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A REVIEW ON THE TREATMENT OF LAUNDRY WASTEWATER USING CONVENTIONAL AND ADVANCED TREATMENT METHODS
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An Important Question: Can Serum Chitotriosidase Enzyme Predict the Activity and Clinical Course of Sarcoidosis Disease?
No full textOBJECTIVE: Serum chitotriosidase (CHIT) is a promising biomarker that has shown high specificity and sensitivity in patients with sarcoidosis. Our study aimed to evaluate the CHIT enzyme concerning the activity, prognosis, and treatment decision of sarcoidosis. MATERIAL AND METHODS: The patients with the following characteristics were included in our single-center study as long as they agreed to participate. These patients were newly or previously diagnosed with sarcoidosis according to American Thoracic Society/ European Respiratory Society/World Association of Sarcoidosis and Other Granulomatous Disorders and consulted the outpatient clinic of chest diseases in our university hospital between August 2020 and April 2021. The patients with sarcoidosis were categorized into 3 groups: 1) diagnosed as sarcoidosis but not having the treatment indication; 2) previouslytreated or currently receiving treatment; and 3) newly diagnosed and having a treatment indication. RESULTS: A total of 126 sarcoidosis patients and 43 healthy volunteers were included. The median value of serum CHIT enzyme levels in patients with sarcoidosis was determined to be 9.8 ng/mL, while it was 5.1 ng/mL in the control group. It was determined that the serum CHIT levels were notably higher in patients with sarcoidosis (P = 0.000). In the serum CHIT levels of the newly-treated patient, a significant reduction was observed after the 6-month treatment (P = 0.008), in comparison with these levels noted at the time of diagnosis. CONCLUSION: Our study demonstrated that the serum CHIT has a high sensitivity and high specificity in the diagnosis of sarcoidosis and a reduction in the level of that enzyme occurs upon treatment
The fundamental physical importance of generic off-diagonal solutions and Grigori Perelman entropy in the Einstein gravity theory
No full textThe gravitational field equations in general relativity (GR) consist of a sophisticated system of nonlinear partial differential equations. Solving such equations in some generic off-diagonal forms is usually a hard analytic or numeric task. Physically important solutions in GR were constructed using diagonal ansatz for metrics with maximum 4 independent coefficients. The Einstein equations can be solved in exact or parametric forms determined by some integration constants for corresponding assumptions on spherical or cylindric spacetime symmetries. The anholonomic frame and connection deformation method allows us to construct generic off-diagonal solutions described by 6 independent coefficients of metrics depending, in general, on all spacetime coordinates. New types of exact and parametric solutions are determined by generating and integration functions and (effective) generating sources. They may describe vacuum gravitational and matter fields solitonic hierarchies; locally anisotropic polarizations of physical constants for black holes, wormholes, black toruses, or cosmological solutions; various types of off-diagonal deformations of horizons etc. The additional degrees of freedom (related to off-diagonal coefficients) can be used to describe dark energy and dark matter configurations and elaborate locally anisotropic cosmological scenarios. In general, the generic off-diagonal solutions do not involve certain hypersurface or holographic configurations and can’t be described in the framework of the Bekenstein-Hawking thermodynamic paradigm. We argue that generalizing the concept of G. Perelman’s entropy for relativistic Ricci flows allows us to define and compute geometric thermodynamic variables for all possible classes of solutions in GR
Bitki Yağları İçeren PCL/Kolajen Nanoliflerinin Elektroeğirme İle Üretimi ve Karakterizasyonu
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Exaggeration-based Fake Cybersecurity News Detection
No full textWe address the challenge of detecting exaggeration in cybersecurity tweets on X, where misinformation spreads rapidly. Our novel framework uses local Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to gather evidence and assess tweets' rhetorical intensity, offering graded exaggeration scores. Validated through a human study and a pilot that matches LLM results with human labels, this work lays the groundwork for improved misinformation detection tools
