Standard Model is well-performing under the three-generation, but in the wake of more physics, fourth-generation leptons and quarks are introduced. In this paper, We will be reviewing various constraints surrounding quarks and leptons of 4th Generation and tried to show the importance of quarks to understand the baryogenesis. We talked about, reviewed existing parameters and set the updated lower limits of t’ and b’ by analysis. And predicted an updated version of lower mass bounds quarks and leptons. It is also reviewed that fourth generation leptons are primarily not possible because of disintegration of Higgs Boson and an interpretation has been setup. And in final, we talked about the Unified Standard Model.

CX3CL1, also named fractalkine or neurotactin, the only known member of the CX3C chemokine family that can chemoattract several immune cells. CX3CL1 exists in both membrane-anchored and soluble forms, each mediating distinct biological activities. CX3CL1 signals are transmitted through its unique receptor, CX3CR1, primarily expressed in microglia of central nervous system (CNS). In the CNS, CX3CL1 acts as a regulator of microglia activation in response to brain disorders or inflammation. Recently, there has been a growing interest in the roles of CX3CL1 in regulating cell adhesion, chemotaxis, and host immune response in viral infection. Here, we provide a comprehensive review of the changes and function of CX3CL1 in various viral infections such as human immunodeficiency virus (HIV), SARS-CoV-2, influenza virus and cytomegalovirus (CMV) infection, to highlight the emerging roles of CX3CL1 in viral infection and the associated diseases.

Coal gasification with carbon dioxide is a process for generating clean gaseous fuels and relieving greenhouse effect. Zhundong coal has high alkali and alkali earth metals (AAEMs) content, medium volatile and low ash in nature. Isothermal CO₂ gasification of char derived from Zhundong coal (R-char) and char from acid washing R-char (AR-char) are performed in thermo-gravimetric analyzer (TGA). The effect of AAEMs is investigated on the gasification behavior in the range of temperatures 1073 K to 1273 K. The carbon conversion increases rapidly with increasing reaction temperature and CO₂ concentration. R-char has high gasification rate and carbon conversion compared with AR-char. The accuracy of the free-model approach for calculating activation energy at different conversions is validated by compared with different kinetic models (volume reaction model, distributed activation energy model). Moreover, R-char gasification with CO₂ shows a compensation effect as the Arrhenius parameters (EA and k0) increase or decrease simultaneously.

In this note, the toy-model of Fourth Generation quarks coupling to Standard Model particles through unknown Lagrangian has been figured. Eventually producing a 103 TeV vector gauge boson, which is a special gauge boson that appears in unification models and here as a mediator for WeakSM4 interaction. Another anomaly of different gauge boson has been reported.

Three kinds of inorganic alkali are introduced into tetraethylenepentamine (TPEA) and polyethyleneimine (PEI) modified MCM-41 as the CO2 adsorbents. FT-IR and TGA are used to characterize the surface groups and the thermal stability of adsorbents. Chemical titration method is used to measure the alkali amounts of adsorbents. Thermo-gravimetric analysis with 10%CO2/N2 as the simulated flue gas is used to test the CO2 adsorption performance of adsorbents. The results show that all three kinds of inorganic alkali containing adsorbents exhibit promoted CO2 adsorption capability than traditional TPEA and PEI modified samples. Ca(OH)2 and PEI modified samples exhibit the highest adsorption capacity and stable regeneration property. The introduction of inorganic alkali changes the chemical adsorption mechanism between CO2 and increases the effective alkali amounts due to its hydroxyl groups. The CO2 adsorption capacities have a linear dependence relation with the alkali amounts of adsorbents, indicating that alkali amount is a critical factor for novel adsorbents exploring.

Quantum loops are processes that constitute quantum objects. In the causal model of quantum loops and quantum objects presented here, the nonlinear processes involve the elementary units of spacetime and the associated elementary units of quantum fields. As such, quantum loop processes are the sources of gravitational fields (i.e., spacetime curvature) and of the quantum objects wave function. The model may be viewed as a derivative of loop quantum gravity, spin networks and causal dynamical triangulation, although significant deviations to these theories exist. The causal model of quantum loops is based on a causal model of spacetime dynamics where space(-time) consists of interconnected space points, each of which is connected to a small number of neighboring space points. The curvature of spacetime is expressed by the density of these space points and by the arrangement of the connections between them. The quantum loop emerges in a nonlinear collective behavioral process from a collection of space points that carry energy and quantum field attributes.

Bacillus Calmette-Guérin (BCG) immunotherapy has been a cornerstone treatment for non-muscle invasive bladder cancer for decades and still faces challenges, such as severe immune adverse reactions, which reduce its use as a first-line treatment. This review examines BCG therapy’s history, mechanisms, and current status, highlighting how nanotechnology and bioengineering are revitalizing its application. We discuss novel nanocarrier systems aimed at enhancing BCG's efficacy while mitigating specific side effects. These approaches promise improved tumor targeting, better drug loading, and enhanced stimulation of anti-tumor immune responses. Key strategies involve using functional BCG cell wall components and co-delivering BCG with chemotherapeutics to achieve synergistic effects. While most studies remain experimental, this research direction offers hope for overcoming BCG's limitations and advancing bladder cancer immunotherapy. Further elucidation of BCG's mechanisms and rigorous safety evaluations of new delivery systems will be crucial for translating these innovations into clinical practice.

Background: Clinical trails have revealed that patients with depression generally accompanied with learning and memory impairment, which critically impact on individual’s health and development. Danggui-Shaoyao-San (DSS), a famous Chinese complex prescription, significantly overcame depression and relieved cognitive disorder based on previous research and publication. However, its effectsand potential mechanism against chronic restraint stress (CRS) remained unknown. Methods: CRS animal model was established and mice were divided to six groups while they were oral administrated with Danggui-Shaoyao-San at doses of 1.25, 2.5 and 5 g/kg for 14 days. Emotional and cognitive performances were detected by behavior tests, meanwhile neural plasticity and its molecular mechanism were examined by Dil staining, western blot and immunofluorescence. Results: DSS treatment dose-dependently improved locomotion ability in open filed test, overcame depression behavior in forced swimming test and elevated plus maze test, enhanced learning and memory ability in Morris water maze test. CRS decreased number of total spines and mushroom spines, while DSS treatment dose-dependently restored these by Dil staining. Expression of BDNF and GluR1 were significantly down-regulated in CRS group, which were significantly normalized by DSS. Conclusions: DSS treatment dose-dependently reversed CRS-induced cognitive impairments by inducing structural remodeling of neurons.

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations and the MG-PLPRs are obtained by statistical calculation to represent sub-group’s preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established to achieving consensus reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city "shared garden" site selection with the participation of citizens.

Activating the cytosolic innate immune sensor cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway via natural and synthetic agonists holds great potential for priming robust antitumor immunity. However, the effectiveness of STING agonists is often impeded by the insufficient tumor accumulation, poor cellular entry, and rapid clearance, thus leading to difficulty of their clinic translation. Herein, we apply the human serum albumin as the single-molecule nanoreactor to encapsulate SR-717 (SH-NPs) for enhanced targeting delivery efficiency and biological potency. SH-NPs with rational size distribution exhibited superior serum stability, considerable cellular uptake and abundant tumor accumulation, thus increasing the STING signaling including phosphorylation of STING-associated proteins (TBK1, IRF3), and cytokines secretion inside tumor region. Subsequently, SH-NPs remarkably reshape the immunosuppressive tumor microenvironment into an immunogenic one that boosts antitumor T-cell immunity and improves the therapeutic efficacy of checkpoint blockade against murine tumors. We also validate SH-NPs in freshly isolated human renal tumor tissues to highlight their clinic translation capability. These findings pave the way towards targeted STING agonist delivery for reinvigorated immunotherapy of intractable cancers.

If we assume that: a. The four fundamental forces of nature are independent waves without rest masses, and their speeds are constant in a vacuum, just like light. b. Light or electromagnetic waves and gravity are comparable in structures. Weak and strong interactions are similar in structures. c. Light and weak interaction have the same speed c_L with spin number +1 or –1. d. Gravity and strong interaction have the same speed c_G without spin. e. The primary particles, namely electrons, electron neutrinos, and dark neutrinos in this paper, are made by the above four waves. We can find and describe some fundamental characteristics of the primary particles (e.g., their sizes, energies, and interactions) and introduce new attractive results from them (e.g., the source of the Pauli exclusion principle, the solution to the Einstein-Podolsky-Rosen paradox, and c_G slightly faster than c_L).

Hybrid scaffolds based on bioactive glass (BAG) particles (<38µm), covalently linked to the gelatin (G*), using 3-glycidoxypropyltrimethoxysilane (GPTMS), have been studied for bone bioengineering. In this study, two glass compositions (13-93 and 13-93B20 [where 20% of the SiO2 was replaced with B2O3]) were introduced in the gelatin matrix. The Cfactor (Gelatin/GPTMS molar ratio) was kept constant at 500. The hybrids obtained were found to be stable at 37°C, in solution; condition at which pure gelatin is liquid. All hybrids were characterized by in vitro dissolution in TRIS solution (for up to 4 weeks) and Simulated Body Fluid (SBF) (for up to 2 weeks). Samples processed with 13-93B20 exhibit a faster initial dissolution and significantly faster precipitation of a hydroxyapatite (HA) layer. The faster ion release and HA precipitation recorded from the G*/13-93B20 samples, is attributable to the higher reactivity of borosilicate compared to the silicate glass. MC3T3-E1 cells behavior, in direct contact with the hybrids, was investigated, showing that the cells were able to proliferate and spread on the developed biomaterials. Tailoring the glass composition allows to better control the material’s dissolution, biodegradability, and bioactivity. Bioactive (especially with 13-93B20 BAG), and biocompatible, the hybrids are promising for bone application.

