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AMO Development LLC has been issued a patent for an automated calibration system for laser and tomography systems used in surgical procedures, particularly in eye surgery. The system aims to improve the calibration process, which can be time-consuming and complex, by automatically calibrating the scanning systems with a fluorescent imaging scan pattern. The patent provides detailed methods for calibrating the laser system with treatment space, including mapping sensor surface locations to the treatment space and using polynomial fitting or lookup tables for calibration. This innovation addresses the need for more efficient and accurate calibration of laser surgical systems, benefiting the field of surgical technology. [Extracted from the article]

Researchers from the University of Alicante in Spain have conducted a study on kurtosis, focusing on the moments of the unconditional distribution of the TGARCH(1,1) model under different specifications for the conditional mean and skewed distributions for the innovations. The research found that skewness of the innovations is the main driver of the skewness of the TGARCH(1,1), while excess kurtosis has a lesser impact. The study also highlighted that asymmetric returns can occur even if innovations are symmetric, and skewed innovations can lead to non-zero cross-correlations, indicating a leverage effect. The findings were applied to empirical data on stock indices, and the research has been peer-reviewed. [Extracted from the article]

A study conducted at the University of Opole in Poland examined the impact of neurological and physical disability on the sexual well-being of individuals with multiple sclerosis (MS). The research found that individuals with MS reported lower levels of sexual activity and motivation compared to healthy individuals. While there was a positive relationship between neurological disability and physical disability, no non-linear relationships were found between disability severity and sexual well-being. The study highlights the importance of addressing sexual dysfunctions in MS patients to improve their overall well-being. [Extracted from the article]

Researchers at Amity University have developed a new method, Quadratic Feynman Polynomial Interpolated and Vector Nearest Centroid-based (QFPI-VNC), to predict genome disorders with improved sensitivity and specificity. By utilizing medical data from a public genomes dataset and applying various filtering and interpolation techniques, the proposed method showed promising results in terms of genome disease detection rate, accuracy, sensitivity, specificity, and convergence speed. This research aims to enhance genomic disorder prediction through innovative mathematical and computational approaches, offering potential advancements in healthcare and biomedical research. [Extracted from the article]

The article discusses the identification of parsimonious pathways of accumulation and convergent evolution from binary data in various processes, including genome evolution and cancer progression. The study focuses on minimizing a topological cost function to determine subgraphs that can generate specific observations before and after certain events. The research aims to measure the predictability and stereotypical nature of evolutionary processes, with applications in antimicrobial resistance, organelle genomes, and cancer progression. The preprint has not yet undergone peer review and provides a software implementation for further exploration. [Extracted from the article]

A recent study conducted at Chongqing University in the People's Republic of China focused on developing a novel framework for diagnosing Alzheimer's Disease (AD) using neuropsychological and neuroimaging data. The researchers proposed a multimodal learning machine framework that accurately calculated the optimal polynomial degree to restore critical clinical information from neuropsychological tests. This approach achieved over 98% accuracy and F1 scores in diagnosing AD, without bias between mild cognitive impairment (MCI) and AD groups, offering clinicians logical recommendations for disease management. The study was supported by Science Innovation Programs in Chongqing and the Science and Technology Research Program of Chongqing Education Commission. [Extracted from the article]

Movano Inc. has been issued a patent for a method to monitor physiological parameters in individuals using radio waves transmitted below the skin surface. The patent aims to coherently combine data generated from an RF-based sensor system to produce a pulse wave signal, potentially aiding in monitoring health parameters like blood glucose levels, blood pressure, and heart rate. This technology could offer a non-invasive and continuous monitoring solution for individuals with diabetes or other health conditions. [Extracted from the article]

