There are several well-described molecular mechanisms that influence cell growth and are relat-ed to the development of cancer. Chemokines constitute a fundamental element which are not only involved in local growth but also affect angiogenesis, tumor spread and metastatic disease. Among them, the C-X-C motif chemokine ligand 12 (CXCL12) and its specific receptor the chemo-kine C-X-C motif receptor 4 (CXCR4) have been widely studied. The overexpression in cell mem-branes of CXCR4 has been shown to be associated with the development of different kinds of his-tological malignancies, such as adenocarcinomas, epidermoid carcinomas, mesenchymal tumors or neuroendocrine neoplasms (NENs). The molecular synapsis between CXCL12 and CXCR4 leads to the interaction of G proteins and activation of different intracellular signaling pathways in both gastroenteropancreatic (GEP) and bronchopulmonary (BP) NENs conferring greater ca-pacity for locoregional aggressiveness, epithelial-mesenchymal transition (EMT) and the ap-pearance of metastases. Therefore, it has been hypothesized how to design tools that target this receptor. The aim of this review is to focus on current knowledge of the relationship between CXCR4 and NENs, with a special emphasis on diagnostic and therapeutic molecular targets.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

Chronic kidney disease (CKD) affects approximately 12% of the global population posing a significant health threat. Inflammation plays a crucial role in the uremic phenotype of CKD contributing to elevated cardiovascular and overall mortality in affected individuals. This study aimed to explore novel metabolic pathways in non-dialysis dependent stage 5 CKD patients using semitargeted metabolomics. In a prospective observational design involving 50 patients, blood samples collected before the initial hemodialysis session underwent liquid chromatography and high resolution mass spectrometer analysis. Univariate (Mann-Whitney test) and multivariate (logistic regression with LASSO regularization) methods identified metabolomic variables associated with inflammation. Notably, adenosine-5’-phosphosulfate (APS), dimethylglycine, pyruvate, lactate, and 2-ketobutyric acid exhibited significant differences in the presence of inflammation. Cholic acid, homogentisic acid, and 2-phenylpropionic acid displayed opposing patterns. Multivariate analysis indicated increased inflammation risk with certain metabolites (N-Butyrylglycine, dimethylglycine, 2-Oxoisopentanoic acid, and pyruvate), while others (homogentisic acid, 2-Phenylpropionic acid, and 2-Methylglutaric acid) suggested decreased probability. These findings unveil potential inflammation-associated biomarkers related to defective mitochondrial fatty acid beta oxidation and branched-chain amino acid breakdown in CKD, shedding light on cellular energy production and offering insights for further clinical validation.

The traditional societies evolved locally with the realms of time in their calendars while modern Gregorian based on the solar movement and some others use lunar or the combination with solar. Unveiling some information from the old manuscripts to gain the realm of traditional Batak people since the ancient times in settlements surrounding the tropical Lake Toba, North Sumatera, Indonesia, revival of cosmos is delivered. Uniquely the ancient Batak count days in a month by observing lunar phase and the period of year is by looking the observable Constellation of Orion & Scorpius within the new phase of the moon. Thus there are years with 12 months and some leap years with 13 months, and we propose to categorize the calendar to be lunar-star, relative to other widely analyzed calendars. In the realization of the use of the cosmogram, we cross the period of the leap year to the examination of contemporary data on agriculture event, i.e.: crop failure, as well as some profiles on weather. The revival of the Batak Calendar opens further interesting conjectures based on the rich cultural and astronomical knowledge embedded in tradition with deep connection between celestial observations and timekeeping.

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

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

This paper reviews the current knowledge of pepper anthracnose in the Philippines. We present research outputs on pepper anthracnose from the last three years. Then, we present evidence of the widespread occurrence of C. acutatum sensu lato in the Philippines. Finally, we highlight some research prospects that would contribute towards developing an integrated anthracnose management program.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

In this paper, carbon nanotubes modified screen-printed electrode (CNTs/SPE) was prepared and the CNTs/SPE was employed for the electrochemical determination of antioxidant substance Chlorogenic acids (CGAs). A pair of well-defined redox peak of CGA was observed at the CNTs/SPE in 0.10 mol∙L^-1 acetic acid-sodium acetate buffer (pH 6.2) and electrode process is adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) method for the determination of CGA were proposed based on the CNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 4.73×10^-7 to 4.45×10^-5 mol∙L^-1, the linear regression equation was Ipa(µA) = 4.1993 C (mol∙L^-1)+1.1039 (r = 0.9976) and the detection limit for CGA could reach 3.25×10^-6 mol∙L^-1. The recovery of matrine was 94.74~106.65% (RSD = 2.92%) in coffe beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA.

Intrahepatic cholangiocarcinoma (ICCA) is a malignant epithelial neoplasm characterized by biliary differentiation within the liver. ICCA is molecularly heterogeneous and exhibits a broad spectrum of histopathological features. It is a highly aggressive carcinoma with high mortality and poor survival rates. ICCAs are classified into two main subtypes: small duct type and large duct types. These two tumor types have different cell origins and clinicopathological features. ICCAs are characterized by numerous molecular alterations, including mutations in KRAS, TP53, IDH1/2, ARID1A, BAP1, BRAF, SAMD4, and EGFR, and FGFR2 fusion. Two main molecular subtypes–inflammation and proliferation–have been proposed. Recent advances in high-throughput assays using next-generation sequencing have improved our understanding of ICCA pathogenesis and molecular genetics. The diagnosis of ICCA poses a significant challenge for pathologists because of its varied morphologies and phenotypes. Accurate diagnosis of ICCA is essential for effective patient management and prognostic determination. This article provides an updated overview of ICCA pathology, focusing particularly on molecular features, histological subtypes, and diagnostic approaches.

