Particles and photons appear to be total opposites; the former has rest mass which requires space to exist; the latter has kinetic energy which requires time to occur (oscillate). But they do share certain properties (e.g., quantization) that remain invariant when one is transformed (swapped) for the other. This gauge invariance is developed in some detail.The symmetry between particle and photon turns out to be one of inversion. It is the equalities of special relativity that support this inversion and the accompanying invariances: mass transformed to energy; space transformed to time. The great advantage of these symmetries (inversions) is that they provide guidance for an object little understood (the photon) based upon an object well understood (the particle). On this basis, progress can be made in the understanding of some long-standing issues: wave-particle duality, time’s arrow, the constant speed of light and nonlocality.

We use a random gap model to describe a metal-insulator transition in three-dimensional semiconductors due to doping and find a conventional phase transition, where the effective scattering rate is the order parameter. Spontaneous symmetry breaking results in metallic behavior, whereas the insulating regime is characterized by the absence of spontaneous symmetry breaking. The transition is continuous for the average conductivity with critical exponent equal to 1. Away from the critical point the exponent is roughly 0.6, which may explain experimental observations of a crossover of the exponent from 1 to 0.5 by going away from the critical point.

Gravitation is interpreted to be an “ontomathematical” force or interaction rather than an only physical one. That approach restores Newton’s original design of universal gravitation in the framework of “The Mathematical Principles of Natural Philosophy”, which allows for Einstein’s special and general relativity to be also reinterpreted ontomathematically. The entanglement theory of quantum gravitation is inherently involved also ontomathematically by virtue of the consideration of the qubit Hilbert space after entanglement as the Fourier counterpart of pseudo-Riemannian space. Gravitation can be also interpreted as purely mathematical or logical “force” or “interaction” as a corollary from its ontomathematical (rather than physical) realization. The ontomathematical approach to gravitation is implicit in general relativity equating it to operators in pseudo-Riemannian space obeying the Einstein field equation and also well-known by the “geometrization of physics”. Quantum mechanics shares the same by the separable complex Hilbert space and defining “physical quantity” by the Hermitian operators.

This systematic literature review explores the burgeoning use of Artificial Intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. In this landscape, AI is crucial in addressing the complexity and dynamism of contemporary manufacturing processes, including machine breakdowns, fluctuating orders and unpredictable job arrivals. This systematic literature review, conducted using the Scopus database and bibliometric tools, pursues two primary objectives. First, it identifies the prevailing trends in solving scheduling problems with AI and identifies the most commonly used AI techniques in the literature. Secondly, it analyses how authors have successfully employed AI to address production scheduling challenges in real-world industrial settings and assesses the benefits obtained by companies. The dynamic nature of manufacturing systems requires adaptive scheduling paradigms. AI, including Particle Swarm Optimization, Neural Networks, and Reinforcement Learning, is applied to optimize production processes, predict machine failures, and achieve substantial benefits. In real-world applications, these AI-driven solutions have led to reduced production costs, enhanced energy efficiency, and more efficient scheduling processes. AI is increasingly recognized as an essential tool in addressing the evolving challenges of modern manufacturing environments.

A key in controlling the SARS-CoV-2 pandemic is the assessment of the immune status of the population. We explored the utility of SARS-CoV-2 virus-like particles (VLPs) as antigens to detect specific humoral immune reactions in an enzyme-linked immunosorbent assay (ELISA). For this purpose, SARS-CoV-2 VLPs were produced from an engineered cell line and characterized by western blot, ELISA and nanoparticle tracking analysis. Subsequently, we collected 42 serum samples from before the pandemic (2014), 89 samples from healthy-, and 38 samples from vaccinated subjects. Seventeen samples were collected less than three weeks after infection, and 44 samples more than three weeks after infection. All serum samples were characterized for their reactivity with VLPs and the SARS-CoV-2 N- and S-protein. Finally, we compared the performance of the VLP-based ELISA with a certified in vitro diagnostic device (IVD). In the applied set of samples, we determined a sensitivity of 95.5 % and a specificity of 100 % for the certified IVD. There were 7 samples with an uncertain outcome. Our VLP-ELISA showed superior performance with a sensitivity of 97.5 %, a specificity of 100 %, and only 3 uncertain outcomes. This result warrants further research to develop a certified IVD based on SARS-CoV-2 VLPs as an antigen.

This paper presents research on the application of trajectory design, optimization, and control to an orbital transfer from Mars-Phobos Distant Retrograde Orbits to the surface of Phobos. Given a Distant Retrograde Orbits and a landing location on the surface of Phobos, landing trajectories for which total Δv for a direct 2-burn maneuver is minimized are computed. This is accomplished through the use of Particle Swarm Optimization in which the required Δv and time of flight are optimization parameters. The non-uniform gravitational environment of Phobos is considered in the computation. Results show how direct transfers can be achieved with Δv in the order of ∼30 m/s.

The up-to-date challenge towards clean road transport are non-exhaust emissions. Important advances regarding measurement systems, including well defined characterisation techniques, as well as regulation, will happen in the next few years. In this work we present detailed results of particle emissions from a test bed for wheel suspension and brakes, consisting of aerosol (size distribution, particle number (PN) and mass (PM)) and electron microscopy measurements (EM) under different load conditions. Standard tyres and brakes from series production were tested with a high-load driving cycle while particle measurements were conducted by gravimetric measurements and with a TSI SMPS, a TSI APS and a GRIMM OPS. Furthermore, samples were analyzed by electron microscopy. A bimodal particle size distribution (PSD) was obtained with the SMPS, with peaks at 20nm and around 400nm. EM analysis of >1400 single particles from the electrostatic sampler matches the PSD results. EM analysis also showed ultrafine particles, mainly from O, Fe, Si, Ba, Mg and S and also fractal particles with high C fractions. Our results suggest, in agreement with previously published literature, that particulate emissions are related to brake disc temperature and occur in significant amounts above a threshold temperature.

These years have witnessed growing research on applying rheology in grinding and flotation treatment of finely disseminated ores. The slurry rheology has long been identified as the comprehensive effect of inter-particle interactions, including their aggregation (coagulation and flocculation) and dispersion states in slurry, which are more impactive under the fine-particle effect. In this regard, rheology has the potential to play a significant role in interpreting the flowing and deforming phenomenon of inter-particle aggregates, particle-bubble aggregates, and flotation froth. Though much attention has been paid to the rheological effect in industrial suspension, this has not been the case for mineral grinding and flotation for fine particles. The influential mechanism of rheology on the sub-processes of mineral processing has not been systemically concluded and revealed thoroughly, without which the underpinning mechanism for enhancing the processing efficiency has been difficult to discover. This paper reviews the current application and importance of rheology in the fine mineral processing, and the potential research direction in the field is proposed.

The literature has well established prestressing in concrete beams and its calculation procedures. However, its use in composite steel-concrete beams has increased despite main normative codes lacking a specific approach for it. Designers must, in most cases, combine the available criteria to calculate the standards of steel and reinforced concrete structures. This study aimed to formulate this optimization problem and analyze CO2 emissions for the optimal design of composite steel-concrete beams with external prestressing. The design variables for the optimization problem include cross-section of laminated or welded profiles, slab height, characteristic strength of concrete to compression, and number of tendons. We obtained a solution for the optimization problem via a genetic algorithm (GA) and a particle swarm optimization one (PSO). Comparative analysis with experimental example and with optimization problem of prestressed steel beam are performed. An parametric analysis to several spams is performed, which enabled to evaluate the factors that most impact CO2 emissions. According to results, the chosen algorithms effectively obtained solutions for the problem, and PSO usually provided better results than GA. Regarding the final solution composition, steel contributes the most to emissions, welded profiles provided better solutions than laminated ones, and laminated spans of up to 17.5m and welded ones up to 27.5m dispensed with tendons.

Natural gas hydrate has gained significant attention in recent years. To safely and sustainably exploit the natural gas from gas hydrate-bearing sediments, it is crucial to understand the long-term mechanical characteristics of the hydrate reservoir. In this study, the influence of hydrate and fine particles on the strain rate dependence of hydrate-bearing sediments under plane strain conditions has been studied. The experimental results show that the strain rate dependency of the mechanical properties of hydrate-bearing sediments is positively correlated with hydrate saturation instead of the morphology of hydrate in sediments. The residual strength of hydrate-bearing sediments is primarily controlled by the hydrate saturation and is independent of the strain rate. Changes in hydrate saturation, and fines content, can affect the relationship between the strain rate and shear band angle. Finally, the local volumetric expansion effect of hydrate-bearing sediments without fines content is more significant and shows a strong strain rate dependence characteristic. Overall, this study provides valuable insights into the long-term mechanical characteristics of hydrate reservoirs. These insights can contribute to the development of a constitutive model of hydrate-bearing sediments with time-dependence in the future, and which is meaningful to the exploitation of natural gas hydrate.

We still do not have satisfactory answers about the inflationary and accelerating expansions of the universe, the missing mass problem, and the origins of the four fundamental forces and the quantum phenomena. Here, we introduce a new model of matter and space and a postulate on a constant of motion, and then show that an equation of motion derived from the constant of motion could provide new understandings of the above problems. For example, we show that there is a natural inflation scenario and that the co-moving distance to the closest accelerating supernova is clearly given as $c/(\sqrt{3} H)$ ($\sim$8.1 billion ly). In addition, we show that, in a system including a proton and an electron, the equation of motion has eight acceleration terms, some of which are suspected to be responsible for the four fundamental forces and the galaxy rotation anomaly. From further discussion, we conjecture that in order to understand the gravitational and strong interactions and the quantum phenomena, it is necessary to consider the expansion of the universe. The lensing mass distribution is explained, based on another postulate on a method of representing superposition.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. So, this paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Non-Linear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) Methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

Single particle analysis with cryogenic electron microscopy (cryo-EM) can determine high resolution structures of macromolecular assemblies. To reconstruct final three-dimension(3D) density map, a reliable initial model is essential and ab-initio reconstruction usually can avoid model bias. The grand challenge for ab-initio reconstruction is to alignment two-dimension(2D) low signal-to-noise ratio(SNR) of particle images with random and unknown parameters. Here we formulate a fast ab initio reconstruction method(named AlignFM) based on Fourier Mellin transform and particle-swarm intelligence optimization(PSO) algorithm. It enables simultaneous 2D alignment and 3D refinement, which can fast generate a reliable 3D map. After experiments on several real datasets. AlignFM shows high performance on fast ab initio reconstruction with a small amount of data.

