Anxiety disorders: phobias

This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of phobias

Impact of Coding Proficiency on Starting Annual Salaries After Graduation

As many companies are going digital in their operations and many startups are driven by software solutions instead of focusing on physical infrastructure, the demand for coding jobs has increased across the globe as it is the programming that drives computer systems. From transport and food to healthcare everything became Information Technology dependent. We can also observe that there is a wide popularity for programming courses, as the coding skills from those courses might fetch high paying jobs. Students who are pursuing their masters in information technology must be good in coding as it will get them a high paying job after graduation. Nowadays many graduate international IT major students are looking for a job in the software industry, which would bring them a high salaried job. Whereas the jobs in the software industries require a candidate with good programming skills, who will play a major role in the growth and development of their company. This study will determine the impact of coding proficiency of IT major international graduate students at Pittsburg State University, as measured by students Grade in GRT-713 (Computer Programming Languages) on their starting annual salaries after their graduation when they enter the job market. Graduate International Information Technology major students who studied at PSU and have recently entered the job market are included in this study with a sample size of 100 students. In this study, a correlational design is used to establish the relationship between grades in GRT-713 and starting annual salaries

Artificial Intelligence in Action: Realtime Payment Fraud Detection and Resolution Systems in High Volume Financial Networks

This chapter provides an overview of recent research on systems employing artificial intelligence in financial networks, where security, consistency, and timeliness are crucial. Such systems continuously scan high-volume payment operations, targeting the detection of any fraud cases, including both particular attacks and new previously unseen patterns. In contrast to many generic machine learning projects that remain in labs and experimental mode, the successful application of artificial intelligence in already fast-working financial payment systems for large corporations is discussed. We summarize the crucial requirements for the data and the models, as well as particular methodologies and algorithms employed in these holistic systems—starting with behavior, context, time, sequence, and relations modeling, verification of diversified machine learning classifiers' combinations for similar environments, and extending to artificial intelligence explanations, interactive interfaces, and operational process integration. Finally, the software structure of the real-time detection workflow of multiple combined unsupervised models, alongside brief empirical validation, is described. In most scenarios, from both business and regulatory compliance perspectives, the time component should be taken into account to address operational risks in super real-time—before the current session closes. Before going deeper into discussing the principles and details of fraud detection and response solutions under consideration, key facts need to be highlighted. Operating in a high data traffic domain, financial detection systems must work automatically all the time without human intervention, from detection trigger to operative case conclusion, to protect the area against sophisticated attacks. Moreover, from a corporate customer’s hard-earned reputation point of view, the speed of fraud case resolution is very important

Longitudinally polarized momentum waves - an expose

Robert Zimmerman of the National Astronomy and Ionosphere Center at Arecibo, Puerto Rico has reported on the discovery of a new longitudinally polarized electromagnetic wave predicated on Vector Potential only. In essence, his experiment launches a wave from a coaxial transmission line waveguide with physical dimensions sufficient to support a Transverse Magnetic (TM 01) wave. Conventional antennas are unable to detect the transmitted signal but parallel plasma tubes aligned parallel to the wave propagation direction do receive the signal if the direction of electric fields for each tube are supplementary. Zimmerman\u27s claim is not unfounded in light of the widely reported Aharonov-Bohm (AB) effect. Electromagneticists Ferraro, Grimes and McGinn (FGM), (Penn State and NIU) dispute Zimmerman\u27s interpretation. FGM postulate that conventional Transverse Electromagnetic Waves are transmitted with accompanying radial electric field spokes and circular magnetic lines of flux. The novel demonstration I am arranging will establish a local time varying magnetic field of annular magnetic rings surrounding the plasma tube. If plasma current is time modulated through altered ion population densities, electrical detection is possible supporting FGM theory

An Integrated Computational and Experimental Approach to Study and Scale-Up Vacuum Drying of Pharmaceutical Products

