The theory of water transport in trees, according to which the main driving force of water movement is the suction created by the evaporation of water by the meniscus (the curved surface of the capillary liquid column) on the evaporating elements, supported from below by root pressure, is flawed. The capillary effect in nature is around one meter. In the case of open-air gaps, the leaf cannot suck in the water against gravity because, in this case, the plant would not be sucking in water, but air through the open-air gap. If not, how do trees transport water? When evaporation occurs, there is no suction towards the canopy, but pressure is due to a reduction in the cross-section of the water pipe caused by heat loss through evaporation. At night, when evaporation stops, a thermal equilibration process is triggered, which restores the original cross-section of the pipe. This generates suction and draws water from the soil. As the hydrostatic pressure in the pipe is high for tall trees, the pipe is cut off. To prove my theory, I have given several examples. Water transport is not based on physical mechanical laws alone. There may be complex physiological, biochemical, and biophysical processes behind the operation of the pipe system.

Pyrethroid pesticides (PYRs) as the third most widely used pesticides, following organophosphorus and carbamate pesticides, which are of significant importance in the analysis and detection of vegetables. However, the current pretreatment technology for PYRs confronts challenges of difficult separation and low enrichment efficiency, resulting in a cumbersome and time-consuming pretreatment process with poor selectivity. Here, a simple and efficient magnetic solid-phase extraction (MSPE) strategy was developed to simultaneously purify and enrich five PYRs in vegetables, with the magnetic covalent organic framework nanomaterial COF-SiO2@Fe3O4 as an adsorbent. The COF-SiO2@Fe3O4 was prepared by a simple solvothermal conditions method, using Fe3O4 as magnetic core, benzidine and 3,3,5,5-tetraaldehyde biphenyl as two building units. COF-SiO2@Fe3O4 could effectively capture the targeted PYRs by virtue of its abundant π-electron system and hydroxyl groups. The impact of various experimental parameters on extraction efficiency was investigated to optimize the MSPE conditions, including adsorbent amount, extraction time, elution solvent type and elution time. Subsequently, method validation was conducted under the optimal conditions in conjunction with gas chromatography-mass spectrometry (GC-MS). Within the range of 5.00–100 μg·kg-1 (1.00–100 μg·kg-1 for bifenthrin and 2.5–100 μg·kg-1 for fenpropathrin), the five PYRs exhibited a strong linear relationship, with determination coefficients ranging from 0.9990 to 0.9997. The limits of detection (LODs) were 0.3–1.5 μg·kg-1, and the limits of quantification (LOQs) were 0.9–4.5 μg·kg-1. The recoveries were 80.2–116.7% with relative standard deviations (RSDs) below 7.0%. Finally, COF-SiO2@Fe3O4, NH2-SiO2@Fe3O4 and Fe3O4 were compared as MSPE adsorbents for PYRs. The results indicated that COF-SiO2@Fe3O4 was an efficient and rapid selective adsorbent for PYRs. This method holds promising prospects for the determination of PYRs in real samples.

In order to the effect of hydrated phase change materials on suppressing coal spontaneous combustion, the thermogravimetric experiment and reaction activation energy analysis experiment were used to explore the changes of combustion characteristic, parameters characteristic temperature and activating energy of gas coal, long flame coal, meagre coal, and lean coal before and after adding hydrated phase change materials. The research results showed that hydrated phase change materials increased the coal samples’characteristic temperature point and have effective inhibitory effects on different stages of oxidation process.However, the effect was best at low temperatures, as hydrated phase change materials undergo phase change and absorb heat when heated at low temperatures, isolating coal from contact with oxygen. The activating energy increased by 1.138 KJmol-1～23.048 KJmol-1 and the mass loss reduced by 1.6%～9.3% after the inhibition of the coal sample,indicating that the oxidation rate of various coal samples can be slowed down and the spontaneous combustion can be suppressed by using hydrated phase change materials.At the same time, this material can also reduce the combustibility index of meagre coal and lean coal, as well as the comprehensive combustion index and maximum loss rate of long flame coal and gas coal.

This study investigates the effect of Re addition on the microstructure evolution and oxidation resistance of Co-Cr-Ta-Ti-C alloys. The alloys tested in this study consist of a Co-rich matrix with mixed HCP and FCC phases, together with TaTiC2 and Ti-based carbides. The addition of Re promotes the precipitation and growth of a CoCr-rich σ phase. Differential thermal analysis (DTA) showed that the melting temperatures did not change drastically up to 10 at% Re addition, whereas 15 at% Re addition significantly increased the melting temperature of the alloys. Oxidation tests in air at 1200°C showed that excessive Re content could lead to deterioration in oxidation resistance, mainly due to the accelerated formation of volatile Re oxides.

This study delves into the atmospheric corrosion behavior of chromium-free complex-phase steel (CP), specifically investigating the influence of wet/dry frequency and ratio in cyclic corrosion tests (CCT). The study employs a modified ISO 14993 standard CCT method, which involves salt spray, dry, and wet stages. After 15 and 30 CCT cycles, mass loss, maximum corrosion depth, and corrosion products were analyzed to gain insights into corrosion mechanisms. In general, increasing the frequency and wet/dry stage ratio in CCT extends the time for autocatalytic reactions to occur, leading to accelerated localized CP corrosion and increased pitting factors. However, as the rust layer thickens, uniform corrosion may also intensify, so careful considerations are necessary. This study underscores the importance of controlling the frequency and ratio of wet/dry stages in CCT for effectively analyzing localized corrosion behavior in specimens.

This study employs the phase-field regularized cohesion model (PF-CZM) to simulate crack propagation and damage behavior in porous granite. We investigate how pore radius (r), initial crack-pore distance (D), and pore-crack angle (θ) impact crack propagation. The simulation findings reveal that, with a fixed deflection angle and initial crack-pore distance, larger pores are more likely to induce crack extension under identical loading conditions. Moreover, with r and θ keeping constant, the crack extension can be divided into two stages: from its initiation to the lower edge of the pore and then from the lower edge to the upper boundary of the model. By varying the values of D and r, we derive multiple combinations of different D/r ratios and pore radii. These results demonstrate that with constant r, cracks tend to deflect toward the pore closer to the initial crack. Conversely, when D is maintained constant, cracks will preferentially deflect toward pores with a larger r. In summary, the numerical simulation of rock pores and initial cracks, based on the PF-CZM, exhibits remarkable predictive capabilities and holds significant potential in advancing rock fracture analysis.

Bimetallic colloidal CoPt nanoalloys, with a very low platinum content, were successfully synthesized following a modified polyol approach. Powder X-ray diffraction, IR spectroscopy, thermogravimetric analysis, and Transmission Electron Microscopy studies were performed to estimate the crystal structure, morphology and surface functionalization of the colloids respectively, while the room temperature magnetic properties were measured using Vibrating Sample Magnetometer. The particles exhibit excellent uniformity, with a narrow size distribution, and display strong room temperature hysteretic ferromagnetic behavior even in the as-made form. Upon annealing at elevated temperatures, progressive formation and co-existence of exchange coupled both chemically ordered and disordered phases, significantly enhance the room temperature coercivity.

Alginate-gelatin hydrogels are extensively used in bioengineering. However, despite different formulations being utilized for growing different cell types in vitro, their pH and its effect, together with the crosslinking ions, of those formulations are still infrequently assessed. In this work we studied how these elements can affect hydrogel stability and printability and influence U2OS and NIH/3T3 cell viability and metabolism on the resulting 3D prints. In this context, 6% alginate + 2% gelatin hydrogels were prepared with 0.1 M MES buffer with pH 5.5, 6.5, 7.0 or 8.0, printed by extrusion-based 3D printing, and crosslinked immediately after printing with either CaCl2 or BaCl2. Our results showed that both the buffer pH and the crosslinking ion (Ca2+ or Ba2+) influence the swelling and degradation rates of the prints. Moreover, the buffer pH influenced the printability of the hydrogel in air, but when printed directly in a fluid-phase CaCl2 or BaCl2 crosslinking bath. In addition, both U2OS and NIH/3T3 cells showed greater cell metabolic activity on one-layer prints crosslinked with Ca2+. Besides, Ba2+ increased cell death of NIH/3T3 cells while had no effect on the U2OS cell viability. The pH of the buffer also caused an important impact on the cell behaviour. U2OS cells showed a 2.25-fold cell metabolism increase on one-layer prints prepared at pH 8.0 in comparison to those prepared at pH 5.5. Whereas, NIH/3T3 cells showed greater metabolism on one-layer prints with pH 7.0. Finally, we observed a difference on cell arrangement of the U2OS cells growing on prints prepared from hydrogels with acidic buffer in comparison to cells growing on those prepared using neutral or basic buffer. These results show that both pH and crosslinking ion influence hydrogel strength and cell behaviour.

Li-rich manganese-based cathode materials (LRMs) are considered one of the most promising cathode materials for the next generation lithium-ion batteries (LIBs) because of their high energy density. However, there are problems such as capacity decay, poor rate performance, and continuous voltage drop, which seriously limit their large-scale commercial applications. In this work, Li1.2Mn0.54Co0.13Ni0.13O2 coated with Li2MoO4 with unique spinel structure was prepared by wet chemistry method and the subsequent calcination process. The Li2MoO4 coating layer with spinel structure can provide a 3D Li+ transport channel, which is beneficial for improving rate performance, while protecting LRMs from electrolyte corrosion, suppressing interface side reactions, and improving cycling stability. The capacity retention rate of LRMs coated with 3wt% Li2MoO4 increased from 69.25% to 81.85% after 100 cycles at 1C, and the voltage attenuation decreased from 7.06 to 4.98 mV per cycle. The lower Rct also exhibits improved magnification performance. The results indicate that the Li2MoO4 coating effectively improves the cyclic stability and electrochemical performance of LRMs.

Early stable deep molecular response (DMR) of nilotinib is associated with goal of treatment-free remission (TFR) for patients with chronic-phase chronic myeloid leukemia (CML-CP). It is significant to early distinguish patients who can obtain DMR and fit for TFR. We performed a multicenter study to explore early cumulative MR4.5 rate by 18 months with nilotinib in patients with newly diagnosed CML-CP (ND-CML-CP) in China. From 29 institutes, 106 ND-CML-CP patients received nilotinib (300mg BID). The cumulative MR4.5 rate of nilotinib by 18 months was 69.8% (74/106). The cumulative MMR and MR4.0 rates of nilotinib by 18 months were 94.3% (100/106) and 84.9% (90/106), respectively. Patients with ultra-early molecular response (u-EMR) at 6 weeks were not significantly different to obtain DMR or MMR by 24 months compared to those without u-EMR (P=0.7584, P=0.9543, respectively). Our study demonstrated that treatment with nilotinib in patients with ND-CML-CP contributed to obtain high early MR4.5.