A patent has been issued for a method and system for generating a therapeutic massage plan by Aescape Inc. The invention involves a robotic control system that adjusts the robot's contact point continuously based on sensed force, allowing for the handling of soft tissue in an automated manner. The system includes motion planning for directing robotic arms to execute specific therapies at different depths of tissue by applying variable pressures. This innovation aims to address the need for a therapeutic massage plan for handling soft body objects in a dynamic or automated system. [Extracted from the article]

A recent study published in the Journal of Nigerian Society of Physical Sciences by researchers from the Faculty of Natural Sciences introduces a novel cluster of hybrid polynomial kernel families in physics. These hybrid kernels aim to reduce error in density estimation tasks compared to traditional kernels by combining classical polynomial kernels using probability axioms. The research demonstrates that the proposed hybrid kernels outperform their classical counterparts across various distribution types and sample sizes, suggesting their potential for enhancing accuracy in statistical modeling and analysis. For more information, readers can access the full article through the provided link. [Extracted from the article]

A study conducted in Faro, Portugal, examined the impact of multicultural attitudes and perceived intergroup threat on attitudes towards immigrants among Portuguese participants. The research, supported by the Intergroup Threat Theory, utilized polynomial regression analysis to explore the relationship between multicultural attitude, perceived intergroup threat, and attitudes towards immigrants. The findings confirmed four hypotheses and highlighted the importance of understanding the complex role of multicultural ideology in predicting attitudes towards immigrants. The study was funded by Fundacao para a Ciencia e a Tecnologia (FCT) and has been peer-reviewed, with potential for further research in this area. [Extracted from the article]

A recent study conducted at Koneru Lakshmaiah Education Foundation in Andhra Pradesh, India, focused on stroke prediction using machine learning techniques. The researchers proposed a model that utilizes polynomial feature transformation and linear regression to capture both linear and non-linear relationships in brain stroke data. The model demonstrated superior performance with a testing accuracy of 99.2%, highlighting the potential of linear regression with polynomial features for accurate predictions and insights into feature-target relationships. This research contributes to the field of stroke prediction by offering a balanced approach that considers model complexity and interpretability. [Extracted from the article]

A research report from the Arctic University of Norway (UiT) discusses the challenges of vaccine shortages during the pandemic and the impact on supply chains. The report introduces the Two-Agent Proportionate Flowshop Scheduling Problem with deadlines, which aims to minimize completion times while meeting demand. The researchers developed two polynomial-time optimization algorithms to solve these scheduling problems. The study provides a foundation for further research in this area. Financial support for the research came from UiT The Arctic University of Norway. [Extracted from the article]

Researchers from the City University of Hong Kong have developed a new algorithm to identify the chirality of polymer knots based on the Alexander polynomial. The Alexander polynomial is commonly used to identify knots in polymer conformations, but it cannot directly discriminate knot chirality. The new algorithm adds an extra step to the calculation of the Alexander polynomial, slightly increasing computational cost but allowing for the identification of knot chirality. This algorithm has been proven mathematically and has implications for physical research, particularly in the study of polymer knots. [Extracted from the article]

Researchers from the Department of Mathematics in Sialkot, Pakistan, have developed a quadratic regression model to analyze the topological characteristics of several antiviral drugs, including favipiravir, baricitinib, fluvoxamine, nirmatrelvir, molnupiravir, lopinavir, and remdesivir. The study aimed to determine the physicochemical properties of these drugs and their potential effectiveness in treating COVID-19. The results showed a significant correlation between the investigated topological coindices and the physicochemical properties of the drugs. This research has been peer-reviewed and published in The Journal of Supercomputing. [Extracted from the article]

This patent application presents an innovative antenna design for electronic devices that improves signal reception and transmission. The design features a series of radiating elements connected in sequence, with gradually decreasing widths. This allows for a smaller antenna size and reduced side lobes. The inventors also propose mathematical functions for designing the curved antenna. The application provides detailed technical specifications and claims for the design, which can be used in detection apparatus and terminals. [Extracted from the article]