: Schizophrenia and schizoaffective disorder are generally treated with second-generation antipsychotic drugs. These drugs are mostly D2 and 5-HT2A antagonists. They improve positive schizophrenia symptoms sufficiently well; however, they ameliorate negative schizophrenia symptoms and cognitive functions to a small extent. We review novel antipsychotic drugs exerting a partial agonism at dopaminergic and serotonergic receptors such as cariprazine, brexipiprazole and lumateperone. Besides, the mechanisms of actions of non-anti-dopaminergic antipsychotic drugs are pointed out. Updated neural networks are used to explain the mechanism of action of M4 and M1 receptor agonists, for example xanomeline combined with trospium or emraclidine, and trace-amine-associated receptor 1 agonists (TAAR1), for example ulataront. Phase 3 clinical trials of new third generation antipsychotic drugs are presented. Novel antipsychotic drugs with a partial agonism at D2 and D3 receptors improve positive and negative schizophrenia symptoms as well as cognitive symptoms better than second generation antipsychotic drugs. Besides, they are well tolerated. M4 and M1 receptor agonists, i.e. xanomeline combined with trospium or emraclidine, and TAAR1 agonists, i.e. ulataront, have promising results in clinical trials; they well improve negative schizophrenia symptoms and cognitive functions. Phase 3 clinical trials offer promising results for anti-dopaminergic and non-anti-dopaminergic novel antipsychotic drugs. These new non-anti-dopaminergic antipsychotic drugs better emend negative schizophrenia symptoms, and they better improve cognitive functions than second-generation antipsychotic drugs. Promising new antipsychotic drugs are cariprazine, brexipiprazole, lumateperone, ulataront, and xanomeline combined with trospium. Although phase 3 clinical studies are not yet completed, they showed a therapeutic effect superior to those achieved by second-generation antipsychotic drugs. They are tolerated very well, and they better treat negative schizophrenia symptoms and improve cognitive functions.

Strong yet ductile alloys have long been desired for industrial applications to enhance structural reliability. This work produced two (CoCrNi)93.5Al3Ti3C0.5 medium-entropy alloys with exceptional strength-ductility combinations, via short/medium (3 min/30 min)-time annealing after hot-rolling. Three types of intergranular precipitates including MC, M23C6 carbides, and L12 phase were detected in both the samples. Noticeably, the high density of intragranular L12 precipitates were only founded in the medium-time annealed sample but not in another one. Upon inspection of deformed substructure, it was revealed that the plane slip is the dominant deformation mechanism of both the alloys. This is related to the lower stacking fault energy, higher lattice friction induced by C solute, and slip plane softening caused by intragranular dense L12 precipitates. Additionally, we noted that the stacking fault and twinning act as the mediated mechanisms in deformation of the short-time annealed alloy, while only the former in the medium-time annealed one. It is attributed to the higher energy stacking faults and the increased critical twinning stress caused by intragranular dense L12 precipitates that inhibited the twinning tendency. Our present findings provide not only a guidance for optimizing the mechanical properties of high/medium-entropy alloys, but also a fundamental understanding of deformation mechanisms.

It is shown that quantum entanglement is the only force able to maintain the fourth state of matter, possessing fixed shape at an arbitrary volume. Accordingly, a new relativistic Schrödinger equation is derived and transformed further to the relativistic Bohmian mechanics via the Madelung transformation. Three dissipative models are proposed as extensions of the quantum relativistic Hamilton-Jacobi equation. The corresponding dispersion relations are obtained.

With the development of the energy sector based on renewable primary sources, structural changes are emerging for the entire national energy system. Initially, it was estimated that energy generation based on fossil fuels would decrease until its disappearance. However, the evolution of CO2 capture capacity leads to a possible coexistence for a certain period with the renewable energy sector. The paper develops this concept of coexistence of the two systems, with the positioning of green hydrogen not only within the renewable energy sector but also as a transformation vector for carbon dioxide captured in the form of synthetic fuels such as CH4 and CH3OH. The paper also analyzes the economic aspects related to price differences for energy produced in the two sectors, as well as their interconnection. The technical aspect, as well as the economic one, for storage through various other solutions besides hydrogen, has been highlighted. The development of the renewable energy sector and its demarcation from the fossil fuel energy sector, even with the transcendent vector represented by green hydrogen, leads to the deepening of dispersion aspects between the electricity sector and the thermal energy sector, a less mentioned aspect in current works but of great importance.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

Acute and chronic respiratory diseases impose a significant economic and health burden. Distal small airway assessment requires invasive procedures such as a bronchoscopy and bronchoalveolar lavage. The Particles in Exhaled Air (PExA) device provides a non-invasive means of assessing small airways; it captures distal airway particles sized around 0.5-7m and contains particles of respiratory tract lining fluid (RTLF) that originate during airway closure and opening. The PExA device can measure particle count and mass according to particle size. Subsequent capture of particles in a thin membrane enables application of various analytical methods to assess a broad range of biomolecules including phospholipids, proteins, inflammatory profile, and other metabolites to characterise inflammation and diseases processes such as asthma, chronic obstructive pulmonary disease, acute respiratory distress syndrome, lung cancer, and more recently acute infections such as COVID-19. PExA analysis provides a promising alternative to traditional investigative methods in the assessment, diagnosis, and management of respiratory conditions. By evaluating specific biomarkers in the RTLF associated with airway inflammation, PExA may offer a more accurate and comprehensive understanding of the molecular-level changes in the small airways of patients with acute and chronic lung diseases.

Cancer, characterized by abnormal cell proliferation driven by genetic mutations, remains a significant health challenge. Tailoring effective therapies depends on factors like tumor location, grade, and stage, alongside patient condition. Leveraging Nano-carrier encapsulated drugs offers a promising avenue, as they accumulate preferentially at tumor sites, enhancing therapeutic efficacy while mitigating side effects. Polymeric Nano-carriers, with their customizable properties, further optimize drug delivery efficiency and specificity. Incorporating active targeting ligands such as antibodies, carbohydrates, peptides, folates, and aptamers-onto polymeric nanoparticle surfaces through techniques like functionalization, adsorption, and conjugation plays a pivotal role in directing therapeutic agents to tumor cells. Consequently, polymeric nanoparticles emerge as the carrier of choice for delivering drugs to various tumor types, including breast, colon, lung, prostate, liver, and spleen tumors.

In previous papers we have derived equations of motion with radiation terms and proved the existence-uniqueness of periodic solution of the 3-body problem of classical electrodynamics. The number of equations of motion – 12 is more than the number of unknown functions – 9. It turns out that 9 equations are independent and the rest 3 ones are consequences of them. These three equations have a solution and they give the expressions for the energy of the moving particles. The main goal of the present paper is to estimate the energy of rounding about the nuclei particles and compare it with the values given by quantum mechanics.

The global issues of overweight and sedentary lifestyles demand comprehensive responses from health systems, yet health education still needs to be more cohesive and less sporadic. This study focuses on primary school children as a specific target for instilling healthy habits, employing ac-tive learning strategies to address healthy diet and physical activity. Through Problem-Based Learning projects, the study engaged fourth-grade primary students in acquiring healthy habits. The study was carried out over four consecutive years at a school with two lines, allowing the employment of an opportunity sample and evaluating intervention and control groups by com-paring their corresponding scores. Control groups integrated a total of 54 students, while the in-tervention group included a total of 159 students. A pre-test and post-test questionnaire were used for data collection. Evaluation differential scores revealed statistically significant higher scores in the intervention groups: 3.62 points in control groups and 6,94 in intervention groups, particu-larly in attitudinal competences. These strategies not only facilitated the development of crucial skills such as information search, synthesis, representation, analysis, decision-making, teamwork, and intrapersonal awareness but also significantly broadened the knowledge acquired in aspects related to healthy habits. This approach proved effective in helping children comprehend the im-portance of healthy choices and encouraged integrating such habits into their daily lives.

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Refractory materials are used worldwide in process equipment. However, gaseous and liquid process products penetrate the surface layer, and deep into the volume of refractories destructing rather expensive constructions complicated to repair. To address this challenge, there is a need to develop protective coatings for refractory that can limit the penetration of working media and extend their operational lifespan. In this work, the application of gas-dynamic cold spraying (CGDS) to produce a coating on the refractory using fine titanium dioxide (TiO2) particles is explored. These particles are accelerated within a nitrogen flow, passing through a Laval nozzle, and then sprayed onto a fireclay surface. The mechanisms of particle deposition and layer formation on porous surfaces through experiments and numerical simulations were investigated. The geometry of a typical refractory pore was determined, which was then incorporated into Computational Fluid Dynamics (CFD) simulations to model the cold spraying process of porous substrates. As a result, the influence of the particle size on its velocity and angle of penetration into pores was established. Experimental findings demonstrate the effective closure of pores and the formation of a particle layer on the refractory surface. Furthermore, the nanoindentation tests for the refractory samples showcase capabilities for checking coating thickness for porous materials.