This study will present a comprehensive review of the two-phase flow boiling heat transfer coefficient of hydrocarbons such propane (R-290), butane (R-600) and iso-butane (R-600a) and ethanol at various experimental conditions. Studying the multiphase flow heat transfer coefficient has a crucial importance for many heat transfer equipment to achieve higher efficiency for more compact design and cost reduction. One reason behind choosing hydrocarbons as refrigerants in this study is because hydrocarbons have zero ozone depletion potential (ODP=0) and insignificant direct global warming potential (GWP = 3). Moreover, thermodynamic and thermophysical characteristics of hydrocarbons qualify them to be a strong candidate for more heat transfer applications. Initially, by constructing a database for the working fluids from various experimental work available in the literature. The current data that this study has collected for the flow boiling of spans wide ranges of parameters, such as: mass flux, heat flux, operating pressure, and saturation temperature, etc. Furthermore, by comparing the experimental multiphase heat transfer coefficient database with the anticipated values of each correlation, the prediction performance of 26 correlations found in the literature was assessed. This study leads to the selection of the best prediction method based on the minimum deviation of predicted results from the experimental database provided by calculated mean absolute error (MAE) from the assessed correlations. The findings of this study can also be useful in the development of more accurate correlation methods for these fluids and improve the prediction of their flow boiling characteristics.

Abstract: Self-regulated Learning has become one of educational psychology's most critical research areas. Contemporary research indicates that student creativity is crucial to the success of arts education. However, there is limited research on creativity in arts education in Self-regulated Learning. Therefore, this study aims to define the creativity characteristics that determine the quality of students’ work. This study implemented a creative competition in painting and composition for first-year undergraduates. It evaluated the characteristics of creativity during the three main phases of the task: planning, execution, and completion. We discovered through semi-open questionnaires (n=178)，and using the content analysis method. According to the competition results, the students with the highest scores in Flow, Inspiration, and Idea Generation in the Performance phase had the best quality work. In the Forethought phase, Analogical Thinking and Idea Generation worked best for most students, while in the Performance phase, Flow Sense, Idea Generation, and Idea Manipulation worked best.Data on creativity characteristics at different work phases and final quality might adequately guide and organize educational procedures. These findings can serve as a theoretical foundation for quantifying the creativity phase in Self-regulated Learning and offer arts educators a reference for creativity instruction.

An exact solution for the Nagel-Schreckenberg model for v_max = 1 is known since the 90s, yet for v_max > 1 it remains an open question. It is conjectured here that this general solution can be generated from the basic solution (for v_max = 1) by a linear coordinate transformation, the shear mapping, which requires no additional parameters in the deterministic case, and two additional parameters otherwise. The proposed solutions show remarkable agreement with existing numerical solutions, except in the congested regime where the Monte Carlo data shows more concavity than the more linear congested branches predicted by the theory. More research is needed to investigate this bias.

Voltage Source Inverters (VSI) are the integral part of Electrical Vehicles (EV) to enhance the reliability of supply power to critical loads in vehicle to load (V2L) applications. Inherent properties of sliding mode control (SMC) makes it one of the best vailable options to achieve desired voltage quality under variable load conditions. Intrinsic characteristic of robustness associated with SMC is achieved generally at the cost of unwanted chattering along the sliding surface. To manage this compromise better, optimal selection of sliding surface coefficient is applied with proposed composite exponential reaching law (C-ERL). The novelty of proposed C-ERL is associated with the intelligent mix of exponential, power and difference functions blended with rotating sliding surface selection (RSS) technique for three phase two level VSI. Moreover, proposed reaching law along with power rate exponential reaching law(PRERL), enhanced exponential reaching law(EERL) and repeatitive reaching law(RRL) are implemented on two level three phase VSI under variable load condtions. Comparative analysis of which strongly advocates the authenticity and effectiveness of proposed reaching law in achieving well regulated output voltage, with high level of robustness, reduced chattering and low %THD.

The term deviation frequency denotes the boundary between the variable part of the amplitude and phase scintillation spectrum and the part of uninformative noises. We suggested the concept of the “characteristic deviation frequency” during the observation period which is defined as the most probable value of the deviation frequency under current local conditions. This work is a case study of the characteristic deviation frequency (fd) registered for GPS, GLONASS, Galileo and SBAS signals under quiet and weakly disturbed geomagnetic conditions in April 2021 at the mid-latitude GNSS station. Our results demonstrated that the fd value for all signal components of GPS, GLONASS and GALILEO signals varied within 15-22 Hz. The characteristic deviation frequency was 20 Hz for the mentioned GNSS signals. In difference, the deviation frequency was limited within 13-20 Hz for SBAS with the lower characteristic deviation frequency at 18 Hz. We assume that the concept of the characteristic deviation frequency can be used to determine the optimal sampling rate of the GNSS carrier phase data for the ionospheric studies. The characteristic deviation frequency can also characterize the state of the regular trans-ionospheric radio channel.

This paper discusses the basic concepts of phase dislocations and vortex formation in the electric fields of fundamental air core mode of hollow core waveguides with specific types of rotational symmetry of the core – cladding boundary. Analysis of the behavior of the electric field phase in the transmission bands shows that the mechanism of light localization in the hollow core waveguides with discrete rotational symmetry of the core – cladding boundary cannot be completely described by the ARROW model. For an accurate description of the phase behavior, it is necessary to account for phase jumps of the magnitude of π when passing through the phase dislocations.

In this work, a small-strain phase-field model is presented, which is able to predict crack propagation in systems with anisotropic brittle and ductile constituents. To model the anisotropic brittle crack propagation, an anisotropic critical energy release rate is used. The brittle constituents behave linear-elastically, in a transversely isotropic manner. Ductile crack growth is realised by a special crack degradation function, depending on the accumulated plastic strain, which is calculated by following the J2-plasticity theory. The mechanical jump conditions are applied in solid-solid phase transition regions. The influence of the relevant model parameters on a crack, propagating through a planar brittle-ductile interface, and furthermore a crack developing in a domain with a single anisotropic brittle ellipsoid, embedded in a ductile matrix, is investigated. We demonstrate that important properties, concerning the mechanical behaviour of grey cast iron, such as the favoured growth of cracks along the graphite lamellae and the tension-compression load asymmetry of the stress-strain response, are covered by the model. The behaviour is analysed on basis of a simulation domain consisting of three differently oriented elliptical inclusions, embedded in a ductile matrix, which is subjected to tensile and compressive load. The used material parameters correspond to graphite lamellae and pearlite.

Two-dimensional mass spectrometry (2D MS) is a method for tandem mass spectrometry that relies on manipulating ion motions to correlate precursor and fragment ion signals. 2D mass spectra are obtained by performing a Fourier transform in both the precursor ion mass-to-charge ratio (m/z) dimension and the fragment ion m/z dimension. The phase of the ion signals evolves linearly in the precursor m/z dimension and quadratically in the fragment m/z dimension. This study demonstrates that phase-corrected absorption mode 2D mass spectrometry improves signal-to-noise ratios by a factor of 2 and resolving power by a factor of 2 in each dimension compared to magnitude mode. Furthermore, phase correction leads to an easier differentiation between ion signals and artefacts, and therefore easier data interpretation.

The aim of this work was to investigate the microstructure and the mechanical properties of la-ser-welded joints combined of DP800 and DP1000 high strength thin steel sheets. The welded joints (WJ) comprised of similar/dissimilar steels with similar/dissimilar thickness were consisted of different zones and exhibited similar microstructural characteristics. The trend of microhard-ness for all WJs was consistent, characterized of the highest value at hardening zone (HZ) and lowest at softening zone (SZ). The degree of softening was more severe and the size of SZ was wider in the WJ combinations of DP1000 than DP800. The tensile test fractures were located at the base material (BM) for all DP800 weldments, while the fractures occurred at the fusion zone (FZ) for the weldments with DP1000 and those with dissimilar sheet thicknesses. The DP800-DP1000 weldment presented similar yield strength (YS) and ultimate tensile strength (UTS) values but lower elongation (EI) in comparison with the DP800-DP800 weldment, which showed similar strength properties as the BM of DP800. However, the EI of DP1000-DP1000 weldment was much lower in comparison with the BM of DP1000. The DP800-DP1000 weldment with dissimilar thicknesses showed the highest YS and UTS values compared with the other weldments, but with the lowest EI. The fatigue fractures occurred at the WJ for all types of weldments. The DP800-DP800 weldment had the highest fatigue limit and DP800-DP1000 with dissimilar thick-nesses had the lowest fatigue limit. The fatigue crack initiated from the weld surface.

In the last years, the Distribution Grid Operators (DGOs) assumed transition strategies of the distribution grids towards an active area associated with the "Smart Grids" concept. They are considering the use of Artificial Intelligence techniques, combined with advanced technologies and real-time remote communication solutions of the enormous data amounts, to develop smart solutions into the small size distribution grids, also called microgrids (μGs). These solutions will provide support for the DGOs to ensure an optimal operation of the technical infrastructure of the μGs. In this context, a bi-level methodology for solving the phase load balancing problem in the μGs with complex topologies and a high number of single-phase consumers, considering a clustering-based selection criterion of the consumers for placement of the switching devices, was proposed in the paper. A real μG from a rural area, with 114 consumers integrated into the Smart Metering System (SMS), belonging to the DGO from Romania, was considered in testing the proposed methodology. An implementation degree of 17.5%, corresponding to the phase load balancing equipment installed to only 20 consumers from the μG, led to a faster computational time with 43% and reducing the number of switching operations by 92% than in the case of a full implementation degree (100%). The performance indicators related to the unbalance factor and energy-saving used in the evaluation of the technical benefits highlighted the efficiency of the proposed methodology.

