Combined Use of Sensitivity Analysis and Hybrid Wavelet-PSO- ANFIS to Improve Dynamic Performance of DFIG-Based Wind Generation

In the past few decades, increasing growth of wind power plants causes different problems for the power quality in the grid. Normal and transient impacts of these units on the power grid clearly indicate the need to improve the quality of the electricity generated by them in the design of such systems. Improving the efficiency of the large-scale wind system is dependent on the control parameters. The main contribution of this study is to propose a sensitivity analysis approach integrated with a novel hybrid approach combining wavelet transform, particle swarm optimization and an Adaptive-Network-based Fuzzy Inference System (ANFIS) known as Wavelet-ANFIS-PSO to acquire the optimal control of Doubly-Fed Induction Generators (DFIG) based wind generation. In order to mitigate the optimization complexity, sensitivity analysis is offered to identify the Unified Dominate Control Parameters (UDCP) rather than optimization of all parameters. The robustness of the proposed approach in finding optimal parameters, and consequently achieve a high dynamic performance is confirmed on two area power system under different operating conditions

Development, cross-cultural adaptation, and psychometric characteristics of the persian progressive aphasia language scale in patients with primary progressive aphasia: A pilot study

Introduction: Primary Progressive Aphasia (PPA) is a neurological condition characterized by progressive dissolution of language capabilities. The Progressive Aphasia Language Scale (PALS) is an easy-to-apply bedside clinical scale capable of capturing and grading the key language features essential for the classification of PPA. The objective of the present study was to develop and validate the Persian version of the PALS (PALS-P) as a clinical language assessment test. Methods: In this cross-sectional study, PALS was translated and adapted into Persian according to the international guidelines. A total of 30 subjects (10 subjects with PPA and 20 control subjects without dementia) were recruited to evaluate the intra-rater reliability and discriminant validity of PALS-P. Results: The intra-rater reliability of the PALS-P within a 14-day interval was excellent for each subtest (ICC agreement range=0.81-1.0). PALS-P results were statistically significant among groups, suggesting its discriminative validity. Conclusion: This preliminary study indicates that PALS-P was successfully developed and translated. It seems to be a valid and reliable screening tool to assess language skills in Persian-speaking subjects with progressive aphasia

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

Transparency and (no) more in the Political Advertising Regulation

The EU has taken its first steps into a sensitive space by proposing a new Regulation on Political Advertising (RPA). Simply put, the RPA does two things, which this commentary will address in turn. First, it replaces national laws on the transparency of political advertising with a single set of rules. These provide progressively more information to citizens who see an ad, to the public through ad libraries, and to regulators and private actors who are authorised to request information. Second, the RPA tightens the GDPR’s ban on using sensitive data for targeted political advertising. It leaves member states free, however, to further regulate the use of political advertising.The RPA takes a number of important steps in political advertising law. It strengthens the transparency of the (so far largely unregulated) online political advertising environment. It expands ad libraries with information on targeting and funding. And it allows a broad range of private actors (including civil society and journalists) to request data from a broad range of companies (including ad agencies and small platforms). At the same time, the RPA not only represents the EU’s most significant effort to address concerns about political advertising’s democratic impact, but (because it fully harmonises transparency) also shapes how individuals, researchers, and national regulators can scrutinise political advertising. It is therefore important to determine whether the regulation lives up to the Commission’s hype

Neurological soft signs in mentally disordered offenders

The study used the Neurological Evaluation Scale to assess neurological soft signs in 351 offenders and 80 healthy comparison subjects. Offenders were also interviewed using the Structured Clinical Interview for DSM-IV and the Hare Psychopathy Checklist. Neurological signs were significantly increased in offenders compared with healthy subjects. Offenders with repeated misdemeanors had higher rates of neurological signs than those with a single felony. Neurological scores were significantly predicted by lifetime diagnoses of psychotic, anxiety, and substance use disorders. Each diagnostic category was associated with a distinct pattern of neurological abnormalities. Copyright © 2007 American Psychiatric Publishing, Inc