For the last two decades, an increasing demand for low-cost adsorbents for drinking water treatment has been challenged in the rural areas of developing countries that have problems with arsenic (As) contamination. In this work, spent bleaching earth (SBE), a residual material that is formed during the process of refining vegetable oil, has been utilized to prepare the new magnetic adsorbent Fe-SBE/C by modifying its surface area through calcination and co-precipitation with iron. The experimental results demonstrate that Fe-SBE/C exhibits a substantial adsorption capacity towards both arsenite (As(III)) and arsenate (As(V)). Optimal pH conditions were found to be pH 10 for As(III) and pH 3 for As(V), resulting in adsorption percentages of 76.7% and 94.1%, respectively. Other experimental parameters such as calcination conditions, adsorbent dosage, iron-loading ratio, and the presence of co-existing ions were thoroughly investigated. The kinetic study revealed that the adsorption processes follow the Elovich and intraparticle diffusion models, suggesting a combined adsorption mechanism of boundary layer control and chemisorption. Furthermore, the isotherm followed the Langmuir model with a maximum adsorption capacity of 202.61 μg g-1 for As(III) and 187.61 μg g-1 for As(V). Through thermodynamic analysis and reinforcement by activation energy data, this research validates that the observed adsorption process is indicative through chemisorption. The regeneration study revealed the potential for multiple cycles of arsenic removal, highlighting the practical applicability of Fe-SBE/C in environmental contexts. In conclusion, the findings of this study underscore the efficacy of Fe-SBE/C in arsenic removal and present it as a promising and environmentally friendly solution for mitigating arsenic pollution.

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

RAMOSA1 (RA1) is a Cys2-His2-type (C2H2) zinc finger transcription factor that controls plant meristem fate and identity and has played an important role in maize domestication. Despite its importance, the origin of RA1 is unknown and the evolution in plants is partially understood. In this paper, we present a well resolved phylogeny based on 73 amino acid sequences from 48 embryophyte species. The recovered tree topology indicates that, during grass evolution, RA1 arose from two consecutive SUPERMAN duplications resulting in three distinct grass sequence lineages: RA1-like A, RA1-like B, and RA1; however, most of these copies have unknown functions. Our findings indicate that RA1 and RA1-like play roles in the nucleus despite lacking a traditional nuclear localization signal. Here we report that copies diversified their coding region and, with it, their protein structure, suggesting different patterns of DNA binding and protein-protein interaction. In addition, each of the retained copies diversified regulatory elements along their promoter regions, indicating differences in their upstream regulation. Taken together, we propose that the RA1 and RA1-like gene families in grasses may have undergone subfunctionalization and neofunctionalization enabled by gene duplication.

In the present study, the corrosion resistance of amorphous coating and composite coatings in 3.5 wt.% NaCl, 0.5 M H₂SO₄ and 10 wt.% NaOH solution were studied. The composite coatings exhibit superior corrosion resistance. When the content of AT13 （Al₂O₃–13 wt.% TiO₂）was 15 wt.%, the composite coating has the lowest corrosion current density (1.75×10^-6 A cm^-2), which is 5.14×10^-5 A cm^-2 for Fe-based metallic glassy coating, and the highest corrosion potential (-411 mV), which is -580 mV for Fe-based metallic glassy coating. The breakdown potential of the passivation film in 3.5 wt.% NaCl solution was much higher than that of 316L.The long-time immersion corrosion tests carried out on different coatings showed that the corrosion protection effect of coating was enhanced with the increase of the amount of AT13 added.

Fe-(9, 19, 28, 37)wt.% Cr alloys were corroded at 700 and 800 ^oC for 70 h under 1 atm of N₂, 1 atm of N₂/3.2%H₂O-mixed gas, and 1 atm of N₂/3.1%H₂O/2.42%H₂S-mixed gas. The corrosion rate of Fe-9Cr alloy increased with the addition of H₂O, and furthermore with the addition of H₂S in N₂ gas. Fe-9Cr alloy was always nonprotective. In contrast, Fe-(19, 28, 37) wt.%Cr alloys were protective in N₂ and N₂/H₂O-mixed gas because of formation of the Cr₂O₃ layer. They were, however, nonprotective in N₂/H₂O/H₂S-mixed gas because sulfidation dominated to form the outer FeS layer and the inner Cr₂S₃ layer containing some FeCr₂S₄.

Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and represented using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which have proved elusive. I develop a framework – the Theory of Intelligences (TIS) – that applies across all systems from physics, to biology, humans and AI. TIS likens intelligence to a calculus, differentiating, correlating and integrating information. Intelligence operates at many levels and scales and TIS distils these into a parsimonious macroscopic framework centered on solving, planning and their optimization to accomplish goals. Notably, intelligence can be expressed in informational units or in units relative to goal difficulty, the latter defined as complexity relative to system (individual or benchmarked) ability. I present general equations for intelligence and its components, and a simple expression for the evolution of intelligence traits. The measures developed here could serve to gauge different facets of intelligence for any step-wise transformation of information. I argue that proxies such as environment, technology, society and collectives are essential to a general theory of intelligence and to possible evolutionary transitions in intelligence, particularly in humans. I conclude with testable predictions of TIS and offer several speculations.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

In this paper, inspired by Jleli and Samet [journal of inequalities and applications 38 (2014) 2 1–8] we introduce two new classes of auxiliary functions and utilize the same to define (q, y)R-weak 3 contractions. Utilizing (q, y)R-weak contractions, we prove some fixed point theorems in the setting 4 of relational metric spaces. We employ some examples to substantiate the utility of our newly proved 5 results. Finally, we apply one of our newly proved results to ensure the existence and uniqueness of 6 solution of a Volterra-type integral equation.

