The growth of solid particles during liquid phase sintering was modeled by the Cellular Automata method. The binary phase diagram and Fickian approach for the diffusion process were applied to simulate the chemical composition variation in liquid and solid phases during sintering. The Oswald-Ripening effect was considered during the dissolution of the solid phase in the liquid phase. It is used to define the probability of solid-phase dissolution by the liquid phase and develop the model to simulate the alloy with solid solubility. So, the microstructure could be modeled in the liquid phase sintering process.

In recent years, the photophysical properties of crystalline metal-organic frameworks (MOFs) have become increasingly relevant for their potential application in light-emitting devices, photovoltaics, nonlinear optics and sensing. The availability of high-quality experimental data for such systems makes them ideally suited for a validation of quantum mechanical simulations, aiming at an in-depth atomistic understanding of photophysical phenomena. Here we present a computational DFT study of the absorption and emission characteristics of a Zn-based surface-anchored metal-organic framework (Zn-SURMOF-2) containing anthracenedibenzoic acid (ADB) as linker. Combining band-structure and cluster-based simulations on ADB chromophores in various conformations and aggregation states, we are able to provide a detailed explanation of the experimentally observed photophysical properties of Zn-ADB SURMOF-2: The unexpected (weak) red-shift of the absorption maxima upon incorporating ADB chromophores into SURMOF-2 can be explained by a combination of excitonic coupling effects with conformational changes of the chromophores already in their ground state. As far as the unusually large red-shift of the emission of Zn-ADB SURMOF-2 is concerned, based on our simulations, we attribute it to a modification of the exciton coupling compared to conventional H-aggregates, which results from a relative slip of the centers of neighboring chromophores upon incorporation in Zn-ADB SURMOF-2.

Among others, road traffic, industrial emissions, commercial activities, smoking and cooking are considered as major contributing factors for the increasing levels of pollutants in atmosphere. High levels of potentially toxic metals and microbes in atmosphere, especially in indoor air, may pose serious threat to human health. Therefore, concentration and associated health risks of potentially toxic trace metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their risk to human health, and microbial load in indoor air was assessed using air condition (AC) filter dust samples collected from 5 locations representing residential, agricultural and industrial settings of Eastern Province, Kingdom of Saudi Arabia. The levels of trace metals varied considerably among sampling areas, with the highest levels of Cr and Cd recorded in the Industrial-area sites followed by the Agricultural and Urban-Residential sites. The highest levels of Pb and Fe were found in the Agricultural area sites followed by the Industrial and Urban-Residential area sites. The metals in dust sample, especially Cd, Cr and Pb, showed a considerable health risk through dermal pathway. Among the sites, the highest hazard quotient for these metals was found for Al-Qatif-Industrial areas sites and among the metals it was the highest for Cd. The cancer risk from the metals contained in AC filter dust was negligible. Samples collected from Agricultural and Industrial area sites were substantially contaminated with bacteria and fungi, respectively. Bacterial contaminants were mostly Gram Negative, with considerable antibiotic resistance and haemolytic activity. Thus, indoor air quality as assessed by AC filter dust depicted that a considerable health risk could be posed by the trace heavy metals and microorganisms for a long-term exposure. Furthermore, this study demonstrated that AC filters dust could be a unique and reliable test sample for the assessment of indoor environment.

Knowledge about the formation energies of compounds is essential to derive phase diagrams of multi-component phases with respect to elemental reservoirs. The determination of formation energies using common (semi-)local exchange-correlation approximations of density functional theory (DFT) exhibits well-known systematic errors if applied to oxide compounds containing transition metal elements. In this work, we generalize, reevaluate and discuss a set of approaches proposed and widely applied in the literature to correct for errors arising from the over-binding of the O2 molecule and from correlation effects of electrons in localized transition-metal orbitals. The DFT+U method is exemplarily applied to iron oxide compounds, and a procedure is presented to obtain U values, which lead to formation energies and electronic band gaps comparable to experimental values. Using such corrected formation energies, we derive the phase diagrams for LaFeO3, Li5FeO4 and NaFeO2, which are promising materials for energy conversion and storage devices. A scheme is presented to transform the variables of the phase diagrams from the chemical potentials of elemental phases to those of precursor compounds of a solid-state reaction, which represents the experimental synthesis process more appropriately. The discussed workflow of methods can directly be applied to other transition metal oxides.

Objective: To examine the burnout syndrome among the healthcare personnel in Puerto Rico during the COVID-19 pandemic. Methods: Descriptive study that pursues to understand burnout syndrome in the clinical personnel in Puerto Rico. The Maslach Burnout Inventory (MBI) was sent via email to healthcare professionals around the island. Furthermore, open questions were asked to the participants. Results: The overall burnout level on the clinical personnel was found to be moderate. Nonetheless, in physicians, 12.1% had severe burnout levels compared to a 13.1% score in nurses. Additionally, 92.4% of physicians and 100% of nurses had moderate to severe burnout. In the three subscales, nurses scored high levels in all of them, and physicians were high in Emotional Exhaustion and moderate level in Depersonalization and Personal Accomplishment at Work. There were high levels of burnout syndrome of the clinical personnel in Puerto Rico. Conclusion: Since the beginning of the COVID-19 pandemic, over 90% of healthcare professionals in Puerto Rico have been working with moderate to severe burnout syndrome, being the nurses the most affected. Key Words: Burnout syndrome, MBI, Clinical personnel, COVID-19, SARS-CoV-2

An electrochemical sensing platinum nanoparticle in the tantalum electrode is provided by means of an Ion Beam Sputtering Deposition (IBSD). The electrode was made with a Pt solution, sputtered simultaneously with hydrochloric acid corrosion on tantalum substrate. In the study of heavy metal ions, for example, the platinum nanoparticle electrodes as prepared were used Square wavelength voltammetry (OSWV) Hg²⁺, Cu²⁺ and Ag²⁺. The porous electrodes were observed in a broader range by the Pt nanostructure electrode for heavy metal ions. Furthermore, the susceptibility to detection has been shown to be saturated as the thickness of the layer electrode exceeded 50 nm. For Hg²⁺ 0,003-1 M, for Cu²⁺ 0,005-3 M and for Ag²⁺ the linear detection scale is 0,009-4 M. There has also been good reusability and repeatability. In addition, a scan electron microscope (SEM) used to study platinum electrode forming process and nanostructure. This electrode will have interesting applications in sensing systems.

Today, contamination from heavy metals in the atmosphere is a global concern. Efficient detection techniques are therefore necessary if heavy metal exposure levels in different media are to be determined. The voltammetry method for in situ detection of heavy metal ions is a very sensitive electrochemical method. This thesis explores emerging developments in electrode alteration, materials production and experimental optimization. An electrochemical sensing platinum nanoparticle in the tantalum electrode is provided by means of an Ion Beam Sputtering Deposition (IBSD). The electrode was made with a Pt solution, sputtered simultaneously with hydrochloric acid corrosion on tantalum substrate. In the study of heavy metal ions, for example, the platinum nanoparticle electrodes as prepared were used Square wavelength voltammetry (OSWV) Hg²⁺, Cu²⁺ and Ag²⁺. The porous electrodes were observed in a broader range by the Pt nanostructure electrode for heavy metal ions. Furthermore, the susceptibility to detection has been shown to be saturated as the thickness of the layer electrode exceeded 50 nm. For Hg²⁺ 0,003-1 M, for Cu²⁺ 0,005-3 M and for Ag²⁺ the linear detection scale is 0,009-4 M. There has also been good reusability and repeatability. In addition, a scan electron microscope (SEM) used to study platinum electrode forming process and nanostructure. This electrode will have interesting applications in sensing systems.

Over the last decades, the coated glass products, and especially the low-emissivity coatings have become a common building material used in modern architectural projects. More recently, these material systems became common in specialized glazing systems featuring solar energy harvesting. Apart from achieving the stability of optical parameters in multilayer coatings, it is also important to have improved control over the design of visual color properties of the coated glass. We prepare metal-dielectric composite (MDC) based multilayer thin-film structures using the RF-magnetron sputtering deposition and report on their optical and chromaticity properties in comparison with these obtained using pure metal-based Dielectric/Metal/Dielectric (DMD) trilayer structures of similar compositions. Experimentally achieved Hunter L, a, b values of MDC-based multilayer building blocks of coatings provide a new outlook on the engineering of future-generation optical coatings with better color consistency and developing approaches to broaden the range of achievable color coordinates and better environmental stability.

Clean production of renewable fuels is a great challenge of our scientific community. Iridium complexes have demonstrated a superior catalytic activity in the water oxidation (WO) reaction, which is a crucial step in water splitting process. Herein we have used a defective zirconium MOF with UiO-66 structure as support of a highly active Ir complex based on EDTA with formula [Ir(HEDTA)Cl]Na. The defects are induced by the partial substitution of tereftalic acid with smaller formiate groups. Anchoring of the complex occurs through a post-synthetic exchange of formiate anions, coordinated at the zirconium clusters of the MOF, with the free carboxylate group of the [Ir(HEDTA)Cl]-complex. The modified material was tested as heterogenous catalyst for the WO reaction by using Cerium Ammonium Nitrate as sacrificial agent. Although TOF and TON values are comparable to those of other iridium heterogenized catalysts, the MOF exhibits iridium leaching not limited at the first catalytic run, as usually observed, suggesting a lack of stability of the hybrid system under strong oxidative conditions.

