Miracle-dz/UniFAR-FTUs huggingface dataset:
Different approaches to Imaging Mass Cytometry data analysis

Summary Imaging Mass Cytometry (IMC) is a novel, high multiplexing imaging platform capable of simultaneously detecting and visualizing up to 40 different protein targets. It is a strong asset available for in-depth study of histology and pathophysiology of the tissues. Bearing in mind the robustness of this technique and the high spatial context of the data it gives, it is especially valuable in studying the biology of cancer and tumor microenvironment. IMC-derived data are not classical micrographic images, and due to the characteristics of the data obtained using IMC, the image analysis approach, in this case, can diverge to a certain degree from the classical image analysis pipelines. As the number of publications based on the IMC is on the rise, this trend is also followed by an increase in the number of available methodologies designated solely to IMC-derived data analysis. This review has for an aim to give a systematic synopsis of all the available classical image analysis tools and pipelines useful to be employed for IMC data analysis and give an overview of tools intentionally developed solely for this purpose, easing the choice to researchers of selecting the most suitable methodologies for a specific type of analysis desired.
Waves in Newton’s bucket

The motion of a liquid in an open cylindrical tank rotating at a constant rate around its vertical axis of symmetry, a configuration called Newton’s bucket, is investigated using a linear stability approach. This flow is shown to be affected by several families of waves, all weakly damped by viscosity. The wave families encountered correspond to: surface waves which can be driven either by gravity or centrifugal acceleration, inertial waves due to Coriolis acceleration which are singular in the inviscid limit, and Rossby waves due to height variations of the fluid layer. These waves are described in the inviscid and viscous cases by means of mathematical considerations, global stability analysis and various asymptotic methods; and their properties are investigated over a large range of parameters $(a,Fr)$ , with $a$ the aspect ratio and $Fr$ the Froude number.
Covid-19 and E-wallet usage intention in Kyrgyzstan

The usage of electronic commerce has continued to expand since the Covid-19 pandemic began indeveloped and developing countries. However, the usage of electronic commerce in Central Asian countries is underresearched. This study aims to determine the factors that influence e-wallet usage intention in Kyrgyzstan. The theoryof planned behaviour (TPB) is extended and adopted in developing the hypotheses. Using surveys, 319 questionnaireswere collected from young and middle-aged adults in Kyrgyzstan. The findings show that attitude, subjective normand perceived behavioral control has a positive relationship with intention to use e-wallets. It is also found thatattitude and intention will be stronger if the perceived behavioral control is high. The findings suggest ways toenhance the use of e-wallets in in Kyrgyzstan.
Monte carlo sampling and regret minimization for equilibrium computation and decision-making in large extensive form games

In this thesis, we investigate the problem of decision-making in large two-player zero-sum games using Monte Carlo sampling and regret minimization methods. We demonstrate four major contributions. The first is Monte Carlo Counterfactual Regret Minimization (MC-CFR): a generic family of sample-based algorithms that compute near-optimal equilibrium strategies. Secondly, we develop a theory for applying counterfactual regret minimization to a generic subset of imperfect recall games as well as a lossy abstraction mechanism for reducing the size of very large games. Thirdly, we describe Monte Carlo Minimax Search (MCMS): an adversarial search algorithm based on *-Minimax that uses sparse sampling. We then present variance reduction techniques that can be used in these settings, with a focused application to Monte Carlo Tree Search (MCTS). We thoroughly evaluate our algorithms in practice using several different domains and sampling strategies.
Latent Growth Curve Model Selection: The Potential of Individual Case Residuals

An individual case residual procedure for model comparison in longitudinal studies is discussed, which can be used as an aid to overall goodness-of-fit indexes in the process of choosing between rival models as means of data description. The method is based on estimates of subject-specific residuals associated with each repeated measure. The approach facilitates assessment of local fit of considered models and can be especially useful with large samples. The outlined procedure is similarly helpful in the general case for fit assessment of competing structural equation models. The method is readily applicable using the popular software Mplus and R, and is illustrated with a simulated data example as well as with empirical data from a large-scale study of older adults.
A Strategic Alternative to Predicting Property Investment Value Through Random Forest and Gradial Enhancing