Drying of Active Pharmaceutical Ingredients (APIs) is an energy intensive process that is often a manufacturing bottleneck due to its relatively long processing times. A key objective is the ability to determine the drying end point, the time at which all solvent has been evaporated from the solid cake. A novel method for determining the end point of pharmaceutical dryers, based on on-line mass spectrometry is developed and tested. The proposed method offers several advantages over existing spectrometric methods, including the ability to detect when the cake is dry from vapor phase measurements and a very simple implementation that does not require chemometric models. The drying end point was determined as the time at which the gas phase solvent concentration measurement from the mass spectrometer converged to a predicted value computed from a solvent mass balance on the oven assuming zero flow rate from the cake. The method was tested on a laboratory scale vacuum dryer over a range of temperatures and pressures using glass beads with three different particle sizes. The method was validated by performing Loss On Drying (LOD) experiments for one combination of pressure, temperature and bead size. The mass spectrometer (MS) was used as a Quality by Design (QbD) tool along with thermocouples to understand dynamics in vacuum tray drying. The data indicated that boiling was the dominant mechanism. A multiphase transport model to predict drying performance was developed. It was found that a two phase transport model with the vapor and solid considered as one phase and the liquid treated as the second phase was capable of qualitatively reproducing the drying dynamics. Adjustable model parameters estimated from experimental data collected over a range of operating conditions exhibited trends that provided further insight into drying behavior. The understanding of drying from a vacuum tray dryer was extended to an Agitated Filter Drier (AFD) using the MS. Further, one of the most challenging issues with the use of an AFD, particle size change, was investigated. The change in particle size of API during drying at different RPMs was studied. The experiments indicated that the process was dominated by breakage of API which showed strong correlation with LOD. Population balance models were used to estimate parameters and derive empirical relations to LOD levels

Precision in Classification: A Comparative Study of Logistic Regression, Naive Bayes, LSTM, and CNN for Spam Email Detection

This research presents a comprehensive comparative study on the precision of classification algorithms applied to spam email detection. The study focuses on four distinct algorithms: Logistic Regression, Naive Bayes, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). The evaluation encompasses a common dataset to ensure a fair and rigorous assessment of each algorithm’s performance. We investigate and analyze precision metrics, considering the trade- offs between true positives and false positives. The comparative study provides insights into the strengths and limitations of each algorithm in effectively identifying spam emails. The results contribute valuable knowledge to the field of email security and classification, guiding practitioners and researchers towards informed algorithm selection based on precision considerations

INSPIROWANE KOJOTAMI PODEJŚCIE DO PRZEWIDYWANIA TOCZNIA RUMIENIOWATEGO UKŁADOWEGO Z WYKORZYSTANIEM SIECI NEURONOWYCH

Systemic Lupus Erythematosus (SLE) is a complicated autoimmune disease that can present with a variety of clinical symptoms, making precise prognosis difficult. Because SLE has a wide range of symptoms and may overlap with other autoimmune and inflammatory disorders, making a diagnosis can be challenging. This study creates a precise and accurate model for the prediction of SLE using the GEO dataset. For cost-effective data collection and analysis, feature selection might be essential in some applications, particularly in healthcare and scientific research. The strength of Artificial Neural Networks (ANN) for Systemic Lupus Erythematosus prediction and the Coyote Optimization Algorithm (COA) for feature selection are combined in this study. The COA is an optimization method influenced by nature and coyote hunting behavior. This study attempts to improve the effectiveness of subsequent predictive modeling by using COA to identify a subset of significant features from high-dimensional datasets linked to SLE. A Multi-layer Feed-forward Neural Network, a potent machine learning architecture renowned for its capacity to discover complex patterns and correlations within data, is then given the chosen features. Because the neural network is built to capture SLE's intricate and non-linear structure, it offers a reliable foundation for precise classification and prediction. The accuracy of the COA-ANN model was 99.6%.Toczeń rumieniowaty układowy (SLE) jest skomplikowaną chorobą autoimmunologiczną, która może objawiać się różnymi objawami klinicznymi, co utrudnia dokładne rokowanie. Ponieważ SLE ma szeroki zakres objawów i może nakładać się na inne choroby autoimmunologiczne i zapalne, postawienie diagnozy może być trudne. Niniejsze badanie tworzy precyzyjny i dokładny model przewidywania SLE z wykorzystaniem zbioru danych GEO. W celu efektywnego kosztowo gromadzenia i analizy danych, wybór cech może być niezbędny w niektórych zastosowaniach, szczególnie w opiece zdrowotnej i badaniach naukowych. W niniejszym badaniu połączono siłę sztucznych sieci neuronowych (ANN) do przewidywania tocznia rumieniowatego układowego i algorytmu optymalizacji Coyote (COA) do wyboru cech. COA to metoda optymalizacji, na którą wpływ ma natura i zachowania łowieckie kojotów. Niniejsze badanie ma na celu poprawę skuteczności późniejszego modelowania predykcyjnego poprzez wykorzystanie COA do identyfikacji podzbioru istotnych cech z wielowymiarowych zbiorów danych powiązanych z SLE. Wielowarstwowa sieć neuronowa Feed-forward, potężna architektura uczenia maszynowego znana ze swojej zdolności do odkrywania złożonych wzorców i korelacji w danych, otrzymuje następnie wybrane cechy. Ponieważ sieć neuronowa została zbudowana w celu uchwycenia skomplikowanej i nieliniowej struktury SLE, oferuje ona niezawodną podstawę do precyzyjnej klasyfikacji i przewidywania. Dokładność modelu COA-ANN wyniosła 99,6%