Let f : V → V be a Cohomological Expanding Mapping¹ of a smooth complex compact homogeneous manifold with $ dim_{\mathbb{C}}(\Vc)=k \ge 1$ and Kodaira Dimension $\leq 0$. We study the dynamics of such mapping from a probabilistic point of view, that is, we describe the asymptotic behavior of the orbit $ O_{h} (x) = \{h^{n} (x), n \in \mathbb{N} \quad \mbox{or}\quad \mathbb{Z}\}$ of a generic point. Using pluripotential methods, we have constructed in our previous paper \cite{Armand4} a natural invariant canonical probability measure of maximal Cohomological Entropy $ \nu_{h} $ such that ${\chi_{2l}^{-m}} (h^m)^\ast \Omega \to \nu_h \qquad \mbox{as} \quad m\to\infty$ for each smooth probability measure $\Omega $ in $\Vc$ . We have also studied the main stochastic properties of $ \nu_{h}$ and have shown that $ \nu_{h}$ is a smooth equilibrium measure , ergodic, mixing, K-mixing, exponential-mixing. In this paper we are interested on equidistribution problems and we show in particular that $ \nu_{h}$ reflects a property of equidistribution of periodic points by setting out the Third and Fourth Main Results in our study. Finally we conjecture that $$\nu_h:=T_l^+ \wedge T_{k-l}^-,$$ $$\dim_\HH(\nu_h)= \Psi h_{\chi}(h) , $$ $$\dim_\HH( \mbox{Supp} T_l^+) \geq 2(k-l) + \frac{\log \chi_{2l}}{\psi_l},$$ $$|\langle \nu_m^x-\nu_h,\zeta\rangle|\leq M \Big[1+\log^+{1\over D(x,\Tc)}\Big]^{\beta/2}\|\zeta\|_{\Cc^\beta} \gamma^{-\beta m/2}$$ and $$|\langle \nu_m^x-\nu_h,\zeta\rangle|\leq M \Big[1 +\log^+{1\over D(x,E_\gamma)}\Big]^{\beta/2}\|\zeta\|_{\Cc^\beta} \gamma^{-\beta m/2}.$$

Human serum albumin (HSA) corona formation at polymer microparticles of a spheroidal shape was studied using dynamic light scattering and laser Doppler velocimetry (LDV). Physicochemical characteristics of the albumin comprising, the zeta potential and the isoelectric point were determined as a function of pH for various ionic strengths. Analogous characteristics of the polymer particles were carried out. The adsorption of albumin on the particles was monitored by electrophoretic mobility measurements (LDV). The stability of the HSA functionalized particle suspensions under various pHs and their electrokinetic properties were determined. The deposition kinetics of the particles on mica, silica and gold sensors were determined in situ by optical microscopy, AFM and by the quartz microbalance (QCM) under diffusion and flow conditions. The obtained results were interpreted in terms of the coarse-grained random sequential adsorption model that allowed to estimate the range of applicability of QCM for determining deposition kinetics of virus and bacteria at abiotic surfaces.

Storage, transmission and processing of data become a significant problem when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here we propose a novel method that allows the scalability of digital assets, including blockchain databases in the download, validation and confidentiality process, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method to generate an information-entropic numerical representation of a digital asset, which can be utilized on its own, or as an optically machine-readable entropic barcode, for data secure transmission, processing, labelling, identification, one-way encryption and compression, validation, and digital tamper-proof check.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

For a given set of plasma parameters, along a single series (Lyman, Balmer etc) the lines with higher principal quantum number(n) lines get progressively wider, closer to each other, and start merging for a certain critical n. In the present work four different codes(with further options) are used to calculate the entire Balmer series for moderate and high electron densities. Particular attention is paid to the relevant physics, such as the cutoff criteria, strong and penetrating electron collisions.

Recommender Systems (RecSys) are crucial in managing information overload and enhancing user satisfaction across various digital platforms, including e-commerce and entertainment. Evolving from traditional models to Deep Neural Networks (DNNs) and, more recently, Large Language Models (LLMs), these systems leverage sophisticated algorithms to analyze user behaviors and preferences. LLMs, such as GPT-4, are trained on extensive datasets to comprehend and generate natural language, significantly advancing their ability to deliver personalized recommendations. This tutorial explores the transformative impact of LLMs on RecSys, discussing their development, application in handling complex datasets, and the integration of contextual insights. Real-world examples illustrate how LLMs enhance recommendation accuracy and user experience, highlighting challenges and future directions in the field.

Abstract: Founders of quantum theory have pointed out the irreducibility of living systems to quantum mechanics. Quantum mechanics is never violated they rightfully argue but works under a set of active macroscopic constraints that selectively order the behavior of the micro components and these constraints are not reducible to the reversible, causally closed formalism of quantum mechanics. The whole of biological evolution is characterized by the production of increasingly more highly-ordered levels of such macroscopic constraints and biological evolutionary theory has no account for this either. It is assumed outside its theoretical first principles. In fact this generative macroscopic ordering, the production of increasingly more highly differentiated levels of order—more things, more kinds of things, cannot be explained within biological theory because it is generic to the universe itself. Irreducible to quantum mechanics this begs the question of fundamentality in physics. Fundamental laws in a universe defined by active macroscopic ordering, level-building, and differentiation must be able to causally cross levels, and more importantly explain why there is any level-building at all to begin with. The laws of thermodynamics are fundamental laws in this sense, and 4th Law path selection explains the generative universal ordering and differentiation (“complexification”).

Though the share of the world population living in extreme poverty declined to 10 percent in 2015, from 16 percent in 2010 and 36 percent in 1990, data shows that the world is not on track in achieving the target of less than 3 percent of people living in extreme poverty by 2030. Hence the study sought to investigate the influence of AI on poverty reduction. Using content analysis one of the unobtrusive research techniques, the study found out that, the availability of relevant data is making AI be able to deliver value to humanity and AI has a strong influence on poverty in areas of relevant data collection through poverty maps, its ability to revolutionize agriculture, education, and the financial sector through digital financial inclusion. The study also discovered that many countries especially developing nations are not collecting as much data to identify the number of poor people and the regions where these people are located. However, the existence of AI is assisting to change this, or instance the study discovered that the research team at Stanford University is using satellite images to provide an alternative to map poverty, to identify the regions where poverty is more concentrated. Also, various robotics and AI programs such as Google and Stanford University’s Sustainability and Artificial Intelligence Lab, are coming forth with AI programs in agriculture which are doing a lot to improve farming, through the identification of diseases, prediction of crop yields, and location of areas prone to a scarcity among several other notable signs of progress in education. Therefore, the study recommends that governments, development institutions and other organizations that are striving to fight poverty to invest more in AI as well as adopting and scaling up its use as it presents benefits in the quest to ensure that poverty is reduced.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Syndactyly is the most common congenital malformation of the hand, leading to the fusion of the digits, frequently affecting the ring and middle fingers. The incidence is one out of 2500 children, predominantly occurring in boys and Caucasians. Clinically, the malformation may present as soft tissue or bony fusion, resulting in the union of the fingers characterised as complete or incomplete. This fusion may involve the phalanges but may also extend to the carpal/tarsal bones, even to the metacarpal’s or metatarsal’s level, rarely to the distal end of the forearm and lower leg. Mostly isolated but may occur together with other disorders or malformations such as synostosis, acro-syndactyly, cleft hand, clinodactyly, or polydactyly. Syndromic syndactyly can be observed in cases of Apert Syndrome, Poland’s Syndrome, Pfeiffer Syndrome and many others. A girl born in June of 2019 was diagnosed with congenital malformation of the right hand at birth —affecting the right middle, ring and little fingers, respectively. After X-ray imaging, the fusion of the third and fourth proximal phalanges to a common metacarpal was identified, forming a unique diagnosis of clino-syndactyly with metacarpal aplasia. Surgical intervention was advocated for, including a wedge osteotomy to correct the synchondrosis at the phalangeal base and a dorsal flap to close the interdigital space created during the correction of the III and IV. fingers. A trapezoid flap for the release of the syndactyly of the IV and V. fingers was applied. The paper aims to present the surgical correction and its results regarding an atypical case of syndactyly with clinodactyly and metacarpal aplasia.

In this paper, we consider the fourth-order parabolic equations with integer and fractional order time derivatives with Neumann boundary conditions. The integer order time derivatives are approximated by backward Euler difference quotients, and the fractional order time derivatives are approximated by L1 interpolation. We propose the block-centered finite difference scheme for fourth-order parabolic equations of integer and fractional order time derivatives. We prove the stability of the block-centered finite difference scheme and the second-order convergence of the discrete L2 norms of the approximate solution and its derivatives of every order. Numerical examples are given to verify the effectiveness of the block-centered finite difference scheme.

High resolution information on nucleotide arrangement in the genome of any or-ganism is critical to understand its genetic makeup, function, and evolutionary his-tory. For years, DNA sequencing has provided crucial breakthroughs in the field of genomics and molecular biology. With the advent of technology, Next Generation Sequencing has now transformed the whole genome sequencing prospects, thus unveiling the novel and interesting features of an organism genome that can behold. Next generation sequencing platforms offer realistic approach to identify and anal-yses whole genome with high throughput, accuracy, scalability in a very short time span. Information obtained can be further used in improving our existing healthcare systems, designing novel and personalized therapeutic modules, making the envi-ronment sustainable and proactively preparing ourselves for future pandemics and outbreaks. This article discusses the various NGS technologies and approaches, their advantages, drawbacks, and futuristic usage in different fields. Though still in nascent stages and marred with flaws, with ongoing advancement, NGS will indeed have promising impacts in the future.