The good investment instruments include the following items gold, stocks, and real estate. Investment in real estate in particularly has increased significantly. Developments in home prices are important for both sellers and buyers because they provide insight into the health of the economy. Property expenses are influenced by a variety of variables, including location, bedroom count, and floor layout. The cost of housing is also influenced by a property's proximity to roads, major highways, schools, retail centers, and local job possibilities. Normal home prediction becomes challenging, hence other methods for predicting house prices have been created. The least transparent sector in our society is real estate. Daily fluctuations in housing prices occur, and they are sometimes overstated instead of based on appraisal. Our group's primary objective is to predict housing values using actual information.
Solution of the euler and navier stokes equations on parallel processors using a transposed/thomas adi algorithm

The Euler and Navier Stokes equations are discretized and numerically solved on distributed memory parallel processors for airfoil geometries. The spatial derivatives are evaluated to second order accuracy with upwind differencing and the equations are solved implicitly using AD1 factorization. The Thomas Algorithm is used to solve the block tridiagonal matrices that result from the implicit AD1 scheme. The recursion inherent in this method is dealt with by transposing the domain amongst the processors so that there is no communication required in order to solve the tridiagonals once the transpose is done. A couple of transpose schemes were considered and results are presented for the most efficient. Very good times are achieved for realistic problems when run on a coarse to medium grain machine. The method is compared with other parallel schemes. The code was developed and run on an nCUBE/2 and also run on a Thinking Machines CM-5 for a performance comparison and to illustrate portability of the code. *Graduate Assistant tPrincipal St& Scientist, Associate Fellow AIAA $Associate Professor, Senior Member AIAA 5 Graduate Assistant, Member AIAA Vcopyright 1993 @by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Liquid Biopsies Poorly miRror Renal Ischemia-Reperfusion Injury

Acute kidney injury (AKI) is the rapid reduction in renal function. It is often difficult to detect at an early stage. Biofluid microRNAs (miRs) have been proposed as novel biomarkers due to their regulatory role in renal pathophysiology. The goal of this study was to determine the overlap in AKI miRNA profiles in the renal cortex, urine, and plasma samples collected from a rat model of ischemia-reperfusion (IR)-induced AKI. Bilateral renal ischemia was induced by clamping the renal pedicles for 30 min, followed by reperfusion. Urine was then collected over 24 h, followed by terminal blood and tissue collection for small RNA profiling. Differentially expressed (IR vs. sham) miRs within the urine and renal cortex sample types demonstrated a strong correlation in normalized abundance regardless of injury (IR and sham: R2 = 0.8710 and 0.9716, respectively). Relatively few miRs were differentially expressed in multiple samples. Further, there were no differentially expressed miRs with clinically relevant sequence conservation common between renal cortex and urine samples. This project highlights the need for a comprehensive analysis of potential miR biomarkers, including analysis of pathological tissues and biofluids, with the goal of identifying the cellular origin of altered miRs. Analysis at earlier timepoints is needed to further evaluate clinical potential.
Comparative study of various release methods for gold surface micromachining

Abstract. A comparison of dry and wet release methods for surface micromachining of metallic structures, such as RF MEMS switches, test structures, bridges, and cantilevers is presented. The dry release process is optimized by varying the concentration of O2 and CF4 plasma and RF power. The plasma ashing of the sacrificial layer typically results in damage to metallic structures or stress-related deformation due to rise in temperature (>80°C). A wet release process using critical point drying (CPD) has been investigated to realize gold-electroplated structures with reduced residual stress. The CPD, being a low-temperature (31.1°C) process, is more suitable for compliant structures without any deformation.
Study of Zernike moments using analytical Zernike polynomials