This work investigated the isothermal holding time dependence of the densification, microstructure, weight loss and tensile properties of Fe-Mn-Si powder compacts. Elemental Fe, Mn and Si powder mixtures with a nominal composition of Fe-28Mn-3Si (in weight percent) were ball milled for 5h and subsequently pressed under a uniaxial pressure of 400 MPa. The compacted Fe-Mn-Si powder mixtures were sintered at 1200 ℃ for 0, 1, 2 and 3 h, respectively. In general, the density, weight loss and tensile properties increased with the increase of isothermal holding time. A significant increase in density, weight loss and tensile properties occurred in the compacts isothermally holding for 1 h, as compared to those with no isothermal holding. However, further extension of isothermal holding time (2 and 3 h) only played a limited role in promoting the density and tensile properties. The weight loss of the sintered compacts was mianly caused by the sublimation of Mn in Mn depletion region on the surface layer of the sintered Fe-Mn-Si compacts. The length of the Mn depletion region increased as isothermal holding time increased. A single α-Fe phase was detected on the surface of all the sintered compacts, and the locations beyond the Mn depletion region were comprised of a dual dominant γ-austenite and minor ε-martensite.

Bamboo is the building material of the past and future. It offers numerous properties that make it versatile for various applications, including construction. Its impressive strength-to-weight ratio enables it to bear substantial loads and stresses, while its good elasticity allows efficient energy absorption. However, its mechanical properties can vary based on factors such as species, age, locations, methods, and treatment. Treating bamboo is essential to enhance its properties and durability. The literature provides various natural and chemical treatments which enhanced some of the properties but also reported a drawbacks on higher temperature, content and duration. This paper reviewed 57 articles from Scopus database, specifically focusing on article-document type publications from the years 2003 to 2023. Additional references were also incorporated to address concerns in properties, treatment and standards to provide systematic understanding. MATLAB was utilized for data text analytics. With extensive assessment on the articles, the following gaps and concerns were observed and recommends for further study and assessment such as for bamboo properties, the development of centralized guidelines and procedures for the preparation and processing; exploration of alternative materials to reinforce bamboo without compromising its ductility; development of joint connections, and testing of mechanical properties considering seismic, wind and vibration. For treatment methods, the standardization of procedures using natural, chemical or combination. Lastly, for bamboo codes and standards, assessment of existing codes and standards for testing the mechanical properties of bamboo highlighting the potential limitations and areas, uniformity, differences with all existing similar standards. By filling these gaps, it can support the reliability and robustness of bamboo as a sustainable material, fostering its promotion and adoption in construction industry.

In this study, Fe-based superconductors (SCs) are described with a model showing a novel attractive force. First, we describe this novel force using an analog from electromagnetism. From electromagnetism, it was found that this force is a Lorentz force in which two electrons orbit around a Fe-ion with the same velocity. Then, we consider a wave function of an electron. Consequently, due to the property of the proposed attractive force and the quantum-field Hamiltonian, we clarified that the Bardeen–Cooper–Schrieffer (BCS) ground state can be reused. Afterward, attractive force energy was calculated according to the presented model. In Fe-based SCs, a structure transition is essential. Considering that the derived attractive force energy is relatively large, we employ the expanded T_c-equation from the BCS theory that includes the structure transition effect and the derived attractive force energy. In addition, we succeed in analytically reproducing T_c-dome diagrams in various types of Fe-based SCs. Moreover, we discuss the universal property a general SC should have as well as the quantum critical point.

Water splitting technology is an efficient approach to generate hydrogen (H2) energy, which can well address the problems of environmental deterioration and energy shortage, as well as establishment of a clean and sustainable hydrogen economy powered by renewable energy sources due to the green reaction of H2 with O2. While the efficiency of H2 production by water splitting technology is intimately related with the reactions on electrode. Nowadays, the efficient electrocatalysts in water splitting reactions are the precious metal-based materials, i.e., Pt/C, RuO2 and IrO2. Ni (Co, Fe)-based layered double hydroxides (LDH) 2D materials are the typical non-precious metal-based materials in water splitting with advantages of low cost, excellent electrocatalytic performance and simple preparation methods, which exhibits a great potential for the substitution for precious metal-based materials. This review summarizes the recent progress of Ni (Co, Fe)-based LDH 2D materials for water splitting, which mainly focuses on discussing and analyzing the different strategies to modify LDH materials towards high electrocatalytic performance. We also discuss the recent achievements including their electronic structure, electrocatalytic performance, catalytic center, preparation process and catalytic mechanism. Furthermore, the characterization progress in revealing electronic structure and catalytic mechanism of LDH is highlighted in this review. Finally, we put forward some future perspectives related to design and explore advanced LDH catalysts in water splitting.

In this study, Fe-Mo-B based hardfacing electrodes containing different amounts of Mo were coated on an AISI 1020 steel substrate using the electric arc welding method. The findings show that molybdenum is highly effective on the microstructure and minor changes in the coating composition affect the phases and morphological properties. In the hardness tests, an increase of 73% was achieved in the Fe14Mo2B4 based hardfacing coating compared to the base material and a 30% increase compared to the Fe16B4 based coating. The highest macro hardness value was measured as 56.4 HRC and the highest phase hardness was measured as 3228 HV in the FeMo2B2 phase. The lowest wear rate was measured in the Fe14Mo2B2 based coating. The wear rate of the Fe14Mo2B4 based coating was 8.1 times lower than that of the substrate material and 4.7 times lower than that of the Fe16B4 based coating. According to corrosion test results, the highest corrosion resistance was obtained in Fe16B4 based coating. The current density value of the Fe16B4 based coating was measured to be 13.6 times lower than that of the substrate material.

Vertically aligned Fe, S, and Fe-S doped anatase TiO₂ nanotube arrays are prepared by electrochemical anodization process using an organic electrolyte in which lactic acid is added as an additive. In the electrolyte, nanotube layers of greater length (12 μm) and high order with inner diameter of approx. 90 nm and outer diameter of approx. 170 nm are achieved. Doping of Fe, S, and Fe-S via simple wet impregnation method substituted Ti and O sites with Fe and S, which leads to enhance the rate performance at high discharge current densities. Discharge capacities of TiO₂ tubes increased from 82 mAh g-1 (bare) to 165 mAh g-1 for Fe-S doped TiO₂ at high current densities of 0.3 mAcm-2 after 100 cycles with exceptional capacity retention of 85% after 100 cycles. Owing to the enhancement of thermodynamic and kinetic properties by doping of Fe-S, Li-diffusion increas2ed resulting in remarkable discharge capacities of 143 mAh g-1 and 89 mAh g-1 at a current density of 7.4 mA cm-2 and 19 mA cm-2, respectively.