Based on the Flashing Ratchet Potential (FRP) and Electro-Spectroscopy detection techniques, a new detection method was experimentally examined. This method allows the detection of sub-micro particles in the human exhaled air such as viral particles and other biological agents. The detection signal created by this method is carried out by electron current buildup forming an Electric Current-Spectrum (ECS) distinguishing the contents of the exhaled air samples. The detection method was developed based on FRP and a beam of free charges passing through the electrodes of the FRP. The ECS is characterized by low-energy free charges interacting with the medium through which they pass, where numerous free charges are scattered (delayed) as a result of these interactions. The resulting ECS is characterized by FRP's capacity to drift back the delayed free charges to their original starting positions. By comparing the ECS of two exhale air samples, this new technology can be utilized to identify the presence of viral and microorganisms’ particles. The time-escalating ECS difference quickly indicates the presence of such particles. Moreover, the FRP's optimal operating settings was experimentally defined, in addition to the experimental evaluation of its functionality to confirm the FRP's promising sensing capabilities, which were statistically examined several times to ensure the reproducibility of the results.

The application of non-uniform spiral gratings to control structure, topological parameters and propagation of orbital angular momentum (OAM) beams was studied experimentally with coherent near-infrared light. Adapted digital spiral grating structures were programmed into the phase map of a high-resolution liquid-crystal-on-silicon spatial light modulator (LCoS-SLM). It is shown that characteristic spatio-spectral anomalies related to Gouy phase shift can be used as pointers to quantify rotational beam properties. Depending on sign and gradient of spatially variable periods of chirped spiral gratings (CSG), variations of rotation angle and angular velocity as a function of propagation distance were measured. Propagation-dependent self-torque is introduced in analogy to known local self-torque phenomena of OAM beams as obtained by the superposition of temporally chirped or phase modulated wavepackets.

This article investigates the fundamental principles that establish universal limits in physical phenomena, such as the uniformity of physical laws and Energy conservation, leading to the concepts of maximum speed, locality, and maximum density. These principles guide me to explore Quantum Black Holes—relativistically rotating, miniature charged black holes with a singular core at the highest possible density. Uniquely, these Quantum Black Holes maintain all quantized charges, including gravitational ones. The study further demonstrates that quarks and leptons are composed of these Quantum Black Holes, redefining them as the actual elementary particles.Exploring these particles reveals that they emit Energy outward, modifying the curvature of nearby space-time. This discovery provides new insights into local space-time dynamics, characterized by a single equation that includes the influence of all charges and hidden Energy. By the conclusion of this article, I show that Quantum Black Holes offer explanations for several Standard Model deficiencies: the nature of luminous, dark, and anti-Matter; the mass, size, and quantized charges of all elementary particles (including gravitational); the nature of neutrino oscillations; and the mechanism by which electric charges remain bound even though the same type of electric charge components must repeal each other.

Particles in classical physics are distinguishable objects, which can be picked out individually on the basis of their unique physical properties. By contrast, in quantum mechanics the standard view is that particles of the same kind (``identical particles'') are completely indistinguishable from each other. This standard view is problematic: Particle indistinguishability is irreconcilable not only with the very meaning of ``particle'' in ordinary language and in classical physical theory, but also with how this term is used in the practice of present-day physics. Moreover, the indistinguishability doctrine prevents a smooth transition from quantum particles to what we normally understand by ``particles'' in the classical limit of quantum mechanics. Elaborating on earlier work, we here discuss an alternative to the standard view that avoids these and similar problems. As it turns out, this alternative approach connects to recent discussions in quantum information theory concerning the question of when identical particles can be considered to be entangled.

This paper designed a new type of generator, transgenerator, that integrates the wind turbine and flywheel into one system, aiming to make the flywheel distributed energy storage (FDES) more modular and scalable than the conventional FDES. The transgenerator is a three-member dual-mechanical-port (DMP) machine with two rotating members (inner and outer rotors) and one stationary member (stator). The transgenerator-flywheel system is introduced with the system configuration, transgenerator overview, flywheel operation principle and power management strategies, and control system. Simulations are performed in MATLAB/ Simulink to verify the system viability, including control system verification and the flywheel storage performance evaluation. Results show that the inner and outer rotors can be controlled independently with accurate and fast control response, and the grid-side control works properly. The flywheel performs well with considerable charging power and storage capacity.

We investigate numerically the interactions of in-phase Airy beams modulated by a fundamental Gaussian beam and fourth-order diffraction in Kerr nonlinear media. Directly numerical simulations show that normal (anomalous) fourth-order diffractions and in-phase (out-of-phase) Gaussian beam affect the interactions of solitons generated from Airy beams in unique manners. Different from previous results that interactions between in-phase (out-of-phase) conventional beams are always attractive (repulsive), many anomalous interactions of Airy beams are obtained. Stable bound states of breathing Airy soliton pairs can be formed with the help of fourth-order diffraction and fundamental Gaussian beam.

Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. Also, there is a lack of standardized evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.

While Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs), it also presents challenges that can affect model accuracy and performance. Practical applications show that RAG can mask the intrinsic capabilities of LLMs. Firstly, LLMs may become overly dependent on external retrieval, underutilizing their own knowledge and inference abilities, which can reduce responsiveness. Secondly, RAG techniques might introduce irrelevant or low-quality information, adding noise to the LLM. This can disrupt the normal generation process, leading to inefficient and low-quality content, especially when dealing with complex problems. This paper proposes a RAG framework that uses reflective tags to control retrieval. This framework evaluates retrieved documents in parallel and incorporates the Chain of Thought (CoT) technique for step-by-step content generation. The model selects the highest quality and most accurate content for final generation. The main contributions include: 1) Reducing the hallucination problem by selectively utilizing high-scoring document, 2) Enhancing real-time performance through timely external database retrieval, and 3) Minimizing negative impacts by filtering out irrelevant or unreliable information through parallel content generation and reflective tagging. These advancements aim to optimize the integration of retrieval mechanisms with LLMs, ensuring high-quality and reliable outputs.

Reviewed are recent efforts leading to chemo-enzymatic syntheses of strongly fluorescent ribosides of nucleobase analogs, potentially applicable in analytical biochemistry and cell biology. Described are syntheses and properties of fluorescent purine, 8-azapurine and etheno-purine ribosides, obtained using various types of the purine nucleoside phosphorylase (PNP) as catalysts of ribosylation, and -ribose-1-phosphate (r1P) as a second substrate. In several instances, the ribosylation sites were different than the canonical purine N9. Some of the obtained ribosides show fluorescence yields close to 100%. Their main applications are assays of PNP, nucleoside hydrolases, and other enzyme activities both in vitro and, possibly, within the living cells, using fluorescence microscopy.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which provides powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

This paper introduces an innovative energy circuit that challenges conventional understandings of energy conservation, marking a significant departure from traditional frameworks. Seeking to enhance energy generation within existing paradigms, this innovation holds promise for addressing pressing global issues such as the energy crisis and environmental sustainability, while also stimulating scientific inquiry. In contrast to prevalent misconceptions rooted in philosophical and scientific constraints, this study refutes the notion of generating energy from nothing and questions the viability of perpetual motion machines as conceptual models. Instead, it proposes a novel circuit design derived from an unconventional electrical short circuit, which is shown to disrupt the established principles of energy conservation. This circuit offers a transformative approach with diverse applications, including self-recharging capabilities for electric vehicles, bolstering microgrid infrastructure, and facilitating the integration of renewable energy sources. Transcending conventional scientific boundaries, this paper prompts philosophical reflections on the evolving nature of scientific exploration. Ultimately, the energy circuit represents a pivotal step towards mitigating the global energy crisis by reducing reliance on finite resources and fostering sustainability. The paper concludes by illustrating the circuit's potential to revolutionize energy systems and contribute to a more resilient and sustainable future.

The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are crucial for disease management and understanding viral evolution. In this study, a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data is presented. By integrating state-of-the-art bioinformatics tools with artificial intelligence, the pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity. Before aligning the reads to the reference genomes, quality control measures and adapter trimming are performed to ensure the integrity of the data. Unmapped reads are subjected to de novo assembly to reveal novel viral sequences and genetic elements. The effectiveness of the pipeline is demonstrated by the identification of virus sequences, illustrating its potential for the detection of known and emerging pathogens. SNP discovery is performed using a custom Python script that compares the entire population of sequenced viral reads to a reference genome. This approach provides a comprehensive overview of viral genetic diversity and identifies dominant variants and a spectrum of genetic variations. The robustness of the pipeline is confirmed by the recovery of complete viral sequences, which improves our understanding of viral genomics. This study highlights the synergy between traditional bioinformatics techniques and modern approaches, providing a robust tool for analyzing viral genomes and contributing to the broader field of viral genomics.

