Miracle-dz/UniFAR-FTUs huggingface dataset:
HyDNN: A Hybrid Deep Learning Framework Based Multiuser Uplink Channel Estimation and Signal Detection for NOMA-OFDM System

Deep learning (DL) techniques can significantly improve successive interference cancellation (SIC) performance for the non-orthogonal multiple access (NOMA) system. The NOMA-orthogonal frequency division multiplexing (OFDM) system is considered in this paper to develop a hybrid deep neural network (HyDNN) model for multiuser uplink channel estimation (CE) and signal detection (SD). The proposed HyDNN uses a combination of a bi-directional long short-term memory (BiLSTM) network and a one-dimensional convolutional neural network (1D-CNN) to optimize errors in the system. The extraction of input signal characteristics from OFDM is carried out using the 1D-CNN model and fed into the time series BiLSTM network to infer the signal at the receiver terminal. The HyDNN model learns through the simulated channel data during offline training. To optimize the loss during learning the model the Adam optimizer is utilized. After successful training, the transmitted symbols in the online deployment are instantly recovered with optimal prediction rates by using the proposed HyDNN model. In comparison to the traditional CE and SD method for the NOMA scheme and other existing DL models, the proposed technique demonstrates satisfactory performance enhancements. In addition, the simulation outcomes show robustness with different training parameters such as minibatch sizes and learning rates.
Transcriptomic analysis of granulosa cells in patients with endometriosis-related infertility: identification of potential molecular mechanisms

Objective: To investigate the impact of endometriosis (EMS) on granulosa cell function and elucidate the molecular mechanisms involved. Methods: RNA sequencing, differential expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis, protein–protein interaction analysis, and RT-qPCR were employed to assess the effects of EMS on granulosa cell function. Results: The results revealed significant differences in gene expression between the EMS and control groups, including genes related to immune regulatory functions and ferroptosis. Hub gene modules and hub genes were identified, including those related to cell cycle and immune and inflammatory pathways. RT-qPCR revealed significant upregulation of the hub genes CCL3 and IL1B in granulosa cells of patients with EMS. Conclusion: The results of RNA sequencing demonstrated that EMS induces significant transcriptional alterations in granulosa cells of affected patients. These findings provide important insights into the diagnosis and treatment of EMS and highlight the importance of further investigation of CCL3 and IL1B as potential biomarkers for EMS.
Modeling pediatric tumor risks in Florida with conditional autoregressive structures and identifying hot-spots

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-inﬂated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al ., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identiﬁed high risk regions discovered by the other researchers.
I-Shaped Dimers of a Plant Chloroplast FOF1-ATP Synthase in Response to Changes in Ionic Strength

F-type ATP synthases play a key role in oxidative and photophosphorylation processes generating adenosine triphosphate (ATP) for most biochemical reactions in living organisms. In contrast to the mitochondrial FOF1-ATP synthases, those of chloroplasts are known to be mostly monomers with approx. 15% fraction of oligomers interacting presumably non-specifically in a thylakoid membrane. To shed light on the nature of this difference we studied interactions of the chloroplast ATP synthases using small-angle X-ray scattering (SAXS) method. Here, we report evidence of I-shaped dimerization of solubilized FOF1-ATP synthases from spinach chloroplasts at different ionic strengths. The structural data were obtained by SAXS and demonstrated dimerization in response to ionic strength. The best model describing SAXS data was two ATP-synthases connected through F1/F1′ parts, presumably via their δ-subunits, forming “I” shape dimers. Such I-shaped dimers might possibly connect the neighboring lamellae in thylakoid stacks assuming that the FOF1 monomers comprising such dimers are embedded in parallel opposing stacked thylakoid membrane areas. If this type of dimerization exists in nature, it might be one of the pathways of inhibition of chloroplast FOF1-ATP synthase for preventing ATP hydrolysis in the dark, when ionic strength in plant chloroplasts is rising. Together with a redox switch inserted into a γ-subunit of chloroplast FOF1 and lateral oligomerization, an I-shaped dimerization might comprise a subtle regulatory process of ATP synthesis and stabilize the structure of thylakoid stacks in chloroplasts.
Comparison of Grip Strength Measurements by Widely Used Three Dynamometers in Outpatients Aged 60 Years and Over