We propose a novel electrochemical system to form precipitation patterns of Cu-Fe-based Prussian blue analogues (Cu-Fe PBA) in agarose gels, using an applied voltage to produce reactant ions. The spatiotemporal evolution, spatial distribution, and crystallite morphologies of the precipitates were investigated by visual inspection, Fe Kα intensity distribution measurements, and optical and scanning electron microscope observations. The precipitation patterns and their evolution depended on the applied voltage. Multicolored periodic precipitation bands were stochastically formed under cyclic alternating voltage (4 V for 1 h and then 1 V for 4 h per cycle). The distances between adjacent bands were randomly distributed (0.30 ± 0.25 mm). The sizes and shapes of the crystallites generated in the gel were position-dependent. Almost cubic but fairly irregular crystallites (0.1–0.8 μm) were formed in the periodic bands, whereas definitely cube-shaped crystallites (1–3 μm) appeared close to the anode. These cube-like reddish-brown crystallites were assigned to Cu-FeII PBA. In some periodic bands, plate-like blue crystallites (assigned to Cu(OH)2) were also present. Future issues for applications of the observed periodic banding were discussed.

The effects of rare earth Y content on the microstructure and high-temperature oxidation properties of 47Fe-36Ni-15Cr-1.5Mn alloy at 1000°C were investigated. The results show that 47Fe-36Ni-15Cr-1.5Mn alloy oxidizes at 1000°C under laboratory atmospheric pressure to form an oxide film layer dominated by Cr2O3 and (Fe, Ni, Mn) Cr2O4 spinel, and the oxidation curve of the alloy follows the parabolic law. After the addition of rare earth Y, rare earth phases precipitated along the grain boundaries in the alloy, and the more Y content, the more precipitated phases in the grain boundaries. The alloy with added rare earths was oxidized at a high temperature of 1000 °C for 2160 min, the oxidation law still followed the parabolic law, while the rare earth phase at the grain boundary reacted with the oxygen atoms infiltrated from the outside to generate a rare earth Y-rich oxide residing at the boundary between the grain boundary and the precipitated phase of the alloy. Add 0.5 wt.% rare earth Y 47Fe-36Ni-15Cr-1.5Mn-0.5Y alloy 1000 ° C high temperature oxidation of the formation of spinel oxide crystalline particles, dense and complete oxide film layer, the surface of the addition of a small amount of (0.5 wt.%) rare earth Y after the refinement of the oxide particles, and in the alloy grain boundaries in the form of oxides reside in the alloy to inhibit the cations in the matrix and the oxygen negative ions. and oxygen-negative ions in the alloy matrix, improving the antioxidant performance of the alloy. With the further increase of rare earth Y, during the oxidation of the alloy at high temperature, the precipitation phase at the grain boundary promotes the inward diffusion of oxygen, resulting in the deterioration of the antioxidant property of the alloy.

The development of high-performance sensing materials for detecting or removing SF₆ decomposition gases is crucial for gas-insulated switchgear (GIS). In this work, the adsorption characteristics of the pristine porphyrin (Pr) and Fe-doped porphyrin (FePr) monolayers towards SF₆ decomposition gases were investigated using the first-principles calculations. Additionally, the electronic properties and sensing-mechanism were also examined. The findings indicate that both Pr and FePr monolayers exhibit thermodynamic stability. The intrinsic Pr monolayer only exhibits a moderate affinity towards the SO₂ molecule, however, its poor sensitivity of the Pr monolayer renders it unsuitable as the sensing-material. In contrast, the FePr monolayer exhibits favorable adsorption strength towards all five toxic gases (H₂S, SO₂, SOF₂, SO₂F₂, HF). The FePr monolayer exhibits chemisorption towards SO₂/SOF₂ gases, Furthermore, the microscopic mechanism of gases-substrate interaction is elucidated through the analysis of charge density difference, charge transfer, and density of states. Moreover, the FePr has excellent sensitivity for H₂S, SO₂, SO₂F₂, SOF₂ and HF molecules owing to the significant variations of the band gap, electrical conductivity and work function. In addition, the τ of H₂S, SO₂, and SOF₂ is 12.6 μs, 18.5 s and 15.4 s at 298 K, respectively. Our calculated results can offer theoretical guidance for the practical application of FePr-based sensing materials to detect SF6 decompositions gases.

A Invasive emerging pollutants from wastewater effluent discharge, such as dyes, pesticides, etc., pose a serious threat to the ecosystem. Advanced oxidation processes (AOPs) under visible light irradiation have emerged as a promising technology to overcome toxic and recalcitrant organic compounds. In this work, the impact of operational factors, including the concentration of Fe3+ or Fe2+, [H2O2], the molar ratio (Oxalate/ Fe3+ or Fe2+), and the initial pH, was studied to obtain high efficiency in the degradation and mineralization of such a food dye to reduce their pollution. The study deals with the comparison of the efficiency of UV, LED, and sunlight irradiation on the photocatalytic degradation of the dye using Fe3+/Lig and Fe2+/Lig complexes. The results showed that sunlight irradiation gave a very rapid kinetic and higher degradation efficiency of over 70%. The optimized conditions for the maximum elimination of the dye with a photocatalytic degradation efficiency (98%) and a mineralization rate of 96% were obtained with the Fe3+/Lig complex, based on the Box Benhken surface design analysis. In the presence of H2O2, the degradation reached an equilibrium stage after 15 min (97.57%) for the Fe(III)/Lig system. Moreover, the inhibition effect of inorganic ions on the photo-Fenton performance of Fe3+/Lig and Fe2+/Lig was studied. The study suggests that the use of nanocrystals of hematite as a Fenton reagent for treating textile effluents needs further investigation. The results showed that the proposed models were well-suited to batch treatment under sunlight. This study not only proposes a Fe3+/Lig and Fe2+/Lig system for the elimination of a food dye without adjusting the pH of the medium, but it also provides insight into the best source of light irradiation for the photocatalytic degradation process.