A recent study conducted at the University of Porto in Portugal focused on the development of green solvents to reduce the environmental impact of separation processes. The researchers used computational chemistry to determine the liquid densities of binary mixtures of different solvents. They compared three different methodologies for correlating the excess volumes of these mixtures and found that the computational chemistry approach provided the most accurate results. This research has important implications for the application of green solvents in industry. [Extracted from the article]

Researchers at the University of Kang Ning in Taipei, Taiwan have conducted a study on astigmatism and the impact of non-toric ortho-k lens decentration. The study aimed to use artificial intelligence (AI) to predict the magnitude and direction of lens decentration and its effects on induced higher-order aberrations (HOAs) and astigmatism. The researchers used a topographer to scan corneas before and after ortho-k wear and developed an AI-based machine learning neural network algorithm to predict decentration. The study found that lens decentration is common and can result in induced HOAs and astigmatism, with the magnitude of decentration affecting the outcomes. The AI-based algorithm showed promise in predicting decentration accurately, potentially leading to better control over ortho-k fitting and clinical outcomes. [Extracted from the article]

A recent study conducted by researchers at the University of Almeria in Spain has found that resistance training can help mitigate side effects during and after breast cancer treatment. The study aimed to evaluate the load-velocity relationship during the row exercise in women survivors of breast cancer in order to provide a new approach for assessing and prescribing the intensity of resistance training in supportive cancer care. The researchers observed a strong relationship between movement velocity and relative load, indicating that monitoring movement velocity during the row exercise can facilitate precise assessment and prescription of resistance training intensity in supportive cancer care. This research provides valuable insights for healthcare professionals working with breast cancer survivors. [Extracted from the article]

A recent report from Eulji University discusses research on pain measurement using frontal channel EEG-based signals. The study investigated various EEG features and machine learning algorithms to identify biomarkers for quantifying pain severity. The researchers found significant correlations between certain asymmetry and phase-amplitude coupling features and pain scores, while band power features did not show meaningful results. The study concludes that complex features such as asymmetry and phase-amplitude coupling are important in pain research and that objectively observing pain intensity using frontal channel-based EEG is feasible and clinically crucial for early intervention. [Extracted from the article]

A recent study conducted by researchers from China Pharmaceutical University confirms the efficacy and safety of cyclin-dependent kinase (CDK) 4/6 inhibitors in the first-line treatment of hormone receptor-positive (HR+) advanced breast cancer. The study analyzed data from six randomized controlled trials involving 2,638 patients and used fractional polynomial modeling methods to evaluate the long-term efficacy and safety outcomes of CDK4/6 inhibitors. The results showed that the use of CDK4/6 inhibitors significantly improves survival in treatment-naive patients with HR+ advanced breast cancer, but also increases the risk of adverse events. Abemaciclib and ribociclib were identified as the best options for prolonging progression-free survival and overall survival, respectively. [Extracted from the article]

A patent application has been submitted for a system that uses interferometry-based techniques to detect small mass changes in objects, such as living cells, observable through microscopy. The system aims to improve the detection of mass changes in objects and track individual object masses over time. It also incorporates image processing techniques to enhance the accuracy of mass measurements. The system may have applications in cancer research and drug development, particularly in identifying optimal therapeutics for individual patients. The patent application describes techniques and devices for detecting small changes in the volume of individual cells within a population of cells, particularly in the context of cancer cells and immune cells. The techniques can be used to identify subpopulations of cells and determine optimal therapeutics or ligands for treatment or activation. The patent application also mentions the use of machine learning and provides specific claims for computer-implemented methods and imaging systems for detecting mass changes of cells. [Extracted from the article]

Researchers at the School of Electronics Engineering in Vellore, India have developed a method for detecting seizures in electroencephalography (EEG) signals using the Gaussian-stockwell transform (GST) and Hermite polynomial features. The researchers applied these features to the Random Forest Classifier (RFC) algorithm and achieved an optimal classification accuracy of 96.4%, with a sensitivity of 97% and a specificity of 96.4%. This research demonstrates the effectiveness of the proposed method in distinguishing seizure activity in EEG signals. [Extracted from the article]