Abstract: This study reviewed aspects of the biology of two members of the glucosinolate family, namely sinigrin and glucoraphanin and their anti-tumour and anti-microbial properties. Sinigrin and glucoraphanin are converted by the β-sulphoglucosidase myrosinase or the gut microbiota into their bioactive forms, allyl isothiocyanate (AITC) and sulphoraphanin (SFN) which constitute part of a sophisticated defence system plants developed over several hundred million years of evolution to protect them from parasitic attack from aphids, ticks, bacteria or nematodes. Delivery of these components from consumption of cruciferous vegetables rich in the glucosinolates also delivers many other members of the glucosinolate family so the dietary AITCs and SFN do not act in isolation. In-vitro experiments with purified AITC and SFN have demonstrated their therapeutic utility as antimicrobials against a range of clinically important bacteria and fungi. AITC and SFN are as potent as Vancomycin in the treatment of bacteria listed by the World Health Organisation as antibiotic-resistant "priority pathogens" and also act as anti- cancer agents through the induction of phase II antioxidant enzymes which inactivate potential carcinogens. Glucosinolates may be useful in the treatment of biofilms formed on medical implants and catheters by problematic pathogenic bacteria such as Pseudomonas aeruginosa and Staphylococcus aureus and are potent antimicrobials against a range of clinically important bacteria and fungi. The glucosinolates have also been applied in the prevention of bacterial and fungal spoilage of food products in advanced atmospheric packaging technology which improves the shelf-life of these products.

Traditional phase change composites usually suffer poor mechanical property and easy collapsing in the phase changing process. Herein, a highly flexible phase change composite is fabricated using thermoplastic elastomer as the basic gel and the expanded graphite/paraffin as the filler. This new phase change composite shows a tensile strength of 2.1 MPa and a breaking elongation of 220%. It has a melting enthalpy of 145.4 J•g^-1 and a thermal conductivity of 2.2 W•m^-1•K^-1 with 70% of expanded graphite/paraffin. The thermoplastic elastomer based phase change composite exhibits great reversible property after 200 heating/cooling cycles. This flexible phase change composite demonstrates good photo-thermal energy charging/discharging property and shows great potential to be applied in the solar thermal energy systems.

Printed electronic devices are attracting significant interest due to their versatility and low cost; however, quality control during manufacturing is a significant challenge, preventing the widespread adoption of this promising technology. We show that terahertz (THz) radiation can be used for the in situ inspection of printed electronic devices, as confirmed through a comparison with conventional electrical conductivity methods. Our in situ method consists of printing a simple test pattern exhibiting a distinct signature in the THz range that enables the precise characterization of {the static} electrical conductivities of the printed ink. We demonstrate that contactless dual-wavelength THz spectroscopy analysis, which requires only a single THz measurement, is more precise and repeatable than the conventional four-point probe conductivity measurement method. Our results open the door to a simple strategy for performing contactless quality control in real time of printed electronic devices at any stage of its production line.

We describe the direct formation of mixed-phase (1T and 2H) MoS2 layers on Si as a photocathode via atomic layer deposition (ALD) for application in the photoelectrochemical (PEC) reduction of water to hydrogen. Without typical series-metal interfaces between Si and MoS2, our p-Si/SiOx/MoS2 photocathode showed efficient and stable operation in hydrogen evolution reactions (HERs). The resulting performance could be explained by spatially genuine device architectures in three dimensions (that is, laterally homo and vertically heterojunction structures). The ALD-grown MoS2 overlayer with the mixed-phase 1T and 2H homojunction passivates light absorber and surface states and functions as a monolithic structure for effective charge transport within MoS2. It is also beneficial in the operation of p-i-n heterojunctions with inhomogeneous barrier heights due to the presence of mixed-phase cocatalysts. The effective barrier heights reached up to 0.8 eV with optimized MoS2 thicknesses, leading to a 670 mV photovoltage enhancement without employing buried Si p-n junctions. The fast-transient behaviors via light illumination show that the mixed-phase layered chalcogenides can serve as efficient cocatalysts by depinning the Fermi levels at the interfaces. A long-term operation of ~ 70 h was also demonstrated in a 0.5 M H2SO4 solution.

In this study, the use of recycled diatomaceous earth as the extraction phase in a the solid phase microextraction (SPME) technique for the determination of polycyclic aromatic hydrocarbons (PAHs) in river water samples, with separation/detection performed by gas chromatography-mass spectrometry (GC-MS), is proposed. The optimized extraction conditions are extraction time 70 min at 80 ºC with no addition of salt. The limits of quantification were close to 0.5 μg L-1 with RSD values lower than 25% (n = 3). The linear working range was 0.5 μg L-1 to 25 μg L-1 for all analytes. The method was applied to samples collected from the Itajaí River (Santa Catarina, Brazil) and the RSD values for repeatability and reproducibility were lower than 15% and 17%, respectively. The efficiency of the recycled diatomaceous earth fiber was compared with that of commercial fibers and good results were obtained, confirming that this is a promising option for use as the extraction phase in SPME.

Batch combustion in wood log stoves is a promising application for latent heat storage (LHS), due to the transient heat production with high peak effects. The current study aimed at designing a compact, passive and durable LHS system storing a substantial part of the heat release during batch combustion and effectively releasing the stored heat to the room for 6 to 10 hours after the last batch. The LHS system consists of a coaxial cylinder located at the top of the wood stove, replacing part of the regular stovepipe. Internal metallic fins were applied as heat transfer enhancement to homogenize the temperature distribution inside the PCM. The effect of radial fin lengths was numerically investigated through a parametric study using five different fin lengths within the PCM. Using 35-mm fins in the 70-mm PCM layer yielded the best trade-off for the application. This configuration enabled achieving a slow but close to complete melting of the PCM within a realistic combustion duration, while avoiding overheating the PCM above its degradation temperature. Thereafter, the discharge allowed releasing the stored latent heat for 6 hours. The exhaust gas inlet temperature proved to have a strong influence on the PCM thermal performance.

A phenomenological constitutive model is developed to describe the uniaxial transformation ratcheting behaviors of super–elastic shape memory alloy (SMA) by employing a cosine–type phase transformation equation with the initial martensite evolution coefficient that can capture the feature of the predictive residual martensite accumulation evolution and the nonlinear hysteresis loop on a finite element (FE) analysis framework. The effect of the applied loading level on transformation ratcheting are considered in the proposed model. The evolutions of transformation ratcheting and transformation stresses are constructed as the function of the accumulated residual martensite volume fraction. The FE implementation of the proposed model is carried out for the numerical analysis of transformation ratcheting of the SMA bar element. The integration algorithm and the expression of consistent tangent modulus are deduced in a new form for the forward and reverse transformation. The numerical results are compared with those of existing model and the experimental results to show the validity of the proposed model and its FE implementation in transformation ratcheting. Finally, a FE modeling is established for a repeated preload analysis of SMA bolted joint

It was demonstrated during the past decade that ultra-short intense laser pulse tightly focused deep inside a transparent dielectric generates the energy density in excess of several MJ/cm$^3$. Such energy concentration with extremely high heating and quenching rates leads to unusual solid-plasma-solid transformation paths overcoming kinetic barriers to formation of previously unknown high-pressure material phases, which are preserved in the surrounding pristine crystal. These results were obtained with the pulse of Gaussian shape in space and in time. Recently it was shown that the Bessel-shaped pulse could transform much larger amount of a material and allegedly create even higher energy density than that was achieved with the Gaussian (GB) pulses. Here we present a succinct review of previous results and discuss the possible routes for achieving higher energy density employing the Bessel beams (BB) and take advantage of its unique properties.

Quantitative phase microscopy (QPM), a technique combining phase imaging and microscopy, enables visualization of the 3-D topography in reflective samples as well as the inner structure or refractive index distribution of transparent and translucent samples. However, as in conventional optical microscopy, QPM provides either a large field of view (FOV) or a high resolution but not both. Many approaches such as oblique illumination, structured illumination and speckle illumination have been proposed to improve the spatial resolution of phase microscopy by restricting other degrees of freedom (mostly time). Therefore, the space bandwidth product (SBP) of QPM becomes enlarged. This paper aims to provide an up-to-date review on the resolution enhancement approaches of QPM, discussing the pros and cons of each technique as well as the confusion on resolution definition claim on QPM and other coherent microscopy.

Owing to the nondiffracting, self-accelerating, and self-healing properties, Airy beams of different nature have become a subject of immense interest in the past decade. Their interesting properties have opened doors to many diverse applications. Consequently, the questions of how to properly design spatial manipulation of Airy beams or how to implement them in different setups have become important and timely in the development of various optical devices. Here, based on our previous work, we present a short review on the spatial control of Airy beams, including the interactions of Airy beams in nonlinear media, beam propagation in harmonic potential, and the dynamics of abruptly autofocusing Airy beams in the presence of a dynamic linear potential. We demonstrate that under the guidance of nonlinearity and external potential, the trajectory, acceleration, structure, and even the basic properties of Airy beams can be adjusted to suit specific needs. We describe other fascinating phenomena observed with Airy beams, such as self-Fourier transformation, periodic inversion of Airy beams, and the appearance of spatial solitons in the presence of nonlinearity. These results have promoted the development of Airy beams, and have been utilized in various applications, including particle manipulation, self-trapping, and electronic matter waves.

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was chosen at last based on the comparison of the binding energy of the complexes between the template (OC) and functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method and NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts with 2 g of MIPs with the best binding properties through gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time.

The theory and working principle of fabric phase sorptive extraction (FPSE) is presented that eloquently explains the mystery behind this new and powerful sample preparation technique. FPSE innovatively integrates the benefits of sol-gel coating technology and the rich surface chemistry of cellulose/polyester/fiberglass fabric, resulting in a microextraction device with very high sorbent loading in the form of an ultra-thin coating. This porous sorbent coating and the permeable substrate synergistically facilitate very fast extraction equilibrium. The flexibility of the FPSE device allows for direct insertion into original, unmodified samples of different origin. Strong chemical bonding between the sol-gel sorbent and the fabric substrate permits the exposure of FPSE devices to any organic solvent for analyte back-extraction/elution and to highly acidic or basic environments (pH 1-12) if required. A sol-gel derived sorbent, highly polar sol-gel poly(ethylene glycol) coating, was generated on cellulose substrates. Five cm2 segments of these coated fabrics were used as the FPSE devices for sample preparation using direct immersion. An important class of environmental pollutants, substituted phenols, was used as model compounds to evaluate the extraction performance of FPSE. The high primary contact surface area (PCSA) of the FPSE device and porous structure of the sol-gel coatings resulted in very high sample capacities and incredible extraction sensitivities for both the compound classes in a relatively short period of time. Different extraction parameters were evaluated and optimized. The new extraction devices demonstrated part per trillion level detection limits for substituted phenols, a wide range of detection linearity, and good performance reproducibility.