Pyrochlore group minerals are the main raw phases in granitic rocks of the Katugin complex-ore deposit that stores Nb, Ta, Y, REE, U, Th, Zr, and cryolite. They are of three main generations: primary magmatic (I), early postmagmatic (II), and supergene (III) pyrochlores. The primary magmatic phase (generation I) is fluornatropyrochlore with high concentrations of Na2O (to 10.5 wt.%), F (to 5.4 wt.%) and REE2O3 (to 17.1 wt.%) but low CaO (0.6-4.3 wt.%), UO2 (to 2.6 wt.%), ThO2 (to 1.8 wt.%), and PbO (to 1.4 wt.%). Pyrochlore of this type is very rare in nature and limited to a few occurrences, such as rare-metal deposits of Nechalacho in syenite and nepheline syenite (Canada) and Mariupol in nepheline syenite (Ukraine). It may have crystallized synchronously with or slightly later than melanocratic minerals (aegirine, biotite, and arfvedsonite) at the late magmatic stage when Fe from the melt became bound making impossible the formation of columbite. Second generation pyrochlore formed at the early postmagmatic stage of the Katugin deposit. It differs from that of first generation in lower Na2O concentrations (2.8 wt.%), relatively low F (4 wt.%), and les occupancy of the A and Y sites at similar contents of other components. Generation III pyrochlore is a product of supergene alteration processes. It is compositionally heterogeneous and contains K, Ba, Pb, Fe, and significant Si concentrations but low Na and F. Its compositions mostly fall within the filed of hydro- and kenopyrochlore.

Trace metals can enter some natural regions with low human disturbance from atmospheric circulation, but little information is available regarding how the canopy can retained trace metals. Therefore, a representative sub-alpine spruce plantation was selected to investigate the net throughfall fluxes of eight trace metals (Fe, Mn, Cu, Zn, Al, Pb, Cd and Cr) of closed canopy and gap-edge canopy from August 2015 to July 2016. Over a one-year observational period, the annual fluxes of Al, Zn, Fe, Mn, Cu, Cd, Cr and Pb were 7.29 kg·ha^-1, 2.30 kg·ha^-1, 7.02 kg·ha^-1, 0.16 kg·ha^-1, 0.19 kg·ha^-1, 0.06 kg·ha^-1, 0.56 kg·ha^-1 and 0.24 kg·ha^-1, respectively, in the deposited precipitation. The annual net throughfall fluxes of these trace metals were 1.73 kg·ha^-1, 0.9 kg·ha^-1, 1.68kg·ha^-1, -0.032 kg·ha^-1, 0.04 kg·ha^-1, 0.018 kg·ha^-1, 0.093 kg·ha^-1 and 0.087kg·ha^-1, respectively, in the gap-edge canopy and -1.6 kg·ha^-1, 1.13 kg·ha^-1, 1.65 kg·ha^-1, -0.10 kg·ha^-1, 0.05 kg·ha^-1, 0.03 kg·ha^-1, 0.26 kg·ha^-1 and 0.15 kg·ha^-1, respectively, in the closed canopy. The closed canopy displayed a greater filter effect on the trace metals from precipitation than did the gap-edge canopy in the sub-alpine forest. In the rainy season, the net filtering ratio of trace metals ranged from -66%-89% in the closed canopy and from -52% to 25% in the gap-edge canopy. However, the net filtering ratio of all trace metals was greater than 50% in the closed canopy in the snowy season. Therefore, the results suggested that the most trace metals moving through the forest canopy are taken up rather than by rainfall leaching; moreover, the closed canopy can efficiently take up trace metals in the snowy season.

We review the latest developments in the field of the metal-insulator transition in strongly-correlated two-dimensional electron systems. Particular attention is given to recent discoveries of a sliding quantum electron solid and interaction-induced spectrum flattening at the Fermi level in high-quality silicon-based structures.

Photoelectrochemical water splitting is a promising field for sustainable energy production using hydrogen. Development of efficient catalysts is essential for resourceful hydrogen production. The most efficient catalysts reported to date have been extremely precious rare-earth metals. One of the biggest hurdles in this research area is the difficulty of developing highly efficient catalysts comparable to the noble metal catalysts. Here, we report that non-noble metal dichalcogenide (Co₃Se₄) catalysts made using a facile one-pot electrodeposition method, showed highly efficient photoelectrochemical activity on a Si photocathode. To enhance light collection and enlarge its surface area even further, we implemented surface nano-structuring on the Si surface. The nano-structured Si photoelectrode has an effective area greater than that of planar silicon and a wider absorption spectrum. Consequently, this approach exhibits reduced overvoltage as well as increased photo-catalytic activity. Such results show the importance of controlling the optimized interface between the surface structure of the photoelectrode and the electrodeposited co-catalyst on it to improve catalytic activity. This should enable other electrochemical reactions in a variety of energy conversion systems.

The chemical synthesis of silver nanoparticles (Ag-NPs) by using an environmentally friendly methodology for their preparation is presented. Thus, considering that plants possess components that can act as reducing agents and stabilizers in the nanoparticles production, in this work, the synthesis of Ag-NPs by using a solution of grape waste extract as reducing and capping agent is studied. First, the total polyphenols content and reducing sugars in extracts produced at different conditions are characterized. After that, Ag-NPs are synthesized regarding the interaction of Ag ions (from silver nitrate) and the grape waste extract. The effect of temperature, contact time, extract/metal solution volume ratio and pH solution in the synthesis of metal nanoparticles are studied too. Different sets of nanoparticle samples are fully characterized by means of Electron Microscopy coupled with Energy Dispersive X-Ray for qualitative chemical identification. Ag-NPs with an average diameter of 27.7 ± 0.6 nm are selected to proof their suitability for sensing purposes. Thus, screen-printed electrodes modified with Ag-NPs are tested for the simultaneous voltammetric stripping determination of Pb(II) and Cd(II). Results indicate good reproducibility, sensitivity and limits of detection around 2.7 µg L⁻¹ for both metal ions.

The research field of two dimensional (2D) materials strongly relies on optical microscopy characterization tools to identify atomically thin materials and to determine their number of layers. Moreover, optical microscopy-based techniques also opened the door to study the optical properties of these nanomaterials. We present a comprehensive study of the differential reflectance spectra of 2D semiconducting transition metal dichalcogenides (TMDCs), MoS₂, MoSe₂, WS₂ and WSe₂, with thickness ranging from one layer up to six layers. We analyze the thickness-dependent energy of the different excitonic features, indicating the change in the band structure of the different TMDC materials with the number of layers. Our work provides a route to employ differential reflectance spectroscopy for determining the number of layers of MoS₂, MoSe₂, WS₂ and WSe₂.

Novel collector lead(II)-benzohydroxamic acid (Pb(II)-BHA) complexes in aqueous solution were characterized by using experimental approaches, including Ultraviolet-visible (UV-Vis) spectroscopy and electrospray ionization–mass spectrometry (ESI-MS), as well as first-principle density functional theory (DFT) calculations with consideration for solvation effects. The Job plot delineated that a single coordinated Pb(BHA)⁺ should be formed first, and the higher coordination number complexes can be formed subsequently. Moreover, the Pb(II)-BHA species can aggregate with each other to form complicated structures, such as Pb(BHA)₂ or highly complicated complexes. ESI-MS results validated the existence of Pb-(BHA)_n=1,2 under different solution pH values. Further, the first-principles calculations suggested that Pb(BHA)⁺ should be the most stable structure, and the Pb atom in Pb(BHA)⁺ will act as an active site to attack nucleophiles. These findings are meaningful to further illustrate the adsorption mechanism of Pb(II)-BHA complexes in mineral processing.

Dental caries is a public health problem worldwide according to WHO data. Among treatments in pediatric dentistry, prefabricated metal crowns (PMCs) have been one of the most successful options since they were introduced in cases of considerable tooth destruction. Our objective was to detect the presence and concentration of iron (Fe), chromium (Cr), and nickel (Ni) in saliva of patients who require rehabilitation with PMCs, before and after their placement. A quasi-experimental study was performed in 32 patients who attended dental care in a pediatric dentistry clinic at a public university and who required rehabilitation with PMCs. Parametric tests (ANOVA and Pearson correlation) were performed, and a p ≤ 0.05 was considered statistically significant. Statistically significant differences were found when comparing the Ni release before, 1 week, and 1 month after placing the crowns. Similarly, we observed a positive correlation between the number of crowns and Ni release. No tests were performed for Fe and Cr because the amounts of these metals were less than 0.1 ppb, which was not detectable by inductively coupled plasma optical emission spectrometry (ICP-OES). The levels of Fe, Cr, and Ni released were below toxic health levels. Studies are required to evaluate whether this release has negative effects at cellular levels.

Addressing global environmental problem, water splitting to produce hydrogen fuel by solar energy is getting so much attention. In the water splitting, the essential problem to solve is the development of efficient　catalysts for oxygen production. In this paper, having the prospect for a practical application of photocatalysts to artificial photosynthesis, molecular mechanisms in the current literature are briefly reviewed. At first, recent progress in the function of Mn cluster at the natural photosystem II is briefly described. The kinds of devices in which oxygen evolution reaction (OER) catalysts are used were designated; water electrolyzers, photoelectrodes, and photocatalysts. Some methods for analyzing molecular mechanism in OER catalysis, emphasized by FTIR method, are shown briefly. After describing common OER mechanisms, the molecular mechanisms are discussed for TiO2 and BiVO4 photoelectrodes with our novel data, followed by presenting of co-catalysts IrO2, RuO2, NiO2, and other metal oxides for OER catalysts. Recent reports describing OER catalysts of perovskites, layered double hydroxides (LDH), metal-organic frameworks (MOF), single atom catalysts, as well as metal complexes are reviewed. Finally, by comparing with natural photosystem, the requiring factors to improve the activity of the catalysts for artificial photosynthesis will be discussed.

