Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Interactions of Small-Sized Neurotransmitters and of Entangled Ionotropic Receptors Accentuate the Impact of Entanglement to Consciousness

This contribution concentrates on the evaluation of quantum processes in the brain that essentially contribute to the protection and activation of entanglement and their impact to consciousness. The corresponding calculations occur in the Fock space that represents discrete quantum fields, where the corresponding computations occur in the following succession. First, three possible weak interactions of emitted, small-sized neurotransmitters are described. These interdependencies are the attraction by electric dipole-dipole interaction, the attraction by the Morse potential and the repulsion characterized by s-wave scattering. Second, this article focus on ionotropic receptors that are embedded in a dense non-rigid grid. Anharmonic oscillators approximate these molecules, where their interactions cause grid vibrations. The determination of the expectation values of the total energy of the oscillating receptors, situated in two entangled ground states, demonstrate the existence of gap functions that shield the entanglement. This protected entanglement represents a bridge to the materialistic consciousness, and as well it refutes the dominant criticism against the quantum processes in the brain that decoherence destroys in picoseconds the entanglement (quantum coherence). The entangled entropy of the protected entangled states is not zero; what is a clear sign of entanglement. Third, consciousness activates the protected entanglement that reveals distinct positive effects, concerning the acquisition of information. Thus, the working space (associative cortices) that operates in a conscious state instantly gets compressed information on the current particular states of the cortical and subcortical components. Thereby, the emergence of consciousness is a synergetic process, which is created by the mutual interdependencies (causal circularity) of the components of the working space (synergetic agents) and the subcortical areas (synergetic “slaves”).
Multiscale Modeling of Cavitation using a Level Set Method with Cavity Detection

An Eulerian-Lagrangian multi-scale two-phase flow model is presented, which includes a micro-scale model for tracking the microbubbles, a macro-scale model for describing the overall flow field and large cavity dynamics, and a transition model to bridge the micro and macro scales. The method also includes a cavity detection scheme which marks each cavity with an ID index so that each can be tracked and treated individually. This approach was implemented in our CFD code 3DYNAFS and used to simulate, for illustration, multiple bubble dynamics simultaneously released at different locations in a Rankine line vortex. The method captures their growth, entrainment into the vortex core, elongation along the vortex centerline, followed by potential splitting and breakup.
Pattern-Multiplicative Average of Nonnegative Matrices Revisited: Eigenvalue Approximation Is the Best of Versatile Optimization Tools

Given several nonnegative matrices with a single pattern of allocation among their zero/nonzero elements, the average matrix should have the same pattern, too. This is the first tenet of the pattern-multiplicative average (PMA) concept, while the second one suggests the multiplicative (or geometric) nature of averaging. The original concept of PMA was motivated by the practice of matrix population models as a tool to assess the population viability from long-term monitoring data. The task has reduced to searching for an approximate solution to an overdetermined system of polynomial equations for unknown elements of the average matrix (G), and hence to a nonlinear constrained minimization problem for the matrix norm. Former practical solutions faced certain technical problems, which required sophisticated algorithms but returned acceptable estimates. Now, we formulate (for the first time in ecological modeling and nonnegative matrix theory) the PMA problem as an eigenvalue approximation one and reduce it to a standard problem of linear programing (LP). The basic equation of averaging also determines the exact value of λ1(G), the dominant eigenvalue of matrix G, and the corresponding eigenvector. These are bound by the well-known linear equations, which enable an LP formulation of the former nonlinear problem. The LP approach is realized for 13 fixed-pattern matrices gained in a case study of Androsace albana, an alpine short-lived perennial, monitored on permanent plots over 14 years. A standard software routine reveals the unique exact solution, rather than an approximate one, to the PMA problem, which turns the LP approach into ‘’the best of versatile optimization tools”. The exact solution turns out to be peculiar in reaching zero bounds for certain nonnegative entries of G, which deserves modified problem formulation separating the lower bounds from zero.
Recent Advances in Electrochemical Biosensors for the Detection of Foodborne Pathogens: Current Perspective and Challenges