The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro unit (PSHU) is the research field of cooperative control and optimal operation of pumped storage, wind power and solar power generation. This paper presents a OGVCS model of PSHU considering the rise rate of the unit rotational speed and the specific node pressure of each hydraulic unit, as well as various complicated hydraulic and mechanical constraints. OGVCS model is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., unit rotational speed and water hammer pressure criteria. In order to realize the efficient solution of the OGVCS model, an enhanced multi-objective bacterial-foraging chemotaxis gravitational search algorithm (EMOBCGSA) is proposed to solve this problem, which adopts population reconstruction, adaptive selection chemotaxis operator of local searching strategy and Elite archive set to efficiently solve the multi-objective problem. Especially, novel constraints-handling strategy with eliminating and local search based on violation ranking is used to balance various hydraulic and mechanical constraints. Finally, simulation cases of complex extreme operating conditions (i.e., load rejection and pump outage) of ‘single tube-double units’ type PSHU system are conducted to verify the feasibility and effectiveness of the proposed EMOBCGSA in solving OGVCS problem. The simulation results indicate that the proposed EMOBCGSA can provide lower rise rate of the unit rotational speed and smaller water hammer pressure than other method established recently while considering various complex constraints in OGVCS problem.

In recent years, due to the detrimental impact of traditional gas-liquid refrigerants on the environment, the pursuit of solid-state refrigeration technology based on elastocaloric effect, as a promising sustainable alternative, has been promoted. However, the traditional trial-and-error method is inefficient and difficult to meet the material application requirements. In this work, machine learning (ML) was employed to accelerate the development of NiTi-based shape memory alloy (SMA) with excellent elastocaloric effect. By means of active learning, we identified 9 novel NiTi-based SMAs in four iterations, the phase transformation-induced entropy change (ΔS) of which are as high as over 90 J/kg·K-1. Moreover, the model exhibits good interpretability, allowing us to understand the relevance between the target and features from the aspect of mechanism.

In this paper, the neural network is developed for solving the inverse photoacoustic problem aiming to estimate thermo-elastic properties and thickness of a semiconductor sample. The idea was that these sample properties be estimated from the phase characteristic because the phase measurements are more sensitive. The neural network has been trained on a large basis of photoacoustic phases calculated from a theoretical Si n-type model in the range of 20Hz to 20kHz, on which random Gaussian noise has been applied. It is shown that high accuracy and precision could be reached in solving of inverse photoacoustic problem using only phase measurement.

This article discusses a focused study on visualizing the flow patterns in a two-phase pulsating heat pipe (PHP) using Fe₃O₄/water as the working fluid at 3 V/V% concentration. The research also aims to meticulously examine phase change phenomena in the heating section, particularly focusing on bubble formation and expansion processes. A high-speed video camera was utilized to capture dynamic insights into the behavior of the Fe₃O₄/water mixture. Based on the findings, a straightforward model was developed to explain bubble generation and growth in the mixture, serving as a useful reference for future PHP designs and optimizations. Visual observations also noted the stable nature of the Fe₃O₄/water nanofluid over a four-day period, confirming its consistency throughout the experiments. Moreover, the impact of heat load variation on the evaporator section was assessed using controlled heat inputs ranging from 10 to 80 Watts. Observations on the arrangement of slugs and plugs at a 50% filling ratio revealed interesting self-adjusting flow patterns in response to increasing heat inputs, providing valuable insights into PHP operational dynamics. Notably, the oscillatory flow behavior of Fe₃O₄/water, the chosen working fluid, exhibited greater activity in comparison to water. This distinctive flow behavior contributed to achieving heightened thermal performance efficiency for the Fe₃O₄/water system, attributed to its faster attainment of the annular flow condition.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

This study aimed to evaluate the safety and tolerability of STP1, a combination of ibudilast and bumetanide, tailored for the treatment of a specific subgroup of patients with Autism Spectrum Disorder (ASD), namely ASD Phenotype 1 (ASD-Phen1). We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 1b study with two 14-day treatment phases (registered at clinicaltrials.gov as NCT04644003). Nine ASD-Phen1 patients were administered STP1, while three received a placebo. We assessed safety and tolerability, along with electrophysiological markers, such as EEG, Auditory Habituation, and Auditory Chirp Synchronization, to better understand STP1's mechanism of action. Additionally, we used several clinical scales to measure treatment outcomes. The results showed that STP1 was safe and well-tolerated, with electrophysiological markers indicating a significant and dose-related reduction of gamma power in the whole brain and in brain areas associated with executive function and memory. Treatment with STP1 also increased alpha 2 power in frontal and occipital regions and improved habituation and neural synchronization to auditory chirps. Although numerical improvements were observed in several clinical scales, they did not reach statistical significance. Overall, this study suggests that STP1 is safe and well-tolerated in ASD-Phen1 patients and shows indirect target engagement in ASD brain regions of interest.

The cucumber mosaic virus (CMV) 2b protein is a suppressor of plant defenses and a pathogenicity determinant. Amongst the 2b protein’s host targets is the RNA silencing factor Argonaute 1 (AGO1), which it binds to and inhibits. In Arabidopsis thaliana, if 2b-induced inhibition of AGO1 is too efficient it induces reinforcement of antiviral silencing by AGO2, and triggers increased resistance against aphids, CMV’s insect vectors. These effects would be deleterious to CMV replication and transmission, respectively, but are moderated by the CMV 1a protein by sequestering sufficient 2b protein molecules into P-bodies to prevent excessive inhibition of AGO1. Mutant 2b protein variants were generated and red and green fluorescent protein fusions used to investigate subcellular colocalization with AGO1 and the 1a protein, and the effects of mutations on complex formation with the 1a protein and AGO1 were investigated using bimolecular fluorescence complementation and co-immunoprecipitation assays. Although we found that residues 56-60 influenced the 2b protein’s interactions with the 1a protein and AGO1, it appears unlikely that any single residue or sequence domain is solely responsible. In silico predictions of intrinsic disorder within the 2b protein secondary structure were supported by circular dichroism (CD) but not by nuclear magnetic resonance (NMR) spectroscopy. Intrinsic disorder provides a plausible model to explain the 2b protein’s ability to interact with AGO1, the 1a protein and other factors. However, the reasons for the conflicting conclusions provided by CD and NMR must first be resolved.

The article is devoted to the research of economic cycles and crises, which are studied within the framework of the theory of long waves by N. D. Kondratiev (K-waves) using the mathematical apparatus of fractional calculus. The object of the study is the mathematical model of S.V. Dubovsky 3 and some of its modifications, which describe the dynamics of the economic system, characterizing the interaction between the efficiency of new technologies and the efficiency of capital productivity. Modifications in the model are determined by the following points: the mathematical model takes into account the dependence of the rate of accumulation on capital productivity, there is an external harmonic influence - the influx of investments and new technological solutions, the economic system has heredity, including time-dependent, which leads to a delayed effect - the system’s response to impact. The property of heredity in the mathematical model is taken into account using fractional derivatives of constant and variable orders. Mathematical model S.V. Dubovsky and its modifications are studied numerically using the Adams-Bashforth-Moulton algorithm. Oscillograms and phase trajectories are constructed, and an interpretation of the simulation results is given.

This study aimed to conduct importance-performance analyses (IPAs) based on the perception of Korean middle-school students toward health management awareness during the post-coronavirus disease 2019 (COVID-19) era. Data were collected from 867 Korean middle-school students via online and offline surveys between May and June 2023. A frequency analysis, a reliability analysis, an IPA based on the entire target students, and an IPA depending on sex were carried out with the collected data, and they revealed the following. First, the IPA results regardless of sex indicated that four factors of the mental health were located in the third quadrant with one factor of the same variable in the fourth quadrant. The three factors of the disease management were located in the third quadrant. As for physical activity, two factors were located in the first quadrant, one in the second quadrant, and one in the third quadrant. As for sleep management, two factors were located in the second quadrant, one in the third quadrant, and one in the first quadrant. As for the eating management, two factors were located in the third quadrant, and one in the fourth quadrant. As for the social distancing variable, all four factors were located in the third quadrant. As for the hygiene management, two factors were located in the first quadrant, one in the third quadrant, and one in the fourth quadrant. Furthermore, the IPA results indicated gender differences in regular sports and vigorous movement activities associated with the physical activity. Additionally, a gender difference was observed in regular diet associated with the eating management. This study proposed possible measures for encouraging adolescents to recognize the importance of health and increase their health-related performance during the COVID-19 endemic phase.

The utilization of selective laser melting (SLM) for high entropy alloys (HEAs) holds significant promise in commercial applications, and substantial experimental research efforts have been directed toward this domain. To take advantage of the reported experimental data of SLM manufactured (SLM-ed) HEAs and reduce unnecessary experimentation, this study incorporates machine learning (ML) techniques for the phase and tensile properties prediction of SLM-ed HEAs, thus presenting a novel avenue for accelerating the discovery of new SLM-ed HEAs. Through the adjustment of material descriptors and machine learning models, a model has been developed with an impressive accuracy of 93.8% in distinguishing between face-centered cubic (FCC), body-centered cubic (BCC), and dual-phase (FCC+BCC). Additionally, optimized models have been devised for the prediction of tensile properties, namely ultimate tensile strength (UTS), yield strength (YS), and elongation (δ), achieving noteworthy MAPEs (mean absolute percentage errors) of 10.33%, 8.55%, and 28.48%, respectively. Furthermore, several HEAs were fabricated using SLM, and the experimental outcomes exhibited a favorable alignment with the predicted results. These efforts carry significant implications in advancing the utilization of SLM for HEAs.

To enhance the applicability of dual-phase high-entropy alloys (HEAs) like Fe32Cr33Ni29Al3Ti3, fabricated via selective laser melting (SLM), a focus on improving their mechanical properties is essential. As part of this effort, heat treatment was explored. This study compares the microstructure and mechanical properties of the as-printed sample with those cooled in water after undergoing heat treatment at temperatures ranging from 1000 to 1200 ℃ for 1 hour. Both pre- and post-treatment samples reveal a dual-phase microstructure comprising FCC and BCC phases. Although heat treatment led to a reduction in tensile and yield strength, it significantly increased ductility compared to the as-printed sample. This strength-ductility trade-off is related to changes in grain sizes with ultrafine grains enhancing strength and micron grains optimizing ductility, also the influence of the content of FCC/BCC phases and dislocation density. In particular, the sample heat-treated at 1000 °C for 1 hour and then water-cooled exhibited a better combination of strength and ductility, a yield strength of 790 MPa, and an elongation of 13%. This research offers innovative perspectives on crafting dual-phase HEA of Fe32Cr33Ni29Al3Ti3, allowing for tailorable microstructure and mechanical properties through a synergistic approach involving SLM and heat treatment.