Objective To measure, exposure to PM2.5 particles in outdoor smoking areas and changes in breathing rates in patients with asthma or chronic obstructive pulmonary disease (COPD). Setting Sixty venues in Czechia, Ireland and Spain, in an open, non-randomised, clinical trial. Participants We studied 60 patients-30 asthma patients (Female 63.3%), with a mean age (+/-standard deviation [SD]) of 47.4 (19.0 SD), and 30 COPD patients (Female 51.6%), mean age 63.5 (10.1 SD), smokers, non-smokers or ex-smokers, recruited through medical clinics. Intervention Patients wore a PM2.5 particle monitor (AirSpeck), and a breath monitor (RESpeck) for 24 hours to determine changes in breathing rates (Br) at rest and during a visit to an outside smoking area. Spirometry and breath CO were measured before and the day after visiting an outdoor smoking area. Results PM2.5 levels in the 60 venues were highly variable, in 1 premises levels of PM2.5 were sustained for at least 15 minutes at ≥ 2,000 µg/m3, in 4 premises, ≥ 500 (range 1,933-539) µg/m3, in 8 premises, ≥ 200(range 480-203) µg/m3, in 9 premises, ≥ 100 (range 170-108) µg/m3, in 8 premises, ≥ 40 (range 80.5- 40.1) µg/m3, in 9 premises, ≥ 25 µg/m3, in 10 premises, ≥ 10 µg/m3, in 8 premises, and ≤10 µg/m3 in only 3 premises, with a single wall. The overall breathing rates/minute (Br)did not change significantly but in 28 patients mean Br increased from 21.47 (1.74 SD) to 22.8 (2.29 SD), change of -1.35 (-1.80-0.91 C.I), p value 0.00 and mean in 29 patients Br decreased from 21.95(2.43 SD) to 20.38(2.79 SD), 1.57(1.03-2.12 C.I), p value 0.00. Conclusion Exposure to high levels of PM2.5, and associated alteration of patients’ breathing rates occurred in outdoor smoking areas despite national comprehensive smokefree laws. These exposure levels support the abolition of such areas. (Words 295) ClinicalTrials.gov ID: NCT03074734 Ethics: Approval Number: Ref: 15-103

The mushroom industry generates large amounts of stems, a co-product with valuable sensory and health characteristics. This work aimed to study the influence of particle size (L — >0.510 mm; LI — 0.510-0.315 mm; SI—0.315-0.180 mm; S — <0.180 mm) and species in the characterization and antioxidant activity of flours from stems of Agaricus bisporus (ABSF) and Pleurotus ostreatus (POSF). ABSF was rich in protein (14 g/100 g), calcium (428.23-700.77 mg/100 g), and sorbitol (22.57-26.60 g/100 g), while POSF in β-glucans (36.62-40.34 g/100 g) and linoleic acid (20.57-39.86 g/100 g of lipid). Both species were flush in amino acids with umami flavor. The highest hydration properties and oil holding capacity were exhibited in the S size of both species. ABSF showed elevated values for emulsifying activity than POSF. ABSF-S recorded the heightened antioxidant capacity in vitro, in consonance with the total phenolic compounds (0.91 mg/g). However, this antioxidant assays in POSF presented a positive correlation with β-glucans content. According to these results, there could be several food applications for these co-products, such as emulsifier stabilizer, sweetener or fortifier in the development of functional food.

Syntactic foam has proven to be a good material with a strong structural strength. Understanding filler particle size variation is important in composite material formation especially in syntactic foam because of their numerous applications such as aerospace, marine and structural purposes. In this present work, the effects of particle variation in different sizes (20-24µm, 25-44µm, 45-49µm, and 50-60µm) on the mechanical properties of the syntactic foam composites with focus on flexural strength, modulus, and fracture surfaces were investigated. The particle sizes were varied into five volume fractions (5, 10, 15, 20, and 25vol%). The results shows that highest flexural strength is 89MPa at 5 vol% fraction of 50-60µm particles size variation which is 69% increase than the neat epoxy. This implies that the incorporation of HGM filler volume fraction and size variation has a strong effect on the flexural strength and bending modulus of syntactic foam. The microstructure of the fracture surfaces and the viscoelastic properties was determined and reported accordingly.

Indonesia's SUMBAGUT 150 kV transmission of High Voltage Alternating Current Network (HVAC) system has considerable power losses. These power losses are a critical problem in the transmission network system. Meanwhile, this study provides one solution to reduce power losses using a High Voltage Direct Current (HVDC) network system. Determining the location to convert HVAC into HVDC is very important. The authors use Particle Swarm Optimization (PSO) to get the optimal location on the 150 kV SUMBAGUT HVAC transmission network system. The study results showed that before using the HVDC network system, the power losses were 122.26 MW. Meanwhile, power losses with one transmission HVDC in the "Paya Pasir-Sei Rotan" are 84.16 MW, "Porsa-P. Siantar" 90.83 MW, "Paya Pasir-Paya Geli" 104.14 MW. Then power losses with two transmission HVDC in "Paya Pasir-Sei Rattan" and "Porsa-P. Siantar" is 71.24 MW, "Paya Pasir-Sei Rotan" and "Paya Pasir-Paya Geli" 77.46 MW, "Porsa-P. Siantar" and "Paya Pasir-Paya Geli" 78.52 MW. The last result, power losses with three transmission HVDC in "Paya Pasir-Sei Rotan," "Porsa-P. Siantar," and "Paya Pasir-Paya Geli" lost 64.57 MW.

In recent years, unquestionable warnings like the negative impacts of CO2 emissions, the necessity of utilizing sustainable energy sources, and the rising demand for municipal electrification have been issued. In this study, by incorporating two significant assumptions, such as electricity production in close proximity to the business location and only renewable energy resource usage, a modest off-grid hybrid energy system is designed. To construct the system, a number of elements such as wind generators (WG), photovoltaic arrays (PV), battery banks, and bi-directional converters are taken into account. Moreover, a real case in Malmö, Sweden, is considered. To optimize the system, a bi-objective problem is developed, and it is solved by proposing a particle swarm optimization (PSO) approach to provide the load requirements (with a maximum allowance of 0.1% unmet) for a nearby supermarket (approximately 1000 m2). Moreover, to verify the obtained results, the developed system is simulated using HOMER Pro software, and the results are compared and discussed. The contribution of this study is to provide off-grid or local clients around the world with a dependable and affordable option by minimizing both the baseline cost of energy and the net current expenditure in the desired system. The best-obtained results by the proposed PSO offered 160 PVs, 5 WGs, and 350 batteries, respectively, while the best solution found by the simulation method was using 384 PVs, 5 WGs, and 189 batteries for the considered off-grid system.

The need to regulate the non-exhaust particulate emissions from vehicles has been discussed worldwide due to the toxicity to the human body as well as the atmosphere. In-depth studies have been conducted on precise analysis of the non-exhaust particulates, in particular, accurate measurement of tire-brake-road wear particles, and their proportion in the atmosphere. In this study, the influence of tire and road wear particles (TRWP) on particulate matter (PM) in the atmosphere was investigated through tire and PM samples. PM samples were collected from the atmosphere using a high-volume sampler equipped with a quartz filter. Additionally, polycyclic aromatic hydrocarbons (PAHs) and heavy metals in tire rubber were analyzed as markers by pyrolysis-gas chromatography/mass spectrometry (GC/MS), GC/MS, and inductively coupled plasma/mass spectrometry (ICP/MS). More vinylcyclohexene was detected than dipentene in the markers measured in the samples of tires equipped with vehicles driving on the road with the high-volume sampler installed, while more dipentene was detected in total suspended particles (TSP) samples. Among the PAHs measured in tire samples, pyrene exhibited the highest concentration. In TSP samples, benzo(b)fluoranthene showed the highest concentration. Among the heavy metals, zinc exhibited the highest concentration in all tire samples and calcium in TSP samples.

In recent years, computer-aided analysis techniques have emerged as valuable tools in assisting dermatologists by providing objective and efficient analysis of skin cancer images. This paper utilizes the combination of the Contourlet Transform (CT) and Local Binary Pattern (LBP) techniques for accurately recognizing borders, contrast changes, and shapes of skin cancer images. These results often contain many features, leading to high computational costs and potential over-fitting issues. Hence, we applied Particle Swarm Optimization (PSO) to select the most informative and discriminating features, reducing the dimensionality while retaining important information for accurate classification. After reducing the feature set with PSO, we applied these sets to Machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN). The results show that SVM has the lowest time complexity of 0.0458 seconds, followed by the Neural Network at 0.08730 seconds, and the Random Forest model has the highest time complexity of 0.1622 seconds. The SVM and Neural Network models are faster to train than the Random Forest model, making them more suitable for real-time or latency-sensitive applications. We also compared our proposed model with the state-of-the-art models and obtained the accuracy of 86.9%, which is the highest among the models.

The pandemic SARS-CoV-2 continues to evolve to give rise to variants of concern that can escape vaccine-induced immunity. As such, more effective vaccines are urgently needed. In this study, we evaluated virus-like particle (VLP) as a vaccine platform for SAS-CoV-2. The spike, envelope, and membrane proteins of the SARS-CoV-2 Wuhan strain were expressed by a single recombinant baculovirus BacMam and assembled into VLPs in cell culture. The morphology and size of the SARS-CoV-2 VLP as shown by transmission electron microscopy was found to be similar to the authentic SARS-CoV-2 virus particle. In a mouse trial, two intramuscular immunizations of the VLP BacMam with no adjuvant elicited spike-specific binding antibodies in both sera and bronchoalveolar lavage fluids. Importantly, BacMam VLP -vaccinated mouse sera showed neutralization activity against SARS-CoV-2 spike pseudotyped lentivirus. Our results indicated that the SARS-CoV-2 VLP BacMam stimulated spike-specific immune responses with neutralization activity.