Aiming to address the issue that the existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive patterns and weak textures in the multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on feature enhancement and weight optimization MVSNet (Abbreviated as FEWO-MVSNet). To obtain accurate and detailed global and local features, we first develop an adaptive feature enhancement approach to obtain multi-scale information from the images. Second, we introduce an attention mechanism and a spatial feature capture module to enable high-sensitivity detection for weak texture features. Third, based on the 3D convolutional neural network, the fine depth map for multi-view images can be predicted and the complete 3D model is subsequently reconstructed. Last, we evaluated the proposed FEWO-MVSNet through training and testing on the DTU, BlendedMVS, and Tanks&Temples datasets. The results demonstrate significant superiorities of our method for 3D reconstruction from multi-view images, with our method ranking first in accuracy and second in completeness when compared to the existing representative methods.

This paper presents the experimental characterization and numerical optimization of the parametric design of inductors used for the induction heating process of metallic samples. Here, the variation of the geometric parameters of the inductor (the distance between the coil and the material, as well as the number of turns and separation between them), was analyzed. In addition, changes in the power input values of the heating system variables were also investigated; and finally, the heating time of the materials was taken into consideration. For the multi-objective numerical optimization process, a full factorial 2K experimental design was devised. Afterward, from preliminary experiments, a response surface was performed, from which the optimal candidates for the heating process were obtained. In the experiments, a methodology was designed for the acquisition and processing of heating data on ASTM E8 tensile test specimens. The main objective of this paper is to determine both the geometric parameters of the coil as well as the power input values of the circuit, which allow for generating uniform heating in the specimens. The objectives of the optimization process are to maximize the heating temperature minimizing the consumption of electrical current in the induction circuit. The data generated in the heating of the specimens were acquired using a Fluke® TI10 thermographic camera, while a BK Precision® regulated power supply was used for the induction circuit. The parameters obtained in the optimization process for the optimal candidates were corroborated through experimental tests. The paper shows the initial (non-optimal) and final (optimal) results obtained from the experimental tests. The heating station created with the optimal coil will be used for mechanical characterization tests on metallic materials at temperatures above room temperature.

Understanding plant species and community status is the most fundamental step in assessing biodiversity and considering environmental conservation, and these depend on the identification of plant species. Plant identification is usually done manually using botanical illustrations, but due to the diversity of plant species, identifying a single species from among many candidates listed in illustrated books is a labor-intensive and time-consuming task, even for experts. Against this background, systems for automatic plant species recognition are in demand by experts and amateurs alike. In this study, we proposed a method for automatic plant identification by applying multifractal analysis to images of leaf veins, including veinlets. In the experiment, we conducted two experiments: one to see if there is a change in the number of dimensions depending on the degree of leaf growth, and the other to see how the number of dimensions changes depending on the taxonomic group. As a result, there was almost no change depending on the degree of growth, and the number of dimensions varied depending on the taxonomic group, suggesting that different taxonomic groups can be classified into different taxonomic groups, suggesting that multifractal analysis can be applied to leaf vein images, including veinlets, to enable automatic plant identification.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities of the myocardium. Understanding genotype-phenotype correlations in these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies and predictive diagnostics. This review explores the major types of inherited cardiomyopathies—hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM)—and provides detailed insights into how different genetic mutations manifest as clinical features. The integration of multi-omics approaches and advanced disease modeling techniques has enhanced our ability to dissect these correlations. The review also discusses the implications of these findings for personalized medicine, including tailored therapeutic strategies, predictive diagnostics, and future research directions.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

This paper presents an enhanced Fully Connected Neural Network (FCNN) framework that incorporates manifold learning and Renormalization Group (RG) methods. By integrating Christoffel symbols, the model adapts to the curvature and topology of the data manifold, improving learning from geometrically complex data. Additionally, RG methods enable effective multi-scale data processing. We demonstrate the enhanced model’s performance on synthetic and real-world datasets, showing improved accuracy compared to standard FCNNs, particularly in complex systems simulation.

Unmanned delivery technology using unmanned aerial vehicles (UAVs) has the potential to solve issues such as traffic congestion and labor shortages. We investigated a method to construct a route between the source and destination nodes in a wireless multi-hop network by installing a radio terminal in each house. If the relay functions of all nodes in the network are in operation, the number of control messages for route construction increases, and the message relay forwarding load of each node increases. Keeping all node pairs multi-hop connectivity, we establish a method to operate the relay function with as few nodes as possible. End nodes that have not activated the relay function connect to the relay node with the closest physical distance. In this paper, we propose methods for selecting nodes to operate the relay function. Specifically, one method is based on random selection, another method is based on the number of adjacent nodes, and a third method is based on the Multipoint Relay (MPR) of the Optimized Link State Routing Protocol (OLSR). For each method, we evaluate whether the route construction is available, the number of relay nodes, the total distance of constructed routes, and the distance in population-dense regions. We clarify the characteristics of each method, and the method based on MPR is the best for constructing a network suitable for UAV navigation routes. In addition, the smaller the number of relay nodes, the more unexpected collisions on the links due to route conflicts. We clarify the minimum node density required to apply the MPR method which reduces the number of relay nodes suppressing route conflicts.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

This study develops a structure for mapping native vegetation in a transition area between Brazilian Cerrado and Atlantic Forest from integrated spatial information from the Sentinel-1 and Sentinel-2 satellites. Most studies use this data separately, however, the integrated use of optical sensors with Synthetic Aperture Radar (SAR) sensors can improve the methodology for classifying land use and land cover (LULC). The fusion of Sentinel-1 and Sentinel-2, for example, can increase the accuracy of mapping algorithms, using complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified, using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings in which the SAR and optical data were evaluated separately (C1 and C2, respectively) and in an integrated manner (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlandsin C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the use of SAR and optical data becomes an alternative to optimize LULC mapping in tropical regions, due to the limited influence of atmosphere on images in the microwave range.

Although the field of geomatics has seen multiple technological advances in the recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; portable laser scanning is on the other hand time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulted working prototype has been put to the test to verify effectiveness of the proposed approach.

Nowadays, Multi-needle electrospinning has used to be an efficient method for producing nanofiber membranes. But in the process, the fluctuation errors in electrospinning fluid flow rate have directly influence the membrane quality, which bringing instability and still have not been solved yet. For finding out the solution to the inaccurate flow control in multi-needle, it is necessary to develop a multi-stage flow runner spinneret for large-scale production of nanofiber membrane. At first, the paper uses the finite element software COMSOL to numerically simulate the polymer flow in the runner of spinneret. Based on it, the velocity field distribution curve was draw and the velocity instability coefficient of the polymer flow was attained, which are providing theoretical guidance for the optimal design of spinneret. Next, the response surface analysis method (RSM) was used to establish a regression model between the average diameter of the nanofiber membrane and the process parameters. Then, above regression model used to perform variance data processing between the average diameter and the process parameter configuration. After that, the 2D and 3D response surface profiles and the significance analysis were completed. Finally, the stability of the nanofibers production was improved, which attained the optimal adjustment of process configuration in multi-needle electrospinning.

Low carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental concerns. LCSCM technologies face a number of challenges that limit their adoption. Eleven hurdles were selected from the literature as well as the recommendations of business and academic experts. These barriers are defined by a number of factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using Multi criteria decision making (MCDM) techniques such as interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL). MICMAC analysis is used to estimate the sensitivity and priority of barriers based on dependency power and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt 'LCSCM techniques' in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers.The objective of this paper is the Evaluation and selection of most influential of Barriers to Adopt Low Carbon Supply Chain Management (LCSCM) in the Indian Manufacturing Sector is interrelated.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

With much more challenging scenarios encountered in engineering, traditional quadrotors couldn’t accomplish all desired missions perfectly. Task-driven based reconfigurable quadrotors have more latent capacity in environment with dense obstacles and narrow corridors. The translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed in this presentation, assumptions, mathematical models and related motion control laws are given subsequently, in order to cope with hazard missions, model reference adaptive control laws are proposed, sufficient numerical simulations demonstrate the validity of the proposed control laws.

This paper addresses the critical issue of trust in Artificial Intelligence systems, especially when users might find it challenging to comprehend the internal decision-making processes of such systems. A relevant topic of research in this respect is Theory of Mind, which involves attempting to understand these systems as if they possessed beliefs, desires, and intentions. We focus on the latter: intentions, and we examine how producing explanations based on them can improve \emph{understandability} while allowing for a better interpretation of how they align with human values. We also review some existing methods for identifying intentions in AI systems and we conclude with a discussion on possible future directions in this line of research.

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, by using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-scale Encoder Vulnerability Detection (MEVD) approach to detect well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model's attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.

It is necessary to manage the power consumption and use fault-tolerance techniques to meet high reliability in Real-Time Embedded Systems, . Hence, in recent years, this is the main reason for providing an ideal approach to decrease power consumption through the worst-case behavior of instantaneous power management (peak-power management), while observing the Thermal Design Power (TDP). Although the multi-core chips in real-time embedded systems provide great opportunity for implementation of Two-phase Triple modular redundancy to meet high reliability, the energy overhead management of concurrently executing tasks is one of the challenges facing designers. In this article, we propose a scheme named Permanent Fault Aware, Two-Phase Peak-Power Management (PFA-TP3M), for scheduling real-time tasks in multi-core systems to eliminate peak power overlap from concurrent running tasks to maintain peak power consumption at the chip TDP. and through mapping different parts of each task on separate cores, prevents consecutive conflicts on cores, which have faced permanent faults to design the fault-tolerant system. Our simulation results show that our proposed scheme provides up to 700x fault tolerance increase (630x on average) compared to the state-of-the-art power management algorithm, an achievement costs up to 14%. (average 11%) increase the length of the task graph compared to it.