A patent has been issued for thermally actuated adaptive optics by inventors Michael Hertwig and Keith M. Murdoch. The patent, assigned to Coherent Inc., describes the use of adaptive optics systems to manipulate the wavefront of a light field. The disclosed adaptive optics are reflective and capable of handling high-intensity and high-fluence laser beams. The thermal actuation of the optics allows for cost savings compared to piezoelectric actuation. The patent provides detailed information on the design and configuration of the thermally actuated adaptive optics. [Extracted from the article]

A study conducted by researchers at Shahrekord University in Iran explores the use of mathematical modeling in cancer gene therapy. The model focuses on the interactions between cancer cells and viruses in oncolytic virotherapy. By applying Caputo fractional derivative and using generalized Bessel polynomials, the researchers were able to solve the optimal problem and demonstrate the accuracy of the method in solving nonlinear systems based on partial differential equations, particularly tumor models. The research has been peer-reviewed and published in Mathematical Methods in the Applied Sciences. [Extracted from the article]

A recent study conducted by researchers at China Pharmaceutical University evaluated the efficacy and safety of cyclin-dependent kinase (CDK) 4/6 inhibitors in the first-line treatment of hormone receptor-positive (HR+) advanced breast cancer. The study analyzed survival data using fractional polynomial modeling methods and found that CDK4/6 inhibitors, including abemaciclib, palbociclib, and ribociclib, significantly improved survival in treatment-naive patients. However, the use of these inhibitors also increased the risk of adverse events, particularly with palbociclib. The study suggests that abemaciclib and ribociclib may be the best options for prolonging progression-free survival and overall survival, respectively. [Extracted from the article]

A study conducted by researchers at the University of Liverpool in the United Kingdom explores the coalgebraic mu-calculus, a semantic framework for fixpoint logics over systems with non-standard branching types. Previous work on this topic has identified an exponential-time upper bound on satisfiability checking, but only for cases where tableau rules for next-step modalities are well-behaved. The researchers in this study prove the same upper complexity bound under more general assumptions, using a generic algorithm that supports on-the-fly satisfiability checking. This research has potential applications in areas such as graded mu-calculus with polynomial inequalities and probabilistic mu-calculus with polynomial inequalities. [Extracted from the article]

Libra Science Ltd. has submitted a patent application for a computer-based method and system that monitors the position of a guidewire during medical procedures. The method involves analyzing baseline images to determine the guidewire's movement during heartbeats and breathing cycles, and then monitoring subsequent images for any deviations from this baseline movement. Techniques such as image analysis, polynomial fitting, and machine learning can be used to track the guidewire's movement. The system aims to enhance the accuracy and safety of medical procedures involving guidewires. The patent application provides further information and claims for this invention. [Extracted from the article]

A recent report from the Netherlands Institute for Neuroscience discusses the challenges of removing stimulation artifacts in neuroprostheses, which are used to stimulate the brain and restore sensory and motor function. The researchers compared six software-based artifact removal methods and found that Polynomial fitting and Exponential fitting provided the best trade-off between recovery quality and computational complexity for a cortical prosthesis. The study highlights the importance of accurately removing artifacts to improve the monitoring of neural signals and closed-loop control of devices. The research was supported by various funding sources, including the European Research Council. [Extracted from the article]

This article discusses the identification markers of activated myofibroblast subsets in the human lung fibrosis ecosystem through integrated omics analysis. The study utilized single-cell RNA sequencing (scRNA-seq) to analyze the phenotypic and functional characteristics of human pulmonary fibrosis (PF). The researchers identified the dynamic changes in the extracellular matrix (ECM) and potential markers of myofibroblasts using a polynomial regression model. Additionally, bulk RNA-seq analysis identified eight new activating markers of myofibroblasts. The findings suggest that understanding the contributions of ECM and myofibroblast subsets could lead to potential therapeutic targets for PF. [Extracted from the article]