This work is aimed at investigating the capabilities and limits of the mid-fidelity numerical solver DUST for the evaluation of wind turbines aerodynamic performance. In particular, this study was conducted by analysing the benchmarks NREL- 5MW and Phase VI wind turbines, widely investigated in literature by from experimental and numerical activities. The work started by simulating simpler configuration of the NREL- 5MW turbine to progressively integrate complexities such as shaft tilt, cone effects and yawed inflow conditions, offering a detailed portrayal of their collective impact on turbine performance. A particular focus was then given to the evaluation of aerodynamic responses from the tower and nacelle, as well as aerodynamic behavior in yawed inflow condition, crucial for optimizing farm layouts. In the second phase, the work was focused on NREL Phase VI turbine due to the availability of experimental data on this benchmark case. Comparison of DUST simulations results with both experimental data and high-fidelity CFD tools shows the robustness and adaptability of this mid-fidelity solver for this applications, thus opening a new scenario for the use of such mid-fidelity tools for the preliminary design of novel wind turbines configurations or complex environments as wind farms, characterised by robust interactional aerodynamics.

Low-cost particulate matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and their cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors but some models of sensors also report particle number concentrations (PNC) segregated into different PM size bins. In this study, 8 units of each Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors to different particle size distributions to quantify and better understand the low cost sensors performance across a range of relevant environmental ranges. The PNC reported by the sensors were analysed. This study showed that there is added value in directly using the PNC reported by the sensors instead of the mass concentrations, which could aid the efforts to calibrate these sensors to a known accuracy. It demonstrated that all sensors tested here could track the fine temporal variation of PNC, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it showed that particle size distribution and composition are more impactful on the sensors measurement than relative humidity.

Electrically conductive adhesives (ECA’s) are used widely to replace or reinforce lead soldering or conductive metal components in electronic packaging applications such as die attachment, solderless interconnections, component repair, display interconnections, and heat dissipation. Their conductive behavior as well as the associated behaviors such as heat conduction and mechanical behavior (strength, rigidity, deformation and viscoelastic behavior, which may be affected by moisture ingression) are affected by adhesive film thickness, volume fraction, size and shape of the conductive filler, as well as uncured base adhesive viscosity, substrate and filler surface treatment and the applied pressure (during bonding and during conduction). The adhesive resistivity decreases precipitously above a characteristic filler volume fraction called the percolation threshold. In general, micron-sized metal fillers mixed in an adhesive (often an epoxy) resulting with different film thicknesses exhibit thickness thresholds for transition from three-dimensional conductivity to two-dimensional conductivity with considerable increases in thickness-direction (z-axis) resistivity when the film thicknesses are smaller than these threshold values. Recently, the use of conductive nanoparticles allowed decreases in percolation threshold levels as well as increases in mechanical strength and durability of ECA’s. Most ECA’s are supplied in liquid or paste form in varying viscosities and therefore, the method of their application also affects their performance. This work intends to provide an understanding of these effects on conduction behavior in (usually high-priced) equipment in which they are used.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

Despite recent improvements in the methods used to eradicate and minimize mosquito populations, they still pose severe threats to human health. This study aimed to investigate the Larvicidal potentials of silver nanoparticles of Ocimum gratissimum on wild Anopheles larvae from Lagos. The plant used in this study was collected from the southeastern part of Nigeria, Ibite-Olo in Ezeagu Local Government Area of Enugu State. Wild Anopheles mosquito larvae were collected from a field at Kosofe Local Government Area of Lagos State, Nigeria where resistant Anopheline populations have been reported in and around Lagos State were brought to the laboratory and kept under standard insectary conditions (25–29 °C;78–82% relative humidity). Aqueous extract of Ocimum gratissimum was made and subsequently into the silver nanoparticles. A bioassay test was performed according to WHO guidelines, with six different concentrations to assess the larvicidal activity and values obtained were subjected to log prohibit regression analysis to obtain LC₅₀ and values with 95% confidence limit. The results of the larvicidal activities of the silver nanoparticles of Ocimum gratissimum showed that the mortality rate was dose-dependent with the percentage mortality increasing at a direct proportional rate as with dose and time of exposure. Ocimum gratissimum gave LC₅₀ at 0.5 ppm and LC₉₅ at 2.5 ppm respectively. The biosynthesized silver nanoparticles augmented the larvicidal activity of the silver nanoparticles of Ocimum gratissimum on wild Anopheles larvae from Lagos. Therefore, this study showed that the silver nanoparticles of Ocimum gratissimum have the potency to be used against an insecticide-resistant mosquito vector and are a suitable candidate for the development of novel natural insecticides.

Solar flares are broadly classified as impulsive or gradual. Ions accelerated in a gradual flare are thought to be accelerated through a shock acceleration mechanism, but the particle acceleration process in an impulsive flare is still largely unexplored. To understand the acceleration process, it is necessary to measure the high-energy gamma-rays and neutrons produced by the impulsive flare. Under such circumstances, on November 7, 2004, a huge X2.0 flare occurred on the solar surface, where ions were accelerated to energies greater than 10 GeV. The accelerated primary protons collided with the solar atmosphere and produced line gamma-rays and neutrons. These particles were received as neutrons and line gamma-rays, respectively. Neutrons of a few GeV, on the other hand, decay to produce secondary protons while traveling 0.06 au in the solar-terrestrial space. These secondary protons arrived at the magnetopause. Although the flux of secondary protons is very low, the effect of collecting secondary protons arriving in a wide region of the magnetosphere (the Funnel or Horn effect) has resulted in significant signals being received by the solar neutron telescope at Mt. Sierra Negra (4,600 m). This information suggests that ions on the solar surface are accelerated to over 10 GeV with an impulsive flare.

Recently, a paper was written to establish semi-structured complex numbers ℍ (a new number set invented to enable division by zero) as a useful mathematical tool providing novel results from its application in engineering and science. Whilst the paper was a milestone, the application of this number set to quantum and classical physics had not been fully explored. Consequently, the aim of this research was to use semi-structured complex numbers to develop a new mathematical model for the mass of an object and in course unite classical and quantum physics in a simple yet effective manner. As its major contributions this paper: (1) develops a new mathematical model for the mass of an object called the wave-mass function using De Broglie’s mass and frequency relation and semi-structured complex numbers; (2) uses the new wave-mass function to determine what happens to an object traveling at the speed of light; (3) derives a relation between Schrödinger’s wave function and the new wave-mass function; (4) derives a relation between Einstein’s Field Equation and the new wave-mass function; (5) uses the wave-mass function to combine Einstein’s Field Equation with Schrödinger’s wave function to form a new quantum gravity equation. These results and their applications provide a firm foundation to advance the number set ℍ as a useful mathematical tool.

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing for medical image segmentation, focusing on Lung CT-Scan and Chest X-ray datasets. Best Cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly in complex Lung CT-Scan images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially in Chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach in advancing the accuracy and reliability of medical image segmentation, paving the way for further research and method integration to enhance this critical healthcare application.

Nanoparticles are of great importance for various scientific and technological applications, drug discovery, ecology, molecular biology, etc. Size is a basic characteristic of any particle, and dynamic light scattering is beyond competition when measuring particle size in dispersions ranging from nanometers to micrometers. Unfortunately, this method has a serious problem in processing and interpreting experimental data. Instrument noise and polydispersity of the sample lead to the situation when one initial autocorrelation function corresponds to several different particle size distributions. Sometimes these different solutions of the inverse task can be found by changing parameters in the software. Sometimes the scientist cannot be suspicious of possible other solutions, having one result from the program. In this article I demonstrate the problem on model and experimental data using three known programs: CONTIN, the Malvern Zetasizer and DynaLS. I also present my own free program Autocor, which allows to test our own hypotheses about the modality and shape of the particle size distribution by optimizing the parameters of our own models with a small number of degrees of freedom.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.

Polyaniline or PANI is a conductive polymer (CPs) with flexibility in turning into conducting properties. The drawback is mainly in terms of limited mechanical properties improvement which attracted the interest in fabrication of PANI with other polymeric materials. PANI-PVL composites film was synthesized and fabricated in this work with chronological steps according to the objectives aligned in two different phases. The first phase determines the optimum parameters to synthesize nanosized PANI. The ideal initial molar ratio, r was 1, stirring speed of 600 rpm, purified by filtration, synthesis temperature of 25 oC, washed by dopant acid, acetone and distilled water. The usage of non-ionic surfactant, Triton X-100 (TX100) with 0.1 % concentration favours the formation of smaller particle size of PANI and good dispersibility compared to the usage of anionic sodium dodecyl sulphate (SDS). The synthesis of PANI in nanosized dimension has the potential to be applied for antimicrobial applications such as gloves and antimicrobial coating of frequent contact points in the near future.

One of the most widely available and extensively produced varieties of industrial hemp, which generates a substantial amount of consumer-increasing waste in the form of hemp cellulose. In this study, recycling with a compressed and high-speed method that combines crushing and alkali treatment effectively converts leftover hemp fiber into ultrafine powder. SEM (Scanning Electron Microscopy), AFM (Atomic Force Microscope), FTIR (Fourier-transform Infrared Spectroscopy), and XRD (X-ray Diffraction) examined the original morphology of hemp fiber treated with alkali, fiber heated to 200 ℃, and crushed powder. Particle size, crystallinity, fiber surface, and strength increased and decreased. It became apparent that fiber strength decreased and fiber roughness significantly increased after alkali treatment. Fiber increased significantly, and crystallinity decreased after crushing. It was simple and effective for converting the leftover hemp fiber into micron-sized powder. In about 3 to 5 minutes, approximately 1 kg of dry ultrafine powder with a particle size of 10.44 μm was produced. This production method will significantly enhance future industrial applications of waste hemp fiber.

The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.

Proteasomes critically regulate proteostasis via protein degradation. Proteasomes are multisubunit complexes composed of the 20S proteolytic core particle (20S CP) that, in association with one or two 19S regulatory particles (19S RPs), generate the 26S proteasome. 26S is the major proteasomal complex in the cells. Native gel protocols are used to investigate the 26S/20S ratio. However, a simple method for detecting these proteasome complexes in the cells is missing. To this end, using CRISPR technology, we YFP tagged the endogenous PSMB6 (b1) gene, a 20S CP subunit, and co-tagged endogenous PSMD6 (Rpn7), a 19S RP subunit, with mScarlet fluorescent protein. We observed colocalization of YFP and mScarlet fluorescent proteins in the cells, with higher nuclear accumulation. Nuclear proteasomal granules are formed under osmotic stress, and all were positive for YFP and mScarlet. Previously we have reported that PSMD1 knockdown, one of the 19 RP subunits, gives rise to a high level of “free” 20S CP. Intriguingly, under this condition, the 20S-YFP remained nuclear, whereas the PSMD6-mScarlet is mostly in cytoplasm, demonstrating the distinct subcellular distribution of uncapped 20S CP. Lately, we have shown that the PSMA3 (a7) C-termininus, a 20S CP subunit, binds multiple intrinsically disordered proteins (IDPs). Remarkably, truncation of the PSMA3 C-terminus is phenotypically reminiscent of PSMD1 knockdown. These data suggest the PSMA3 C-terminal region is critical for the 26S proteasome integrity.