Chromatographic separation is a critical technique in the manufacturing processes of both chemical products and biopharmaceuticals. Its principle relies on exploiting the differences in distribution between the stationary and mobile phases to achieve the separation of mixtures. The precision of substance concentration during separation directly impacts the quality and usability of the final product. Therefore, the development of an effective and precise separation control technique has long been a vital concern in the field of chromatographic separation control. Currently, the employment of Simulated Moving Bed (SMB) technology for chromatographic separation, which allows for continuous feed, has been recognized as a cutting-edge technique. SMB is a continuous process technology that enhances the efficiency of adsorbents within the bed. This sequential multi-column SMB technology not only increases production capacity but also reduces the consumption of solvents and water. It is acknowledged as one of the cleanest manufacturing technologies in the biopharmaceutical industry. However, multi-column SMB involves various variables such as flow rates in multiple sections and valve switching times, rendering its system control highly complex. Unlike traditional control objectives that aim to minimize control output errors, the control objective of SMB is to achieve a specific proportion of substance concentration. Consequently, designing and implementing traditional control theories for SMB is challenging, leading to SMB systems largely being controlled based on simple PLC controls. Moreover, these controls are often applicable only to single or a few-column SMB setups, limiting the effectiveness of high-capacity applications. To achieve effective control of SMB, this study employs an adjustable intelligent fuzzy controller with a structure like an approximate neural network (NN) for SMB control research. Simulation results demonstrate that the intelligent controller effectively achieves desirable control outcomes for the SMB system.

This work tackles the problem of image restoration, a crucial task in many fields of applied sciences, focusing on removing degradation caused by blur and noise during the acquisition process. Drawing inspiration from the multi-penalty approach based on the Uniform Penalty principle introduced in [Bortolotti et al. arXiv.math.NA/2309.14163], we develop here a new image restoration model and an iterative algorithm for its effective solution. The model incorporates pixel-wise regularization terms and establishes a rule for parameters selection, aiming to restore images through the solution of a sequence of constrained optimization problems. To achieve this, we present a modified version of the Newton Projection method, adapted to multi-penalty scenarios, and prove its convergence. Numerical experiments demonstrate the efficacy of the method in eliminating noise and blur while preserving the image edges.

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2 % and 108.5 % improvement over DQN methods and RL method respectively in handling large-scale multi-agent problems in M-MIMO networks.

This study proposes a method for image matching between infrared(IR)-RGB images using a deep learning network to estimate the deformation field. We propose a deformation field generator (DFG) that estimates the deformation field of the transformation matrix to match each pixel or IR image to the RGB image. DFG is a network that receives IR and RGB images as input; the output size is two channels and has the sample resolution as the input image. By warping the IR image through a grid-sampler that warps the image according to the value of the deformation field, we can obtain a warped IR image that matches the RGB image. Additionally, to check whether the warped IR image matched the RGB image, the masking images detecting the segmentation of objects were photographed in two images. Without directly comparing IR and RGB images, we proposed mask loss that warps the IR mask image through the deformation field and grid sampler and then compares the warped IR mask image with the RGB mask image. Mask loss solves the spatial similarity comparison problem with multi-modality images, such as IR and RGB images, by comparing the mask image with the same modality image as the mask image.

This paper proposes a position recognition method for the receiver coil by the secondary coil current. Based on the practical value of the current of the receiver coil, the position recognition of the receiver coil in the WPT system is realized. This paper proposes a 3×3 array multi-transmitter coil grouping and control logic. The mathematical model of mutual inductance between the receiver and transmitter coil at different positions on the X-Y plane is established. A method for identifying the position of the receiver coil according to the current of the receiver coil is proposed. Compared with the traditional position recognition method of the receiver coil, this method does not need to add a detection coil and position sensor and can realize the position recognition of the receiver coil on the 2D plane.

With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

Reading is a complex cognitive task involving processes from different systems. The present work aims to identify some points of divergence reported in the reading literature and discuss them in a new experimental paradigm framework. Inspired by the paradigms of perceptual identification and rapid parallel presentation (RPVP), we emphasize that the originality of our experimental paradigm lies in the recruitment of multi-stable Arabic percepts within the region where low-level processing occurs (i.e., the visual span area). With good flexibility, the current paradigm has reached higher-order processing levels. In agreement with previous works highlighting the parafoveal-on-foveal effect, results suggest parallel word processing. Furthermore, they suggest a rapid extraction of syntactic and semantic information from words in sentences while attributing an advantage to semantic processing in the emergence of the sentence superiority effect.

The effects of various drawing parameters and annealing processes on the structure and properties of Cu-Ag wires, containing 1 wt% silver, were investigated using specialized equipment including fine wire drawing machines, very fine wire drawing machines, heat treatment equipment, tensile testing machines, microcomputer-controlled electronic universal testers, resistance testers, and scanning electron microscopes. The results revealed that continuous drawing of Cu-1%Ag alloy wires led to elongation of the grains, resulting in a uniform and tightly fibrous microstructure. Moreover, the tensile strength of the alloy wire increased from 670 MPa to 783.9 MPa after a single-pass with a deformation of 14%. Subsequently, when the wire was drawn at a speed of 500 m/min, the tensile strength further increased to 820.1 MPa. After annealing the Փ0.08 mm Cu-1% Ag alloy wire, an increase in annealing temperature up to 500°C resulted in the wire's tensile strength decreasing from 820.1 MPa to 377.5 MPa. Simultaneously, the elongation increased from 1.94% to 15.21%, and the resistivity decreased from 1.931×10-8Ω·m to 1.723×10-8Ω·m. Additionally, when annealing was conducted at a rate of 80 m/min, the wire resistivity dropped to 1.635×10-8Ω·m.

Buildings are responsible for approximately 40% of the world’s energy consumption and 36% of the total carbon dioxide emissions. Building occupancy is essential, enabling Occupant-Centric Control for zero emissions and decarbonization. Although existing machine learning and deep learning methods for building occupancy prediction have achieved remarked progress, their analyses remain limited when applied to complex real-world scenarios. Besides, there is a high expectation for Transformer algorithms to predict building occupancy accurately. Therefore, this paper presents an Occupancy Prediction Transformer network (OPTnet). We fuse and feed multi-sensor data (building occupancy, indoor environmental conditions, HVAC operations) into a Transformer model to forecast the future occupancy presence in multiple zones. We perform experimental analysis and compare it to different occupancy prediction methods (e.g., Decision Tree, Long Short-Term Memory networks, Multi-layer perceptron) and diverse time horizons (1,2,3,5,10,20,30mins). The performance metrics (e.g., Accuracy and Mean Squared Error) are employed to evaluate the effectiveness of prediction algorithms. Our OPTnet method achieves superior performance on our experimental two-week data compared to existing methods. The improved performance signifies its potential to enhance HVAC control systems and energy optimization strategies. We will make the code publicly available at https://github.com/kailaisun/occupancy-prediction-binary to promote transparency and reproducibility.

Buckground: Although ceftazidime/avibactam (CAZ/AVI) has become important option for treating adults and children, no recommendations exist for neonates. We report a neonatal sepsis case due to CAZ/AVI-resistant KPC-2 producing-Klebsiella pneumoniae carrying blaVEB-25 and the use of a customized active surveillance program in conjunction to infection control measures. Methods: The index case was an extremely premature neonate hospitalized for 110 days that had been previously treated with multiple antibiotics. Customized molecular surveillance was implemented at hospital level and enhanced infection control measures were taken for early recognition and prevention of outbreak. Detection and identification of blaVEB-25 was performed by next-generation sequencing. Results: That was the first case of a bloodstream infection caused by KPC producing K. pneumoniae that was resistant to CAZ/AVI without the presence of a metalo-β-lactamase in the multiplex PCR platform in a neonate. All 36 additional patients tested (12 in the same NICU and 24 from other hospital departments) carried wild-type blaVEB-1 but they did not harbor blaVEB-25. Conclusion: The emergence of VEB-25 is signal for horizontal transfer of plasmids at hospital facilities and it is of greatest concern for maintaining a sharp vigilance for surveillance of novel resistance mechanisms. Molecular diagnostics can guide appropriate antimicrobial therapy and early implementation of infection control measures against antimicrobial resistance.

Current effort with light field displays is mainly concentrated on the widest possible viewing angle, while a single viewer only needs to view the display in a specific viewing direction. To make light field display a practical practice, a super multi-view light field display is proposed to compress the information in the viewing zone of a single user by reducing the redundant viewpoints. A quasi-directional backlight is proposed and a lenticular lens array is applied to achieve the restricted viewing zone. Eye-tracking technique is applied to extend the viewing area. Experimental results show that the proposed scheme can present vivid 3D scene with smooth motion parallax. Only 16.7% conventional light field display data are required to achieve 3D display. Furthermore, an illumination power of 3.5 watt is sufficient to lighten a 31.5-inch light field display，which takes up 1.5% of the illumination power required for planar display of similar configuration.

In this paper, we extend the Merton model of investment in discrete time to the cases when there is a finite number of investors and the market is with frictions represented by convex penalty functions defined for each investor. In the main result of this paper, we proved the existence of optimal strategy of investment by using a new approach based on the formulation of an equivalent general equilibrium economy model.

For the ground-state properties of gas-phase nanomolecules with multi-reference character, thermally-assisted-occupation density functional theory (TAO-DFT) has been recently found to outperform the widely used Kohn-Sham density functional theory, when the conventional exchange-correlation energy functionals are employed. Aiming to explore solvation effects on the ground-state properties of nanomolecules with multi-reference character at a minimal computational cost, we combine TAO-DFT with the polarizable continuum model (PCM). To show its usefulness, TAO-DFT-based PCM (TAO-PCM) is employed to study the electronic properties of linear acenes in three different solvents (toluene, chlorobenzene, and water). According to TAO-PCM, in the presence of these solvents, the smaller acenes should have nonradical nature, and the larger acenes should have increasing polyradical nature, showing remarkable similarities to the past findings in the gas phase.