The endophytic fungus Aspergillus sp. SPH2 was isolated from the stems of the endemic plant Bethencourtia palmensis and its extracts were found to have strong fungicidal effects against Botrytis cinerea and ixodicidal effects against Hyalomma lusitanicum at different fermentation times. In this study, the fungus was grown using three different culture media and two methodologies, Microparticulate Enhancement Cultivation (MPEC) and Semi-Solid Fermentation (SSF), to increase the production of secondary metabolites in submerged fermentation. The addition of an inert support to the culture medium (SSF) resulted in a significant increase in extract production. However, when talcum powder was added to different culture media, unexpected results were observed, with a decrease in the production of the biocompounds of interest. Metabolomic analyses showed that the production of aspergillic, neoaspergillic, and neohydroxyaspergillic acids peaked in the first few days of fermentation, with notable differences observed among the methodologies and culture media. Mellein production was particularly affected by the addition of an inert support to the culture medium. These results highlight the importance of surface properties and morphology of spores and mycelia during fermentation by this fungal species.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

In this work we have investigated the accumulation of ryanoids in different plant parts (leaves, stems roots) of aeroponically grown Persea indica cloned trees (one year old cloned individuals) and a selected mature wild tree. We have tested the insect antifeedant (against Spodoptera littoralis, Myzus persicae and Rhopalosiphum padi) and nematicidal (against Meloidogyne javanica) effects of ethanolic extracts from these different plant parts. The HPLC-MS analysis of P. indica extracts showed that the mature tree (wild) leaves had 2 times more chemical diversity than the stems. The aeroponic plants showed lower differences in chemical diversity between leaves and stems, with the lowest diversity found in the roots. The ryanodane epiryanodol (1) was present in all the plant parts, with the mature stems (wild) having the highest amount. The aeroponic stems also accumulated ryanoids including 1, cinnzeylanol 2 and cinnzeylanone 4. The insect Spodoptera littoralis was strongly affected by the stem extracts while the leaf ones were moderately active. Based on the predicted vs. the real antifeedant values we conclude that the ryanoid content (1 or a combination of 2, 4 and 1) explained the antifeedant effects of the stem extracts while addition-al components contributed to the activity of the leaf ones. Therefore, careful individual selection of P. indica seedlings should be carried out prior to proceed with its aeroponic cultivation in or-der to obtain ryanodane-rich stem or leaf extracts with strong antifeedant effects on S. littoralis.

Abstract: Panfacial fractures are complex injuries involving multiple regions of the facial skele-ton simultaneously, which may necessitate multiple surgeries over a relatively short period. They are often associated with polytrauma and other injuries including brain trauma, which re-quire either immediate airway management, prolonged intubation, or repeated intubations for staged surgeries. Given the frequent need for intermaxillary fixation and the potential involve-ment of the central nasal complex or skull base, conventional oro-tracheal or nasotracheal intu-bation often presents challenges. Submental intubation is the most reported method of airway management with the aim to avoid tracheostomy and its related complications. A review of the different techniques of airway management in the elective treatment of panfacial fractures was performed focusing on the advantages and disadvantages of each method. Most of the literature consisted of retrospective studies, with only one prospective study comparing submental intu-bation to tracheostomy in panfacial fractures. There is abundant literature that favours the sub-mental intubation for its rapid establishment and low complication rate. Other techniques less reported were retromolar intubation, nasotracheal to orotracheal switch, tracheostomy or crico-thyroidotomy. An algorithm for the management of the airway in panfacial fractures is pre-sented.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Iron-based materials are widely applied in Fenton chemistry and they have promising prospects in the processing of wastewater. The composition complexity and rich chemistry of iron and/or oxides, however, hamper the precise understanding on the active sites and the working mechanism which still remain highly controversial. Herein, iron oxides of four different model systems are designed through a conventional precipitation method plus H2 reduction treatment. These systems feature Fe@Fe3O4 with abundant oxygen vacancy, Fe0 and Fe3O4 particles with interface structures, and Fe3O4-dominated nanoparticles of different sizes. These materials are applied in the decomposition of methyl orange as a model reaction to assess the Fenton chemistry. The Fe@Fe3O4 with core-shell structures exhibited significantly higher decomposition activity than the other Fe3O4-rich nanoparticles. A thin Fe3O4 layer formed by auto-oxidation of iron particles when exposing to air can boost the activity as compared with the Fe0 and Fe3O4 particles with interface structures but poor oxygen vacancy. The unique hetero-structure with the co-existence of both metallic iron and oxygen vacancy displayed excellent redox propensity which might account for the superior Fenton activity. This finding provides a new perspective to understand and design highly efficient iron-based Fenton catalysts.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Increasing pesticide resistance in plant pathogens is major concern in agriculture production. Research on ecofriendly alternatives of chemical pesticides are more in demand in pesticide industry. In the current study, an ethyl acetate extract from the endophytic fungus Trichoderma sp. EFI 671, isolated from the stem parts of the medicinal plant Laurus sp., was screened for bioactivity against plant pathogens (Fusarium graminearum, Rhizoctonia solani, Sclerotinia sclerotiorum and Botrytis cinerea), insect pests (Spodoptera littoralis, Myzus persicae, Rhopalosiphum padi) and plant parasites (Meloidogyne javanica). The bioactive components have been characterized following a bioassay-guided isolation against M. persicae. The chemical study of this bioactive extract resulted in the isolation of 1-oleoyl-2-linoleoyl-3-palmitoylglycerol (1), eburicol (2), (24R)-stigmast-4-ene-3-one or β-sitostenone (3), ergosterol (4) and ergosterol peroxide (5). The free fatty acids present in compound 1 (oleic, linoleic and palmitic) showed strong dose-dependent aphid antifeedant effects against M. persicae. Liquid (PDB, and SDB) and solid (corn, sorghum, pearl millet and rice) growth media were tested in order to optimize the yield and bioactivity of the fungal extracts. Pearl millet and corn gave the highest extract yields. All the extracts from these solid media had strong effects against M. persicae with sorghum being the most active. Corn increased the content in linolenic, pearl millet the oleic and stearic and sorghum oleic and linolenic acids compared to rice. Their antifeedant effects correlated with linoleic /oleic acids. The phytotoxic effects of these extracts against Lolium perenne and Lactuca sativa varied with culture media, with sorghum being the least toxic.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover genetic determinants of GBM progression and susceptibility to various therapies. Furthermore, we discuss ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Compared to other 2D materials, MBenes are at an early stage in both experimental and theoretical approaches. However, the wide range of possible 2D structures leads to novel and challenging properties and their consequent applications. From all the possible stoichiometries, we have performed a theoretical study of orthorhombic and hexagonal M2B2 MBenes in the framework of the Density Functional Theory. We have found that both symmetries of Cr2B2, Fe2B2, and Zr2B2 show a metallic behavior and could be grown under certain conditions, as they are demonstrated to be dynamically stable. Moreover, the values of the magnetic moment, in specific ferromagnetic cases exceeding 2.5 μB/M2B2, make them suitable as robust 2D magnets. All our findings represent an important step in the understanding of MBenes and open several windows to future research in fields like energy conversion and storage, sensing, catalysis, biochemistry, or nanotechnology, among others.