Background: Recurrent approximation of retention parameters in reversed-phase HPLC is effective for revealing anomalies difficult to detect otherwise, namely, the reversible hydration of analytes. This was demonstrated previously for restricted sets of analytes with acetonitrile–water eluents. Expanding the number of analytes and eluents seems to be a topical problem. Two kinds of derivatives of aromatic carbonyl compounds were characterized: unsubstituted hydrazones and oximes. Methods: If analyte demonstrates no anomalies in dependences of retention times vs. concentration of organic modifier, the recurrent approximations of these dependences are linear. To explain the features of recurrent approximations, the numerical experiments were proposed and considered. The artificial shifting of one, two, or more points allows modeling the different kinds of deviations of approximations from linearity. Results: It was shown that hydrazones are the class of analytes having no anomalies of retention parameters. On the contrary, several anomalies were detected for oximes. Downward deviations of points in the plots of recurrent approximations of retention times are the signs of reversible hydration. This effect for methanol–water eluents was detected for the first time. Some of oximes underwent hydrolysis. Conclusion: Recurrent approximation of retention times allows detecting chemical transformations of analytes during RP HPLC analysis.

Efficient commencement of clinical trials is pivotal for their successful execution. The purpose of this study was to determine and examine the factors that contribute to trial start-up delays. The study assessed 20 clinical trials' start-up stages in order to identify relevant variables. Results showed that trials with an emphasis on devices, trials that did not outsource, trials that utilized less auxiliary services, and trials that employed interventional designs had quicker start-up times than observational studies. Furthermore, approval times were substantially shortened by using a centralized Institutional Review Board (IRB) rather than a local IRB. Furthermore, compared to studies that successfully enrolled subjects, those who failed to do so required more time to complete contracts and budgets and secure IRB clearance. This study underscores the diverse impact of clinical trial features on start-up timelines. Understanding these factors is crucial for optimizing the initiation process of clinical trials.

Objective: The stability of the flight phase in ski jumping is crucial for athletes' performance and safety. This study aims to investigate the influence of unfavorable conditions on aerodynamic characteristics and flight stability through computational fluid dynamics (CFD) numerical simulations. Methods: The ski jumper and the skis are considered as a multi-body system. A detailed three-dimensional (3D) model of this multi-body system under a commonly observed posture during flight is established. The partially averaged Navier-Stokes (PANS) turbulence model is employed, and CFD simulations are conducted to predict the aerodynamic characteristics of the multi-body system under lateral environmental wind and asymmetric postures during flight phase. The conditions of asymmetric postures include yaw rotation and roll rotation. Results: (1) Lateral environmental wind generated yaw force, yaw moment, and roll moment, which influenced the lift, drag, and pitch moment of the athlete. These forces and moments were relatively small at lower wind speeds (less than 3 m/s) and became more significant at higher wind speeds (greater than 4.5 m/s). (2) Under the influence of yaw rotation or roll rotation, the multi-body system exhibited noticeable yaw force, yaw moment, and roll moment, all showing a monotonic increasing trend. Moreover, they had a significant impact on the lift, drag, and pitch moment of the multi-body system. Conclusion: (1) The influence of unfavorable conditions is complex, resulting in significant yaw force, yaw moment, and roll moment on the multi-body system. The adverse effects of roll rotation were generally greater than those of yaw rotation. (2) The multi-body system exhibited self-stabilizing tendencies in yaw and roll. This phenomenon can provide a solution to maintain flight stability by employing appropriate yaw or (and) roll rotation angles, effectively compensating for or even eliminating the adverse effects of lateral environmental wind. (3) Understanding the mechanisms of how unfavorable conditions affect the aerodynamic characteristics and stability during flight in ski jumping can provide valuable assistance for real-time prediction and decision-making during competitions, as well as scientific guidance for training athletes stable flight control and techniques and improving their sport performance.

Tight junctions (TJ) are cell-cell adhesive structures that define the permeability of barrier-forming epithelia and endothelia. In contrast to this seemingly static function, TJs display a surprisingly high molecular complexity and an unexpected dynamic regulation, which allows the TJs to maintain a barrier in the presence of physiological forces and in response to perturbations. Cell-cell adhesion receptors play key roles during the dynamic regulation of TJs. They connect individual cells within cellular sheets and link sites of cell-cell contacts to the underlying actin cytoskeleton. Recent findings support roles of adhesion receptors in transmitting mechanical forces and promoting phase separation. In this review, we discuss newly discovered functions of cell adhesion receptors localized at the TJs and their role in the regulation of the barrier function.

This article presents the results of studies of the structure and conductive properties of Na3Fe2(PO4)3 polycrystals synthesized by solid-phase and melt (using concentrated optical radiation) methods. It has been established that with the melt method, the synthesis of the material occurs under significantly nonequilibrium temperature-gradient conditions, which leads to the creation of deformations in the sample that contribute to an increase in ionic conductivity. The ability of the Na3Fe2(PO4)3 structure to adapt to anomalous deformations that occur under temperature-gradient conditions indicates the elasticity of the crystalline framework {[Fe2(PO4)]3-}3∞. Through a comparative assessment of the structural parameters and conductive properties of these materials, it is shown that polycrystals obtained by the melt method have a better texture and greater ionic conductivity. The advantage of this method is that the duration of the synthesis of α-Na3Fe2(PO4)3 is halved. It is concluded that this method is applicable to obtain effective polycrystals of other compositions.

This work investigates a new nanostructured gas-diffusion-layer (nano-GDL) to improve performance of air-cathode Single-Chamber-Microbial-Fuel-Cells (a-SCMFCs). The new nano-GDLs improves the direct oxygen-reduction-reaction by exploiting the best of nanofibers from electrospinning in terms of high surface ratio to volume and high porosity, and laser-based processing to promote adhesion. Nano-GDLs by electrospinning were fabricated directly collecting two nanofibers mats on the same carbon-based electrode, acting as the substrate. Each layer was designed with a specific function: water resistant, oxygen permeable polyvinylidene-difluoride (PVDF) nanofibers served as a barrier to prevent water-based electrolyte leakage, while an inner layer of cellulose nanofibers was added to promote oxygen diffusion towards the catalytic sites. The maximum current density obtained for a-SCMFCs with the new nano-GDLs is (132.2 ± 10.8) mA m^-2, and it doubles the current density obtained with standard PTFE-based GDL (58.5 ± 2.4 mA m^-2), used as reference material. The energy recovery (EF) factor, i.e. the ratio of the power output to the inner volume of the device, was then used to evaluate the overall performance of a-SCMFCs. a-SCMFCs with nano-GDL provided an EF value of 60.83 mJ m^-3: one order of magnitude higher than the value of 3.92 mJ m^-3 obtained with standard GDL

Using straightforward and cost-effective methods, persimmon leaves were converted into a high-quality powder. This powder was applied as an adsorbent for the removal of Cu(II) and Cd(II) from aqueous solutions. Scanning electron microscopy (SEM) indicated the presence of particles with non-homogeneous sizes and rough texture. Elemental analysis and the EDX technique confirmed the presence of elements such as sulfur, phosphorus, nitrogen, and oxygen. The results of ¹³C nuclear magnetic resonance (¹³C-NMR), obtained through the cross-polarization technique, suggest the presence of groups containing sulfur and oxygen. Infrared spectroscopy (FTIR) indicated the existence of amine and hydroxyl groups. The material was used in solid-phase extraction of Cu(II) and Cd(II) in batch experiments, and its adsorption capacity was evaluated as a function of time, pH, and analyte concentration. Kinetic equilibrium was reached within 5 minutes, and the experimental data were fitted to the pseudo-second-order kinetic model. The optimum pH for adsorption of both metal species was observed to be around 5.0. The adsorption isotherms were adjusted using the modified Langmuir equation, and the maximum amount of metal species extracted from the solution was determined to be 0.213 mmol g^-1 for Cu(II) and 0.215 mmol g^-1 for Cd(II), with high linear correlation coefficients for both metals. The persimmon leaves are typically abundant during the growing season, and since they are seasonal, the Diospyros kaki L.f. tree undergoes the natural process of leaf abscission, ensuring the availability of leaves for application.

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with nearest neighbors and the external magnetic field. We establish the partition function of the model by means of the transfer matrix. Under a null boundary condition, we compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the model’s associated iterative equations, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.

The application of warm-mixing technology brings considerable economical and environment benefits by decreasing the mixing temperature during warm asphalt mixture(WMA) production. However, the possible water residue also generates concerns in moisture susceptibility. For deep investigation on the influencing factors and mechanism of the moisture susceptibility of WMA, surface free energy(SFE) test and laboratory tests are applied in this research. A novel indicator based on SFE namely effective adhesion work is proposed to assess the asphalt-aggregate adhesion with different moisture contents. Then, given the mixing procedure of dry mixing method, an advanced three-phase model as form of asphalt-aggregate-warm mixing additive is introduced improving from the conventional two-phase asphalt-aggregate model for better reflecting the separate addition of warm mixing additive during mixing. Afterwards, the influence of aggregate types, asphalt type, aggregate moisture contents, warm-mixing agent types and warm-mixing process on the moisture susceptibility of WMA is analyzed utilizing the models and indicators proposed. Finally, the validity of the SFE indicator is verified by comparing the calculation of effective adhesion work with freeze-thaw splitting test result. The results show that all of the above factors impact the moisture susceptibility of WMA by influencing the interfacial adhesion, with the effect of moisture content being the most significant. Meanwhile, effective adhesion work and the three-phase model brought out in this research are proven to be feasible to characterize the adhesion properties of WMA, offering theoretical support to the research on warm mixing technology.

This work exclusively focuses on the assessment of the thermal performance of a multifunctional facade panel incorporating PCM in foam layers, recurring to a hot box heat flux meter method to determine the thermal transmittance (U-value). The experimental setup is based on the steady-state approach using climatic chambers, assuring a stable thermal environment. Even small fractions of PCM achieved a small reduction in thermal amplitude. Numerical simulations using Ansys Fluent were developed to evaluate the performance of PCM use over a wide range of temperature boundary conditions and operating modes. These numerical models were calibrated and validated using the results of experimental tests, achieving a correlation factor of 0.9674, thus accurately representing a real-world scenario. The decrement factor (f) was used to analyse the data. It was identified that the efficiency of the panel and size of the optimum region increased with the PCM fraction growth. The simulated behaviour was optimum when the input mean temperature is of 20 °C for a room temperature of between 18-20 °C. The results show the significant potential of the multi-layered panel and the thermal regulator effect of the PCM incorporated on indoor space temperature to reach good thermal comfort levels.