Biochar is a carbon-rich material that recently received attention due to its increasing agronomical potential. The agricultural utilization of biochar relates to its potential to act in the soil as a soil conditioner, nevertheless, the complex information on the direct dependence of biochar physical properties (texture, particle size) and corresponding leaching and availability of organic molecules (e.g. the polycyclic and heterocyclic organic compounds) and inorganic mineral salts (based on micro– and macroelements) are still not consistent. These aspects were addressed in our study. A multi-elemental analysis (ICP-OES) was used to access the information on the aqueous-extractable and total contents of inorganic macro- and macroelements in used commercial biochar samples (purchased from Sonnenerde GmbH, Novoterrra Sonnenerde GmbH, Biouhel.cz, s.r.o.). Potassium (K) was identified as the major macroelement, responsible for alkaline pH and the conductivity of biochar extract. Our results indicate an indirect relation between the size fraction of biochar and the content of extractable K, sodium (Na), and calcium (Ca) and a direct relation with the contents of phosphorus (P) and magnesium (Mg). As the total determined amounts of inorganic elements were significantly higher compared to aqueous-extractable amounts, we believe it could indicate the potential for repeated release to the surrounding soil. These findings were also confirmed by the analysis of biochar samples obtained after the cultivation (duration of 2 years) in selected representative soil samples from the Czech Republic (Cambisol, Fluvisol, Regosol, Chernozem). Moreover, gas chromatography (GC) coupled with mass spectroscopy (MS) was further used to reveal the concentrations of available polycyclic aromatic and heterocyclic compounds in biochar samples. The detected concentration of these types of organic compounds were far below the certified limits. We believe our in-depth analysis could provide the necessary insight into the description of biochar mineral content, its connection to biochar texture, corresponding physicochemical properties and potential availability to leach nutrients to the soil. These findings could help in further assessment of the potential applications of biochar in modern agriculture as a soil conditioner.

Pharmaceutical aerosols play a key role in the treatment of lung disorders, but also systemic diseases, due to their ability to target specific areas of the respiratory system (RS). This article focuses on identifying and clarifying the influence of various factors involved in the generation of aerosol micro- and nanoparticles on their regional distribution and deposition in the RS. Attention is given to the importance of process parameters during aerosolization of liquids or powders and the role of aerosol flow dynamics in RS. The interaction of deposited particles with the fluid environment of the lung is also pointed out as an important step in the mass transfer of the drug to the RS surface. The analysis presented highlights the technical aspects of preparing the precursors to ensure that the properties of the aerosol are suitable for a given therapeutic target. Analyzing existing technical limitations, several potential strategies for enhancing the efficiency of targeted therapeutic delivery to or through the respiratory system are identified and presented, including the use of smart inhaling devices incorporating AI algorithms.

This study aimed to investigate the interactions between corn particle size (PS) and conditioning temperature (CT) on the performance, carcass traits, intestinal morphology, and immune responses in broilers fed a corn-soybean meal-based diet. A total of 360, one-day-old male broiler chicks (Ross 308) were randomly allocated into six dietary treatments in a 2×3 factorial arrangement, consisting of two corn PS (finely ground with geometric mean diameter (GMD) of 357 µm (PSF) vs. coarsely ground corn with GMD of 737 µm (PSC), and three CT [unconditioned (CTU), conditioned at 75◦C (CT75) and 90◦C (CT90)]. Birds accommodated in 30 pens with five replicates and 12 chicks per each. There was no interaction between corn PS and CT on growth performance and immune response of broilers at any growth phases. However, during the starter (0-10d) period, the average daily weight gain (ADWG) and feed conversion ratio (FCR) of PSF-fed birds were significantly improved compared to those fed PSC (P<0.05). During the starter (0-10d) and grower (11-24d) periods, increasing the conditioning temperature of corn increased the ADWG, while in the starter phase only the CT75 caused a lower FCR (P<0.05). Broilers fed PSF corn showed lowest FCR during the finisher (25-42d) period compared to those fed PSC (P<0.05). Conditioning corn at 75ºC reduced FCR during the finisher (25-42d) period compared to the birds fed CTU and CT90 corn (P<0.05). In whole experimental periods (1-42d), PSF and CT75 treatment increased the ADWG compared to the PSC and CTU (P<0.05). The CT75 treatment improved primary total anti-sheep red blood cell (SRBCs) titer (IgT) and IgM and secondary IgT and IgG responses compared to the other experimental groups (CTU and CT90) (P<0.05). No significant PS × CT interaction was found on Newcastle disease (ND) antibody titer of broiler chickens (P>0.05). Feeding CT75 corn reduced duodenum and jejunum relative length compared to the birds fed diets containing CTU corn. Significant PS×CT interactions (P<0.05) were observed for villus height, villus height to crypt depth, crypt depth, muscle thickness, and absorption surface area of jejunum. The highest carcass yield observed in PSF-CT75-group (P<0.05). In conclusion, the use of finely ground corn (PSF) conditioned at 75℃ (CT75) was beneficial to growth performance, development of the digestive tract, jejunum histomorphometry and the immune responses of broilers.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

We consider a new model of a branching random walk on a multidimensional lattice with continuous time and one source of particle reproduction and death, as well as an infinite number of sources in which, in addition to the walk, only absorption of particles can occur. The asymptotic behavior of the integer moments of both the total number of particles and the number of particles at a lattice point is studied depending on the relationship between the model parameters. In the case of the existence of an isolated positive eigenvalue of the evolution operator of the average number of particles, a limit theorem is obtained on the exponential growth of both the total number of particles and the number of particles at a lattice point.

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model's response closely matches the experimental data, with a maximum prediction error of 10% or less. Furthermore, the interpolated model's response shows good agreement with the experimental data, with a maximum percentage error of less than 8.5%.

Motivated by the PEACOC project on metal recovery from solid wastes, an innovative magnetic density separation (MDS) process has been developed for granular material sorting. It has intrinsic advantages over the existing industrialized MDS by eliminating fluid turbulence and particle jamming problems. The new MDS applies an inclined planar magnet and a horizontal basin containing a static magnetic fluid as the separation medium. A particle sliding phenomenon is identified as a feature of the process which could help the separation. The MDS successfully sorted shredded PCBAs to concentrate valuable metals and sorted shredded wires to reduce metallic contaminants in the plastic fractions. A pilot scale facility is introduced to show the design to achieve continuous production and to reduce the consumption of ferrofluid.

The economical load dispatch problem is a critical concern in thermal power systems to ensure cost-effective operation. This paper conducts a comparative analysis between the Social Spider Algorithm (SSA) and Particle Swarm Optimization (PSO) for economical load dispatch in a 6-unit thermal power system with the integration of 11,000 plug-in electric vehicles (PEVs). Both SSA and PSO are population-based optimization techniques designed to minimize the overall fuel cost while satisfying the load demand by determining the optimal power output of each unit. SSA emulates the social behavior of spiders, while PSO simulates the collective intelligence of a swarm of particles. Performance evaluation of the algorithms considers power system data, such as unit fuel costs, minimum and maximum loads, and total load demand. The objective is to minimize fuel cost while meeting the load demand. Comparative analysis of SSA and PSO includes convergence speed, solution quality, and computational efficiency. Experimental results indicate that SSA outperforms PSO in achieving a more optimal and economical load dispatch solution for the 6-unit thermal power system. SSA demonstrates faster convergence and provides superior-quality solutions compared to PSO. This paper contributes to the field by highlighting SSA's effectiveness in achieving cost-efficient operation of thermal power systems. The findings suggest that SSA holds promise as an optimization technique for similar power system optimization problems, enhancing overall operational efficiency and reducing costs for thermal power plants.

The transportation of feed pellets via pneumatic conveying plays an improtant role in feed industry. In recent years, many efforts are made both exprimentally and numerically to gain a better understanding of the underlying mechanism for pneumatic conveying. Though few of those research is directly related to feed pellets, it can be beneficial to draw a full picture of rencent development in pneumatic conveying, with emphasis on aspects extendable to feed transportation. This paper reviews first the major factors concerning feed pellets pneumatic conveying, such as particle breakage, pipe wear and electrostatic. Building on that, new advancement including emerging new technologies in experiments and simulations are discussed, with a focus on the experimental measurements and mathematical models, repectively. The needs for future research and the analogy to feed pellets tranportation is given lastly.

The reactive nature of traditional frequency stability methods can lead to delayed corrective actions and unnecessary loss-of-load. This work presents a distributed model predictive control method for proactive power system frequency stability. Dynamic state estimation model is derived through a particle filter. By being able to estimate the future state of frequency, corrective actions can be taken before the system reaches a critical condition. Proactive approach makes it possible to optimize the response to a disturbance, which results in a decrease of the amount of compensation utilized. The method is tested via Matlab simulations based on Kundur's Two-Area System, and the IEEE 14-Bus system. Performance metrics are provided and evaluated against other contemporary solutions found in literature. Easy-to-derive model, without hard-to-design parameters, indicate potential towards real-life applications.

In this work, we investigated whether the Pseudo-Newtonian theory (PNP) is applicable to black hole solution in modified gravity. In this regard, as an example, a static spherically symmetric 4D AdS Schwarzschild Yang-Mills black hole in EinsteinGauss-Bonnet gravity is considered. Also, a brief discussion on the structure and horizons of this black hole spacetime is presented. We calculated angular momentum and energy in the framework of the general theory of relativity as well as in PNP theory. In both of these framework, a graphical comparison of angular momentum has been studied

The solid particle erosion at room and elevated temperatures of pure and filled hot-curing epoxy resin on anhydride hardener was experimentally tested using accelerated method on special bench. Micro-sized dispersed industrial wastes were used as fillers: fly-ash from a power plant and spent filling material from a copper mining and processing plant. The results showed that the unfilled resin wear significantly decreases with increasing temperature, while the dependence on the temperature of the wear intensity at an impingement angle of 45o is linear inversely proportional, and at an angle of 90o non-linear. The decrease in wear intensity is probably due to an increase in elasticity (an increase in the fracture strain limit) because of heating. Solid particle erosion of the examined filled epoxy compounds is considerably higher than that of unfilled compounds at impingement angles of 90o and 45o. Filled compounds showed ambiguous dependences of intensity of wear from temperature (especially at an angle of attack 45o), probably, character of dependence is defined by a filler share and the structural features of the samples caused by distribution of particles of the filler. Intensity of wear of the considered compounds at impingement angles 90o and 45o has high direct correlation with density and the modulus of elasticity (i.e., increases with their increase), and low correlation with bending strength of the considered materials. The data set for determining the correlation between mechanical properties and wear included characteristics due to both compound filling and test temperature.