Grip strength (GS) is widely used in various fields such as sports, rehabilitation, and geriatrics to assess muscle strength, and to diagnose sarcopenia and frailty in older adults. There is a potential for measurement differences among different dynamometers available, and studies comparing GS measurements by variable tools have conflicting results. The two most frequently used dynamometers are the Jamar hydraulic (Jamar) and spring-type hand grip dynamometers, and Jamar has not been compared to Jamar PLUS+ Digital (Jamar+) dynamometer in older adults. So, we aimed to assess GS measurements with the Jamar as the reference standard against Jamar+, and spring-type Takei T.KK. 5401 (Takei) digital dynamometers. One hundred and ten outpatients aged >60 years were included. Inter-instrumental reliability was determined. The differences between dynamometers were evaluated by Bland–Altman plots and measurement error. The measurements with Jamar+, and Takei dynamometers were reliable and valid regarding the Jamar dynamometer. Takei and Jamar+ dynamometers overestimated GS over the Jamar dynamometer. Though the differences in the measured values might be disregarded in clinical practice, individuals defined to have low GS varied by the use of different dynamometers. Grip strength better be measured by the same dynamometer in serial measurements of older individuals.
Even-Odd Sorting Network for Fast Binary Counters

In various digital signal processing devices, the efficiency of data multiplication is a common performance limiter and the critical route includes the parallel summing of multiple operands. Therefore, high compression is used to speed up the summation process. Binary counters with specific design are required to reduce some efficiency parameters in the partial products compared to traditional one’s counters. In this project, we introduce a technique for quickly saturated binary counters used to design multipliers. This method involves a design of a sorter network using different sorting algorithm that is "Even-Odd" sorting. By using this technique, (15,4) and (31,5) binary counters are designed and further optimization of the parameters are compared in terms of latency and area. These counters can be further implemented in8 × 8, 16 × 16 bit multipliers and area-delay product, powerdelay product values can be obtained. These sorter networks and design of binary counters are programmed and synthesized in Xilinx Vivado tool.
Phase Calibration of Synchrotron RF Signals

In the scope of FAIR’s scientific program higher beam intensities will be achieved and several new synchrotrons (including storage rings) are being built [1]. The low-level radio frequency (LLRF) systems of FAIR have to support multi-harmonic operations, barrier bucket generation and bunch compression in order to meet the desired beam quality requirements. All this imposes several demands on the LLRF systems. For example the phase error of the gap voltage of a specific RF cavity must be less than ±3 [2]. Thus, each individual component must have a better accuracy. The RF reference signals for the FAIR synchrotron RF cavity systems are generated by direct digital synthesis (DDS). Four so-called Group DDS modules are mounted in one crate. In the supply rooms, the reference signals of such a crate are then distributed to local cavity LLRF systems [3]. Therefore, the precise phase calibration of Group DDS modules is of importance to prevent phase errors in LLRF signals of FAIR. A phase calibration method with respect to absolute phases of DDS modules defined by means of the FAIR Bunch Phase Timing System (BuTiS) is developed, and its precision is under evaluation.
Advances in photonic reservoir computing

Abstract We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.
Biochemical and Radiological Factors for Prognostication of Traumatic Brain Injury: An Institutional Experience

Introduction Traumatic brain injury (TBI) necessitates identifying patients at risk of fatal outcomes. Classic biomarkers used clinically today in other organ systems are quantitative in nature. This aspect largely restricts the prognostic ability of a theoretical quantitative brain biomarker. This study aimed to explore biochemical markers and imaging findings reflecting the severity of cerebral damage to predict outcomes. Methodology In this study, 61 TBI cases with moderate to severe brain injury were prospectively observed, and various indices including random blood sugar (RBS), hemoglobin, international normalized ratio (INR), lactate dehydrogenase (LDH), cortisol, and CT findings were assessed. Glasgow Outcome Scores (GOS) determined the outcomes. Statistical analysis was carried out to assess correlations. Results The mean RBS level of those who did not survive was 259.58 mg/dL, whereas in those who survived the value was 158.48 mg/dL. Analysis indicated that patients with high RBS value on admission had a higher risk of mortality (p=0.000). We noted that the mean serum cortisol levesl on both Days 1 and 5 were higher in patients who died and were able to establish a statistically significant correlation between both the values and outcome. A statistically significant negative correlation between Day 1 and Day 5 serum LDH levels and outcomes was evident from our study (p=0.000 for both). Among the components of the Rotterdam score, the presence of intraventricular hemorrhage (IVH) in the CT scan had a significant association with unfavorable outcomes (p=0.01) while midline shift was significantly associated with a low GCS (p=0.04). Conclusion Biochemical markers such as INR, RBS, serum cortisol, and LDH at admission can serve as valuable indicators of prognosis in TBI patients. Furthermore, a persistent increase in LDH and cortisol levels between Days 1 and 5, along with the Glasgow Coma Scale and Rotterdam Scoring system, are good predictors of mortality.
Basket Option Pricing and the Mellin Transform