In the perspective of designing complex concentrated alloys (CCAs) for structural applications, sigma (σ) phase is important. It is generally known to be an embrittling phase leading to a degradation in the mechanical properties of CCAs. This necessitates detailed investigations on the evolution of σ phase in these alloys. In the present study, an equiatomic CoCrCuMnNi CCA is chosen, in which σ phase appears at 800°C, as reported in a recent publication [1]. To this end, the present investigation aims at understanding the evolution of this phase so that its presence can be controlled for the purpose of preventing degradation in mechanical properties. In the present work, a vacuum arc melted alloy, after homogenisation (at 400°C for 5h), is heat-treated for different times ranging from 1 to 9h at 800°C followed by water quenching. Extensive characterisation has been carried out using X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) techniques to understand the sequence of σ phase precipitation at 800°C.

In the paper, a coordinated control methodology of single-phase (1-P) end-users switching oper-ations on the phases of an active electric distribution network (AEDN) has been proposed to ob-tain a minimum unbalance degree at the coupling common point (CCP) level with the main dis-tribution system. The phase load balancing (PLB) process considers the smart devices that switch from one phase to another phase the 1-P end-users (consumers and prosumers) to compensate for the phase load unbalance. The proposed methodology has been tested successfully in an AEDN belonging to a Romanian Distribution Network Operator (DNO) containing 114 end-users (104 consumers/10 prosumers) integrated into the Smart Metering System (SMS). The op-timal solution leads to a value of the objective function by 1.00004, represented by the unbalance factor, very close to the ideal target, 1.00. A comparative analysis was conducted considering other possible PLB cases (the consumer-level PLB and prosumer-level PLB), obtaining similar values of the UF (1.027 vs. 1.028), slightly higher than in the hybrid-level PLB. Also, the signifi-cant technical benefits were quantified through an energy-saving of 58.73% and decreasing the phase voltage unbalance rate by 91 % compared to the initial case (without PLB). These results emphasized the positive impact of the proposed coordinated control methodology on the PLB process and evidenced its effectiveness and applicability in the AEDNs.

Alterations in the carnitine shuttle system may be an indication of the presence of cancer. As such, in-depth analyses of this pathway in different malignant tumors could be important for the detec-tion and treatment of this disease. The current study aims to assess the profiles of carnitine and acylcarnitines in gliomas with respect to their grade, the presence of isocitrate dehydrogenase (IDH) mutations, and 1p/19q co-deletion. Brain tumors obtained from 19 patients were sampled on-site using solid-phase microextraction (SPME) immediately following excision. Analytes were desorbed and then analyzed via liquid chromatography-high resolution mass spectrometry. The results showed that SPME enabled the extraction of carnitine and 22 acylcarnitines. An analysis of the correlation factor revealed presence of two separate clusters: short-chain and long chain car-nitine esters. Slightly higher carnitine and acylcarnitine concentrations were observed in the higher malignancy tumor samples (high vs low grade), and in those samples with worse projected clinical outcomes (without vs with IDH mutation; without vs with 1p/19q co-deletion). Thus, the proposed chemical biopsy approach offers a simple solution for on-site sampling that enables sample preservation, thus supporting comprehensive multi-method analyses.

The work is devoted to the study of a MEMS resonator dynamics under the action of phase-locked and automatic gain control loops. Particular attention is directed to the study of the nonlinearity factor of the resonator elastic restoring force. It was found that the determination of control system parameters based on the stability analysis of the operating resonant mode, in the general case, does not provide the required phase adjustment and stabilization of the oscillation amplitude. Stable multifrequency modes of oscillations are found, an analytical study of the mechanisms of their appearance and evolution is carried out under variation of the key parameters of the system. The real regions of the control system stable operation are determined (which do not coincide, as was found, with the regions of stability of the operating resonant mode, due to the presence of hidden attractors in the phase space of the system). A methodology has been developed for identifying such areas of stable operation. A significant complication of the structure of possible motions in the system with an increase in the Q-factor of the resonator is revealed.

Based on electromagnetic heat transfer and metal phase transformation co-simulations, we modeled an AISI 1045 specimen under high-frequency heat treatment. Hardening zone predictions were confirmed through cooling and metal phase transformation simulations after obtaining the results from electromagnetic heat transfer simulations. The cooling process was modeled by applying the cooling coefficient of the cooling water in the same way as the actual heat-treatment process. To obtain the current flowing through the coil during high-frequency induction heating, the voltage was measured and applied using the resistance–inductance–capacitance circuit calculation method. Experimental and simulated results of the heating temperature and curing depth of an AISI 1045 specimen with a carbon content of 0.45% were compared; the comparison indicated good agreement between the two. Using the simulation results, we established a method for obtaining the current flowing through the induction coil for predicting the extent and depth of the hardening zone during high-frequency induction heat treatment.

The contact materials in high-voltage vacuum interrupter require properties such as high conductivity, high density, and high hardness to minimize arc heat damage. In this study, copper–molybdenum-chromium alloys contact materials are examined for a high voltage contact material. Ball milling process was carried out after analyzing the raw materials of copper, chromium, and molybdenum powders. A green compact was produced using a high press with the mixed powder. Afterwards, composite was produced by sintering method according to temperature and infiltration method according to Cu content in green compact. The composite of sintering method showed a density of 8.55 g/cm3 (relative density 93%) a hardness of 217 HV, and an electrical conductivity of 40.7 IACS% at 1200 °C. The composite of 10 wt.% Cu produced by the Cu infiltration method showed a density of 8.7 g/cm3 (relative density 94%), Hardness of 274HV and electrical conductivity of 39 IACS% at 1300 °C. The measurements of physical properties showed the new possibility of using the Cu–Cr–Mo alloy as a contact material for high-voltage vacuum interrupters.

When focusing on materials science in civil engineering, the current trend is to investigate the use of innovative solutions in order to enhance thermal and energy performances. This trend is amplified with the need for a sustainable development strategy for the construction sector. This paper assesses the integration of a Phase Change Material (PCM) in cement intended for building construction. The key characteristic of PCMs is their capacity to absorb energy and restore it. In building construction, this feature could be harnessed to save energy by incorporating PCMs in the materials used. In this study, passive integration of PCM in cement was tested and thermal properties of such an integration was assessed. The results provide insights into how PCMs affect cement as part of the concrete mixture, thus identifying the contribution of PCM-based cements in concrete mixtures.

This paper reports 2 × 4 hybrid Mach-zehnder interferometer (MZI) - multi-mode interferometers (MMI) based compact thermo-optical switch consisting of slab waveguides on silicon-on-insulator, SOI, platform. The device consists of two identical MMIs, each of 6 μm wide and 140 μm long connected with two phase shifters MMIs each with 2 μm wide and 8 μm long and linear tappers each 4 μm long, connected at both ends of the MMIs to minimize the power coupling loss. The loss for linear taper is found to be below 0.02dB. The footprint of the whole device is six 6 μm × 324 μm. This structure is based on unique multimode region shape, which leads optical switch to have less coupling loss and reduced cross-talk. The average thermo-optical switching power consumption is 1.4 mW, the excess losses are 0.8 dB, and the imbalances are 0.1 dB. Aluminum is used as a heating pad, and a trench is created around this pad to prevent from spreading of heat and reduce power loss almost by a factor of 2 to the adjacent phase shifter. Our new heating method has advantages of compact size and ease of fabrication with the current CMOS technology.

This paper proposes a novel three-phase voltage source inverter dead-time compensation strategy for accurate compensation in wide current regions of the inverter. In particular, an analysis of the output voltage distortion of the inverter, which appears as parasitic components of the switches, has been conducted for proper voltage compensation in the low current region, and an on-line compensation voltage controller has been proposed. Also, a new trapezoidal compensation voltage implementation method using the current phase is proposed to simplify realizing the trapezoidal shape of the three-phase compensation voltages. Finally, when the proposed dead-time compensation strategy is applied, the maximum phase voltage magnitude in the linear modulation voltage regions is defined to achieve smooth operation even at high modulation index. Simulations and experiments were conducted to verify the performance of the proposed dead-time compensation scheme.

The out-of-equilibrium dynamics of many body systems has recently received a burst of interest, also due to experimental implementations. The dynamics of both observables, such as magnetization and susceptibilities, and quantum information related quantities, such as concurrence and entanglement entropy, have been investigated under different protocols bringing the system out of equilibrium. In this paper we focus on the entanglement entropy dynamics under a sinusoidal drive of the tranverse magnetic field in the 1D quantum Ising model. We find that the area and the volume law of the entanglement entropy coexist under periodic drive for an initial non-critical ground state. Furthermore, starting from a critical ground state, the entanglement entropy exhibits finite size scaling even under such a periodic drive. This critical-like behaviour of the out-of-equilibrium driven state can persist for arbitrarily long time, provided that the entanglement entropy is evaluated on increasingly subsytem sizes, whereas for smaller sizes a volume law holds. Finally, we give an interpretation of the simultaneous occurrence of critical and non-critical behaviour in terms of the propagation of Floquet quasi-particles.

Determination of time-weighted average (TWA) concentrations of volatile organic compounds (VOCs) in air using solid-phase microextraction (SPME) is advantageous over other sampling techniques, but is often characterized by insufficient accuracies, particularly at longer sampling times. Experimental investigation of this issue and disclosing the origin of the problem is problematic and often not practically feasible due to high uncertainties. This research is aimed at developing the model of TWA extraction process and optimization of TWA air sampling by SPME using finite element analysis software (COMSOL Multiphysics). It was established that sampling by porous SPME coatings with high affinity to analytes is affected by slow diffusion of analytes inside the coating, an increase of analytes concentrations in the air near the fiber tip due to equilibration, and eventual lower sampling rate. The increase of a fiber retraction depth (Z) resulted in better recoveries. Sampling of studied VOCs using 23-ga Car/PDMS assembly at maximum possible Z (40 mm) was proven to provide more accurate results. Alternative sampling configuration based on 78.5 x 0.75 mm i.d. SPME liner was proven to provide similar accuracy at improved detection limits. Its modification with the decreased internal diameter from the sampling side should provide even better recoveries. The developed model offers new insight into optimization of air and gas sampling using SPME.