This study investigates job satisfaction, burnout, and well-being among first-generation migrant physicians in anesthesia and intensive care medicine in Germany, comparing them to their native German counterparts. Utilizing a cross-sectional survey design, the study collected data from 513 physicians, 110 of whom identified as having a migration background. Job satisfaction was measured using the German version of the Warr-Cook-Wall (WCW) Job Satisfaction Scale, burnout was assessed with the Copenhagen Burnout Inventory (CBI), and well-being was evaluated using the WHO-5 Well-Being Index. The job satisfaction ratings revealed no significant differences between migrant and German physicians in most dimensions, including physical workload, freedom to choose work methods, satisfaction with colleagues, responsibility, income, skill utilization, and variety in work tasks. However, migrant physicians reported significantly higher satisfaction with recognition received for their work and lower dissatisfaction with working hours. Burnout assessments showed that migrant physicians experienced higher psychological strain, perceiving every work hour as more exhausting and having significantly less energy for family and friends. Notably, migrant physicians reported higher difficulty and frustration in working with patients. Well-being ratings indicated that migrant physicians felt less energetic and active but found their daily life more filled with interesting activities. The findings underscore the unique challenges faced by migrant physicians, particularly in terms of recognition and patient-related burnout. These results highlight the need for targeted interventions to support migrant physicians, including cultural competence training and flexible working hours to enhance their job satisfaction and overall well-being. Addressing these issues is crucial for maintaining the quality of patient care and the occupational health of migrant physicians in Germany.

It is shown that if there exists in nature a pair of heavy elementary particles each with a charge equal to that of the proton and with mass energy , then their direct interaction gives rise to a spontaneous fractionation of the two charges into three pairs of elementary particles according to two equiprobable channels, one hadronic and one leptonic. The leptons and quarks produced are in agreement with the first generation of the Standard Model of matter. Fractionation suggests the relative abundances of the elementary particles, introducing some questions about the possibility of the real existence of a currently unknown particle and its role in the formation of an apparent matter-antimatter asymmetry in the universe.

A single water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and suitable in industrial use. Magnetic chitosan polymers offer the advantage of easy separation from the dye solution. In this work, a Chitosan-Fe polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water. The polymer was characterized in terms of surface morphology (SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), X-ray powder diffraction (XRD) and density functional theory (DFT). The reported results hint that 1 g and pH 7.0 were the best conditions to carry out both kinetic and isotherm models. To understand the adsorption behavior of Direct Red 83:1 onto the polymers, adsorption rate and maximum adsorption capacity were calculated using kinetic tests and isotherm curves respectively. The kinetic data and mechanism of the adsorption process were analyzed by three models and the equilibrium data by three adsorption isotherms. The results indicated that the adsorption process follows a pseudo-second-order kinetics (R2= 0.999) and fits the Temkin isotherm (R2= 0.946), indicating that the adsorption occurred on heterogeneous surfaces. The newly synthesized Chitosan-Fe polymer exhibited good adsorption properties and easy separation of cleaned water.

This study aimed to enhance the compatibility of glass ionomer cement (GIC) with tooth tissue by chemically modifying its primary component, calciumfluoroaluminosilicate (CFAS), through the incorporation of nanorod hydroxyapatite (nHA). The process involved binding L-glutamic acid to nHA, followed by activation and binding of albumin to create nHA immobilized with albumin. This modified nHA was then used to surface-modify CFAS particles, producing nHA-CFAS powder. The modified powder was mixed with poly(acrylic acid) and UV-cured to form GIC containing nHA-CFAS (GIC-nHA). Fourier transform infrared spectroscopy and scanning electron microscopy (SEM) confirmed the successful modification. Cytocompatibility tests with osteoblasts showed that GIC-nHA had superior cell viability and bone formation capabilities compared to the control GIC. The improved histocompatibility is attributed to nHA enhancing the biological activity of osteoblasts, indicating that the surface modification method significantly improves the functional integration of GIC with tooth tissue.

The quest for accurate and more efficient methods for solving periodic/oscillatory problems is gaining more attention in recent time. This paper presents the construction and implementation of a family of exponentially–fitted Obrechkoff methods. A single–step Obrechkoff method involving terms up to the fourth derivatives was used as the base method. We also present the stability and convergence properties of the constructed family of methods. Two numerical examples were use to illustrate the performance of the constructed methods.

Abstract: Discussed is an expansion of SRT to fourth-order of space-time including any velocities and possible negative kinetic energies by real rest mass in analogy of the model amplitude of enforced damped oscillation. This paper serves an introduction to developed SRT including superluminar translating velocities without tachyons or imaginary restmass. Focused is on those parts, which develop the elaborated lorentz-transformations. Discussed are the problems of this theory.

In this study, a new type of temperature sensor device was developed. The circular electrode of the thermal sensitive sensor was modified with TMT and O₂ plasma to enhance the conductivity by forming a thin SnOxCy layer on the electrode surface. The Nano-Au particles were subjected to O₂ plasma pretreatment to form peroxide groups on the surface. The thermally sensitive sensor was made by mixing the above-treated Nano-Au particles with N-isopropylacrylamide (NIPAAm) to form solution and then UV-induced grafting polymerization of the NIPAAm-containing solution onto the electrode substrate. The composite hydrogels on the electrode introduce thermo-sensitive polymeric surface films for temperature sensing. Using ambient environment resistance test to measure the resistance, the LCST (lower critical solution temperature) of Nano-Au (MUA) mixed with NIPAAm hydrogel was found to be 32 °C. At ambient temperatures higher than LCST, the electrode resistance decreases linearly.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

This study reports on the development of bio-based hydrophobic coatings for packaging papers through deposition of polyhydroxybutyrate (PHB) particles in combination with nanofibrillated cellulose (NFC) and plant wax. In a first approach, PHB particles in micrometer range (PHB-MP) were prepared through a phase-separation technique providing internally nanosized structures. The particles were transferred as a coating by dip-coating filter papers in the particle suspension, followed by sizing with a carnauba wax solution. This approach allowed partial to almost full surface coverage of PHB-MP over the paper surface resulting in static water contact angles of 105° to 122° and 129° to 144° after additional wax coating. In a second approach, PHB particles with submicron sizes (PHB-SP) were synthesized by an oil-in-emulsion (o/w) solvent evaporation method, and mixed in aqueous suspensions with 0 to 7 wt% NFC. After dip-coating filter papers in PHB-SP/NFC suspensions and sizing with a carnauba wax solution, static water contact angles of 112° to 152° were obtained. The intrinsic properties of the particles were analyzed by scanning electron microscopy, thermal analysis and infrared spectroscopy, indicating higher crystallinity for PHB-SP than PHB-MP. The chemical interactions between the more amorphous PHB-MP particles and paper fibers were identified as an esterification reaction, while the morphology of the NFC fibrillar network was playing a key role as binding agent in the retention of more crystalline PHB-SP at the paper surface, hence contributing to higher hydrophobicity.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

The wastewater problem is becoming an increasingly significant environmental issue affecting various aspects of human activities. The generation of reactive oxygen species (ROS) in water through certain physicochemical processes, which can oxidize or degrade various organic and chemical compounds, offers an effective purification method. The main advantage of these methods is the elimination of chemical reagents. Aim of this work is to investigate the possibility of regulating the ROS content in water by altering the characteristics of the plasma discharge that occurs under hydrodynamic cavitation in the water flow. ROS concentration was determined using the permanganate oxidation titration technique. It was found that the content of various reactive oxygen species in water after treatment can be controlled by varying the power of the electrical pulses applied. Additionally, the study obtained the dependence of ROS concentration in treated water samples on storage time, indicating a prolonged effect of sonoplasma treatment on the antibacterial properties of water. Analysis of water samples using a biological test culture of Salmonella typhimurium revealed bacteriostatic properties of the water for at least 48 hours after sonoplasma discharge treatment.

The Standard Model (SM) of particle physics successfully describes electromagnetic, weak, and strong forces but omits gravity. We introduce the Kinematrix Field Theory (KFT), integrating gravity into this fundamental force framework through a novel particle, the kinematon. This gauge boson, existing in both massless and massive forms, advances the SM's scope to address enigmas like dark matter and neutrino masses. We detail the mathematical structure of the KFT and establish Feynman rules for kinematon interactions with fermions and gauge bosons. The KFT, synergizing with the Higgs mechanism and general relativity, aspires to a unified quantum theory of gravity. We explore the kinematon's phenomenological implications and potential experimental signatures, forecasting significant advancements in fundamental physics and setting a course for future explorations in the SM.

There are unsolved problems related to inflation, gravity, dark matter, dark energy, missing antimatter, and the birth of the universe. Some of them can be better answered by assuming the existence of aether and hypoatoms. Both were created during the inflation in the very early universe. While aether forms vacuum, hypoatoms, composed of both matter and antimatter and believed to be neutrinos, form all observable matter. In vacuum, aether exists between the particle-antiparticle dark matter form and the dark energy form in a dynamic equilibrium: A + A-bar = gamma + gamma. The same reaction stabilizes hypoatoms and generates a 3-dimensional sink flow of aether that causes gravity. Based on the hypoatom structure, the singularity does not exist inside a black hole; the core of the black hole is a hypoatom star or neutrino star. By gaining enough mass, ca. 3 X 10²² M_sun, to exceed neutrino degeneracy pressure, the black hole collapses or annihilates into the singularity, thus turning itself into a white hole or a Big Bang. The universe is anisotropic. Its center, or where the Big Bang happened, is at 0.66^+0.03_-0.01 times the radius of the observable universe at Galactic coordinates (l, b) = (286⁺¹⁰_-10, -43⁺⁷_-6). If we look from the Local Group to the center of the universe, the universe is rotating clockwise.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

This paper treats the connection problem of expressing sums of finite products of Chebyshev polynomials of the third and fourth kinds in terms of five classical orthogonal polynomials. In fact, by carrying out explicit computations each of them are expressed as linear combinations of Hermite, generalized Laguerre, Legendre, Gegenbauer, and Jacobi polynomials which involve some terminating hypergeometric functions ${}_2 F_0, {}_2 F_1$, and ${}_3 F_2$.