Text document clustering using mayfly optimization algorithm with k-means technique

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS)

Eosinophilic cholangitis: A case report of diagnostically challenging eosinophilic infiltrative biliary obstruction.

BACKGROUND: Evaluation of biliary strictures primarily focuses on ruling out malignancy in older age groups. With endoscopic tools such as endoscopic ultrasound (EUS) and cholangioscopy, improved biliary visualization has enhanced the investigation of intraluminal biliary lesions and provided modalities for targeted biopsies. Benign biliary strictures, however, may pose a diagnostic dilemma. CASE SUMMARY: A 71-year-old female with past medial history of hypothyroidism presenting for abnormal biliary imaging. Patients previous evaluation was concerning for common bile duct dilation with cholelithiasis, for which she underwent a cholecystectomy. Due to persistent symptoms and worsening liver function tests, she presented to our institution for further workup. Subsequently, the patient underwent an EUS and multiple ERCPs with cholangioscopy; biliary biopsies revealed no evidence of malignancy but concerning for prominent eosinophilic infiltration. After further review of multiple pathology specimens and the benign clinical course, we diagnosed the patient with eosinophilic cholangitis. CONCLUSION: Eosinophilic cholangitis is a rare disease and can present as a challenging case diagnostically. This case raises the potential utility of quantitative eosinophilic infiltration reporting in creating an objective diagnostic metric for eosinophilic cholangitis

Comparative Study into the Interlayer Bond Strength of 3D Printed Mortar: Underwater vs. Air Environments

For 3D printable concrete, interlayer bond strength is an important consideration. Several studies have confirmed the lack of bond strength attributed to air entrapment, lack of surface roughness, thixotropy of mix, water evapo- ration and printing parameters. However, the same conclusion may not be extended to understand the layer bonding behavior for underwater printed specimens. The present study utilizing anti-washout admixture (AWA) shows that AWA-mixed concrete (H7) when printed underwater has 13.9% and 18.2% lower bond strength in the Y and Z directions, respectively compared to the specimens printed in air. Notably, the absence of AWA in the control mix (C) resulted in further reduction of bond strength (up to maximum 41.4%) in the underwater environment with 5-minute interlayer time gap. Micro-CT analysis of pore structure revealed that the volumetric pore size distribution in the interlayers significantly influences the bond strength behavior of specimens printed in both air and underwater. In-line monitoring of the printing process detected the settlement of washed binder near the layer interface, leaving a distinct white line mark in the hardened printed specimen. Optimal interlayer time gaps were found to be beneficial for enhancing bond strength of mixtures containing AWA, attributed to pore structure refinement due to settled binder and increased extrusion pressures during underwater printing