In a complex working environment, the fault signal of the gearbox is greatly affected by the outside world, and fault feature recognition is difficult, so the fault diagnosis accuracy is difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the Particle Swarm Optimization algorithm in Adaptive Weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that, compared with existing commonly used methods of fault diagnosis, the fault diagnosis model proposed in this paper can reduce the influence of noise in the original signal can better learn the deep-level features in the original signal and has higher diagnosis accuracy, faster diagnosis speed and good generalization in fault diagnosis.

In this work, a generalized analogue to the Pauli spin matrices is presented and investigated. The proposed Hermitian spin matrices exhibit a general symmetry. The generalized spin projection operators are derived and the electrodynamics for hypothetical fermions are explored using the proposed spin matrices. The fermionic quantum Heisenberg model is constructed using the proposed spin matrices and comparative studies against simulation results using the Pauli spin matrices are conducted. Further analysis on key findings as well as discussions on extending the proposed spin matrix framework to describe hypothetical bosonic systems (spin-1) are provided.

Particle acceleration, and the thermalisation of energetic particles, are fundamental processes across the universe. Whilst the Sun is an excellent object to study this phenomenon, since it is the most energetic particle accelerator in the Solar System, this phenomenon arises in many other astrophysical objects, such as active galactic nuclei, black holes, neutron stars, gamma ray bursts, solar and stellar coronae, accretion disks and planetary magnetospheres. Observations in the Extreme Ultraviolet (EUV) are essential for these studies but can only be made from space. Current spectrographs operating in the EUV use an entrance slit and cover the required field of view using a scanning mechanism. This results in a relatively slow image cadence on the order of minutes to capture inherently rapid and transient processes, and/or in the spectrograph slit `missing the action'. The application of image slicers for EUV integral field spectrographs is therefore revolutionary. The development of this technology will enable observations of EUV spectra from an entire 2D field of view in seconds, over two orders of magnitude faster than what is currently possible. The Spectral Imaging of the Solar Atmosphere (SISA) instrument is the first integral field spectrograph proposed for observations at 180A combining the image slicer technology and curved diffraction gratings in a highly efficient and compact layout, while providing important spectroscopic diagnostics for characterization of solar coronal and flare plasmas. SISA's characteristics, main challenges and the on-going activities to enable the image slicer technology for EUV applications are presented in this paper.

We investigate the Friedmann–Lemaitre–Robertson–Walker cosmological models within the framework of Rastall gravity incorporating particle creation. The modified field equations for Rastall gravity are derived and exact solutions are obtained under various choices of scale factors. The qualitative behaviour of our solutions depends on the Rastall coupling parameter ψ=kλ. Following the literature, we have restricted the Rastall coupling parameter ψ(k=1) to the range −0.0001 < ψ < 0.0007 at 68% CL from CMB+BAO data. Further, we have discussed the distinct physical behaviour of the derived models in detail.

Space exploration introduces astronauts to many challenges such as space radiation and microgravity. Researchers have investigated vitamin C as a potential radiation mitigator and antioxidants for sustaining astronaut health. Building on our own studies, which indicate vitamin C's life-saving radioprotective effects and its potential as a radiation mitigator, our research highlights its promise, even when administered 24 h post-exposure. This is particularly relevant in scenarios where astronauts may be exposed to sudden large solar particle events, potentially resulting in lethal doses of space radiation. The success of vegetable cultivation on the International Space Station using NASA's Veggie system offers fresh, vitamin C-rich food. While approved supplements address somatic function, further research is needed to optimize vitamin C's efficacy in humans and develop antioxidant cocktails for space missions. The variable vitamin C content in vegetables underscores the necessity for utilization of artificial intelligence (AI) to assist astronauts in selecting and cultivating vitamin C-rich vegetables that are best suited to combat high levels of space radiation and microgravity. Particularly, AI algorithms can be utilized in analyzing various factors such as nutritional content, growth patterns, and cultivation methods. In conclusion, vitamin C shows significant potential for mitigating space radiation, and ongoing research aims to enhance astronaut health through optimal dietary strategies.

In this work, the nearest neighbor distances and Voronoi cell features of the Cu-Ag deposits were analyzed and fitted with Lognormal, Weibull, and Gamma distributions. The nearest neighbor distance distributions of the samples were compared with those of complete spatially random points, showing spatial inhomogenity due to the nucleation exclusion effect. The radial distribution function was calculated, showing both influences from the grain size and the nucleation exclusion effect. Voronoi cells were generated based on the shape of the grains. The size, occupancy, and coordination of the Voronoi cells were examined and fitted. The results show that despite the Cu-Ag deposits seem to be governed by instantaneous nucleation mode, the spatial distribution of the nuclei is more impacted by the nucleation exclusion effect than the Cu-only samples. This behavior is also justified by the grain size distribution generated with Voronoi cell size and occupancy distributions.

An axial wind turbine's performance is analyzed numerically using geometrical and operational parameters. The performance of an axial wind turbine is simulated using three-dimensional computational fluid dynamics (CFD) and compared with experimental data, which confirms the validity of the assumptions and method used. A study of the effects of air in clean and sand particlesy conditions is then conducted. A study was also conducted on mechanical power and the impact of the blade tip on the extension of the vortex. It has been found that different sand particle sizes have a significant impact on wind performance numerically from 0.1 to 0.9, and the results indicate that mechanical power and thrust increase as wind velocity increases, but larger sand particle sizes reduce power, thrust, and torque as wind velocity increases. The turbulence intensity causes the pressure distribution on the walls to be irregular, thereby increasing mechanical power and propulsion force. Moreover, as the wind velocity increases, the tip speed ratio increases and the vortexes are subsequently extended.

In current study, the production of microparticles from fifteen commercial sugarcane pulp (SCP) lunch-boxes into different types of food under different conditions was investigated, where we used DI water, 4% HAc, and 95% EtOH to simulate aqueous, acidic, and fatty foods. Results showed that compared with DI water and 95% EtOH, 4% HAc caused the degradation of sugarcane fibers, resulting in the highest number of microparticles released, which was significantly greater than the specified limit value. It was estimated that consumers might intake a total number of 36,400 ~ 231,700 items per meal per lunch-box, of which the proportion of microparticles with particle size between 100 ~ 500 μm was the largest, with a number of 12,376 ~ 130,795 items. Moreover, of all SCP lunch-box except S-11, Al and Fe are the main metals, ranging between 35.16 ~ 1244.04 and 44.71 ~ 398.52 mg/kg, respectively, followed by Pb, Ti, and Sr. Interestingly, unlike other lunch-boxes, the content of Pb in S-11 was the highest, with a number of 136 mg/kg. This study provides important information that the safeness including both the production of microparticles and the metallic elements should be considered for SCP lunch-boxes when in contact with food.

A recently proposed single image parasite quantification (SIMPAQ) platform based on a Lab-On-a-Disc (LOD) device has been earlier successfully tested in field conditions demonstrating the efficiency in soil-transmitted helminths (STH) egg detection and analysis. A related study (Micromachines 2021, 12, 1032) revealed the effects that can limit the performance of a SIMPAQ method due to the action of the Euler and Coriolis forces, and the interaction of the moving eggs with the walls of the LOD chamber. Here we propose a new improved design that allows to overcome those limitations and enhance the yield of the SIMPAQ LOD device which is demonstrated in the experiments with synthetic particle model system and real parasite eggs.

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE), to include constrained optimization in it, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a deferential amplifier, a two-stage operational amplifier, and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works.

The exceptional performance advantages of the fiber optic gyroscope (FOG) position it as a dominant player in middle and high-end inertial navigation systems. To prevent the loss of sensor precision caused by algorithm design and simplify the complex modeling strategy in traditional methods. We gradually demonstrate the significant role of Convolutional Neural Network (CNN) in the navigation system based on FOG, and utilize the particle swarm optimization algorithm (PSO) to expedite the convergence of the network. The experimental results demonstrate that the initial alignment method based on deep learning is more accurate than the traditional method. The attitude angle error is reduced by 81.25%, 92.54% and 36.53% respectively. The research provides support for the future application of deep learning in optical navigation systems.

The interest on amorphous carbon thin films with low secondary electron yield (SEY) has been increasing in the last years, to mitigate electron multipacting in particle accelerators and in RF devices. Previous works found that the SEY increases with the hydrogen amount and correlates with the Tauc gap. In this work, we analyse films produced by magnetron sputtering with different contents of hydrogen and deuterium, incorporated via target poisoning and sputtering of CxDy molecules. XPS was implemented to estimate the phase composition of the films. The maximal SEY was found to decrease linearly with the fraction of the graphitic phase in the films. These results are supported by Raman scattering and UPS. The graphitic phase decreases almost linearly for hydrogen and deuterium concentrations between 12% and 46% (at.), but abruptly decreases when the concentration reaches 53%. This vanishing of the graphitic phase is accompanied by a strong increase of SEY and of the Tauc gap. These results suggest that the SEY is not dictated directly by the concentration of H/D but by the fraction of graphitic phase. The results are supported by a model used to calculate SEY of films consisting of a mixture of graphitic and polymeric phases.

The Dirac and Klein-Gordon equations have proven to be some of the most accurate theories for describing particle behaviour and have laid the foundations of Relatavistic Quantum Mechanics. We extend the formulations to consider a 5 Dimensional spacetime with two time dimensions (as first conceptualised by Sajjad Zahir) and extend the notions to Quantum Field Theory to arrive at the behaviour of particles through the Dirac and Klein-Gordon counterparts of the 5 Dimensional spacetime. The implications of this to anti-matter are discussed.