Here we consider sums of finite products of Chebyshev polynomials of the third and fourth kinds. Then we represent each of those sums of finite products as linear combinations of the four kinds of Chebyshev polynomials which involve the hypergeometric function ₃F₂.

In this work, satellite data from the Clouds and Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments is analyzed to determine how the global absorbed sunlight and global entropy generation rate have changed during the time period from 2002 to 2023. The data is used to test hypotheses derived from the Maximum Power Principle (MPP) and Maximum Entropy Production Principle (MEP) about the evolution of earth’s surface and atmosphere. The results indicate that the amount of absorbed sunlight has increased over the last 20 years but rising surface temperatures have caused the entropy generation rate to remain approximately constant despite that decrease in albedo. The results are simpler to explain using the MPP but do not completely rule out the MEP. Given the acceptance of the MPP or MEP, some extensions and nuances are discussed.

In this research, catalytic steam reforming acetic acid derived from the aqueous portion of bio-oil for hydrogen production was investigated by using different Ni/ATC (Attapulgite Clay) catalysts prepared by precipitation, impregnation and mechanical blending methods. The fresh and reduced catalysts were characterized by XRD, N₂ adsorption-desorption, TEM and H₂-TPR. The comprehensive results demonstrated that the interaction between active metallic Ni and ATC carrier was significantly improved in Ni/ATC catalyst prepared by precipitation method, and in which the mean Ni particle size was the smallest (~13 nm) resulted in the highest metal dispersion (7.5%). The catalytic performance of the three catalysts was evaluated through the process of steam reforming of acetic acid in a fixed-bed reactor under atmospheric pressure at two different temperatures, such as 550 ℃ and 650 ℃. Results showed that the Ni/ATC (PM-N/ATC) prepared by precipitation method, achieved the highest H₂ yield of ~82% and little lower acetic acid conversion efficiency of ~85% than that (~95%) of Ni/ATC (IM-NATC) prepared by impregnation method. In addition, the deactivation catalysts after reaction for 4 h were analyzed by XRD, TGA-DTG and TEM, which demonstrated that the catalyst deactivation was not caused by the amount of carbon deposition, but owed to the significant agglomeration and sintering of Ni particles in the carrier.

Prior studies have focused on the overall behavior of randomly moving particle swarms. However, the characteristics of the stochastic-constrained particles that form ubiquitously within these swarms remain oblivious. This study demonstrates a generalized diffusion equation for stochastic-constrained particles that considers the velocity and location aggregation effects observed from their parent particle swarm (i.e., a completely random particle swarm). This equation can be approximated as the form of Schr\"odinger equation in the microcosmic case (low relative density) and describe the dynamics of the total mass distribution in the macrocosmic case (high relative density). The predicted density distribution of the particle swarm in the stable aggregation state is consistent with the total mass distribution of massive, relaxed galaxy clusters (at least in the range of r < r_s), preventing cuspy problems in the empirical Navarro-Frenk-White (NFW) profile. This study opens a window to observe the dynamics of stochastic-constrained particles from a third perspective, from which the aggregation effect of particles without gravitation can be saw.

children is challenging. An important advance is the 15-questions Pictorial Personality Traits Questionnaire for Children. A study on students from 10 to 13 years old in Poland validated the questionnaire but with some observations. Thus, there is a need for replication and stronger evi-dence in this age group. In Chile, we replicated the study with 3,423 4th-graders (9 to 12 years old). Teachers, in regular sessions, applied the questionnaire to their entire classes. We found similar results, including that asexual pictograms worked well in both genders. We also found positive relationships between conscientiousness, openness, and extraversion with mathematical performance. Furthermore, a combination of these three traits has a relationship with math performance twice as big as each trait alone. Moreover, students with the lowest scores in this combination of personality traits (6.6% of students) have 0.27 standard deviations less in mathematical performance than those with the highest score, which is 74.3% of students. To the best of our knowledge, this is the first time that a study finds a strong relationship between a combination of personality traits gathered with a 15-question questionnaire and fourth-graders´ math perfor-mances.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

Background (1) In the time of the 4th Industrial Revolution or Industry 4.0, a conglomerate of technical and social inventions, political contexts, socio-cultural circumstances, environmental policies, business models, and economic policies has emerged. Sustainability policy in theory and practice aims to deal with the effects of all these factors and to try to make decisions that ensure both social and economic development sustainably. The question is how to familiarize oneself with the current knowledge about the relationship between Industry 4.0 and sustainability?; (2) Methods: This research utilizes an automated content analysis method to analyses scientific journals, newspapers and magazines. The comparison of results of both research group shows that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development; (3) We found that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers, as well as some latest research journals, include articles of the COVID-19 outbreak and its effect on the economy and the environment. Indeed, the outbreak of the virus brings a new thought to the reorganization of the complex relationships between consumers, businesses and the state; (4) Conclusions: According to the comparison of the analyses of the results, it can is that the analyses of both types of literature, both scientific and professional, shows that there are common topics they write about, which are related to the field of clean production, emissions, renewable energy, climate change, sustainable investments and corporate sustainability. An urgent global issue that extends all over the world is the promotion of energy-saving technologies and reduction of carbon dioxide emissions.

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.

Puerto Rico's geographic and climatic conditions render it highly susceptible to natural disasters, particularly hurricanes. The devastation caused by Hurricane Maria in 2017 highlighted the fragility of the island's centralized energy infrastructure, which led to prolonged power outages and severe disruptions in essential services. This review explores the potential of biomass energy to enhance resilience in post-disaster scenarios, focusing on Puerto Rico as a case study. Biomass energy, derived from organic materials like agricultural residues, woody biomass, and municipal solid waste, offers a renewable and locally sourced power solution that can be rapidly deployed. Key biomass conversion technologies, including direct combustion, gasification, anaerobic digestion, and pyrolysis, are examined for their applicability and benefits in emergency energy provision. The implementation of biomass-powered microgrids in Puerto Rico, such as those by Casa Pueblo and Arensis, demonstrates the feasibility and effectiveness of decentralized energy systems in disaster recovery. These microgrids provide reliable power to critical infrastructure, reduce dependence on fossil fuels, and support waste management efforts. The review also discusses ongoing research and development in advanced biomass technologies, such as second-generation biofuels, algae-based biomass, and biomass-to-hydrogen conversion, which can further enhance energy efficiency and sustainability. Additionally, the economic, environmental, and social impacts of biomass energy are evaluated, highlighting its role in job creation, waste reduction, emission control, and community resilience. Policy recommendations include streamlining permitting processes, enhancing financial incentives, developing feedstock supply chains, and incorporating biomass into disaster recovery plans. The findings suggest that integrating biomass energy into Puerto Rico’s energy strategy can significantly improve its capacity to respond to and recover from natural disasters, promoting long-term sustainability and resilience.

We consider the inverse function $z=g(w)$ of a (normalized) starlike function $w=f(z)$ of order $\alpha$ on the unit disk of the complex plane with $0<\alpha<1.$ Krzy{\. z}, Libera and Z\l otkiewicz obtained sharp estimates of the second and the third coefficients of $g(w)$ in their 1979 paper. Prokhorov and Szynal gave sharp estimates of the fourth coefficient of $g(w)$ as a consequence of the solution to an extremal problem in 1981. We give a straightforward proof of the estimate of the fourth coefficient of $g(w)$ together with explicit forms of the extremal functions.

This paper proposes two fully-automatic machine-extracted linguistic features from an unlimited text input for Mandarin prosody generation. One is the punctuation confidence (PC) which measures the likelihood of inserting a major punctuation mark (PM) at a word boundary. Another is the quotation confidence (QC) which measures the likelihood of a word string to be quoted as a meaningful or emphasized unit in text. Because a major PM in a text is highly correlated with a prosodic break, and a quoted word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is first realized by employing conditional random field (CRF)-based models to predict major PMs, quoted word string locations, and their associated confidences, i.e., the PC and the QC, for each word boundary. Then, the predicted punctuations and their confidences are combined with traditional contextual linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the one without the proposed features. So, the proposed PC and QC are promising features for Mandarin prosody generation.

The automotive market is very competitive and presents a demand for the consistent improvement of the used technologies and reduction of the product cost and dimensions with each product model iteration. Hence, it is important to have access to well-defined reference designs of high quality products, in order to evaluate the new ideas and technologies. This paper provides the readers with a numerical model of such a high quality product, i.e., the IPMSM type traction motor from the 4th generation of the Toyota Prius hybrid transaxle. In the introductory section, the brief history of the Prius model is given together with the references to the reverse engineering reports. Afterwards, the machine dimensions, material properties and winding configuration are described. Then, the model is validated with the torque measurements at constant speed. In the next chapters the simulation results are presented and the numerical source data are supplied to the reader. Finally, the design philosophy of the Toyota drive is briefly discussed in comparison with the BMWi3 drive and the results are concluded in the last section.

During the last decade, new unusual physical phenomena have been discovered in studying the optics of dielectric mesoscale particles of an arbitrary three-dimensional shape with the Mie size parameter near 10 (q ~ 10). The paper provides a brief overview of these phenomena from optics to terahertz, plasmonic and acoustic ranges. The different particle configurations (isolated, regular or Janus) are discussed, and the possible applications of such mesoscale structures are briefly reviewed herein in relation to the field enhancement, nanoparticle manipulation and super-resolution imaging. The number of interesting applications indicates to a new promising scientific direction emerged in optics, terahertz and acoustic ranges, and plasmonics. In this paper we present the authors' view of these problems.