In this research, the analytical Zernike polynomial s written in Cartesian coordinates and in polar coo rdinates were used to calculate the Zernike moments instead of th e numerical complex Zernike polynomials used before . The results show that there is no difference between th em except in time calculating where this method tak es more time.
Nonlinear Analysis of Physiological Signals: A Review

This paper reviews various nonlinear analysis methods for physiological signals. The assessment is based on a discussion of chaos-inspired methods, such as fractal dimension (FD), correlation dimension (D2), largest Lyapunov exponet (LLE), Renyi's entropy (REN), Shannon spectral entropy (SEN), and approximate entropy (ApEn). We document that these methods are used to extract discriminative features from electroencephalograph (EEG) and heart rate variability (HRV) signals by reviewing the relevant scientific literature. EEG features can be used to support the diagnosis of epilepsy and HRV features can be used to support the diagnosis of cardiovascular diseases as well as diabetes. Documenting the widespread use of these and other nonlinear methods supports our thesis that the study of feature extraction methods, based on the chaos theory, is an important subject which has been gaining more and significance in biomedical engineering. We adopt the position that pursuing research in the field of biomedical engineering is ultimately a progmatic activity, where it is necessary to engage in features that work. In this case, the nonlinear features are working well, even if we do not have conclusive evidence that the underlying physiological phenomena are indeed chaotic.
An Improved Parameter Design Method of Active Clamping Circuit for Voltage Balancing of Series-Connected IGBTs

Connecting multiple insulated gate bipolar transistors (IGBTs) in series is the most direct and effective way to meet the voltage rating requirements of high power and high voltage converters, but there is severe voltage imbalance during the switching process of series-connected IGBTs. The active clamping circuit has been widely used for its simple structure and high reliability for voltage balancing of series-connected IGBTs. Based on the existing research, this paper presents an improved parameter design scheme for the active clamping circuit. Through theoretical analysis and experiments, it’s verified that the active clamping circuit using the proposed parameter design method can achieve static voltage balancing without additional power-side static voltage balancing resistance, and the influence of clamping circuit parameters on voltage balancing effect and IGBTs switching loss is also discussed. The work of this paper provides suggestions for the design and selection of appropriate active clamping circuit parameters in practical applications.
Comparative Analysis of ST, ECRL and Static Logic Style at Different Process Technologies

In the lower VLSI process technologies, to design the low-power VLSI circuits selection of suitable logic style becomes important to minimize the chip’s power to meet the power density need with minimum sacrifice in the speed. This study’s emphasis is on the design and optimization of digital code converters. To compare propagation delay, power consumption and power delay product (PDP) at 32 nm and 22 nm process technologies, sub-threshold (ST) logic style implementations of Gray code to Binary code (GB), Binary code to Gray code (BG), and BCD code to Excess-3 code (BE3) code converters are used. These implementations are compared with Efficient Charge Recovery Logic (ECRL) and static CMOS logic style implementations.
NF-κB Transcriptional Activity Indispensably Mediates Hypoxia–Reoxygenation Stress-Induced microRNA-210 Expression

Ischemia–reperfusion (I-R) injury is a cardinal pathophysiological hallmark of ischemic heart disease (IHD). Despite significant advances in the understanding of what causes I-R injury and hypoxia–reoxygenation (H-R) stress, viable molecular strategies that could be targeted for the treatment of the deleterious biochemical pathways activated during I-R remain elusive. The master hypoxamiR, microRNA-210 (miR-210), is a major determinant of protective cellular adaptation to hypoxia stress but exacerbates apoptotic cell death during cellular reoxygenation. While the hypoxia-induced transcriptional up-regulation of miR-210 is well delineated, the cellular mechanisms and molecular entities that regulate the transcriptional induction of miR-210 during the cellular reoxygenation phase have not been elucidated yet. Herein, in immortalized AC-16 cardiomyocytes, we delineated the indispensable role of the ubiquitously expressed transcription factor, NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) in H-R-induced miR-210 expression during cellular reoxygenation. Using dominant negative and dominant active expression vectors encoding kinases to competitively inhibit NF-κB activation, we elucidated NF-κB activation as a significant mediator of H-R-induced miR-210 expression. Ensuing molecular assays revealed a direct NF-κB-mediated transcriptional up-regulation of miR-210 expression in response to the H-R challenge that is characterized by the NF-κB-mediated reorchestration of the entire repertoire of histone modification changes that are a signatory of a permissive actively transcribed miR-210 promoter. Our study confers a novel insight identifying NF-κB as a potential novel molecular target to combat H-R-elicited miR-210 expression that fosters augmented cardiomyocyte cell death.
Favorable Appendicular Skeletal Muscle Mass Changes in Older Patients With Type 2 Diabetes Receiving GLP-1 Receptor Agonist and Basal Insulin Co-Therapy