For order-of-addition experiments, the response is affected by the addition order of the experimental materials. Consequently, the main interest focuses on creating a predictive model and an optimal design for optimizing the response. Van Nostrand (1995) proposed the pairwise-order (PWO) model for detecting PWO effects. Under the PWO model, the full PWO design is optimal under various criteria but is often unaffordable because of the large run size. In this paper, we consider the D-, A- and M.S.-optimal fractional PWO designs. We first present some results on information matrices. Then, a flexible and efficient algorithm is given for generating these optimal PWO designs. Numerical simulation shows that the generated design has an appealing efficiency in comparison with the full PWO design, though with only a small fraction of runs. Several comparisons with existing designs illustrate that the generated designs achieve better efficiencies, and the best PWO designs and some selected 100% efficient PWO designs generated by the new algorithm are reported.

In this paper, we discussed the motion of neutral and charged particles in Schwarzschild black hole immersed in an electromagnetic universe (SEBH). The SEBH represents a mass M coupled to an external electromagnetic field that was derived from the metric of colliding electromagnetic waves with double polarization. Our study examine how the external electromagnetic field affects the trajectory of neutral and charged particles. First, the trajectories of both photon and test particle are studied. Then, the exact solutions for the trajectories are obtained in terms of the Jacobi-elliptic integrals for all possible energy and angular momentum of test particle and photon. We have also discussed the motion of charged particle and obtained the radius of the innermost circular orbit (ISCO). It is especially interesting when the charge sign is positive since this creates a conflict between gravitational (attraction force) and electric (repulsion force). Furthermore, we discuss the Lyapunov exponent and the effective force influencing particles in SEBH spacetime.

We find at least 4 quantum properties that are indeed universal in some number systems; and we can use them for quantum consciousness. Using the sets B={0,1}, N, Z, or Q has the +4 quantum properties that one can trust, as archetypes of easy communication to friends and foes, to ignoranti or cognoscenti, and for quantum computing. This paper proposes the use of Bayesian geometry to reduce the space of possible results, reducing or increasing initial beliefs by successive experimentation. This is exemplified in biology and mathematics, and can be applied to physics and other sciences. The conclusion is that an experimental fact can be absolutely verified. Any mathematical choice (as verified by computers) militating against R=Q, is under an absolutely low chance to succeed. Infinitesimals are denied based on 3^16 independent factors. This is not a probabilistic result, but based on reduction of the Bayes geometry for updating beliefs. This work applies to non-probabilistic and unknown phenomena, such as those found in oscillations (neutrino, health), evolution, and quantum computing, denies Gödel's uncertainty, and solves the liar's paradox by reflection. This connects different scientific fields with one another, in an internet, i.e., in a network of connections of multiple open networks, using the set Q, resembling a hologram, and includes entanglement.

The interactions of metal ions with fulvic acids resulted in changes in the particle size and charge. Particle size distribution was trimodal for fulvic acids, bimodal for fulvic complexes with calcium and magnesium, and copper-fulvic complexes had only one size fraction. The compensation of negative charge of carboxylic and phenolic functional groups by positive charged metal ions resulted in the increase in zeta potential which was getting closer to zero in the case of copper-fulvic complexes. However, all metal humic complexes behaved as colloidally unstable which resulted in visually observable sedimentation. Calorimetric measurements provided positive values of changes in enthalpy which indicated endothermic processes. In contrast quantum chemical calculations as well as experiments with model compounds provided negative values indicated exothermic processes. Changes in Gibbs energy were determined as negative and changes in entropy as positive which meant spontaneous processes.

The aeroelastic response of lightweight low-speed aircraft with slender wings under extreme flow turbulence intensity is not well-understood. Experiments on a commmercial unmanned aerial vehicle (UAV) with a 3-meter wingspan and aspect ratio of 13.6 were performed in a large open-return wind tunnel with extreme flow turbulence intensity of T∞≈10%. The structural dynamics of the wing in the bending mode and the flow beneath the wing to capture the effect of aileron deflection was measured using laser displacement sensors and tomographic particle image velocimetry (PIV) techniques. The unsteady lift produced by the wing was also measured using a high-capacity load cell at an angle of attack, α of 2 degrees for three freestream velocities U∞ of 13.4 m/s, 17.9 m/s, and 26.8 m/s representing the UAV’s stall through cruise speed. It was found that high flow turbulence intensity with large integral length scales relative to the wing chord plays a dominant role in the large unsteady lift and wing displacements measured. The power spectral density (PSD) of wing structural vibration shows that flow shedding from the wing and the integral length scales have a significant impact on the overall power inherent in the bending vibration of the wing. Computation of vorticity iso-surfaces in the flow measurement volume surrounding the aileron reveal a striking observation; aileron deflection, δa of 10∘becomes less effective in producing additional spanwise vorticity, which is proportional to circulation and lift, at U∞ of 26.8 m/s because the freestream already has elevated levels of vorticity. A paradigm shift in design is suggested for light aircraft structures with slender wings operating in highly turbulent flow, which is to employ multiple control surfaces in order to respond to this flow and mitigate large bending or torsion displacements and the probability of structural failure.

In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian Coherent Structures (LCS) in large-scale Direct Numerical Simulation (DNS) datasets across incompressible, supersonic, and hypersonic flow regimes. LCS play a significant role in turbulent boundary layer analysis, and our proposed methodology offers valuable insights into their behavior in various flow conditions. Our novel owning-cell locator method enables efficient, constant-time cell search, and the algorithm draws inspiration from classical search algorithms and modern multi-level approaches in numerical linear algebra. The proposed method is implemented for both multi-core CPUs and Nvidia GPUs, demonstrating strong scaling up to 32,768 CPU cores and up to 62 Nvidia V100 GPUs. By decoupling particle advection from other problems, we achieve modularity and extensibility, resulting in consistent parallel efficiency across different architectures. Our methodology was applied to calculate and visualize the FTLE on four turbulent boundary layers at different Reynolds and Mach numbers, revealing that coherent structures grow more isotropic proportional to the Mach number, and their inclination angle varies along the streamwise direction. We also observed increased anisotropy and FTLE organization at lower Reynolds numbers, with structures retaining coherency along both spanwise and streamwise directions. Additionally, we demonstrated the impact of lower temporal frequency sampling by upscaling with an efficient linear upsampler, preserving general trends with only 10% of the required storage. In summary, we present a particle search scheme for particle advection workloads in the context of visualizing LCS via FTLE that exhibits strong scaling performance and efficiency at scale. Our proposed algorithm is applicable across various domains requiring efficient search algorithms in large structured domains. While this manuscript focuses on the methodology and its application to LCS, an in-depth study of the physics and compressibility effects in LCS candidates will be explored in a future publication.

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 process of shock wave focusing can make the strength of shock waves be continuously accumulated and turned into detonation wave in Pulse Detonation Engine (PDE). However, its effective application needs the inlet jets be in high temperature and velocity, which is difficult to be satisfied under certain conditions. Therefore, in this paper, metal magnesium assisted detonation initiation is proposed and the effect of magnesium particle addition on the shock wave focusing process in a kerosene-fueled PDE with cavity configuration is investigated through numerical simulation. The result showed that when the temperature of the premixed fuel/air jets injected in opposite direction was set as 650 K, the collision of leading shock waves on the central axis was the main source of energy deposition and the shock wave focusing could make the detonation be initiated in the cavity. When the temperature of jets is reduced to 550 K, fuel ignition and detonation could not be achieved through shock wave focusing. Then adding metal magnesium particles into the combustor made the energy deposition be enhanced and the detonation be induced. The diffusion of metal particles can significantly change the structure, motion, merging and dissipation of vortices in the flow field. Generally, the shock wave focusing process is basically not affected with metal particles injection. Therefore, this method can be successfully employed for detonation initiation in the cavity when the fuel/air premixed jet temperature is not high for PDE.

With the engineering system becoming more and more complex, the uncertainty factors have a more and more profound influence on the reliability and security of the engineering system. In recent years, Reliability-Based Design Optimization (RBDO) has been studied extensively in complex engineering system design. The research on reliability optimization design considering stochastic uncertainty has been comprehensive and widely used. However, the reliability optimization design considering mixed uncertainty has the disadvantages of large computation and imprecise convergence. This study proposes an RBDO method based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to overcome this challenge. In the method, PSO is used to solve the most probable point, while SA has excellent global optimization capability to acquire the global optimal solution. Finally, three examples are given to illustrate the advantages of the proposed method.

Observations of ocean surface winds from Indian scatterometer SCATSAT-1 have been combined with background wind field from a numerical weather prediction (NWP) model available at National Centre for Medium Range Weather Prediction (NCMRWF) to generate a 6-hourly gridded hybrid wind product. A distinctive feature of the study is to produce a global gridded wind field from SCATSAT-1 scatterometer passes with spatio-temporal data gaps at regular synoptic hours relevant for forcing models and other NWP studies. This is done by making use of concepts from the modern particle filter technique, which does not represent the model probability density function (PDF) following the Gaussian technique. The 6 hourly hybrid wind is generated for the entire year of 2018 and is validated using the wind speed from daily gridded level-4 SCATSAT-1 winds (L4AW), Cross Calibrated Multi-Platform dataset (CCMP) and global buoy data from National Data Buoy Centre (NDBC). The results indicate potential of the technique to produce scatterometer winds at the desired temporal frequency with significantly less noise and along swath biases. The study shows the generated hybrid winds have very high quality with respect to the already existing daily product available from ISRO.

In this paper, the effects on the rheological properties, particle size distribution, and fluorescence properties of red wine were investigated under microwave irradiation, and the mechanism of the effects of microwave irradiation on the sensory properties of red wine was discussed. The results showed that the effect of microwave on the rheological properties in red wine could be fitted by the Power-law model and Carson model, through the analysis of the rheological constants, yield stress, and viscosity coefficient of wine, it was found that microwave treatment could improve leg phenomenon and thickening effect by the change of rheological properties; the particle size distribution in wine indicated that microwave irradiation did change the particle size distribution through friction effect and oxidative polymerization, resulting in the wine’s visual effect and mouthfeel; the wine quality could be improved by enhancing the fluorescence intensity under different microwave conditions, which meant that microwave technology could speed up the formation of fluorescent substances, such as the polymerization of flavan-3-ols. In conclusion, microwave treatment could modify the sensory characteristics of wine, and further provide the theoretical basis for the application of microwave in winemaking.