We review some properties of a relativistic classical massless charged particle with spin interacting with an external electromagnetic field. We give in particular a proper definition of kinetic energy and total energy, the latter being conserved when the external field is stationary. We find that the particle’s velocity may differ from c as a result of the spin - electromagnetic field interaction, without jeopardizing Lorentz invariance.

Background: The antisense oligonucleotide against APOC3 mRNA volanesorsen was recently introduced to treat Familial Chylomicronemia Syndrome (FCS). Cases of decreased platelet count are reported among patients treated with volanesorsen. The aim of the study is to evaluate platelet function and thrombin generation (TG) assessment in FCS patients receiving volanesorsen. We performed a cross-sectional study on FCS patients treated with volanesorsen. Methods: changes in PLC were assessed from baseline to Tw12 and Tw36. To assess TG, samples were processed by CAT (with PPP-reagent LOW). The results were expressed by the trombogram graphic (thrombin variation over time); lagTime; Endogenous Thormbin Potential(ETP); peak; time to reach peak(ttpeak), startTail e velocity Index. Platelet aggregation was assessed by testing different agonists using turbidimetry method. Results: Four FCS patients and 4 matched healthy controls were included in the present study. Changes in PLC at Tw12 was 30% and 34% at T36w. Thrombin generation results show values in normal range (for patients and controls respectively lagTime:10,42±4,40 and 9,25±0,99; ttPeak:14,33± 4,01 and 13,10±0,67; startTail:32,13±3,54 and 29,46±1,69; velocityIndex:20,21±3,63 and 33,05±13,21; ETP 599,80±73,47 and 900,2±210,99; peak value 76,84±1,07 and 123,30±39,45) and no significant difference between cases and controls. Platelet aggregation test showed values in range with no significant difference with healthy controls Conclusions: Our study showed for the first time that no significant changes in the general haemostasis assessed by TG and in platelet function have been observed in FCS patients receiving volanesorsen.

Electricity consumption is often the hotspot of life cycle assessment (LCA) of products, industrial activities, or services. The objective of this paper is to provide a consistent, scientific, region-specific electricity-supply-based inventory of electricity generation technology for national and regional power grids. Marginal electricity generation technology is pivotal in assessing impacts related to additional consumption of electricity. China covers a large geographical area with regional supply grids; these are arguably equally or less integrated. Meanwhile, it is also a country with internal imbalances in regional energy supply and demand. Therefore, we suggest an approach to achieve a geographical subdivision of the Chinese electricity grid, corresponding to the interprovincial regional power grids, namely the North, the Northeast, the East, the Central, the Northwest, and the Southwest China Grids, and the China Southern Power Grid. The approach combines information from the Chinese national plans on for capacity changes in both production and distribution grids, and knowledge of resource availability. The results show that nationally, marginal technology is coal-fired electricity generation, which is the same scenario in the North and Northwest China Grid. In the Northeast, East, and Central China Grid, nuclear power gradually replaces coal-fired electricity and becomes the marginal technology. In the Southwest China Grid and the China Southern Power Grid, the marginal electricity is hydropower towards 2030.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

Equine hepacivirus (EqHV, Flaviviridae, Hepacivirus) is a small, enveloped, RNA virus generally causing sub-clinical hepatitis with occasional fatalities. EqHV is reported in equids worldwide, but for Italy data are limited. To address this, a survey study was set up to estimate prevalence at national level and among different production categories (equestrian, competition, work and meat and reproduction) and national macro-regions (North, Central, South and Islands). Data obtained testing 1801 horse serum samples by Real-Time RT PCR were compared within the categories and regions. The NS3 fragment of the PCR-positive samples was sequenced by Sanger protocol for phylogenetic and mutational analysis. The tertiary structure of the NS3 protein was also assessed. The estimated national prevalence was 4.27% [1.97-6.59, 95%CI] and no statistical differences were detected among production categories and macro-regions. The phylogenesis confirmed the distribution in Italy of the three known EqHV subtypes, suggesting also a possible fourth sub-type that however requires further confirmation. Mutational profiles that could also affect the NS3 binding affinity to the viral RNA were detected. The present paper demonstrates that EqHV should be included in diagnostic protocols when investigating causes of hepatitis and in quality control protocols for blood derived products, due to its parental transmission.

It’s very well known that the world's attention is currently focused on the energy transition. The energy security, in addition to the drive to reduce greenhouse gas emissions in order to limit global warming, and the generalization of the access to energy, have contributed to adoption of the Moroccan Energy Strategy with a strong focus on Renewable Energies (RE). Morocco is notoriously poor in conventional fossil primary energy resources with an energy dependence on the order of 90%. The energy crisis and that resulted from Covid-19 pandemic and the geopolitical conflicts, combined compounded with steady increase in demand have heavily affected the security and the stability of the country's energy situation. The transition to RE, by strongly engaging in the implementation of several solar, wind and hydro energy projects benefiting from the country's excellent solar and wind energy potential, has made the country the leader in RE in Africa. By 2030, the share of RE in the installed capacity is expected to reach 52%. In this work, an overview of the current situation of RE (particularly solar energy) in Morocco is provided, including, the potentials, obstacles and challenges, and future perspectives. Thanks to its high solar potential location, it’s predictable that Morocco effort will be focused on this field: the Erasmus Plus Innomed Project is a virtuous example of international cooperation, aiming at create a Solar Energy Network, in synergy with EU Partners.

Flammability was evaluated and the results showed the values of critical flux at extinguishment (CFE) as 49.5kW/㎡ on average, total heat release (Qt) as 0.21MJ on average, and peak heat release rate (Qp) as 0.66kW on average, which satisfied all of the thresholds presented by the IMO. Therefore, flame-retardant plywood, of which the flame-retardant performance was evaluated, was determined to be an applicable finishing material that satisfies fire safety standards.

Paper presents proposition of possible extension of The Standard Model Classification of Particles and Fields in way to include gravitation into it.

Particles are considered temporarily identical if the interchange of these particles does not cause any observable physical effects within a time interval Δt. An indistinguishability hypothesis is proposed for such particles, suggesting that two particles (or systems) with nearly the same degree of internal freedom will actually interchange under certain circumstances, thereby affecting the subsequent dynamics. This indistinguishability implies that, theoretically, it is possible to capture an elementary particle with extraordinarily large internal freedom by trading it for an isolated composite system of ordinary matter particles with almost the same total internal freedom as the large-freedom particle. Following this, the ordinary particles will instantly disperse into the environment, while the large-freedom particle will initially be captured and then gradually delocalized into the environment, an observable phenomenon. If this hypothesis is proven true in the future, it might provide a novel strategy for detecting dark matter particles.

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws were derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N ̅ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N ̅∝t^(-m). Simultaneously, the scaling behavior of the average cluster size S ̅ is characterized by S ̅∝t^m. Our findings reveals the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induce alterations in the dynamic procession of the system.

Confinement can induce substantial changes in the physical properties of macromolecules in suspension. Soft confinement is a particular class of restriction where the boundaries that constraint the particles in a region of the space are not well-defined. This scenario leads to a broader structural and dynamical behavior than the observed in systems enclosed between rigid walls. In this contribution, we study the ordering and diffusive properties of a two-dimensional colloidal model system subjected to a one-dimensional parabolic trap. Increasing the trap strength makes it possible to go through weak to strong confinement, allowing a dimensional transition from two- to one-dimension. The non-monotonic response of the static and dynamical properties to the gradual dimensionality change affects the system phase behavior. We find that the particle dynamics is connected with the structural transitions induced by the parabolic trap. In particular, at low and intermediate confinement regimes, complex structural and dynamical scenarios arise, where the softness of the external potential induces melting and freezing, resulting in faster and slower particle diffusion, respectively. Besides, at strong confinements, colloids move basically along one direction, and the whole system behaves structurally and dynamically similar to a one-dimensional colloidal system.

Polymeric particles made up of biodegradable and biocompatible polymers such as poly(lactic-co-glycolic acid) (PLGA) are promising tools for several biomedical applications including drug delivery. Particular emphasis is placed on the size and surface functionality of these systems as they are regarded as the main protagonists in dictating the particle behavior in vitro and in vivo. Current methods of manufacturing polymeric drug carriers offer a wide range of achievable particle sizes, however, they are unlikely to accurately control the size while maintaining the same production method and particle uniformity, as well as final production yield. Microfluidics technology has emerged as an efficient tool to manufacture particles in a highly controllable manner. Here, we report on tuning the size of PLGA particles at diameters ranging from sub-micron to microns using a single microfluidics device, and demonstrate how particle size influences the release characteristics, cellular uptake and in vivo clearance of these particles. Highly controlled production of PLGA particles with ~100 nm, ~200 nm and >1000 nm diameter is achieved through modification of flow and formulation parameters. Efficiency of particle uptake by dendritic cells and myeloid-derived suppressor cells isolated from mice is strongly correlated with particle size and is most efficient for ~100 nm particles. Particles systemically administered to mice mainly accumulate in liver and ~100 nm particles are cleared slower. Our study shows the direct relation between the particle size varied through microfluidics and the pharmacokinetics behavior of particles, which provides a further step towards the establishment of a customizable production process to generate tailor-made nanomedicines.