Foodborne pathogens cause many diseases and significantly impact human health and the economy. Foodborne pathogens mainly include Salmonella spp. , Escherichia coli, Staphylococcus aureus, Shigella spp. , Campylobacter spp. and Listeria monocytogenes, which are present in agricultural products, dairy products, animal-derived foods and the environment. Various pathogens in many different types of food and water can cause potentially life-threatening diseases and develop resistance to various types of antibiotics. The harm of foodborne pathogens is increasing, necessitating effective and efficient methods for early monitoring and detection. Traditional methods, such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA) and culture plate, are time-consuming, labour-intensive and expensive and cannot satisfy the demands of rapid food testing. Therefore, new fast detection methods are urgently needed. Electrochemical biosensors provide consumer-friendly methods to quickly detect foodborne pathogens in food and the environment and achieve extensive accuracy and reproducible results. In this paper, by focusing on various mechanisms of electrochemical transducers, we present a comprehensive overview of electrochemical biosensors for the detection of foodborne pathogens. Furthermore, the review introduces the hazards of foodborne pathogens, risk analysis methods and measures of control. Finally, the review also emphasizes the recent research progress and solutions regarding the use of electrochemical biosensors to detect foodborne pathogens in food and the environment, evaluates limitations and challenges experienced during the development of biosensors to detect foodborne pathogens and discusses future possibilities.
Consensus Gene Network Analysis Identifies the Key Similarities and Differences in Endothelial and Epithelial Cell Dynamics after Candida albicans Infection

Endothelial and epithelial cells are morphologically different and play a critical role in host defense during Candida albicans infection. Both cells respond to C. albicans infection by activating various signaling pathways and gene expression patterns. Their interactions with these pathogens can have beneficial and detrimental effects, and a better understanding of these interactions can help guide the development of new therapies for C. albicans infection. To identify the differences and similarities between human endothelial and oral epithelial cell transcriptomics during C. albicans infection, we performed consensus WGCNA on 32 RNA-seq samples by relating the consensus modules to endothelial-specific modules and analyzing the genes connected. This analysis resulted in the identification of 14 distinct modules. We demonstrated that the magenta module correlates significantly with C. albicans infection in each dataset. In addition, we found that the blue and cyan modules in the two datasets had opposite correlation coefficients with a C. albicans infection. However, the correlation coefficients and p-values between the two datasets were slightly different. Functional analyses of the hub of genes from endothelial cells elucidated the enrichment in TNF, AGE-RAGE, MAPK, and NF-κB signaling. On the other hand, glycolysis, pyruvate metabolism, amino acid, fructose, mannose, and vitamin B6 metabolism were enriched in epithelial cells. However, mitophagy, necroptosis, apoptotic processes, and hypoxia were enriched in both endothelial and epithelial cells. Protein–protein interaction analysis using STRING and CytoHubba revealed STAT3, SNRPE, BIRC2, and NFKB2 as endothelial hub genes, while RRS1, SURF6, HK2, and LDHA genes were identified in epithelial cells. Understanding these similarities and differences may provide new insights into the pathogenesis of C. albicans infections and the development of new therapeutic targets and interventional strategies.
Acousto-optic spectrometer for speckle imaging

We report on the acousto-optic spectral imaging system designed for speckle imaging observations and interferometry. The setup is based on a non-collinear paratellurite acousto-optic tunable filter (AOTF). Breadboard prototype of the system with an in-house fabricated AOTF has been designed and commissioned. The prototype spectral range covers the band from 3600 to 5800 Å. Arbitrary spectral transmission function synthesis of the AOTF was applied. The AOTF spectral transmission bandwidth is programmable in the range from 44 to 875 cm−1 (12.5–250Å at the wavelength of 5050Å). The AOTF is synchronized with the CCD readout and can be used as a global electronic shutter with on/off switching time of 12 μs. The exposure is adjusted as any integer multiple of 4.5 μs.
Conditional Phase-Type Distribution Under Doubly Stochastic Jump Markov Processes with Observed Covariates