Researchers from Shahrekord University in Iran have developed a new approach to finding optimal solutions for hematopoietic stem cell models. Hematopoietic stem cells play a crucial role in various biological activities, including immune system regulation and inflammation response. The researchers used a fractional mathematical model and introduced a new method based on generalized Bessel polynomials to solve a system of nonlinear equations. Their findings suggest that this approach is superior to other methods. The research has been peer-reviewed and published in The European Physical Journal Plus. [Extracted from the article]

A patent has been issued to NantBio Inc. for interferometry-based systems that can detect small mass changes of an object in a solution. The patent describes techniques for using interferometry to measure phase differences in light passing through objects, such as living cells, to determine mass changes. The invention aims to improve the detection of mass changes in objects observable through microscopy, which could have applications in fields such as cancer research and immunotherapy. The patent outlines methods for background correction, segmentation, and motion tracking to track changes in mass over time. [Extracted from the article]

A recent study conducted in Zhoushan, People's Republic of China, explored the relationship between social support, family resilience, and psychological resilience among Chinese individuals receiving maintenance hemodialysis (MHD). The study found that social support and family resilience had a combined positive linear association with psychological resilience when there was agreement between the two variables. However, when there was disagreement, the combined effect manifested as a negative linear relationship. These findings emphasize the importance of considering both social support and family resilience when developing interventions to improve the psychological resilience of individuals receiving MHD. [Extracted from the article]

Researchers from Prince Sattam Bin Abdulaziz University in Saudi Arabia have developed a comprehensive model using mass transfer and machine learning to improve understanding of drug release behavior for tissue regeneration. The study utilized three regression models - Decision Tree Regression, Passive Aggressive Regression, and Quadratic Polynomial Regression - to analyze a dataset of drug release. The Decision Tree Regression model demonstrated exceptional predictive accuracy, while the Passive Aggressive Regression and Quadratic Polynomial Regression models also showed commendable performance. The research provides valuable insights into drug release kinetics and has implications for the development of novel drug formulations. [Extracted from the article]

A new report from the Central University of Venezuela presents fresh data on obesity, fitness, and wellness. The research focuses on dose conformity in radiotherapy and radiosurgery, which measures the correspondence between the dose distribution derived from a Treatment Planning System (TPS) and the actual volume to be treated. The study uses a method based on Zernike polynomials to define a general criterion of dose conformity. The results indicate agreement between the proposed dose conformity index and the Conformation Number CN, and the researchers suggest that the method could be extended to AI applications. [Extracted from the article]

A study conducted by researchers at the University of Notre Dame in Indiana focuses on the use of computational models in diagnosing, treating, and preventing cardiovascular disease. The study explores the application of Multifidelity Monte Carlo estimators for sensitivity analysis in an idealized model of the common carotid artery. The researchers found that these estimators, which combine three-dimensional fluid-structure interaction simulations with reduced-order models, offer superior variance reduction compared to traditional Monte Carlo estimators. The study also compares the multifidelity Monte Carlo estimators with polynomial chaos expansion estimates and discusses the computational cost of each method. [Extracted from the article]

A new report from the University Medical Center Utrecht discusses the association between age at menarche (the onset of menstruation) and cardiovascular disease (CVD). The study found a U-shaped relationship between age at menarche and myocardial infarction (heart attack) and ischaemic stroke, with higher risks in women aged under 12 and over 16 compared to those aged 13. However, a Mendelian randomisation analysis revealed no non-linear relationship between genetically proxied age at menarche and the risk of myocardial infarction, ischaemic stroke, or haemorrhagic stroke. The study suggests that there is no causal link between age at menarche and these cardiovascular conditions. [Extracted from the article]