The fourth industrial revolution (4IR) can offer significant benefits to the construction sector, improving productivity, efficiency, collaborative efforts, and product quality while promoting safety and sustainability. However, research on the application of 4IR technologies in construction is scarce in developing countries. It is crucial to understand the ability of construction companies to adopt new technologies and identify factors influencing the success of technology implementation. In this study, a questionnaire-based survey was conducted with construction professionals to evaluate the level of technological development of the construction market in an emerging economy, assess the potential for innovation implementation, and identify factors that might influence technological development. Although respondents recognized the importance of digital transformation, the results showed that most innovations are in the early stages of implementation in the construction sector, particularly technologies that are essential for the consolidation of the 4IR. Understanding the factors underlying this reality can support the development of strategies to foster innovation and harness the advantages of the 4IR in the construction industry in developing countries.

Different from national systems of innovation, a knowledge-based economy is grounded in the volatility of discursive knowledge enabling us to specify expectations. Expectations can be improved by testing against observations. Furthermore, expectations can differently be codified; for example, in terms of market perspectives and technological opportunities. The Triple Helix of university-industry-government relations provides first a (neo-)institutional model. However, three functions are recombined at the systems level in each instantiation: wealth generation (by industry), novelty production (academia), and legislation and regulation (government). The Triple-Helix synergy indicator enables us to use the institutional arrangements as instantiations of the knowledge-dynamics and thus to assess the generation of options and reduction of uncertainty in information-theoretical terms. The Fourth Industrial Revolution entails the transition to the reflexive entertaining of expectations in terms of models as increasingly the sources of innovations.

Finite element discretizations of problems in computational physics often rely on hand-generated initial mesh and adaptive mesh refinement (AMR) to preferentially resolve regions containing important features during simulation. We propose Adaptnet, a Graph Neural Networks (GNNs) framework for learning mesh generation and adaptation. The model is composed of two GNNs: the first one, Meshnet, learns mesh parameters commonly used in open-source mesh generators, to generate an initial mesh from a Computer Aid Design (CAD) file; while the second one, Graphnet, learns mesh-based simulations to predict the components of an Hessian-based metric to perform anisotropic mesh adaptation. Our approach is tested on structural (Deforming plate - Linear elasticity) and fluid mechanics (Flow around cylinders - steady-state Stokes) problems. Our results show it can accurately predict the dynamics of the system and adapt the mesh accordingly. The adaptivity of the model supports learning resolution-independent dynamics and can scale to more complex state spaces at test time.

We are calling for a paradigm shift in engineering education. In times of the Fourth Industrial Revolution (“4IR”), a myriad of potential changes is affecting all industrial sectors leading to increased ambiguity that makes it impossible to predict what lies ahead of us. Thus, incremental culture change in education is not an option any more. The vast majority of engineering education and training systems, having remained mostly static and underinvested in for decades, are largely inadequate for the new 4IR labor markets. Some positive developments in changing the direction of the engineering education sector can be observed. Novel approaches of engineering education already deliver distinctive, student centered curricular experiences within an integrated and unified educational approach. We must educate engineering students for a future whose main characteristics are volatility, uncertainty, complexity and ambiguity. Talent and skills gaps across all industries are poised to grow in the years to come. The authors promote an engineering curriculum that combine timeless didactic tradition, such as Socratic inquiry, project-based learning and first-principles thinking with novel elements (e.g. student centered active and e-learning by focusing on the case study and apprenticeship pedagogical methods) as well as a refocused engineering skillset and knowledge. These capabilities reinforce engineering students’ perceptions of the world and the subsequent decisions they make. This 4IR engineering curriculum will prepare engineering students to become curious engineers and excellent communicators better navigating increasingly complex multistakeholder ecosystems.

This study was conducted to investigate the correlations between social-emotional competence (SEC) and academic achievement (AA) among nursing students and to compare students’ level of each core skill of SEC (critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, and collaboration) and academic achievement (clinical performance and subjective academic achievement). A cross-sectional design was adapted. Data were collected from 195 nursing students in the junior and senior years who had participated in clinical practicum from four universities in South Korea. General characteristics, levels of critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, collaboration, and academic achievement were collected via self-reported questionnaire. Canonical correlation analysis was performed to evaluate the relationship between SEC and AA. The canonical correlation coefficient between SEC and AA was .762. Critical thinking disposition (Rs = .89), problem-solving (Rs = .86), cooperation (Rs = .80) made the most important contributions to SEC. Clinical performance (Rs=.95) and subjective AA (Rs=.57) were correlated with AA. SEC should be addressed to improve the AA of nursing students. All core skills of SEC should be regularly promoted. It is particularly urgent for nursing students to improve their creativity.

Vaccination has been one of the most successful and the most significant scientific advances in human health and life expectancy all around the globe. The World Health Organization considers that immunization should be recognized as the main component of human health right, due to the fact that vaccination prevents 2.5 million deaths annually (World Health Organization, 2011). The most successful vaccines have been developed using conventional methods that follow the paradigm established by Pasteur: "to isolate, inactivate and inject" the pathogen microorganism and mimic a natural infection. Recently, metagenomics have played an important role in the discovery of new immunogens for vaccine design and the selection of antigens based on genomic information. The main approach that has used this strategy has been called "reverse vaccinology". This promising and arising field allows the screening of the entire potential antigenic repertoire of an organism using predictive bioinformatic tools. Once the antigenic protein or proteins have been selected, they are expressed and purified using molecular cloning and in vitro expression techniques. Following the in vitro production step, they are probed in animal models to evaluate the in vivo protective strength of the immune response. The main aim of this in vivo approach is to evaluate the ability of the immune response to eliminate or neutralize the pathogen at the time of infection. Those antigens capable of generate a specific immune response with neutralizing activity for natural infections are the best candidate vaccines. In this review we summarize the evolution of vaccinology since its inception, with special emphasis on the development of VLPs as vaccine platforms and their future in preventive medicine and we introduce a new recombinant platform for antigen presentation based on Junin virus VLPs (JUNV-VLPs).

MgAl oxide coatings containing MgO and MgAl2O4 phases were doped with CeO2 particles using plasma electrolytic oxidation (PEO) of AZ31 magnesium alloy in 5 g/L NaAlO2 water solution, with CeO2 particles added at concentrations up to 8 g/L. They underwent extensive inve-stigation into their morphology, chemical and phase compositions, and most importantly, photoluminescent (PL) properties and photocatalytic-activity (PA) in photodegradation of methyl orange (10 cm3 of 8 mg/L). The concentration of CeO2 particles in the aluminate electrolyte influences the amount of CeO2 particles incorporated into MgAl oxide coatings, but CeO2 particles have no signi-ficant effect on the surface morphology, thickness, or phase structure of the coatings. The PL emi- ssion spectrum of MgAl oxide coating is divided into two bands: one in the 350-600 nm range related to structural defects in MgO, and another much more intense in the 600-775 nm range attributed to F+ centres in MgAl2O4. Incorporated CeO2 particles do not have a significant effect on the PL intensity of the band in the red spectral region, but the PL intensity of the first band increases with the concentration of CeO2 particles. PA of MgAl/CeO2 oxide coatings is higher than that of pure MgAl oxide coatings. The highest PA was observed for MgAl/CeO2 oxide coatings formed in aluminate electrolytes with the addition of 2 g/L of CeO2 particles. The MgAl/CeO2 oxide coatings remained chemically and physically stable across multiple cycles, indicating their potential for applications.

Orodispersible film (ODF) is one of the novel formulations that have been currently made available that disintegrates rapidly in the mouth without the requisite for water and offer several benefits over conventional oral solid dosage formulations. This delivery system serves as a discreet and convenient mode of administration, especially in patients who have dysphagia, fluid restriction, being beneficial to pediatric, geriatric, and bedridden patients. A novel sildenafil ODF containing sildenafil citrate is formulated to be used in patients with erectile dysfunction (ED). This review discusses the advantages of ODF in improving compliance and satisfaction in these patients and describes the bioequivalence and stability studies of sildenafil ODF. This formulation offers unique benefit to patients with ED by improving their acceptance, compliance, and respecting their privacy. Moreover, the comparison of pharmacokinetic parameters between the sildenafil ODF administered with and without water and the conventional film-coated tablet were similar. It also demonstrated reliable performance that yielded a consistent product, meeting all specifications at release and after three weeks of storage under stressed conditions (60oC). Sildenafil ODF warrants improved ease of intake, taste, portability, storage, and compliance among ED patients, making it the potential most preferred formulation and drug of choice.

The persistence of diseases that affect construction workforce as a result of activities on construction sites poses a danger to the sustainable development of the industry. This resulted to a huge loss of skilled labour and economic development of the industry and the entire country. The arrival of the fourth industrial revolution (4IR) technologies urges an urgent need to assess the effect of the technology’s biological driver on the construction occupation related diseases. Therefore, this study is aimed at assessing the effect of 4IR on the construction occupation related diseases in Gauteng, South Africa. The study is quantitative in design and questionnaire survey were administered to project and Health and Safety (H&S) managers in Gauteng, South African construction sector using a proportionate simple random sampling technique. For data analysis, the Warp PLS-SEM 8.0 software algorithm was used for the analysis of the collated data. The study found that the effects of the 4IR’s biological driver variables ranges between moderate to high effects for genome sequencing (GENSE) and Neurotechnology (NEURO) respectively. The combined predictive relevance of the two (2) variables predicts 64% of the construction occupation related diseases. This implies that the adoption of the driver would help reduce the causes of construction-related diseases. Hence, implies that continuous deployment of 4IR technologies would ensure that construction occupation related diseases are easily identified and put on alert.