Empirical evidences on corporate bond found e.g. in Frydman (2005) and Frydman and Schuermann (2008) suggest the facts that the attribution of constant intensity in the credit rating dynamics, represented by finite-state jump Markov processes, is not borne out by actual data. On the other hand, it is known facts that continuous-time finite-state jump Markov processes can be represented by means of Poisson process and embedded discrete-time Markov chains, see, e.g., Ch. 7 of Pardoux (2008), Sec. 5.10 of Resnick (2002) and and Jakubowski and Nieweglowski (2010, 2008). Motivated by the above facts, we consider representation of jump Markov processes in terms of doubly stochastic Poisson process (see e.g. Cox (1955), Kingman (1964), Serfozo (1972), and Bremaud (1981)) whose intensity is driven by observed explanatory covariates. However, we impose weaker conditions than that of specified in Kingman (1964) and Jakubowski and Nieweglowski (2010, 2008) in the construction of such jump Markov process in which the conditional rate of jump arrival in the conditional Poisson process follows the landmarking approach. This approach has been recently introduced in event history analysis by van Houwelingen (2007) and van Houwelingen and Putter (2012). By this approach, we do not require the whole trajectory (dynamics) of the observed covariates. Analogous to Cox (1955) and Jakubowski and Nieweglowski (2010), we call such representation as doubly stochastic jump Markov processes. We derive lifetime distributions until its absorption of doubly stochastic finite state absorbing jump Markov process, and propose generalization of the phase-type distribution introduced in Neuts (1981, 1975). Also, we derive conditional forward intensity of future occurrences of the jump Markov process. The new distribution and intensity are given in closed form and have ability to capture explanatory covariates and heterogeneity. The results can be seen as an alternative structural approach to the reduced-form model of credit default discussed in Duffie et al. (2009, 2007), and Duan et al. (2012). Some numerical examples are discussed to motivate the main results.
An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal Ultrasound

A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model’s ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals.
Factors Affecting Firm Value Through Profitability As A Mediation Variable

This study examines the impact of Capital Structure, Firm Size, and Increase in sales on Firm wealth in manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2020, with Ability to generate income as an intervening variable. A total of 72 companies were included as a sample in this investigation. The result of this research are capital structure affect firm wealth positively significant, firm size affect firm wealth negatively insignificant, increase in sales affect firm wealth negatively significant, capital structure affect ability to generate effect on ability to generate income, increase in sales affect ability to generate income positively significant, ability to generate income affect firm wealth positively significant, ability to generate income is unable to mediate effect of capital structure and increase in sales on firm wealth, and ability to generate income is able to mediate effect of firm size on firm wealth.
An Intensive Technical Analysis of Home Automation using IOT with Node MCU

Home automation has become an ever-increasing number of well-known lately. It targets assisting individuals with dealing with home appliances openly and constructing an autonomous climate in the home. The Internet of Things (IoT) with remote control is an innovation that computerized basic home administrations and choices overall over the Web utilizing PCs or cell phones. Associating home apparatus electrical gadgets with the web or distributed storage can bring about home automation. The Internet of Things (IoT) is an augmentation of the webserver to give correspondence, association, and inter' networking between different gadgets or physical objects also known as "Things''. Node-MCU is the most important component of this system and it can proceed as a miniature web server it goes probably as a mark of cooperation for the extensive variety of hardware modules. To control lights, fans, and other home machines which are associated with the handoff framework, the framework offers exchanging functionalities. This task expects to carry out a voice control home computerization framework utilizing a framework that is executed utilizing common domestic devices.
Drug Delivery Strategies and Nanozyme Technologies to Overcome Limitations for Targeting Oxidative Stress in Osteoarthritis