Given the importance of menstrual blood in the pathogenesis of endometriosis and the multifunctional roles of menstrual mesenchymal stem cells (MenSCs) in regenerative medicine, this issue has gained prominence in the scientific community. Moreover, recent reviews highlight how robust the integrated assessment of omics data is for endometriosis. To our knowledge, no study has applied the multi-omics approaches to endometriosis MenSCs. It is a case-control study at a university-affiliated hospital. MenSCs transcriptome and proteome data were obtained by RNA-seq and UHPLC-MS/MS detection. Among the differentially expressed proteins and genes, we emphasize ATF3, ID1, ID3, FOSB, SNAI1, NR4A1, EGR1, LAMC3, and ZFP36 genes and MT2A, TYMP, COL1A1, COL6A2, and NID2 proteins that were already reported in the endometriosis. Our functional enrichment analysis reveals integrated modulating signaling pathways such as epithelial-mesenchymal transition (↑) and PI3K signaling via AKT to mTORC1 (↓in proteome), mTORC1 signaling, TGF beta signaling, TNFA signaling via NFkB, and response to hypoxia via HIF1A targets (↑in transcriptome). Our findings highlight primary changes in the endometriosis MenSCs, suggesting that the chronic inflammatory endometrial microenvironment can modulate these cells, providing opportunities for endometriosis etiopathogenesis. Moreover, they identify challenges for future research leveraging knowledge for regenerative and precision medicine in endometriosis.

The prediction of drug-target interactions plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the drug-target interaction prediction problem as an instance of multi-instance learning. We address the problem in three stages, first organizing given drug and target sequences into instances via a private-public mechanism, then identifying the predicted scores of all instances in the same bag, and finally combining all the predicted scores as the output prediction. A comprehensive evaluation demonstrates that the proposed method outperforms other state-of-the-art methods on three benchmark datasets.

Land cover mapping provides spatial information on the physical properties of the Earth’s surface, for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems such as destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping one of the most widespread application areas in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet, that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the Channel Attention mechanism and Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP). In the experimental analysis, in order to to verify the validity of the proposed method, we test AMM-FuseNet applied to four datasets whilst comparing it to the 6 state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we also demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state-of-the-art, achieving less accuracy loss even for the case with 1/20 of the training samples.

Pedestrian multi-target tracking technology plays an important role in artificial intelligence, driverless, virtual reality and other fields. The pedestrian multi-target tracking algorithm DeepSORT based on detection is widely used in industry. It mainly tracks multiple pedestrian targets continuously and keeps their ID unchanged. In order to improve the applicability and tracking accuracy of DeepSORT algorithm, this paper improved the IOU distance measurement in the matching process. At the same time, ResNet50 is used as the feature extraction backbone network, and combined with FPN (Feature Pyramid Network), the appearance features of multi-layer pedestrians are fused to improve the tracking accuracy of DeepSORT algorithm. The proposed algorithm is verified on the public data set MOT-16 and it’s tracking accuracy is enhanced to 4.1%.

The present paper is concerned with the study of the quality of life index. Such an index has become an important index for measuring the well-being of individuals. However, the quality of life index is always a subjective, intangible, and often hard to quantify with precision due to the lack of quantitative models dealing with. The main goal of the present paper is thus to propose a mathematical, quantitative model for the measurement of a quality of life index. The main novelty is firstly the construction of a wavelet dynamic multiscale model to quantify and investigate the effect of time scale on the quality of life index measuring. The proposed procedure is acted empirically on a sample corresponding to Saudi Arabia as a case of study during the period from 2003 to 2020 as part of the 2030-vision plan. Saudi Arabia has implemented the so-called 2030-vision plan where the quality of life improvement is one of the main goals to be attempted. The findings show that wavelets are capable to localize the time-wise behavior of the index contrarily to classical studies which estimate a global view of the index. Moreover, the study shows the link between the quality of life behavior and many other indices.

In this study, a new method for synthesizing Au-NaYF4:Yb3+/Er3+-DSPE-PEG2K nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Au nanoparticles were doped into upconversion nanomaterials and modified with DSPE-PEG2k up-conversion nanomaterials. This material is known as Ag-UCNPs-DSPE-PEG2k, it improves both the luminous intensity because of the doped Au nanoparticles and has low cytotoxicity because of the DSPE-PEG2k modified. Exciting UCNPs with a wavelength of 980nm near-infrared light will emit light with a wavelength of 520nm to further excite gold nanoparticles to convert light energy into heat. Successful synthesized gold nanoparticles was confirmed using transmission electron microscopy (TEM). The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Au nanoparticles. Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Au nanoparticles. The cytotoxicity of Au-UCNPs-DSPE-PEG2K was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) method.

Background: The COVID-19 pandemic has disproportionately affected people from more deprived communities. The experience of Long Covid is similarly distributed but very few investigations have concentrated on the needs of this population. The aim of this project was to co-produce an acceptable intervention for people with Long Covid, living in communities recognised as more deprived. Methods: The intervention was based on a multi-disciplinary team using approaches from sport and exercise medicine and functional rehabilitation. The co-production process was undertaken with a stakeholder advisory group and patient public involvement representation. This study identified participants by postcode and the indices of multiple deprivation (IMD); recruitment and engagement were supported by an existing health and wellbeing service. A virtual ‘clinic’ was offered with a team of professional practitioners who met participants three times each; to directly consider their needs and offer structured advice. The acceptability of the intervention was based on the individual’s participation and their completion of the intervention. Results: Ten participants were recruited with eight completing the intervention. The partnership with an existing community health and wellbeing service was deemed to be an important way of reaching participants. Two men and six women ages ranging from 38 to 73 were involved and their needs were commonly associated with fatigue, anxiety and depression with overall de-conditioning. None reported serious hardship associated with the pandemic although most were in self-employment/part-time employment or were not working due to retirement or ill-health. Two older participants lived alone, and others were single parents and had considerable challenges associated with managing a household alongside their Long Covid difficulties. Conclusions: This paper presents the needs and perspectives of eight individuals involved in the process and discusses the needs and preferences of the group in relation to their support for self- managed recovery from Long Covid.

A graph based path planning method consisting of off-line part and on-line part for multi-UAV system in windy condition is proposed. In the off-line part, the task area is divided into grids. When two nodes is close enough, they are connected and weighted by the cost, obtained by solving an optimization problem based on difference method, between them. In order to ensure the accuracy of the difference method, the adjacent radius is set to be small enough. In order to ensure the generated paths close to analytical solution, the grid size is set to be much smaller than the adjacent radius. The shortest path algorithm is used to update the adjacent matrix and path matrix. In the on-line part, the target position assignment and optimal velocity can be obtained by accessing the adjacent matrix and path matrix. At the end of this paper, some numerical examples are taken to illustrate the validity of our method and the influence of the parameters.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

Identifying cracks in the incipient state is essential to prevent the failure of engineering structures. Detection methods relying on the analysis of the changes in modal parameters are widely used because of the advantages they present. In our previous research, we have found that eigenfrequencies were capable of indicating the position and depth of damage when sufficient vibration modes were considered. The damage indicator we developed was based on the relative frequency shifts (RFS). To calculate the RFSs for various positions and depths of a crack, we established a mathematical relation that involved the squared modal curvatures in the healthy state and the deflection of the healthy and damaged beam under dead mass, respectively. In this study, we propose to calculate the RFS for beams with several cracks by applying the superposition principle. We demonstrate that this is possible if the cracks are far enough from each other. In fact, if the cracks are close to each other, the superposition method does not work and we distinguish two cases: (i) when the cracks affect the same beam face, the frequency drop is less than the sum of the individual frequency drops, and (ii) on the contrary, cracks on opposite sides cause a decrease in frequency, which is greater than the sum of the frequency drop due to individual damage. When the RFS curves are known, crack assessment becomes an optimization problem, the cost function being the distance between the measured RFSs and all possible RFSs for several vibration modes. Thus, the RFS constitutes a benchmark that characterizes damage using only the eigenfrequencies. We can accurately locate multiple cracks and estimate their severity trough experiments and thus prove the reliability of the proposed method.

The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with traditional manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing. STEP-NC using boundary representation has better capability of depicting complex internal structures for additive processes. By using exact model of the final product represented by STEP-NC, the paper demonstrates improvements in data size reduction, slicing accuracy, and precise manipulation of internal structure.