Basket Option Pricing and the Mellin Transform Derek J. Manuge Advisor: University of Guelph, 2013 Dr. Peter T. Kim Option pricing has been an increasingly popular area of study over the past four decades. The use of the Mellin transform in such a context, however, has not. In this work we present a general multi-asset option pricing formula in the context of Mellin transforms, extending previously known results. The analytic formula derived computes European, American, and basket options with n underlying assets driven by geometric Brownian motion. Aside from the usual given parameters, the pricing formula requires three components to compute: (i) the Mellin basket payoff function, (ii) the characteristic function (or exponent) of a multivariate Brownian motion with drift, and (iii) the Mellin transform of the early exercise function. A fast discretization is solved, providing option prices at incremental values of initial asset prices. As an application, European put option prices are computed for Canadian bank stocks (n = 1) and foreign exchange rates (n = 2) with USD denomination.
Critical current enhancement driven by suppression of superconducting fluctuation in ion-gated ultrathin FeSe

The framework of phase transition, such as superconducting transition, occasionally depends on the dimensionality of materials. Superconductivity is often weakened in the experimental conditions of two-dimensional thin films due to the fragile superconducting state against defects and interfacial effects. In contrast to this general trend, superconductivity in the thin limit of FeSe exhibits an opposite trend, such as an increase in critical temperature (Tc) and the superconducting gap exceeding the bulk values; however, the dominant mechanism is still under debate. Here, we measured thickness-dependent electrical transport properties of the ion-gated FeSe thin films to evaluate the superconducting critical current (Ic) in the ultrathin FeSe. Upon systematically decreasing the FeSe thickness by the electrochemical etching technique in the Hall bar-shaped electric double-layer transistors, we observed a dramatic enhancement of Ic reaching about 10 mA and corresponding to about 107 A cm−2 in the thinnest condition. By analyzing the transition behavior, we clarify that the suppressed superconducting fluctuation is one of the origins of the large Ic in the ion-gated ultrathin FeSe films. These results indicate the existence of a robust superconducting state possibly with dense Cooper pairs at the thin limit of FeSe.
Switching in Boolean Circuits and Modelling Cognition through Neuroids

In computational complexity theory, Hastad’s switching lemma is a vital analytical tool for proving lower bounds on the size of constant-depth Boolean circuits. Using the switching lemma, Hastad showed in 1986 that Boolean circuits of depth k, in which only AND, OR, and NOT gates are allowed, require size exp ( Ω ( n 1 k−1 )) for computing the PARITY function on n variables. In essence, the switching lemma says that, given an arbitrary formula in disjunctive normal form, if we set some fraction of the variables randomly, then with high probability, the restricted function can be computed by a decision tree of small depth. In 2012, Impagliazzo, Matthews and Paturi formulated an extended switching lemma which says that, given a sequence of formulas in conjunctive and/or disjunctive normal form on the same set of variables, if all of them are hit by the same random restriction, then it is exponentially unlikely that there is a large subset of formulas where each formula contributes a large number of variables to their joint decision tree. In the first chapter of this thesis, we begin by discussing Hastad’s switching lemma, and how it is useful in proving that PARITY does not belong to AC0. We then move on to discussing the extended switching lemma and its proof. In the second chapter, we critique the neuroidal model for cognition proposed by Valiant. We start off by discussing the motivation behind Valiant’s work. We then explore the physiology of the brain and some insights from cognitive psychology that aided Valiant’s formulation of the neuroidal model. Next, we describe the actual model in extensive detail and present the algorithm put forward by Valiant for implementing unsupervised memorization within his model as a case study. Finally, we conclude by discussing the relevance of the neuroidal model to the current attempts by researchers to build cognitive computing systems.
An Updated Review on Monkeypox Viral Disease: Emphasis on Genomic Diversity