Researchers at Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University in China have conducted a study on sensorineural hearing loss (SNHL). SNHL is the most common form of sensory deprivation and often goes unrecognized by patients, leading to both auditory and non-auditory symptoms. The researchers used data-driven classifier modeling with a combination of neural static and dynamic imaging features to effectively classify SNHL individuals and healthy controls. The study found that machine learning-based models could provide useful biomarkers for the classification and diagnosis of SNHL. [Extracted from the article]

A patent has been issued to Hologic Inc. for a system that uses curved compression elements to improve breast imaging. The system includes a radiation source, compression paddles with non-planar surfaces, a detector, and a memory and processor. The method involves filling the paddles with liquid, passing radiation through them and a radiolucent surface, detecting the radiation, generating a correction image, and creating a correction map. The correction map is used to correct the image data and generate an image of the breast. This technology aims to address patient discomfort during breast imaging and ensure accurate and comprehensive imaging of breast tissue. [Extracted from the article]

A new study from Open University explores the use of polynomial regression analysis to better understand anxiety disorders. The study examines the relationship between implicit and explicit measures of self-esteem and their impact on depressive symptoms, anxiety, and aggression. The results suggest that explicit self-esteem plays a significant role in depression and anxiety, while the discrepancy between implicit and explicit self-esteem does not. The study recommends further research using polynomial regression to better understand the roles of explicit and implicit self-esteem in psychological outcomes. [Extracted from the article]

Topcon Corporation has been issued a patent for an ophthalmologic information processing apparatus. The apparatus includes a generator that generates a phase difference profile, an extractor that extracts a phase drift from the profile, and a phase correction unit that corrects the phase of complex OCT data. The patent also covers an ophthalmologic apparatus and an ophthalmologic information processing method. The invention aims to improve the accuracy of measurements in optical coherence tomography (OCT) systems. [Extracted from the article]

A patent application has been submitted for a method of extracting respiratory information from a bio-impedance signal. The method involves using a Savitzky-Golay low-pass filter with a 3rd degree polynomial fit to accurately and efficiently extract respiratory information from the signal. The filter is able to smooth the data without distorting it, making it useful for extracting qualitative information from noisy signals. The method can be applied to bio-impedance signals measured using electrodes placed on the torso of a subject. Additionally, the method can differentiate a respiratory effort signal to extract respiratory flow information and assess the presence of upper airway obstructions. The patent application provides detailed embodiments and claims for the method. [Extracted from the article]

A recent study conducted at the University of Guelph in Canada explored the use of digital image analysis to evaluate the sensory attributes of protein-enriched whole-wheat bread. The researchers aimed to determine if color images could provide an accurate method for assessing sensory qualities during the product development stages. The study found that polynomial regression models using features extracted from the images yielded higher R2 values than linear regression models. While image-based sensory assessment cannot replace human sensory evaluation, it can provide valuable supplementary information for decision-making processes. [Extracted from the article]

A study conducted at Tongling University in China has explored the predictive strength of interactions among physical examination indicators regarding the risk of type 2 diabetes. The study utilized the LightGBM polynomial kernel model and the PolynomialFeatures method to derive high-order interaction data from physical examination indicators. The LightGBM model, built on high-order interaction factors, showed significant potential for predicting the risk of diabetes in populations undergoing physical examinations. The study's findings suggest that this model could be useful for early diabetes screening and prognosis. [Extracted from the article]

A study conducted by the University of Ulsan College of Medicine in Seoul, South Korea, aimed to develop a Z score calculation model for coronary artery diameter in children and adolescents. The study collected data from 16 institutions across South Korea and analyzed the relationship between coronary artery diameter and demographic parameters. Separate calculation models were developed for each sex, and the final model used a polynomial formula with an exponential relationship between coronary artery diameter and body surface area. The researchers believe that this new model will help establish a consensus-based Z score model for clinical practice. [Extracted from the article]