Microwave Kinetic Inductance Detector (MKID) Camera Testing for Submillimeter Astronomy

Developing kilopixel focal planes for incoherent submm- and mm-wave detectors remains challenging due to either the large hardware overhead or the complexity of multiplexing standard detectors. Microwave kinetic inductance detectors (MKIDs) provide a efficient means to produce fully lithographic background-limited kilopixel focal planes. We are constructing an MKID-based camera for the Caltech Submillimeter Observatory with 576 spatial pixels each simultaneously sensitive in 4 bands at 230, 300, 350, and 400 GHz. The novelty of MKIDs has required us to develop new techniques for detector characterization. We have measured quasiparticle lifetimes and resonator Qs for detector bath temperatures between 200 mK and 400 mK. Equivalent lifetime measurements were made by coupling energy into the resonators either optically or by driving the third harmonic of the resonator. To determine optical loading, we use both lifetime and internal Q measurements, which range between 15,000 and 30,000 for our resonators. Spectral bandpass measurements confirm the placement of the 230 and 350 GHz bands. Additionally, beam maps measurements conform to expectations. The same device design has been characterized on both sapphire and silicon substrates, and for different detector geometries. We also report on the incorporation of new shielding to reduce detector sensitivity to local magnetic fields

Design and Performance of A High Resolution Micro-Spec: An Integrated Sub-Millimeter Spectrometer

Micro-Spec is a compact sub-millimeter (approximately 100 GHz--1:1 THz) spectrometer which uses low loss superconducting microstrip transmission lines and a single-crystal silicon dielectric to integrate all of the components of a diffraction grating spectrometer onto a single chip. We have already successfully evaluated the performance of a prototype Micro-Spec, with spectral resolving power, R=64. Here we present our progress towards developing a higher resolution Micro-Spec, which would enable the first science returns in a balloon flight version of this instrument. We describe modifications to the design in scaling from a R=64 to a R=256 instrument, as well as the ultimate performance limits and design concerns when scaling this instrument to higher resolutions

Study of water's physico-chemical characteristics in the southern Caspian Sea

This study was conducted to determine the physico-chemical characteristics of water during four season and 8 transects (Astara, Anzali, Sefidroud, Tonekabon, Nowhshar, Babolsar, Amirabad, Bandar Tourkman) in the Southern of Caspian Sea in 2009-2010. 480 samples were collected at different water layers and then physicochemical parameters were measured based on standard methods. Result of this study showed that surface water temperature was varied from 7.2 to 29.8◦C in winter and summer, respectively. Minimum fluctuation of water temperature was observed at 100 m depth (6.8-10.3◦C). Mean value of water transparency was obtained 4.91±0.24 m. This value increased from inshore to offshore. pH value was fluctuated from 7.15 to 8.83 unit with variance of 1.54. Maximum DO concentration was observed at surface layer (8.40±0.08 mg/l) with 137±18 saturation and minimum was at 100 m depth (6.46±0.18) with 86.1±2.8 saturation. The nitrite, nitrate and ammonium concentration were ranged 0.0-0.2, 0.0-4.6 and 0.05-7.12 µM, respectively. Maximum value of TN was observed at inshore and minimum at offshore (100m). Nitrite concentration decreased from inshore to offshore but increased from surface to the bottom (100 m). The inorganic phosphorous increased at surface water and also at the bottom. Trend of inorganic and TP was similar. Minimum of N/P ratio were observed at summer (5.48±0.38) and maximum value at winter (9.13±0.46). This value of N/P ratio showed that the growth of phytoplankton was limited by nitrogen. The dissolved silicate was decreased from spring (230.7±6.65 µg/l) to winter

Ureter tracking and segmentation in CT urography (CTU) using COMPASS

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/1/mp1412_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/2/mp1412.pd