Multi-criteria decision-making (MCDM) methods and techniques have been applied to many real-world problems in different fields of engineering science and technology. The evaluation based on distance from average solution (EDAS) method is a new and efficient MCDM method. The aim of this study is to propose a modification to address two exceptional cases in which the EDAS method fails to solve an MCDM problem.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used to define the building blocks to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

The proportion of area under various crops at a given point in time, known as a cropping pattern, plays an essential role in determining the level of agricultural production. In this study, cropping patterns of three sub-counties in Murang’a County, a typical African smallholder farming area in Kenya, were mapped. Specifically, we compared the performance of eight classification scenarios for mapping cropping patterns; namely using (i) only Sentinel-2 reflectance bands (S2), (ii) S2 and S2 derived vegetation indices (VIs); (iii) S2 and S2 vegetation phenology (VP); (iv) S2 and Sentinel-1 radar backscatter data (S1); (v) S2, VIs, and S1; (vi) S2, VP, and S1; (vii) S2, VIs and VP, and (viii) S2, VIs, VP and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The guided regularized random forest (GRRF) algorithm was used to select the optimum variables and to perform the respective classification for each scenario. The most accurate result of the overall accuracy of 93.16% was attained from the scenario (viii) S2, VIs, VP, and S1. The McNemar’s test of significance did not show significant differences (p≤0.05) among the tested scenarios. Our study demonstrated the strength of GRRF and the synergetic advantage of S2 and S1 derivatives to map cropping patterns in a heterogeneous landscape where high resolution imagery are inaccessible. Our cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

The interaction between single or a fixed number of photons with a single absorber is of fundamental interest in quantum technology. The harnessing of light matter interactions at the single particle limit has several potential applications ranging from quantum communication and quantum metrology to quantum imaging. In this letter, a setup for heralded two-photon absorption at the single absorber level is proposed. The setup is based on a heralded two-photon source utilizing spontaneous parametric down-conversion, entanglement swapping and sum frequency generation for joint detection. The feasibility of the scheme is discussed by reviewing recent achievements in utilizing entangled and correlated photons for two-photon absorption as well as single photon absorption experiments at the limit of single absorbers in the context of applications in imaging (here mainly microscopy) and spectroscopy.

Changes in land-use have been observed in banana-based systems in the African Great Lakes region affected by Xanthomonas wilt disease (XW) of banana. Through participatory focus group discussions (FGDs) and the 4-cell method, changes in land-use were retrospectively assessed in 13 XW-affected landscapes/villages along a 230 km transect from Masisi (XW arrived in 2001) to Bukavu (XW arrived around 2014) in eastern Democratic Republic of Congo during 2015. The four-cell chart ranked land-use by mapping the area under production and the number of households involved in production. Farmers’ perceptions on the sustainability of new land-uses were also documented. Soil nutrient content and erosion levels were measured for five major land-use options/ trajectories on 147 fields across 55 farms in three landscapes along the transect. From being ranked the most important crop (92% of landscapes i.e. produced on large areas of land and by many households) before XW outbreaks, its importance had declined with most households in 36% of the landscapes growing it on smaller farms while in 64% of cases by few households on smaller pots. Farmers uprooted entire banana mats or fields, expanding land under other crops, mainly beans, taro, sweet potato, cassava, maize, coffee and eucalyptus. Species richness did not change at landscape level, though 21 crops were introduced at farm level. Land-use for banana is however still perceived to be more sustainable due to its multi-functional roles. Soils under banana plots were found in general to be better in their chemical attributes while high erosion levels (Mg ha-1 year-1) were observed under cassava (1.7-148.9) compared with banana (0.3-10.7) and trees (0.3-5.9). The current shift away from banana could thus have profound effects on supply of key services and sustainability of the production systems. This study offers a good basis/entry point for interventions in the XW-affected landscapes.

Accurate channel estimation is of utmost importance for massive MIMO systems that allow providing significant improvements in spectral and energy efficiency. In this work, we investigate the spectral efficiency performance and present a channel estimator for multi-cell massive MIMO systems subjected to pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without prior knowledge of the inter-cell large-scale channel coefficients and noise power. The estimator approximates the performance of a linear Minimum Mean Square Error (MMSE) as the number of antennas increases. Following, we derive a lower bound closed-form spectral efficiency of the Maximum Ratio Combining (MRC) detector in the proposed channel estimator. The simulation results highlight that the proposed estimator performance approaches the linear minimum mean square error (LMMSE) channel estimator asymptotically.

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10.

Existing methods for detecting magnetic leakage signals from damaged wire ropes require axial saturation magnetisation, and the accuracy of the detection equipment depends on the saturation degree of magnetisation. Moreover, the expected magnetisation effect is usually difficult to achieve owing to the special characteristics of the rope structure, diameter, and operating environment of the wire rope. Consequently, in addition to other issues, the detection accuracy and versatility of the equipment are low. In this study, a method based on spatial multidimensional orthogonal array loop magnetisation for detecting mine wire rope defects is proposed. Firstly, a new sensor excitation structure model is developed. Then a method of radiant magnetic flux of permanent magnet array is analysed, and the influence law of the number of permanent radiant magnetic blocks on the magnetisation effect is studied. In addition, a method of wire rope detection based on the principle of clustering is investigated, according to which the influence law of the shape, structure, and size of the polyurethane device on the effect of magnetisation is discussed. Finally, through laboratory experiments, the test model and proposed method are verified. The results show that the magnetisation effect is better and more cost-effective when the number of permanent magnet radiation magnetic block is n=8, and the proposed detection method can effectively distinguish damaged wire rope joints. Furthermore, the proposed equipment achieved signal-to-noise ratio of the signals, improving the reliability of damage detection.

Negative corona discharges occur widely in high voltage transmission lines and charged aircraft, which can cause strong electromagnetic interference. Negative corona discharge is typically performed simultaneously at multiple discharge points. In this study, the current and its EM radiation characteristics of single-needle and multi-needle negative corona discharge in different conditions were tested. The current and electromagnetic radiation characteristics of the two discharge structures were compared. The dipole radiation model was established to analyze the EM radiation characteristics of the negative corona discharge.The results show that, It is only when the voltage reaches a certain threshold that the current and electromagnetic radiation fields of the multi-needle discharge structure will be superimposed and their amplitudes will increase significantly. The frequency of electromagnetic radiation signal does not change with the number of needles, cathode geometry and applied voltage, but only depends on ambient pressure. It provides a basis for detecting corona discharge sources under different conditions.

In the oil palm industry, stands age is an important parameter to monitor the sustainability of cultivation, to develop the growth yield model, to identify the disease or stressed area, and to estimate the carbon storage capacity. This research is focused to estimate and distinguish oil palm stands age based on crown/ canopy density obtained using Forest Canopy Density (FCD) model derived from four indices as follows; Advanced Vegetation Index, Bare Soil Index, Shadow Index, and Thermal Index. FCD model employs multi temporal image analysis resulting four classes of oil palm stands age categorized as seed with FCD value of 29–56% (0 years), young with FCD value of 56–63% (1–9 years), teen with FCD value of 63–80% (10–15 years), and mature with FCD value of >80% (>15 years). Minimum canopy density value is 29% even in the zero years old indicates incomplete land clearance or the type of seed planted in the land.

With quick improvement in the field of network, everything becomes online. Remote user authentication is a mechanism in which remote server verify the correctness of user over common or public channel. Remote user authentication can be Single server authentication or Multi server authentication. The disadvantage of single server authentication is that the user needs to recall user id and password for each service, he/she need to utilize, however it can overcome by Multi-server authentication in which user needs to register himself with Registration Center (RC) for the first run through and after that onwards he can utilize each service (which are given by servers, associated with RC) by recollecting only one user id and password. In this paper, we analyze Chen's authentication scheme (based on multi server authentication) and show that this scheme is vulnerable to password guessing attack, replay attack, RC spoofing attack, session key verification attack and perfect forward secrecy attack. In this Paper, we propose a biometric based remote user authentication scheme in multi-server environment. Proposed scheme is more secure and efficient as compared to chen's scheme\cite{23}.

UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

There is an ongoing global pandemic of human respiratory syncytial virus (RSV) infection that results in substantial annual morbidity and mortality. In Australia, RSV is the major cause of acute lower respiratory tract infections (ALRI). Nevertheless, little is known about the extent and origins of genetic diversity of RSV in Australia, nor the factors that shape this diversity. We conducted a genome-scale analysis of RSV infections in New South Wales (NSW). RSV genomes were successfully sequenced for 144 specimens collected between 2010-2016. Of these, 64 belonged to the RSVA and 80 to the RSVB subtype. Phylogenetic analysis revealed a wide diversity of RSV lineages within NSW and that both subtypes evolved rapidly in a strongly clock-like manner, with mean rates of approximately 6-8 x 10^-4 nucleotide substitutions per site per year. There was only weak evidence for geographic clustering of sequences, indicative of fluid patterns of transmission within the infected population, and no evidence of any clustering by patient age such that viruses in the same lineages circulate through the entire host population. Importantly, we show that both subtypes circulated concurrently in NSW with multiple introductions into the Australian population in each year, and only limited evidence for multi-year persistence.

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

Abstract: Comparing outcrop data to laboratory results is important to verify and validate experiments of analogue and reservoir materials especially regarding conditions for deformation experiments. This is important better understand highly complex carbonate reservoir strata and their response to changes in subsurface conditions, reducing subsurface uncertainty. This study develops methods to allow for a more straightforward comparison of outcrop data (m-scale) with experimentally created fracture arrays developed in cylindrical samples (cm-scale). The main objective is to assess usefulness of experimentally-produced fracture networks as analogues for subsurface structures, typically at the meter and above scale by developing new techniques to use the lab deformation. It analyses key characteristics of laboratory-induced fracture networks by adapting scanline methods to use with x-ray tomography (XRT) images to allow for comparison with outcrop and field data. To test and verify these new methods two low permeability carbonate samples were used for deformation testing and analysis. Applying the different scanline methods we show that they can be used to analyse lab induced fractures (mm to cm-scale) identified in XRT images for comparison with outcrop data (m-scale). In addition, these methods also allow for quantification of fracture network attributes e.g. fracture spacing, fracture apertures, orientation. This new data bridges the gap between micro-scanlines using thin sections and outcrop scanlines.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

Recent analyses of cosmic microwave background surveys have revealed hints that there may be a non-trivial running of the running of the spectral index. If future experiments were to confirm these hints, it would prove a powerful discriminator of inflationary models, ruling out simple single field models. We discuss how isocurvature perturbations in multi-field models can be invoked to generate large runnings in a non-standard hierarchy, and find that a minimal model capable of practically realising this would be a two-field model with a non-canonical kinetic structure. We also consider alternative scenarios such as variable speed of light models and canonical quantum gravity effects and their implications for runnings of the spectral index.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

The airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) problem is critical for ramp work safety management, because AGHS equipment malfunctions affect airport ramp work safety. Appropriate vendor selection can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. The AGHSEVS problem is a time-consuming and complex process that requires professional knowledge and experience to make judgments. Specifically, AGHSEVS is a multi-criteria decision-making (MCDM) problem. Previous research has seldom integrated MCDM methods with linear and goal programming to solve the AGHSEVS problem. The objective of this study was to develop a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We test the proposed approach on an AGHS company in Taiwan.