Background And Aims: Maintaining appendicular skeletal muscle mass is important for maintaining the quality of life of elderly patients with type 2 diabetes. The possibility of GLP-1 receptor agonists for maintaining appendicular skeletal muscle mass has previously been reported. We investigated changes in appendicular skeletal muscle mass, measured by body impedance analysis, in elderly patients who were hospitalized for diabetes self-management education. Methods: The study design was a retrospective longitudinal analysis of the changes in appendicular skeletal muscle mass in hospitalized patients over the age of 70 years. The study subjects consisted of consequential patients who received GLP-1 receptor agonist and basal insulin co-therapy or received basal insulin therapy. Body impedance analysis was performed on the day after admission and on the ninth day of admission. All patients received standard diet therapy and standard group exercise therapy 3 times per week. Results: The study subjects consisted of 10 patients who received GLP-1 receptor agonist and basal insulin co-therapy (co-therapy group) and 10 patients who received basal insulin (insulin group). The mean change in appendicular skeletal muscle mass was 0.78 ± 0.7 kg in co-therapy group and −0.09 ± 0.8 kg in the insulin group. Conclusions: This retrospective observational study suggests the possibility of favorable effects of GLP-1 receptor agonist and basal insulin co-therapy for maintaining appendicular skeletal muscle mass during hospitalization for diabetes self-management education.
The matching polytope does not admit fully-polynomial size relaxation schemes

The groundbreaking work of Rothvo{\ss} [arxiv:1311.2369] established that every linear program expressing the matching polytope has an exponential number of inequalities (formally, the matching polytope has exponential extension complexity). We generalize this result by deriving strong bounds on the polyhedral inapproximability of the matching polytope: for fixed $0 < \varepsilon < 1$, every polyhedral $(1 + \varepsilon / n)$-approximation requires an exponential number of inequalities, where $n$ is the number of vertices. This is sharp given the well-known $\rho$-approximation of size $O(\binom{n}{\rho/(\rho-1)})$ provided by the odd-sets of size up to $\rho/(\rho-1)$. Thus matching is the first problem in $P$, whose natural linear encoding does not admit a fully polynomial-size relaxation scheme (the polyhedral equivalent of an FPTAS), which provides a sharp separation from the polynomial-size relaxation scheme obtained e.g., via constant-sized odd-sets mentioned above. Our approach reuses ideas from Rothvo{\ss} [arxiv:1311.2369], however the main lower bounding technique is different. While the original proof is based on the hyperplane separation bound (also called the rectangle corruption bound), we employ the information-theoretic notion of common information as introduced in Braun and Pokutta [this http URL], which allows to analyze perturbations of slack matrices. It turns out that the high extension complexity for the matching polytope stem from the same source of hardness as for the correlation polytope: a direct sum structure.
Feature Weakening, Contextualization, and Discrimination for Weakly Supervised Temporal Action Localization