Oxidative stress is an important, but elusive, therapeutic target for osteoarthritis (OA). Antioxidant strategies that target oxidative stress through the elimination of reactive oxygen species (ROS) have been widely evaluated for OA but are limited by the physiological characteristics of the joint. Current hallmarks in antioxidant treatment strategies include poor bioavailability, poor stability, and poor retention in the joint. For example, oral intake of exogenous antioxidants has limited access to the joint space, and intra-articular injections require frequent dosing to provide therapeutic effects. Advancements in ROS-scavenging nanomaterials, also known as nanozymes, leverage bioactive material properties to improve delivery and retention. Material properties of nanozymes can be tuned to overcome physiological barriers in the knee. However, the clinical application of these nanozymes is still limited, and studies to understand their utility in treating OA are still in their infancy. The objective of this review is to evaluate current antioxidant treatment strategies and the development of nanozymes as a potential alternative to conventional small molecules and enzymes.
A Review of Myoelectric Control for Prosthetic Hand Manipulation

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.
Identification of Fraudulent Healthcare Claims Using Fuzzy Bipartite Knowledge Graphs

Health insurance is one of the most important services that people depend on for paying the bills related to hospital and clinical services. This dependency on health insurance lures some healthcare service providers to commit insurance frauds which has become a grave concern. The majority of healthcare fraud is committed by a very small number of untrustworthy providers. Yet, such fraudulent actions damage the reputation of the health service providers and cost the system billions of dollars. In this article, we specifically focus on the fraudulent claim identification problem and develop different solution schemes to identify the fraudulent cases in healthcare claims with minimal data. We present a solution to the fraudulent claim identification problem that translates diagnoses and procedure code's relations into Bipartite Graphs with Fuzzy Edges (BiGFuzzE). We also investigate the extension of BiGFuzzE using vector representations of clinical codes instead of non-negative matrix factorization (NMF). Our experimental evaluations demonstrate significant outcomes.
Load balancing clustering and routing for IoT‐enabled wireless sensor networks

Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy‐efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c‐means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO‐based clustering (GWO‐C) and Cat Swarm Optimization based Energy‐Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.
Pairwise interaction extended point-particle model for a random array of monodisperse spheres

This study introduces a new point-particle force model that attempts to account for the hydrodynamic influence of the neighbouring particles in an Eulerian–Lagrangian simulation. In previous point-particle models the force on a particle depends only on Reynolds number and mean volume fraction. Thus, as long as the mean local volume fraction is the same, the force on different particles will be estimated to be the same, even though the precise arrangement of neighbours can be vastly different. From direct numerical simulation (DNS) it has been observed that in a random arrangement of spheres that were distributed with uniform probability, the particle-to-particle variation in force can be as large as the mean drag. Since the Reynolds number and mean volume fraction of all the particles within the array are the same, the standard models fail to account for the significant particle-to-particle force variation within the random array. Here, we develop a model which can compute the drag and lateral forces on each particle by accounting for the precise location of a few surrounding neighbours. A pairwise interaction is assumed where the perturbation flow induced by each neighbour is considered separately, then the effects of all neighbours are linearly superposed to obtain the total perturbation. Faxén correction is used to quantify the force perturbation due to the presence of the neighbours. The single neighbour perturbations are mapped in the vicinity of a reference sphere and stored as libraries. We test the pairwise interaction extended point-particle (PIEP) model for random arrays at two different volume fractions of $\unicode[STIX]{x1D719}=0.1$ and 0.21 and Reynolds numbers in the range $16.5\leqslant Re\leqslant 170$ . The PIEP model predictions are compared against drag and lift forces obtained from the fully resolved DNS simulations performed using the immersed boundary method. Although not perfect, we observe the PIEP model prediction to correlate much better with the DNS results than the classical mean drag model prediction.
Comment modéliser les préférences au moyen de fonctions d'utilité auditives

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