Single-particle inductively coupled plasma mass spectrometry (spICP-MS) has been used for particle size measurement of diverse types of individual nanoparticles and micrometer-sized carbon-based particles, such as microplastics. However, its applicability to the measurement of micrometer-sized non-carbon-based particles such as silica (SiO2) is unclear. In this study, the applicability of spICP-MS to particle size measurement of non-porous/mesoporous SiO2 micro-spheres with a nominal diameter of 5.0 µm or smaller was investigated. Particle sizes of these microspheres were measured using both spICP-MS based on a conventional calibration approach using an ion standard solution and scanning electron microscopy (SEM) as a reference technique and the results were compared. The particle size distributions obtained using both techniques were in agreement within analytical uncertainty. The applicability of this technique to the detection of metal-containing protein-binding mesoporous SiO2 microspheres was also investigated. Bound iron (Fe)-containing proteins (i.e., lactoferrin and transferrin) of mesoporous SiO2 microspheres were detected using Fe as a presence marker for the proteins. Thus, spICP-MS is applicable to the particle size measurement of large-sized and non-porous/mesoporous SiO2 microspheres; it has considerable potential for element-based detection and qualification of bound proteins of mes-oporous SiO2 microspheres in a variety of applications.

In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL-magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the generated magnetic field using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

This study aimed to improve sensitivity of ferrous particle sensors used in various mechanical systems such as wind power gearboxes to detect abnormalities by measuring the amount of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, its ability to detect abnormalities is limited because it only measures amounts of ferrous particles collected on the top of the sensor. To overcome this limitation, this study evaluated sensibility of an existing sensor using a multi-physics analysis method and improved it by changing the core shape inside the sensor. The detection ability of the improved sensor was analytically shown to be better than the existing sensor. This study highlights the use of analytical methods and multi-physics analysis for developing a ferrous particle sensor and presents a method to compare sensibility analytically. Results of the improved sensor are expected to enhance the ability to detect abnormalities in the lubrication system where ferrous wear particles are generated. This study will contribute to the development of more effective ferrous particle sensors, which can potentially improve the performance and reliability of mechanical systems.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

River bed materials serve multiple environmental functions as a habitat for aquatic invertebrates and fishes. At the same time, the particle size of the bed material reflects the tractive force of the flow regime in a flood and provides useful information for flood control. The traditional river bed particle size surveys, such as sieving, require time and labor to investigate river bed materials. The authors proposed a method to classify aerial images taken by unmanned aerial vehicle (UAV) using convolutional neural networks (CNN), our previous study showed that terrestrial riverbed material could be classified with high accuracy. In this study, we attempted to classify riverbed materials distributed in shallow waters where the bottom can be seen from UAVs. After training the CNN to classify the images with the same grain size as being in the same class even if the surface flow types taken overlapping the riverbed material were different, the total accuracy reached 90.3%. Moreover, the proposed method was applied to the wide-ranging area to determine the distribution of the particle size. In parallel, the microtopography was surveyed using Lidar-UAV, and the relationship between the microtopography and particle size distribution was discussed. In the steep section, coarse particles were distributed and formed a rapid. Fine particles were deposited on the upstream side of those rapids, where the slope had become gentler due to the damming. There was good agreement between the microtopographical trends and the grain size distribution.

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, which is being developed in recent years, uses not only coarse but also microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the dose of MB, the effect of their application may be both positive, but also negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=2dddpρp, where dd is MB size; dp and ρp respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f= 0). In the area between the optimal MB doses, the deep minimum in copper recovery is observed, which value is by several percent lower compared to the value in the reference test.

In steel recycling, the optimization of electric arc furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie-Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie-Mayr model. A novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

Abstract: Composite sintered bodies comprising silicon dioxide (SiO2) nanoparticles dispersed in β-tricalcium phosphate (β-TCP) were prepared. The addition of nano-sized colloidal SiO2 to the β-TCP produced well-dispersed secondary phase nanoparticles that promoted densification by suppressing grain growth and increasing volume shrinkage of the sintered bodies. The SiO2 was found not to react with the β-TCP at 1120 °C and the substitution of silicon for phosphorous to produce a solid solution did not occur. This lack of a reaction is ascribed to the absence of avail-able calcium ions to compensate for the increase in charge associated with this substitution. The SiO2 nanoparticles were found to be present near the intersections of grain boundaries in the β-TCP. A β-TCP composite sintered body containing 4.0 wt% SiO2 exhibited a bending strength comparable to that of cortical bone and hence could potentially be used as a bone filling material.

Background: In today’s competitive market, predicting customer churn with high accuracy is crucial for enterprises to maintain growth and profitability. Traditional predictive models often lack in accuracy due to the complexity of customer behavior.Objective: This research aims to improve the accuracy of predicting customer churn by utilizing the Particle Swarm Optimization (PSO) algorithm for optimizing the hyperparameters of a composite deep learning model. The performance of this enhanced model is evaluated against traditional models such as LSRM_GRU, LSTM, GRU, and CNN_LSTM to demonstrate the effectiveness of PSO in hyperparameter tuning.Methods: A composite deep learning approach was employed, integrating various neural network architectures to leverage their strengths in modeling complex customer interactions. The PSO algorithm was used to optimize the model’s hyperparameters. Customer transaction and interaction data from different business operations served as the dataset for testing and analyzing the model’s performance. Evaluation metrics including accuracy, precision, recall, F1 score, and ROC AUC were utilized for a detailed comparison with established models.Findings: The PSO-enhanced composite deep learning model showed superior performance across all metrics, significantly outperforming the LSRM_GRU, LSTM, GRU, and CNN_LSTM models. Notably, improvements in ROC AUC and F1 score highlight the robustness and balanced precision-recall trade-off of the proposed model, demonstrating its effectiveness in identifying potential churners.Conclusion: The integration of PSO for hyperparameter optimization in composite deep learning models for customer churn prediction has proven to significantly enhance predictive accuracy and performance metrics over conventional models. This underscores the potential of evolutionary algorithms in improving deep learning applications. Future research should explore the scalability of this approach across various sectors and further refine predictive accuracy with evolving customer data.

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants, the pollution reduction and carbon emissions reduction of coal-fired power plants will be a key task in the future. This article proposes an optimization operation of electrostatic precipitators(ESP), based on the working mechanism and historical data of ESP, voltage-current characteristic model and outlet dust concentration prediction model are constructed by deep learning algorithm, Particle Swarm Optimization(PSO) is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate prediction of secondary current and outlet dust concentration of ESP has been achieved, the Mean Absolute Percentage Error(MAPE) of secondary current is less than 1.5%, and the MAPE of outlet dust concentration of ESP on the test set is less than 5.2%. Finally, the optimization experiment is carried out in a 330MW coal-fired power plants, the results showed that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization, according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely.

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For Autonomous Vehicles (AV's), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach however is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMaps have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 centimeters of the ground truth significantly faster than the state of the art methods.

The use of cemented aeolian sand-fly ash backfill (CAFB) material to fill the mining area to improve the surface subsidence damage caused by underground coal mining is in the development stage. Their performance with large overflow water and strength loss is not well understood. Few research has been conducted to understand the effects of aeolian sand and coal gangue on the rheological properties of CAFB with plasticizers. Therefore, the aim of this study is to investigate the effects of a plasticizer on the rheological properties, specifically yield stress and viscosity, of CAFB prepared with aeolian sand and coal gangue. CAFB mixes containing 0%, 0.05%, and 0.1% plasticizer were prepared, and yield stress and viscosity were determined at different time intervals over the period. Additional tests, such as thermal analysis and zeta potential analysis were also conducted. It was found that the rheological properties of CAFB is the comprehensive manifestation of the composite characteristics of various models. Reasonable particle size distribution and less amount of plasticizer can ensure the stability of slurry structure, reduce the slurry settlement and the risk of pipe blocking. The findings of this study will be beneficial in the design and production of CAFB material.

The effect of coal hydrophilic particles in water-glycerol drops on the maximum diameter of spreading along a hydrophobic solid surface is experimentally studied by analyzing the velocity of internal flows by Particle Image Velocimetry. The grinding fineness of coal particles was 45-80 μm and 120-140 μm, their concentration was 0.06 wt.% and 1 wt.%. The impact of particle-laden drops on a solid surface occurred at Weber numbers (We) from 30 to 120. It is revealed the interrelated influence of We and the concentration of coal particles on changes in the maximum absolute velocity of internal flows in a drop within the kinetic and spreading phases of the drop-wall impact. A physical model is formulated for internal convective flows in the longitudinal section of a drop parallel to the plane of the solid wall. The kinetic energy of translational motion of coal particles in a spreading drop compensates for the energy expended by the drop on sliding friction along the wall. At We=120, the inertia-driven spreading of the particle-laden drop is mainly determined by the dynamics of the deformable Taylor rim. An increase in We contributes to more noticeable differences in the convection velocities in spreading drops. When the drop spreading diameter rises at the maximum velocity of internal flows, a growth of the maximum spreading diameter occurs. The presence of coal particles causes a general tendency to reduce the drop spreading.

NUSES is a planned space mission aiming to test innovative observational and technological approaches related to the study of low-energy cosmic rays, gamma rays and high-energy astrophysical neutrinos. Two scientific payloads will be hosted onboard the NUSES space mission: Terzina and Zirè. Terzina will be an optical telescope readout by SiPM arrays, for the detection and study of Cerenkov light emitted by Extensive Air Showers generated by high-energy cosmic rays and neutrinos in the atmosphere. Zirè will focus on the detection of protons and electrons up to a few hundred MeV and to 0.1-10 MeV photons and will include the Low Energy Module (LEM). The LEM will be a particle spectrometer devoted to the observation of fluxes of low-energy electrons in the 0.1-7 MeV range and protons in the 3-50 MeV range along the Low Earth Orbit (LEO) followed by the hosting platform. The detection of Particle Bursts (PBs) in this Physics channel of interest could give new insight into the understanding of complex phenomena such as eventual correlations between seismic events or volcanic activity with the collective motion of particles in the plasma populating van Allen belts. With its compact sizes and limited acceptance, the LEM will allow the exploration of hostile environments such as the South Atlantic Anomaly (SAA) and the inner van Allen belt, in which are expected electron fluxes of the order of 106−7 cm−2 sr−1 s−1. Concerning the vast literature of space-based particle spectrometers, the innovative aspect of the LEM resides in its compactness, within 10x10x10 cm3, and in its “active collimation” approach dealing with the problem of multiple scattering at these very low energies. In this work, the geometry of the detector, its detection concept, its operation modes and the hardware adopted will be presented. Some preliminary results from the Monte Carlo simulation (Geant4) will be shown.