The neurodegenerative diseases (Alzheimers, Parkinsons, amyotrophic lateral sclerosis, Huntingdons) and the prion disorders, have in common a dysregulation of metalloprotein chemistry involving redox metals (Cu,Fe,Mn). The consequent oxidative stress gives rise to protein plaques and neuronal cell death. An equilibrium exists between the functional requirement of the brain for Cu and Fe and their destructive potential with the production of reactive oxygen species. The importance of the brain barrier is highlighted in regulating the import of these metals. Upregulation of key transporters occurs in foetal and neonatal life when brain metal requirement is high and is down-regulated in adult life when need is minimal. By contrast a neonatal mode of CTR1 upregulation persists in feral N.Ronaldsay sheep. This has led to the premise that metal regulation may return to the default setting in ageing with implications for neurodegenerative disease.

This study presents a combined electrochemical impedance spectroscopy (EIS) and atomic force microscopy (AFM) and Scanning Kelvin Probe Force Microscopy(SKPFM) investigation on the natural passivation process of stainless steels (AISI 316 and AISI 304) and nickel-based alloy (INCONEL 600) as a function of immersion time in an alkaline medium (pH = 13). EIS measurements show an increase in the resistance of the film with increasing immersion time in the electrolyte. After 3 days of immersion, for the three alloys, the films formed constitute an effective barrier to the dissolution of the alloys as shown by AFM and SKPFM. The oxide film growth on each substrates is only influenced by trenches formed during the polishing step and don’t depend on the chemical composition.

Keywords: Amorphous-precipitate, Nitride, Morphological stability, Diffusion, Internal stresses, Modelling, Simulation

Microbial-mediated biogeochemical cycling of iron is widespread in anaerobic environments such as sediments and deep Earth aquifers, and is one of the major pathways for metabolising iron in nature. Organic matter containing quinone groups can act as electron shuttles to transfer electrons between microorganisms and Fe(III) oxides, facilitating the biotransformation of iron; this process also influences the transport transformation of pollutants. In this paper, the reduction of Fe(III) oxides by aniline in the heterogeneous iron-reducing bacterium Shewanella oneidensis MR-1 mediated by the electron shuttle AQS was investigated. The results showed that aniline significantly promoted the reduction of Fe(III) by MR-1 in the AQS-mediated reaction, and the best Fe(III) reduction was achieved in the reaction group of "GT+MR-1+0.5mM AQS+3μM aniline", in which the Fe(II) production was significantly higher than that in the reaction groups of only microorganisms and no aniline reaction. The amount of Fe(II) produced was significantly higher than that of the microorganism-only and aniline-free reaction groups, and the addition of aniline also significantly increased the consumption of sodium lactate by MR-1 in the reaction system. The solid mineral Fe(II)-O content increased to 41.32% after the reaction. However, the structure of needle ferrite is relatively stable, and the surface morphology did not change significantly by SEM, and the generation of other secondary minerals was not observed in the XRD results. The results of this experimental study can help to understand the biogeochemical cycle of iron in multicomponent reactions and are of great significance for environmental management.