Most of the phase change materials (PCMs) have been limited to use as functional additions or sealed in containers, and extra auxiliary equipment or supporting matrix is needed. The emergence of 3D printing technique has dramatically advanced the developments of materials and simplified production processes. This study focuses on a novel strategy to model thermal energy storage crystalline gels with three-dimensional architecture directly from liquid resin without supporting materials through light-induced polymerization 3D printing technique. A mask-projection stereolithography printer was used to measure the 3D printing test, and the printable characters of crystalline thermal energy storage P(SA-DMAA) gels with different molar ratios were evaluated. For the P(SA-DMMA) gels with small fraction of SA, the 3D fabrication was realized with higher printing precision both on mili- and micro-meter scales. As a comparison of 3D printed samples, P(SA-DMAA) gels made by other two methods, post-UV curing treatment after 3D printing and UV curing using conventional mold, were prepared. The 3D printed P(SA-DMAA) gels shown high crystallinity. Post–UV curing treatment was beneficial to full curing of 3D printed gels, but did not lead to the further improvement of crystal structure to get higher crystallinity. The P(SA-DMAA) crystalline gel having the highest energy storage enthalpy that reached 69.6 J·g⁻¹ was developed. Its good thermoregulation property in the temperature range from 25 to 40 °C was proved. The P(SA-DMAA) gels are feasible for practical applications as one kind of 3D printing material with thermal energy storage and thermoregulation functionality.

Using a nonclassical symmetry of nonlinear reaction-diffusion equations, some exact multi-dimensional time-dependent solutions are constructed for a fourth-order Allen-Cahn-Hilliard equation. This models a phase field that gives a phenomenological description of a two-phase system near the critical temperature. Solutions are given for the changing phase of a cylindrical or spherical inclusion, allowing for a 'mushy zone' with mixed state that is controlled by imposing a pure state at the boundary. The diffusion coefficients for transport of one phase through the mixture, depend on the phase field value, since the physical structure of the mixture depends on the relative proportions of the two phases. A source term promotes stability of both of the pure phases but this tendency may be controlled or even reversed through the boundary conditions.

The LiCl and LiNO₃ water solutions in the presence of small amounts of 3-helium have been investigated by means of multinuclear resonance spectroscopy. The resulting concentration dependences of the ³He, ^6,7Li⁺, ¹⁴NO₃^¯ and ³⁵Cl^¯ resonance radiofrequencies are reported in the infinite limit. This data along with new theoretical corrections of shielding lithium ions was analyzed by a known NMR relationship method. Consequently, the nuclear magnetic moments of ⁶Li and ⁷Li were established against that of the helium-3 dipole moment: μ(⁶Li)=+0.822046(5)μ_N and μ(⁷Li)=+3.256418(20)μ_N. The new results were shown to be very close to the previously obtained values of the (ABMR) atomic beam magnetic resonance method. This experiment proves that our helium method is well suited for establishing dipole moments from NMR measurements performed in water solutions. This technique is especially valuable when gaseous substances of the needed element are not available. All shielding constants of species present in water solutions are consistent with new nuclear magnetic moments and these taken as a reference. Both techniques – NMR and ABMR – give practically the same results providing that all shielding corrections are properly made.

Zinc oxide (ZnO) nanostructures have been studied extensively in the past years due to the novel electronic, photonic, mechanical and electrochemical properties. Recently, more attention has been paid to assemble nanoscale building blocks into three dimensional (3D) complex hierarchical structures, which not only inherit the excellent properties of the single building blocks but also provide potential applications in the bottom-up fabrication of functional devices. This review article focuses on 3D ZnO hierarchical nanostructures, and summarizes major advances in the solution phase synthesis, applications in environment, and electrical/electrochemical devices. We present the principles and growth mechanisms of ZnO nanostructures via different solution methods, with an emphasis on rational control of the morphology and assembly. We then discuss the applications of 3D ZnO hierarchical nanostructures in photocatalysis, field emission, electrochemical sensor, and lithium ion batteries. Throughout the discussion, the relationship between the device performance and the microstructures of 3D ZnO hierarchical nanostructures will be highlighted. This review concludes with a personal perspective on the current challenges and future researches.

This paper studied the nonlinear vibrations of top tensioned cantilevered pipes conveying pressurized steady two-phase flow under thermal loading. The coupled axial and transverse governing partial differential equations of motion of the system were derived based on Hamilton’s mechanics with the centreline assumed to be extensible. Multiple scale perturbation method was used to resolve the governing equations, which resulted to an analytical approach for assessing the natural frequency, mode shape and the nonlinear coupled axial and transverse steady state response of the pipe. The analytical assessment reveals that at some frequencies the system is uncoupled, while at some frequencies a 1:2 coupling exists between the axial and the transverse frequencies of the pipe. Nonlinear frequencies versus the amplitude displacement of the cantilever pipe conveying two-phase flow at super critical mixture velocity for the uncoupled scenario exhibit a nonlinear hardening behaviour, an increment in the void fractions of the two-phase flow resulted to a reduction in the pipe’s transverse vibration frequencies and the coupled amplitude of the system. However, increasing the temperature difference, pressure and the presence of top tension were observed to increase the pipe’s transverse vibration frequencies without a significant change in the coupled amplitude of the system.

Frequency, amplitude and phase information of the grid voltage are of great importance in constructing a robust controller structure for grid connected inverter systems. This paper presents a simple and robust approach for the instantaneous estimation of positive and negative sequence voltage components under distorted voltage conditions. A second order generalized integrator (SOGI) is used to filter the distorted voltage and to generate orthogonal voltage components for each of the three phases. These filtered and orthogonal components are used for instantaneous calculation of symmetrical components. The implemented method is frequency adaptive; the method is demonstrated and compared to a conventional phase locked loop (PLL) technique with both MATLAB/Simulink simulations and experiments utilizing the dSPACE ds1103 digital controller.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using Multi-Agent Reinforcement Learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments.

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new dataset called Fish52 (Multi-scene Fish dataset) was constructed, on which the model achieved an mAP of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish dataset) and Fish30 dataset and tested them on the F4k and FishNet dataset. The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet datasets reaches 91.72%, 98.7%, 88.6%,and 81.5% respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses high generalization ability and has reached advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and anew module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection header module, introducing classes with independent parameter sand regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar target sin other environments, and offers assistance for the construction of smart fisheries and digital fisheries.

This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple input multiple output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML) based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP) consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks.

Investigation of functional magnetic resonance imaging (fMRI) data with machine learning (ML) techniques, including also deep learning (DL) methods, have been widely used to study Autism Spectrum Disorder (ASD). This disorder is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for treatment development. This study compares traditional ML techniques with deep learning (DL) methods in the analysis of functional connectivity measures obtained from the time series of multicentric ABIDE dataset. Specifically, Support Vector Machines (SVM) classifiers, with both linear and Radial Basis Function (RBF) kernels, as well as eXtreme Gradient Boosting (XGBoost) classifiers, are compared against the TabNet classifier, which is a DL architecture customized for tabular data analysis and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. SVMs achieve performances, in terms of AUC, around 75%, compared to the best TabNet and MLP results, which are 65% and 71%, respectively. Additionally, this work investigates the brain regions that contribute most to the classification task, which are found to be those primarily responsible for sensory and spatial perception, as well as attention modulation, known to be altered in ASD.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before they are sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP.

The radiative heat transfer in arc plasma models is considered from the point of view of its description in terms of a net emission coefficient, the method of spherical harmonics in its lowest order, and the discrete ordinate method. Net emission coefficients are computed applying approximate and numerical approaches, the multi-band representation of the spectral absorption coefficient with three kinds of its averaging and two datasets. The self-consistent access to the radiative heat transfer is applied to a two-dimensional axisymmetric model of free-burning arc in argon at atmospheric pressure. The results obtained from the models employing the net emission coefficient, the method of spherical harmonics, and the discrete ordinate method are compared.

Mitral regurgitation (MR) is a broadly diffuse valvular heart disease (VHD) with a significant impact on healthcare system and patient’s prognosis. Transcatheter mitral valve interventions (TMVI) are now well-established techniques included in the therapeutic armamentarium for managing patients with mitral regurgitation, either primary or functional MR. Even if guidelines give indications regarding the correct management of this VHD, the wide heterogeneity of patient’s clinical background, valvular and heart’s anatomy make each patient a unique case, in which the appropriate device’s selection requires a multimodality imaging evaluation and a multidisciplinary discussion. Proper pre-procedural evaluation plays a pivotal role to judge the feasibility of TMVI, as well as a cooperative works between imagers and interventionalist is crucial for procedural success. This manuscript aims to provide an exhaustive overview of the main parameters that need to be evaluated for appropriate device selection, pre-procedural planning, intraprocedural guidance and post-operative assessment in the setting of TMVI. In addition, it tries to give some insights about next perspective of structural cardiovascular imaging.

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

CuAl8Fe5Ni4Zn4Sn1 (OF 2238) is a new lead-free copper alloy for plain bearing applications that was first officially presented in a scientific journal in 2020. Soon after its invention, the use of the alloy for connecting rod bushings in heavy duty internal combustion engines was promoted and validated with customers. The aim of this article is to describe the material properties of the new alloy in more detail than previously and explain how the advantageous properties of CuAl8Fe5Ni4Zn4Sn1 are generated. At the beginning of the article, the general development trends in the field of copper alloys for sliding applications are presented. This is followed by a presentation of the latest research results about CuAl8Fe5Ni4Zn4Sn, before concluding with a brief summary and future outlook, emphasizing on the fact that recent trends in copper-based sliding applications show great additional potential for further material developments.

Hyperuricemia is mainly caused by the overproduction of uric acid (UA) catalyzed from xanthine by xanthine oxidase (XO) in abnormal purine metabolism in human body. There is an increasing demand for developing novel XO inhibitors of plant origins. The inhibition kinetics and type as well as actions of hesperetin against XO were investigated in vitro using multi-spectroscopic methods including UV-Vis absorption, Fourier transform infrared spectroscopy (FT-IR), fluorescence spectroscopy and molecular docking simulation. Results indicated that hesperetin reversibly inhibited XO in a competitive manner with an inhibition constant (Ki) value of (2.15 ± 0.05) × 10-6 mol/L. Hesperetin had a higher scavenging rate against O2- radical generated by the XO reaction system. The binding of hesperetin to the active site of XO altered the protein’s secondary structure as demonstrated by FT-IR and fluorescence spectra. Molecular docking study revealed that hesperetin had highly affinity to XO and the bonding of hesperetin to the molybdopterin (Mo-pt) active center inhibited not only substrate entry, but also prevented oxygen binding to flavin adenine dinucleotide (another active center in XO). Additionally, hydrophobic interactions and hydrogen bonds contributed to the stabilization of the hesperetin-XO complex. The results suggested that hesperetin be a promising natural XO inhibitor to alleviate hyperuricemia and gout.