This study examines the effects of particle size and heat pipe angle on the thermal effectiveness of a cylindrical screen mesh heat pipe using silver nanoparticles as the test substance. The experiment investigates three different particle sizes (30nm, 50nm, and 80nm) and four different heat pipe angles (0°, 45°, 60°, and 90°) on the heat transmission characteristics of the heat pipe. The results show that the thermal conductivity of the heat pipe increased with an increase in heat pipe angle for all particle sizes, with the highest thermal conductivity attained at a 90° heat pipe angle. Furthermore, the thermal resistance of the heat pipe decreased as the particle size decreased for all heat pipe angles. The thermal conductivity measurements of the particle sizes - 30, 50, and 80 nm - were 250 W/mK, 200 W/mK, and 150 W/mK, respectively. The heat transfer coefficient values for particle sizes 30nm, 50nm, and 80nm were 5500 W/m2K, 4500 W/m2K, and 3500 W/m2K, respectively. The study also found that the heat transfer coefficient increased with increased heat pipe angle for all particle sizes, with the highest heat transfer coefficient obtained at a 90° heat pipe angle.

The sPHENIX experiment is a state-of-the-art jet and heavy flavor physics detector which successfully recorded its first Au + Au collision data at 200 GeV at RHIC. sPHENIX will provide heavy flavor physics measurements covering an unexplored kinematic region and unprecedented precision at RHIC to probe the parton energy loss mechanism, parton transport coefficients in quark-gluon plasma, and the hadronization process under various medium conditions. At the center of sPHENIX, the Monolithic-Active-Pixel-Sensor (MAPS) based VerTeX detector (MVTX) is a high-precision silicon pixel detector. The MVTX provides excellent position resolution and the capability of operating in continuous streaming readout mode, allowing precise vertex determination and recording a large data sample, both of which are particularly crucial for heavy flavor physics measurements. In this work, we will show the general performance of heavy-flavor hadron reconstruction. In addition, we will discuss the commissioning experience with sPHENIX. Finally, we will provide the projection of b-hadron and jet observables and discuss the estimated constraints on theoretical models.

This study aims to examine the non-volatile Particle Number (PN) emissions of a retrofitted Heavy-Duty Spark-Ignition (HD-SI) engine powered by liquefied petroleum gas (LPG) under both steady-state and transient conditions. The engine was tested under seven steady-state operating points to investigate the PN behavior and Particle Size Distribution (PSD) upstream and down-stream of the Three-Way Catalyst (TWC). This analysis intends to assess the impact of including particles with diameters ranging from 10 nm to 23 nm in the total particle count, a consideration for future regulations. The study employed the World Harmonized Transient Cycle (WHTC) for transient conditions to encompass the same engine working region used in the steady-state analysis. A Dekati FPS-4000 diluted the exhaust sample to measure the PSD and PN for particle diameters between 5.6 nm and 560 nm using the TSI-Engine Exhaust Particle Sizer (EEPS) 3090. Findings indicate that PN levels tend to increase downstream of the TWC under steady-state conditions in operating points with low exhaust gas temperatures and flows (equal to or less than 500 °C and 120 kg/h). Furthermore, the inclusion of particles with diameters between 10 nm and 23 nm leads to an increase in PN emissions by 17.70% to 40.84% under steady conditions and by an average of 40.06% under transient conditions, compared to measurements that only consider particles larger than 23 nm. Notably, in transient conditions, most PN emissions occur during the final 600 seconds of the cycle, linked to the most intense phase of the WHTC.

The relaxation processes induced by an exposure of the Ar matrices doped with CH4 (0.1 – 10%) to an electron beam were studied with a focus on the dynamics of radiolysis products – H atoms, H2 molecules, CH radicals, and energy transfer processes. Three channels of energy transfer to dopant and radiolysis products were discussed: by free charge carriers, free excitons and photons from the “intrinsic source” provided by emission of the self-trapped excitons. Radiolysis products along with the total yield of desorbing particles were monitored in a correlated manner. Analysis of methane transformation reactions induced by free excitons showed that the CH radical can be considered as a marker of the CH3 species. The competition between exciton self-trapping and energy transfer to the dopant and radiolysis products has been demonstrated. A nonlinear concentration behavior of the H atoms in doped Ar matrices has been established. Real-time correlated monitoring of optical emissions (H atom and CH3 radicals), particle ejection, and temperature revealed a nonmonotonic behavior of optical yields with a strong luminescence flash after almost an hour of exposure, which correlated with explosive pulse of particle ejection and temperature. The connection of this phenomenon with the processes of energy transfer and radical-radical recombination is discussed.

Waste heat recovery stands out as a promising technique to tackle both energy shortages and environmental pollution. Currently, this valuable resource, generated from processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a travelling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy (R^2=0.9959), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. By doing so, it is possible to get an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision.

The research introduces the concept of Strong Energy (SE) as a force that encompasses all the different forms of energy in the universe. The study aims to create a mathematical framework that can describe the relationship between these different types of energy and how they can be converted from one form to another. The paper explores the theoretical implications of this model and investigates how it aligns with established physics principles like the conservation of energy and fundamental forces. The paper also discusses potential experiments and the impact this research could have on cosmology, particle physics, and our understanding of energy. This work sets the stage for future research in this area of study.

We investigate a single-particle density of states in the three-dimensional system described by effective two-band Hamiltonian which describes a ground state in two distant electronic phases: the semimetalic nodal-loop phase and the insulating gapped phase. Analysis of valence bands and Fermi surfaces in both phases indicates that the density of states crucially depends on the parameter in the Hamiltonian of the system that controls a topological alternation of the Fermi surface. The signature of that alternation is expected to play an important role in all quantities closely related to the density of electronic states, such as charge transport and optical conductivity of the system for example.

The compilation summarizes results so far gathered in a project that investigates the atom with classical (Newtonian) physics. It posits that the atom’s wave-particle dual nature is real, objective and intrinsic; the wave gives rise to the cosmic vacuum field while the particle, of course, manifests condensed matter. Light’s π radians oscillation manifests, on molar scale, significant radial tension in the vacuum field, the effect, amongst others, causes molecular matter to precipitate out of the vacuum – so-called ‘molecular outflow’. Cosmic vacuum field radial tension relates to molecular mass, in other words, a mass-radius (M-R) relationship is uncovered. It is argued that using the M-R relation it is possible to perform a valid simulation of the cosmic physical reality. The three states of matter are meaningfully accounted for using the atom’s e-m radiation wavelength. It is explained that force, be it of any kind whatsoever, originates in the atomic waveform, i.e., the cosmic vacuum field.

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.

The base excitation of mechanical equipment can result in pipeline system vibration which seriously affects the safe operation of the equipment. Particle damping technology is based on the particle collision dissipation principle to realize the structural vibration energy dissipation. This paper introduces the particle damping method into the vibration control of pipeline system. A particle damper has been designed for the structural characteristics of the pipeline. The influence of the material and structure of the pipeline particle damper, the external excitation and other parameters on the energy dissipation characteristics of particle damping are revealed. The energy dissipation mechanism of the pipeline particle collision damper under various parameter is analyzed. A pipeline vibration reduction test system with particle damping has been established. The effects of different excitation conditions, damper materials and number of unit structures of particle damper on the vibration damping effect of pipeline systems are investigated comparatively. The influence of damper parameters on the vibration suppression of pipeline system is obtained. The research in this paper can provide technical references for vibration suppression of pipeline systems.

In this research, the concept of the instability of tension in Microgrid still becomes the focus of study and purpose. Scrutinously, First-Swing Instability occurs in the system in noted changes with properties of making the system unstable or restore to a state of stability. The latter calls itself as a phenomenon of re-synchronization. Allied to aspects of system instability, it is evident that by connecting fast electric chargers for battery-electric vehicles to the distribution system from microgrids, they increase the necessary basic electrical charges and reduce the stability of the electrical energy system. Transient electrical currents have the characteristic of severely impacting microgrids not prepared in connected states or island of the main system. The occurrences of the electromagnetic incompatibility of the inrush current, due to their unpredictability and intermittent characteristic, have complex identification studies. With the elevation of electricity demand by battery-electric vehicle chargers, concerns about the effects of the integration of the electricity grid increase proportionally. The research described in this article emphasizes the influence of fast chargers’ initialization on the power variation related to the re-synchronization phenomenon and its mitigation of the stability of electricity microgrid. An optimization algorithm based on particle swarm optimization (PSO) implements the development of virtual impedances. The PSO optimization algorithm analyzes all possible operating points and aims to maximize the stability index of microgrid, maintaining reactive energy incompatibilities at the minimum level supported. The fractional objective function facilitates the service to these goals simultaneously. The optimization algorithm is implemented in a case study and the corresponding virtual impedance is incorporated into the microgrid. Voltage levels are verified as a condition in the optimization process, comparing system effectiveness.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

The paper presents a new type of hydraulic clutch operating with magnetorheological (MR) fluids and the results achieved from both theoretical analysis and experimental measurement. The hy-draulic clutch system of its own design with MR working fluid and a rotating magnetic field lo-cated is designed. The principle of this clutch is to use a rotating magnetic field created by an al-ternating current electromagnet to set the MR fluid in motion. In the tests of the hydraulic clutch with a rotating magnetic field, MR fluid produced on their own, consisting of solid iron particles of various diameters mixed with a silicone oil, are used as working fluids. The rheological properties of MR fluids are assessed on the basis of tests carried out with a Brookfield DV2T rheometer equipped with a magnetic device for generating the magnetic field of its own design. The char-acteristics of the hydraulic clutch with MR working fluid and the rotating magnetic field are tested on a specially built test stand. As a result of the research, it has been found that the torque transmitted by the clutch is the greater, the higher the rotational speed of the magnetic field and the lower the rotational speed of the beaker in which the working fluid is placed, and that the greatest torque occurs for the working fluid with the highest iron content. Based on the analysis of the structure and characteristics of the clutch in which the magnetic field is used, it has been shown that the design of the developed clutch is similar to that of an induction clutch, and its characteristics correspond to the characteristics of the eddy current clutch. Therefore, the proposed new clutch working with MR fluid and the rotating magnetic field can be applied to stationary power transmission system similarly to an eddy current clutch.