Cylindrical Fe-6.5wt.% Si bars with 7.5 mm in diameter were successfully fabricated from as cast ingot through three rolling stages within 10 passes: rough rolling at 850–900 °C and 8–10 m/min, medium rolling at 800–850 °C and 10–15 m/min, and finish rolling at 800–850 °C and 12–18 m/min. The evolution of microstructure, texture and ordered structure, and the mechanical property are investigated. The results showed that the grains were refined by the hot bar rolling. Area fractions of the {100}<011> oriented grains and the {011}<100> oriented grains decreased to 0 during the hot bar rolling. Whereas, the {100}<001> component, the {011}<211> component and the {112}<110> component increased, and γ fiber with {111}<110> component was dominant. DO₃ ordered phase were suppressed, and B2 ordered domains were refined after the hot bar rolling. Ductility of the as rolled bar was higher than that of the rotary swaged bar, due to the absence of the DO₃ ordered phase and the fine grains in the rolled bar. Hence, the hot bar rolling technology is an excellent process to fabricate the Fe-6.5wt.% Si bars.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Introduction The prevalence of adolescent electronic cigarette (e-cigarette) use has increased in most countries. This study determines the relation between the frequency of e-cigarette use and the frequency and intensity of cigarette smoking. Furthermore, it evaluates the association between the reasons for e-cigarette use and the frequency of its use. Materials and Methods Participants were 68,043 middle and high school students aged 13–18 years from the 2015 Korean Youth Risk Behavior Web-Based Survey. Of the 68,043 participants, we analyzed 6,655 adolescents with an experience of e-cigarette use. Results The prevalence of ever using and current (past 30 days) use of e-cigarettes was 10.1% and 3.9%, respectively. Of the ever e-cigarette users, approximately 40% used e-cigarettes for ≥1/month and 8.1% used e-cigarettes daily. Daily e-cigarettes users were 10 times greater among daily cigarette smokers than among cigarette users for <1/month (18.1% vs. 1.8%) and 16 times more prevalent among those smoking ≥20 cigarettes/day than among those smoking <1 cigarette/month (38.9% vs. 2.4%). The most common reason for e-cigarette use was curiosity (22.9%), followed by less harmful than conventional cigarettes (18.9%), smoking cessation (13.1%), and indoor use (10.7%). Curiosity was the most common reason among less frequent e-cigarette users; however, smoking cessation and indoor use were the most common reasons among more frequent users. Conclusions Results showed a positive relation between frequency or intensity of conventional cigarette smoking and frequency of e-cigarette use among Korean adolescents, and frequency of e-cigarette use differed according to the reason for the use of e-cigarettes.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

A portable laser photoacoustic sensor based on a Field-Programmable Gate Array (FPGA) is reported for methane detection. A tunable DFB diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane (CH₄) is demonstrated.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

In directional forming processes such as rolling and extrusion, the microstructure is strongly elongated in the forming direction. This results in a preferred alignment of the crystal orientations of the initially randomly oriented grains. These preferred orientations – the texture – and elongations of the grains are the cause of the material anisotropy. This anisotropy leads to phenomena such as earing, which occur during further forming processes, e.g., during deep drawing of sheet metal. Considering anisotropic properties in numerical simulations allows to investigate the effects of texture-dependent defects in forming processes and the development of possible solutions. Purely phenomenological models for modelling anisotropy work by fitting material parameters or applying measured R-values in experiments which are applied to all elements of the part and remain constant over the duration of the simulation. In contrast, methods such as the Visco-Plastic Self-Consistent (VPSC) model, which has been used in this work, provide a deeper insight into the development of the material microstructure. By experimentally measuring the initial texture and using it as an initial condition for the simulations, it is possible to predict the evolution of the microstructure and the resulting effect on the mechanical properties during forming operations. The results of the simulations with VPSC show good qualitative agreement with tests typically used for rolled stock.

Iron is an essential micronutrient for Citrus, playing an important role in photosynthesis and yield. The aim of this paper is to evaluate the tolerance to Fe deficiency of five Citrus rootstocks: sour orange (S), Carrizo citrange (C), Citrus macrophylla (M), Troyer citrange (T) and Volkamer lemon (V). Plants were grown for 5 weeks in nutrient solution that contained the following Fe concentrations (in µM): 0, 5, 10, 15 and 20. At the end of the experiment, biomass (dry weight), leaf area, total leaf chlorophyll (CHL) and the activity of root chelate activity (FCR) were registered. Additionally, the mineral composition of roots (R) and shoots (S) was evaluated. Principal Component Analysis was used to study the relationships between all parameters and, subsequently, the relations between rootstocks. In the first component, N-S, P-S, Ca-S, Cu-S, Zn-S, Mn-S and Zn-R and Mn-R concentrations were related to leaf CHL and FCR. Increases in leaf CHL, Mg-R, DW (shoots and roots) were inversely related to Cu-R, which was shown in the second component. The values obtained were consistent for V10, C15, and C20, but in contrast for S0 and S5. Overall, the Fe/Cu ratio was significantly related to CHL, which may be used to assist rootstock performance.

With an increase in energy consumption globally, Fischer-Tropsch (FT) synthesis is a good alternative for producing fuels and chemicals from coal, natural gas or biomass. Among them, coal to liquids has been put into production in countries that have large coal reserves. In this process, Fe-based catalysts are commonly used due to their earth abundance, comparatively wide operation range and ready availability to handle low H2/CO ratio from coal. Despite their extensive applications, the kinetic and mechanistic understandings of Fe carburization and FT reaction on Fe-carbides are relatively limited due to the complexity of phase composition of the applied catalysts. This review summarizes the current state of knowledge of FT synthesis on Fe-carbide with an emphasis on underlying mechanism. Specifically, the employment of model catalyst, such as Raney-Fe, could provide a convenient way to furnish kinetic information regarding Fe carburization and subsequent FT reaction. A major challenge for further understanding catalytic reactions occurring at the Fe-carbide surface is correlating FT activity and selectivity to specific active site. To address this issue, the advancements of both DFT calculations and surface science techniques are highly demanded.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

