults wavelet Analysis (WT) is used. Faults are detected by the fault indices and compared with defined threshold value. The location of fault is done by artificial neural networks (ANN) on MG connected transmission system using detailed (D1) coefficients of energy current signals. This proposed algorithm is tested and more effective for the detection, classification and location of faults on MG interconnected transmission system. This algorithm is accurate and independent of fault inception angle (FIA), fault impedance and fault distance on line.

Frequent Sequential Patterns (FSP) Algorithm for Finding Mutations in BRCA2 Gene

The Sequential Pattern Mining (SPM) is a fundamental task in data mining. The SPM mines subsequences from given sequence which can be used for various analyses. This paper aims to propose an efficient method for mining frequent sequential patterns in biological data. It also includes the k-mer for decomposing the sequence according to the user defined threshold value. The input data used is breast cancer gene BRCA2 normal and mutated BRCA2 gene. The parameters used for analyses are suffix, candidate pattern and frequent pattern. The suffix value is increased for mono-,di and tri-nucleotide in mutated gene and in frequent pattern tri-nucleotide has increased nucleotide in mutated gene. So this abnormal increase in pattern may leads to cancer in the human