Electrolysis of water is one of low-cost green hydrogen production technologies. The challenge is designing and developing low-cost and high-activity catalysts. Herein, we present a strategy to fabricate flexible electrocatalysts based on nickel-iron (NiFe) alloy coatings. NiFe coatings were plated on the flexible copper-coated polyimide surface (Cu/PI) using the low-cost and straightforward electroless metal plating method and morpholine borane as a reducing agent. It was found that Ni90Fe10, Ni80Fe20, Ni60Fe40, and Ni30Fe70 coatings were deposited on the Cu/PI surface, then the concentration of Fe2+ in the plating solution was 0.5 mM, 1 mM, 5 mM, and 10 mM, respectively. Morphology, structure, and composition of NixFey/Cu/PI catalysts have been examined using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and inductively coupled plasma optical emission spectroscopy (ICP-OES), whereas their activity has been investigated for hydrogen evolution (HER) and oxygen evolution (OER) reactions in 1 M KOH using linear sweep voltammetry (LSVs). It was found that the Ni80Fe20/Cu/PI catalyst exhibited the lowest overpotential value of –202.7 mV for the HER to obtain a current density of 10 mA cm–2 compared to Ni90Fe10/Cu/PI (–211.9 mV), Ni60Fe40/Cu/PI (–276.3 mV), Ni30Fe70/Cu/PI (–278.4 mV), and Ni (−303.4 mV). On the other hand, the lowest OER overpotential (344.7 mV) was observed for the Ni60Fe40/Cu/PI catalyst to obtain a current density of 10 mA cm–2 as compared with the Ni30Fe70 (369.9 mV), Ni80Fe20 (450.2 mV), Ni90Fe10 (454.2 mV) coatings, and Ni (532.1 mV). The developed Ni60Fe40/Cu/PI catalyst exhibit a cell potential of 1.85 V at 10 mA cm−2. The obtained catalysts seem to be suitable flexible catalysts for HER and OER in alkaline media.

The discharge of industrial effluents containing toxic heavy metals in water sources has serious consequences for human health and the environment, and biosorption appears to be an environmental-friendly and cost-effective alternative that can be used for wastewater treatment. The use of different types of agricultural waste as biosorbents for the removal of toxic heavy metals, although an alternative, is quite difficult to apply in practice because these wastes have many other uses. Based on these considerations, in this study, soybean biomass (SB), soybean waste biomass obtained from oil extraction (SBW) and biochar obtained from soybean waste pyrolysis (BC-SBW) were testes as biosorbents form the removal of Pb(II) and Cd(II) ions, in batch systems. Under optimal conditions (pH = 5.4, 4.0 g biosorbent/L, room temperature (25 ± 1 C), contact time = 180 min), the biosorption capacity increase in the order: SB < SBW < BC-SBW for both metal ions (Pb(II) ions (69.43 mg/g < 99.81 mg/g < 116.83 mg/g) and Cd(II) ions (25.63 mg/g < 36.12 mg/g < 49.10 mg/g)), indicating that BC-SBW has the highest efficiency in removing toxic heavy metals. In addition, experiments on wastewater samples have shown that, in addition to significant reducing the content of heavy metals, BC-SBW also significant improved other quality indicators (such as: pH, TSM, CCO-Cr, CBO5), compared to the other biosorbents (SB and SBW). Quantitative evaluation of the biosorptive performance of each biosorbent (SB, SBW and BC-SBW) shows that BC-SBW has a real chance of being used on an industrial scale for wastewater treatment. All these aspects allowed the development of a circular approach for the use of soybean biomass in the removal processes of toxic heavy metals. This approach minimizes the shortcomings of using biomass as a biosorbent and increase the chance of using these materials in industrial practice.

Titanium-based Metal Matrix Composites (MMCs) manufactured by additive manufacturing offer tremendous lightweighting opportunities. However, processing the high reinforcement contents needed to substantially improve elastic modulus while conserving significant ductility remains a challenge. Ti-TiC MMCs fabricated in this study report fracture strains in tension up to 1.7% for a Young’s modulus of 149 GPa. This fracture strain is 30% higher than previously reported values for Ti-based MMCs produced by Laser Powder Bed Fusion (LPBF) displaying similar Young’s moduli. The heat treatment used after the LPBF process leads to the doubling of the fracture strain due to the conversion of TiCx dendrites into equiaxed TiCx grains. As-built microstructure shows both un-dissolved TiC particles and sub-stoichiometric TiC dendrites resulting from a partial dissolution of TiC particles. Reduction of the C/Ti ratio in TiC during the process results in an increase in the reinforcement content, from a nominal 12 vol% to an effective 21.5 vol%. The variation of the TiC lattice constant with its stoichiometry is measured, and an empirical expression is proposed for its effect on TiC’s Young’s modulus. The lower TiC initial powder size distribution displayed the best mechanical performance.

We investigated theoretically the electron-surface optical phonon interaction across the long-range Fröhlich coupling in monolayer transition metal dichalcogenides such as WS2, WSe2, MoS2, and MoSe2 monolayers on SiC and hexagonal BN dielectric substrates. We employed the effective Hamiltonian in the 〖K_(+ ) (K〗_-) valley of the hexagonal Brillouin zone, to assess electronic energy shifts induced by the interaction between electronic states and surface polar optical phonons. Our results indicate that the interaction between electron and surface optical phonon depends upon the polar nature of the substrate. We have also calculated the polaronic oscillator strength as well as the polaronic scattering rate of the lower polaron state in monolayer WS2, WSe2, MoS2, and MoSe2 on SiC and hexagonal BN dielectric substrates. As a result, we have proved theoretically; firstly, the enhancement of polaronic scattering rate with temperature, and secondly, the notable influence of the careful selection of surrounding dielectrics on both Polaronic oscillator strength and polaronic scattering rate. Thus, the optimal dielectrics would be those exhibiting both elevated optical phonon energy and a high static dielectric constant.

To enhance the corrosion resistance of molybdenum metal in molten zinc, this study successfully fabricated TiB2 coatings on molybdenum substrates using the molten salt electrophoretic deposition technique and investigated their corrosion resistance in molten zinc. Initially, TiB2 nanoparticles with a size ranging from 50 to 150 nm were synthesized using the borothermal reduction method in NaF-AlF3 molten salts at 1238 K. Subsequently, electrophoretic deposition experiment was conducted by applying a cell voltage of 1.2 V (corresponding to an electric field of 0.6V/cm) for 1 h in the molten salts containing TiB2 nanoparticles, resulting in a uniform, continuous, and dense TiB2 coating with a thickness of 35 μm on the molybdenum substrate. Moreover, the corrosion resistance of the TiB2 coated molybdenum metal to molten zinc was tested through continuous immersion. After 120 h of immersion, the TiB2 coating showed no signs of cracking or peeling off, successfully protecting the molybdenum metal substrate from corrosion by molten zinc. The results confirm that the molten salt electrophoretic deposition technique can be used to prepare TiB2 coatings with good resistance to molten zinc corrosion on molybdenum metal.

The research paper investigates the synthesis of metal matrix composites (MMCs) through the amalgamation of aluminum and silicon carbide particles, employing a two-step mixing methodology called the stir casting method. Metal matrix composites are materials that enhance the capabilities of a metallic matrix by incorporating another material. This leads to better characteristics such as tensile and shear moduli, fatigue and fracture properties, thermal expansion coefficient, and more. Aluminum-matrix composites are a class of materials that possess distinct characteristics that may be modified by altering factors such as the type of reinforcing material, the amount of reinforcement, and the fabrication technique employed to produce the composite. The concept aims to combine the desirable properties of metals and ceramics by incorporating durable refractory particles into a malleable metal structure. The fabrication process employs the stir casting technique, where reinforcing phases are mechanically stirred into a molten matrix to create cast composites with reinforcement volume percentages of up to 30%. The study utilizes Response Surface Methodology to enhance the mechanical properties of the composites through the optimization of manufacturing parameters, including stirring temperature, time, and speed. The study entails analyzing the correlation between different casting conditions and the resulting changes in hardness and tensile strength.

Along time, many minted coins were withdrawn from circulation, being replaced with new ones. The returned obsolete metal coins were melted in order to sustainable reuse of the alloy for other purpose. Between the withdrawing and melting, some of the metal coins were cancelled by destruction of the original shape and dimension, using some adequate tools. In the first part, the paper is focused to present some aspects insight of the cancelling method used on the Romanian old nickel coins; also, there are presented some examples. The introductive part also includes the literature review in the field of coin manufacturing, covering the subjects as metal behavior under the striking load, aspects about the 3D modeling and FEM analysis or explaining some striking errors. Result the paper main purpose, to study the particularities of one or another cancelling method applied on subjected coins, which conducted to more or less valuable collection metal pieces. In the paper second part, is introduced the adequate 3D model, computed for the cancelling dies, respectively the coin. Then, there are introduced the assembled models, corresponding to each cancelling case, which consist by the obverse and reverse cancelling dies, having the coin inside them. For the each model, the finite element analysis is realized and is achieved for different initial conditions. The final part of the paper presents the analysis results and also the discussions and conclusions.