Weakly-supervised Temporal Action Localization (W-TAL) aims to train a model to localize all action instances potentially from different classes in an untrimmed video, using a training dataset that has video-level action class labels but has no detailed annotations on the start and end timestamps of action instances. We propose to solve the W-TAL problem from the feature learning aspect, with a new architecture, termed F3-Net, which includes (1) a Feature Weakening (FW) module that can identify and randomly weaken either the most discriminative action or the most discriminative background features over the training iterations to force the network to precisely localize the action instances in both discriminative and ambiguous action-related frames, without spreading to the background intervals; (2) a Feature Contextualization (FC) module that can infer the global contexts among video segments and attentionally fuse them with the local contexts from individual video segments to generate more representative features; and (3) a Feature Discrimination (FD) module that can highlight the most discriminative video segments/classes corresponding to each class/segment, respectively, for localizing multiple action instances from different classes within a video. Experimental results on THUMOS14 and ActivityNet1.3 demonstrate the state-of-the-art performance of our F3-Net, and the FW and FC are also effective plug-in modules to improve other methods. This project will be available at https://moniruzzamanmd.github. io/F3-Net/https://moniruzzamanmd.github. io/F3-Net/
New limb-darkening coefficients for modeling binary star light curves

We present monochromatic, passband-specific, and bolometric limb-darkening coefficients for a linear as well as nonlinear logarithmic and square root limb-darkening laws. These coefficients, including the bolometric ones, are needed when modeling binary star light curves with the latest version of the Wilson-Devinney light curve progam. We base our calculations on the most recent ATLAS stellar atmosphere models for solar chemical composition stars with a wide range of effective temperatures and surface gravitites. We examine how well various limb-darkening approximations represent the variation of the emerging specific intensity across a stellar surface as computed according to the model. For binary star light curve modeling purposes, we propose the use of a logarithmic or a square root law. We design our tables in such a manner that the relative quality of either law with respect to another can be easily compared. Since the computation of bolometric limb-darkening coefficients first requires monochromatic coefficients, we also offer tables of these coefficients (at 1221 wavelength values between 9.09 nm and 160 micrometer) and tables of passband-specific coefficients for commonly used photometric filters.
Awake/Sleep Mechanism Using LEACH Protocol in WSN - An Energy Efficient Approach

The hierarchical clustering, like the LEACH protocol, utilizes the Cluster Head system for collecting and aggregating and transmitting data. The Cluster Heads are chosen in arbitrary after each round based on the information transmitted by the sensor node. Cluster Heads or the non-Cluster Head nodes which are at a greater distance from the Base Station will consume extra energy in sending information from a similar distance. The ASLEEP mechanism is induced in the traditional LEACH protocol. Accordingly, the new methodology expands the lifetime of the network to a greater extent. The time of talk interval should be set with the base time expected to effectively get the whole message from all the sensor nodes. The sleep mechanism in LEACH consequently changes the sensor nodes exercises, in this way accomplishing low power utilization and low message inactivity. This drags out the network's lifetime by a component that directly relies upon the level of redundancy. In any event, during the low duty cycle, the sleep time of adjoining nodes times are coordinated.
Analysis of Breast Cancer in Early Stage by UsingMachine LearningAlgorithms: A Review

The goal of using Machine Learning (ML), a branch of Artificial Intelligence (AI) technology, is to increase the accuracy and efficiency of doctors' work. After skin cancer, breast cancer is the kind of cancer that affects women the most often. Breast cancer is caused by an unchecked multiplication of breast cells. According to the national cancer institute, 287,850 are the Estimated New Cases in 2022, with 15% of all new cancer cases. 43,250 will be the. Estimate deaths in 2022 in the USA. In the United States, female breast cancer accountsfor 15% of all newly diagnosed instances of cancer. An analysis of breast cancer data from 2018 in India revealed 1,62,468 fresh recorded cases and 87,090 deaths from the condition. According to the findings of the most recent research, the state of Kerala has the highest cancer incidence in all of India. This review paper will understand the various stages of Breast cancer, look at datasets and evaluate supervised and unsupervised Machine Learning (ML) algorithms to see which ones are best for early breast cancer prediction made possible by the use of machine learning or deep learning.