Recent studies, typically using patient cerebrospinal fluid (CSF), have suggested that different autoantibodies (Aabs) acting on their respective receptors, may underlie neuropsychiatric disorders. The NR1 subunit of the N-methyl-D-aspartate receptor (NMDAR) has been identified as a target of anti-NMDAR Aabs in a number of central nervous system (CNS) diseases including encephalitis and autoimmune epilepsy. However, the role or the nature of Aabs responsible for effects on neuronal excitability and synaptic plasticity is yet to be established fully. Patient-derived anti-NMDAR Aabs have been shown to bind to specific regions within the NR1 subunit; here, peptide immunisation was used to generate Aabs against selected specific NR1 extracellular sequences. ‘Protein A’ purification was used to obtain total IgG and further peptide purification used to obtain a greater percentage of NMDAR-target specific IgG Aabs. Binding and specificity of these anti-NMDAR Aabs was determined using a range of methodologies including enzyme-linked immunosorbent assay, immunocytochemistry and immunoblotting. Functional effects were determined using different in vitro electrophysiology techniques: two-electrode voltage-clamp in Xenopus oocytes and measures of long-term potentiation (LTP) in ex vivo hippocampal brain slices using multi-electrode arrays. We show that anti-NMDAR Aabs generated from peptide immunisation had specificity for NR1 immunisation peptides as well as target-specific binding to the native protein. Anti-NMDAR Aabs had no clear effect on isolated NMDARs in an oocyte expression system. However, we show that peptide-purified anti-NMDAR Aabs prevented the induction of LTP at Schaffer collateral-CA1 synapses in ex vivo brain slices. Our data demonstrate that anti-NMDAR Aabs cause synaptic NMDAR hypofunction at a network level. This work provides a solid basis to address outstanding questions regarding anti-NMDAR Aabs mechanisms of action and, potentially, development of therapies against CNS diseases.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rain water. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima (2019). The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increases approximately 17%~18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

Abstract Background: Numerous studies have demonstrated that the amalgamation of populations belonging to the same breed or closely related breeds leads to enhanced accuracies in genomic predictions (GP). Extensive experimentation with diverse Bayesian and Genome-enabled best linear unbiased prediction (GBLUP) models has been conducted to explore multi-breed genomic selection (GS) in livestock, ultimately establishing they as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to examine the efficacy of BayesR and GBLUP model with different weighted genomic relationship matrices (GRM) in making genomic predictions for three distinct beef cattle breeds. Subsequently, we conducted a comparative analysis of the predictive accuracy pertaining to various marker densities and genetic correlations across three distinct beef cattle breeds. This investigation aimed to identify the optimal approach for enhancing the predictive accuracy of multi-breed genomic selection in beef cattle. Results：Genetic relationship matrices revealed moderate similarities between YL and the other breeds, with a striking genetic similarity of 0.87 between WG and HX. In HX cattle, BayesR demonstrated an enhancement in prediction accuracy, achieving 0.52 with HD and 0.46 with WGS, a marked improvement over 0.41 with HD and 0.42 with WGS in GBLUP. In WG and YL breeds, both methods showed comparable accuracies with HD, but BayesR slightly outperformed GBLUP with WGS. Further, multi-breed GS analysis indicated that BayesR consistently surpassed GBLUP in prediction accuracy, particularly with WGS data. For instance, in a combined HX and WG reference population, BayesR achieved a superior accuracy of 0.53 with WGS in HX cattle, a significant enhancement over GBLUP models. The study also underscores the advantage of incorporating multiple breeds in the reference population, which improved prediction accuracy, underscoring the value of broad-based genomic selection strategies. Conclusion: The results show that accuracy of multi-breed genomic predictions was higher with BayesR than with GBLUP, especially for the distantly genetic relationship between reference and validation breeds. Further improvements of multi-breed accuracy of genomic predictions could be achieved by increasing the density of the SNP marker. These findings underscore that BayesR providing a substantial improvement in genomic prediction and the importance of considering genetic relationships in the development of GS strategies for multi-breed cattle populations. Further research is warranted to optimize GRM construction and to explore alternative models for genomic prediction across breeds.

Photovoltaic systems and wind energy systems are generally used in distributed system. However, zero-sequence and harmonic currents occur due to photovoltaic systems, and positive-sequence harmonic currents occur due to wind turbines. Thus, the sheath current in high voltage cables increases due to these currents and poor power quality in distributed generation plants. Metal parts of the cable are grounded to prevent the negative effects of sheath currents. When long high voltage cable lines are grounded with the method in the literature, high sheath current and voltage occur on the cable sheath. Therefore, the SSBLR method is designed as a new method for the distributed generation system. The SSBLR method has been optimized with prediction methods and multi-objective optimization methods to prevent negative effects of the sheath current. Pareto optimal method is used as multi-objective optimization method, and artificial neural network, hybrid artificial neural network and regression methods are used as prediction method. The most dominant result is searched using different estimation and optimization methods in pareto optimal method. When the artificial neural network-genetic algorithm hybrid method is used as a prediction method, and genetic algorithm used as optimization method, significant decreases are observed in sheath current and voltage.

Liquid argon technology is widely used by many previous and current neutrino experiments, and it is also promising for future large-scale neutrino experiments. When detecting neutrinos using liquid argon, many hadrons are involved, which can also interact with argon nuclei. In order to gain a better understanding of detection processes, and to simulate neutrino events, knowledge of hadron-argon cross sections is needed. This paper describes a new procedure, which improves upon the previous work with a multi-dimensional unfolding, to measure hadron-argon cross sections in a liquid argon time projection chamber. Through a simplified version of simulation, we demonstrate the validity of this procedure.

In this paper, we investigate a financial market in which asset prices evolve based on a multi-dimensional Brownian motion process and a multi-dimensional Poisson process with different credit and deposit rates We proceed to evaluate European options by establishing upper and lower hedging prices through a transition to a suitable auxiliary market. Additionally, we address the minimization of shortfall risk and no-arbitrage price bounds in incomplete markets within this framework.

One of the fastest growing customer service delivery platforms is online banking. However, the increasing number of attacks on online banking platforms is driving the banks to constantly review their security. This study developed an enhanced Multi-factor authentication scheme with a track and trace capability to reduce these threats. Five modalities of authentication were incorporated in the proposed scheme: The traditional username, password, personal identification number (PIN) and one-time PIN (OTP), augmented with fingerprints or facial scans, registered smart devices, and a time locked user's location. One of the scheme's most impressive accomplishments is its capacity to seamlessly detect undesired activities and send alerts in the form of secretly obtained photographs and location triangulation.

In recent decades, porous scaffolds for bone tissue engineering (BTE) have gained significant attention. Considerable research efforts have been dedicated to investigating the impact of scaffold pore size on a diverse array of biological processes, including cell-scaffold interactions, substance transportation, and vascularization. However, the multi-scale hierarchical porosity, a key structural characteristic of natural bone, has rarely been replicated in BTE scaffolds due to the challenges of controlling the scaffold structure across multiple length scales. With the advancement of manufacturing technology, there has been increasing research on biomimetic multi-scale hierarchical materials, which are also gaining favor in the field of BTE. Therefore, there is an urgent need to review multi-scale hierarchical porous BTE scaffolds. This paper aims to review the role and fabrication methods of multi-scale porous BTE scaffolds at different length scales, the design and manufacturing of new BTE scaffolds for bone regeneration.

Rollover prevention of partially-filled tank trucks is an industry teaser, with the core challenge being real-time perception and observation of the liquid state inside the tank. In order realize re-liable observation of sloshing liquid, this article firstly proposes a sloshing modeling method based on multi-degree-of-freedom pendulum model, and derives the double mass trammel pendulum model (DMTP, 2DOF) accordingly, which accurately reflects the sloshing dynamics under more pervasive working conditions. Secondly, a free surface fluctuation sensor is designed based on magnetostriction, capable of measuring the inclination and height of the liquid level inside tanks filled with hazardous chemical. Finally, the unscented Kalman filter (UKF) is utilized to synthesize the information of the two, establishing a credible real-time observation of the sloshing liquid. Verified through vehicle-fluid coupled co-simulation, under condition of con-secutive double lane change, observation error of the proposed method is only 25.9% that of the open-loop calculation, providing a secure guarantee for the observation of the state variables of the single pendulum model (SP) used for most kinds of anti-rollover controller.

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) have a tough time adopting new technologies. Numerous technological obstacles nega-tively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0 might resolve several technological challenges. A sustainable city is a difficult structure whose economical, societal, and ecological components interact and compete with one another. There is a dearth of actual methodologies for measuring interaction. The objective of Industry 4.0 is to obtain a better degree of performance effectiveness and profitability, and greater automation. Conse-quently, the purpose of the research is to determine the influence of Industry 4.0 in fostering economic efficiency in small and medium enterprises' sustainability. A Sustainable City using Multi-Criteria Decision Making (SC-MCDM) system is designed in this research to test and achieve sustainable developmental goals. This paper then gives a technique for calculating the interaction between various standards, such as static interactions and dynamical pattern resemblance, as well as the weight variables of every indication generated by the connection. Furthermore, the ap-plication of the suggested technique is proved by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional inequalities in sustainability. Indicator scores suggest that the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit are the most significant limiting factors for most communities. Furthermore, the growth of tertiary industries, the improvement of energy performance, the expansion of green areas, and the reduction of pollution emissions are the key driving forces for enhancing sustainability. Compared to other methodologies, Multi-Criteria Decision Making (MCDM) considers the interplay between conditions, which is an excellent way to assess the sustainability of a city. The experimental findings show the impact of MCDM and sustainability to achieve sustainable development goals.

Some tumors occur in anatomical regions that are hard to biopsy with a needle. Such regions include the brain, spinal cord, liver, and lungs. For the latter two, magnetic nanoparticle-loaded macrophages could be intravenously infused and driven via an MRI machine into the tumor or tumors. Once there, they can be induced to phagocytose whole tumor cells. They would keep their target in a non-digested form by inhibiting phagosome maturation - and be directed via magnetotaxis or chemotaxis to an extraction point in the body where they can be more easily collected via needle.

Recently, deep learning models have been widely applied to modulation recognition, which have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the Iterative Dual Scale Attention Module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5\% at 10dB and 0.6232 at full SNR. The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.