Monkeypox virus has remained the most virulent poxvirus since the elimination of smallpox approximately 41 years ago, with distribution mostly in Central and West Africa. Monkeypox (Mpox) in humans is a zoonotically transferred disease that results in a smallpox-like disease. It was first diagnosed in 1970 in the Democratic Republic of the Congo (DRC), and the disease has spread over West and Central Africa. The purpose of this review was to give an up-to-date, thorough, and timely overview on the genomic diversity and evolution of a re-emerging infectious disease. The genetic profile of Mpox may also be helpful in targeting new therapeutic options based on genes, mutations, and phylogeny. Mpox has become a major threat to global health security, necessitating a quick response by virologists, veterinarians, public health professionals, doctors, and researchers to create high-efficiency diagnostic tests, vaccinations, antivirals, and other infection control techniques. The emergence of epidemics outside of Africa emphasizes the disease’s global significance. Increased monitoring and identification of Mpox cases are critical tools for obtaining a better knowledge of the ever-changing epidemiology of this disease.
Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.
Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter

An interlaced matrix Kalman filter, which is based on vector observations and gyro measurements, is proposed for spacecraft attitude estimation in this paper. It combines the matrix Kalman filter and cubature Kalman filter to estimate spacecraft attitude and gyro drift bias, respectively. The defects of the original matrix Kalman filter, which could only estimate the attitude parameters of spacecraft, are addressed by the proposed interlaced matrix Kalman filter. In addition, the dimensions of cubature Kalman filter for conventional attitude estimation method are reduced by the designed recursive algorithm. It is noted that the two filters are not independent with each other. Firstly, the attitude quaternion of spacecraft is estimated by the modified matrix Kalman filter. Then, the estimated quaternion is input for the recursive cubature Kalman filter, which is used to estimate the gyro drift bias. Finally, the estimated gyro drift bias is compensated for the measurements of the gyros. Therefore, the precision of the estimated attitude of spacecraft is improved by the interacting process of the modified matrix Kalman filter and recursive cubature Kalman filter. A simulation test is designed to verify the advantage of the proposed method by comparing with the previous method, and the results indicate that the proposed algorithm has better performance on convergence rate and stability.
Improving Access to Care for Patients Taking Opioids for Chronic Pain: Recommendations from a Modified Delphi Panel in Michigan

Purpose About 5–8 million US patients take long-term opioid therapy for chronic pain. In the context of policies and guidelines instituted to reduce inappropriate opioid prescribing, abrupt discontinuations in opioid prescriptions have increased and many primary care clinics will not prescribe opioids for new patients, reducing access to care. This may result in uncontrolled pain and other negative outcomes, such as transition to illicit opioids. The objective of this study was to generate policy, intervention, and research recommendations to improve access to care for these patients. Participants and Methods We conducted a RAND/UCLA Modified Delphi, consisting of workshops, background videos and reading materials, and moderated web-based panel discussions held September 2020–January 2021. The panel consisted of 24 individuals from across Michigan, identified via expert nomination and snowball recruitment, including clinical providers, health science researchers, state-level policymakers and regulators, care coordination experts, patient advocates, payor representatives, and community and public health experts. The panel proposed intervention, policy, and research recommendations, scored the feasibility, impact, and importance of each on a 9-point scale, and ranked all recommendations by implementation priority. Results The panel produced 11 final recommendations across three themes: reimbursement reform, provider education, and reducing racial inequities in care. The 3 reimbursement-focused recommendations were highest ranked (theme average = 4.2/11), including the two top-ranked recommendations: increasing reimbursement for time needed to treat complex chronic pain (ranked #1/11) and bundling payment for multimodal pain care (#2/11). Four provider education recommendations ranked slightly lower (theme average = 6.2/11) and included clarifying the spectrum of opioid dependence and training providers on multimodal treatments. Four recommendations addressed racial inequities (theme average = 7.2/11), such as standardizing pain management protocols to reduce treatment disparities. Conclusion Panelists indicated reimbursement should incentivize traditionally lower-paying evidence-based pain care, but multiple strategies may be needed to meaningfully expand access.