This work compares the multi-element analysis of human hair and nails using inductively coupled plasma mass spectrometry (ICP-MS) with an easy, fast, cheap, non-destructive method using energy dispersive x-ray fluorescence (EDX-RF). The ICP-MS-based method was more sensitive (over 30 elements could be quantified) and costly (requiring more time, sample, and chemicals). The EDX-based method required laboratory and certified reference materials made of hair for instrument calibration. It was less sensitive (16 elements could be quantified: S, Si, Ca, Br, Fe, Cu, Cr, Mg, Si, K, Mn, Ni, Zn, Se, Sr, Pb), but it allowed to replace troublesome grinding with the dissolution of keratin-based material with alkalic agent (tetramethylammonium hydroxide, TMAH) and formation of stable for days pellets. The method is simple, enables automation, and, due to the modification of wells in the autosampler of the EDX system by immersion of home-designed inserts, it requires smaller amounts of biological material and binder (down to 70 mg instead of 500 mg required by commercially available instrument) to perform analysis. It was concluded that the EDX-based method offers complementary selectivity and sensitivity to ICP-MS with the possibility of the reuse of the sample for further analysis.

Gastric outlet obstruction (GOO) poses a common and challenging clinical scenario, characterized by mechanical blockage in the pylorus, distal stomach, or duodenum, resulting in symptoms such as nausea, vomiting, abdominal pain, and early satiety. Its diverse etiology encompasses both benign and malignant disorders. The spectrum of current treatment modalities extends from conservative approaches to more invasive interventions, incorporating procedures like surgical gastroenterostomy (SGE), self-expandable metallic stents (SEMS) placement, and the advanced technique of endoscopic ultrasound-guided gastroenterostomy (EUS-GE). While surgery is favored for longer life expectancy, stents are preferred in malignant gastric outlet stenosis. The novel EUS-GE technique, employing a lumen-apposing self-expandable metal stent (LAMS), combines the immediate efficacy of stents with the enduring benefits of gastroenterostomy. Despite its promising outcomes, EUS-GE is a technically demanding procedure requiring specialized expertise and facilities.

In this study, robust composite solid electrolytes were developed and employed them to enhance the performance of Li-metal batteries significantly. The robust composite solid electrolytes are composed of a soft polymer, poly(ethylene oxide), a Li salt, bis(trifluoromethanesulfonyl)imide (LiTFSI), and super ionic conductive ceramic fillers such as Li1.5Al0.5Ti1.5(PO4)3 (LATP), and Li6.4La3Zr1.4Ta0.6O12 (LLZTO). The main goal of this study is to enhance the electrochemical stability and ionic conductivity. The ionic conductivities of the composite solid electrolytes were found to be 2.08 × 10-4 and 1.64 × 10-4 S cm-1 with the introduction of LATP and LLZTO fillers, respectively. The results prove that the fabricated solid electrolyte was electrochemical stable at voltage exceeding 4.25 V vs. Li/Li+. The internal resistance of the solid electrolyte significantly reduced as compare to gel electrolyte. This reduction can be attributed owing to the alleviation of bulk electrolyte, charge-transfer, and interfacial electrolyte/electrode impedance. When LiFePO4 cathode sheets are coated with a composite solid electrolyte containing LATP powders, the resulting Li-metal battery displays high capacity at 5 C (with a capacity retention of 65.2% compared to the original capacity at 0.2 C) as well as superior cyclic stability and excellent Coulombic efficiency (&gt; 99.5%, over 200 cycles). These results confirm that the composite solid electrolyte acts as a protective layer which have ability to prevent the growth of Li dendrites. Consequently, the fabricated electrolyte configuration can be engineered to enable high energy/power density and electrochemical stable cyclability in Li-metal batteries.

Municipal solid waste incineration fly ash (MSWI FA) predominantly consists of compounds comprising elements such as calcium, aluminum, silicon, sodium, and others. Additionally, it encompasses a complex mixture of heavy metals, chlorides, sulfates, organic pollutants, and other constituents. The effective and economically viable treatment of MSWI FA poses a formidable challenge for the current stage of resource cycling. In this research report, we will adopt a novel low-temperature sintering method called "Cold Sintering Process" (CSP) as a means to immobilize heavy metals within the fly ash. By utilizing a Taguchi orthogonal array, we will adjust five control factors in the CSP, including sintering temperature, uniaxial pressure, sintering time, initial water addition, and sodium carbonate dosage. The leaching of cadmium element from the fly ash, as measured by TCLP, will serve as the quality indicator. Through the application of CSP, the MSWI FA will be transformed into structurally stable ceramic blocks, effectively immobilizing the heavy metals within the blocks. The results of the experiments showed that MSWI FA under the conditions of a temperature of 300℃, uniaxial pressure of 312 MPa, sintering time in 60 minutes, 25 wt% water addition, and 9 wt% Na2CO3 addition could effectively reduce the leaching of cadmium by 77.71%, lead by 21.14%, zinc by 42.37%, and chromium by 99.99%, as compared to the original MSWI FA TCLP results.

Reductive amination is a powerful tool in sustainable organic synthesis that allows chemists to access a wide range of valuable amine products using renewable feedstocks, mild reaction con-ditions, and with minimal waste generation. Practical applications can be found in various fields, including pharmaceuticals, contributing to greener and more sustainable chemical processes. In this work we present a heterogeneous (Rh and Pt) catalysed protocol for the fast and efficient synthesis of ractopamine hydrochloride (-adrenergic drug) under MW-assisted reductive ami-nation protocol starting from raspberry ketone and octapamine. Microwave (MW) successfully accelerated the hydrogenation reaction and reduced the reaction time from 13 hours to only 3 hours under mild conditions (50°C at 10 bar). The best catalysts were Pt/C and Rh/C, which led to high conversion and selectivity towards ractopamine:HCl. The solvent has a significant effect on the reaction outcome. The replacement of methanol with other solvents had a negative impact on the reaction yield. We also replaced the raspberry ketone with other ketone substrates, especially cyclohexanone. These preliminary experiments may be useful for further process improvements in the synthesis of β-adrenergic agonists and related structures.

A new Pd(II) complex of the previously reported ligand N,N′-bis[(2,2′-dihydroxybiphen-3-yl)methyl]-N,N′-dimethylethylenediamine (L) was obtained from a DMF/H2O mixture. It differs from a Pd(II) complex of L previously obtained by sole DMF. The roles in the solid state assembly of solvent molecules and molecular conformation (driven by specific transition metal ions) were studied by comparing six complexes of L containing different metal centers (Ni(II), Cd(II), Cu(II), Pd(II)). Hirshfeld surface analysis, 2D fingerprint plots and energy frameworks calculations were used to investigate the intermolecular interactions within the crystal packing of the six complexes. As suggested by interaction energies, Pd(II) and Ni(II) complexes arrange to form ribbons, while Cd(II) and Cu(II) complexes form 3D networks. Interactions between complexes and solvent molecules in the previous Pd(II) complex are replaced by complex-complex interactions in the new Pd(II) complex, while BuOH and MeOH are interchangeable in the interactions within the crystal packing of the two Ni(II) complexes. Finally, solvent molecules are not involved in the crystal packing of the Cu(II) complex. The present study suggested that the solid state assembly of these systems is mainly driven by the molecular conformation, that depends in turn by the metal ion involved, while the solvent only plays a minor role.

This paper presents a novel approach of welding thick steel plates that offers time and energy savings compared with conventional techniques. The combination of gas metal arc welding (GMAW) and hot-wire technology simplifies the joint configuration and enhances the process tolerance. In this study, a square butt joint was prepared with as-cut edges and a thickness of 15 mm. The relationship between the welding current and the deposition rate of solo GMAW showed limitations and low process tolerance. Increasing the welding current led to a larger deposited volume with unnecessary weld penetration. An independent deposition volume due to hot-wire insertion was used to improve process tolerance. This approach provided an additional volume without increasing the welding current and reduced unnecessary penetration. With optimized parameters, full-penetration single-pass welding was achieved. Compared with the formation of a typical single-v butt joint at a similar welding speed of 30 cm/min, the proposed process reduced the minimum arc time and power consumption by approximately 83% and 62%, respectively. Moreover, a single pass at a travel speed of 60 cm/min was achieved with approximately 91% and 81% less arc time and power consumption, respectively. In summary, the combined process simplifies the joint configuration, enables full-penetration single-pass welding, and reduces time and energy requirements.

The global context of research for new sustainable energy storage technologies makes it a very active sector with significant scientific and economic challenges. Indeed, due to the irregular development of renewable energies and the shutdown of traditional power facilities, it is difficult to maintain a stable balance in terms of supply and demand: energy storage can help in particular for substantial changes in the latter. Metal air batteries have a higher energy density and are safer than other available energy storage devices. Based on the existing and proven lead-acid battery technology, this paper proposed an open cell foam manufactured by the Excess Salt Replication process for use as an anode for lead-air battery cellsies with sulphuric acid as the electrolyte. This will save lead and reduce the battery weight. A 25% antimonial lead alloy was used to produce open cell foams with a cell diameter between 2 mm and 5 mm for the antimonial lead-air battery. Preliminary results of the effective electrical conductivity of self-discharged primary battery cells, measured experimentally, showed that all antimonial lead foam-air battery cellsies performed better than that made from the same dense non porous antimonial lead alloy. This is generally due to their important specific surface area where oxidation-reduction reactions took place. A correlation between the effective electrical conductivity and the cell diameter has been established and the highest conductivity was obtained with a cell diameter of 5mm. The feasibility of such an electrical system has been demonstrated.