The present study conducts the phase analysis, microstructural characteristics, and mechanical property evaluation on the as-cast and quenched Ti-15Mo-xFe alloys with high iron content ranging from 4 to 12 weight percent. All the four alloys were produced in a button-arc melting furnace. Heat treatment in the form of solution treatment was performed in a muffle furnace at a temperature of 1100°C, with a 1-hour holding time. The samples were rapidly quenched in ice-brine. Various characterization techniques were used to analyze samples both in the as cast and quenched conditions. X-ray diffractometer (XRD) was used to analyses the phases present in each alloy whereas the optical microscope (OM) was employed to track the microstructural evolution. The mechanical properties of the alloys were evaluated using a tensile test method while the micro-Vickers hardness measurements were conducted to evaluate hardness of the alloys. OM micrographs revealed equiaxed β grains and substructures in as-cast TMF0, dendritic structure, and equiaxed β grains with pores forming around the grain boundaries in TMF1, and micrograph full of dendrites and pores was observed in the as-cast TMF2 and TMF3 alloys. The quenched TMF0 showed only β equiaxed grains whereas TMF1, TMF2, and TMF3 revealed micrographs full of equiaxed grains of β phase with pores throughout the grain boundaries. The XRD patterns of cast TMF0 showed peaks belonging to the β and orthorhombic α″ martensitic phases and the quenched sample showed the bcc β phase only. The TMF1, TMF2, and TMF3 alloys in as-cast condition indicated peaks belonging to β , α″ and intermetallic B2 TiFe phases, however, their quenched samples showed the presence of β and Fe·Ti·O2 phases in TMF2 and TMF3 alloys. The tensile properties such as ultimate tensile strength (UTS) and elastic modulus (E) of as-cast TMF0 were 264 MPa and 79 GPa and these properties changed upon quenching to 411 MPa and 66 GPa respectively. The elastic modulus of TMF1 in as-cast condition was 74 GPa. The UTS and E of TMF1, TMF2, and TMF3 in as cast and quenched conditions were not recorded due to the fragility of the samples that failed prior to yielding any useful data. The micro-Vickers hardness in as cast and quenched conditions showed a similar trend with hardness increasing slightly upon quenching for TMF0, TMF1, and TMF3 alloys but slightly decreased in the case of TMF2. 6). The fracture surfaces of TMF0 exhibited both ductile and brittle fracture in as cast and quenched conditions whereas the only brittle fracture mode was observed in the TMF1, TMF2 and TMF3 alloys in both conditions.

Endophytic insect pathogenic fungi have a multifunctional lifestyle; in addition to its well-known function as biocontrol agents, it may also help plants respond to other biotic and abiotic stresses, such as iron (Fe) deficiency. This study explores M. brunneum EAMa 01/58-Su strain attributes for Fe acquisition. Firstly, direct attributes include siderophore exudation (in vitro assay) and Fe content in shoots and in the substrate (in vivo assay) were evaluated for three strains of Beauveria bassiana and Metarhizium bruneum. The M. brunneum EAMa 01/58-Su strain showed a great ability to exudate iron siderophores (58.4% surface siderophores exudation) and provided higher Fe content in both dry matter and substrate compared to the control and was therefore selected for further research to unravel the possible induction of Fe deficiency responses, Ferric Reductase Activity (FRA), and relative expression of Fe acquisition genes by qRT-PCR in melon and cucumber plants.. In addi-tion, root priming by M. brunneum EAMa 01/58-Su strain elicited Fe deficiency responses at transcriptional level. Our results show an early up-regulation (24, 48 or 72 h post inoculation) of the Fe acquisition genes FRO1, FRO2, IRT1, HA1, and FIT as well as the FRA. These results highlight the mechanisms involved in the Fe acquisition as mediated by IPF M. brunneum EAMa 01/58-Su strain.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Fe[C5H5N]2[N(CN)2]2 (1) was synthesized from a reaction of stoichiometric amounts of NaN(CN)2 and FeCl2·4 H2O in a methanol/pyridine solution. Single-crystal and powder diffraction show that 1 crystallizes in the monoclinic space group I2/m (no. 12), different from Mn[C5H5N]2[N(CN)2]2 (P21/c, no. 14) due to tilted pyridine rings, with a = 7.453(7) Å, b = 13.167(13) Å, c = 8.522(6) Å, β = 114.98(6)° and Z = 2. ATR-IR, AAS, and CHN measurements confirm the presence of dicyanamide and pyridine. Thermogravimetric analysis shows that π-stacking interactions of the pyridine rings play an important role in structural stabilization. Based on DFT-optimized structures, a chemical bonding analysis was performed using a local-orbital framework by projection from a plane-wave basis. The resulting bond orders and atomic charges are in good agreement with the expectations based on the structure analysis. SQUID magnetic susceptibility measurements show a high-spin state FeII compound with predominantly antiferromagnetic exchange interactions at lower temperatures.

Conventional models do not fully explain composition of the solar system – for example, the presence of p-nuclei and post-post-Fe-nuclei remains not yet understood (and is one of the great unresolved puzzles of nuclear astrophysics in general); other puzzles exist. We offer a hypothesis which can explain the appearance of non-native elements in the solar system, and a feasible scenario for its implementation. The hypothesis suggests that a nuclear-fission "event" occurred in the inner part of the solar system at the time currently defined as the birth of the system. Conventional models have never considered fission as a contributing nuclei-production mechanism. Upon examination of the existing models and factual data (presented in volumes of publications but never combined into an aggregate), we identified one plausible scenario by which a fission event (not demolishing the entire solar system) could occur: an encounter with a compact super-dense stellar "fragment" (with specific properties) and its "explosion" in fission-cascades. Such scenario also helps resolve other long-standing puzzles of the solar system. For example, it provides that the fission-produced nuclei subsequently transformed into the material that (eventually) accreted into the "rocky" objects in the system (terrestrial planets, asteroids, etc.) and enriched the pre-existed hydrogen-helium objects (the Sun and the gaseous giants) – this offers an explanation for the planets’ inner position and compositional differences within the predominantly hydrogen-helium rest of the solar system. Other implications also follow.

The flow behavior of Fe-6.5wt.%Si alloys during hot compression was investigated at temperatures 650–950 °C and strain rates 0.01–10 s^-1. The results showed that the flow stress depended distinctly on the deformation temperatures and strain rates. The flow stress and work hardening rate increased with the decrease of temperature and the increase of strain rate. The activation energy under all the deformation conditions was calculated to be 410 kJ/mol. The constitutive equation with hyperbolic sine function and Zener–Hollomon parameter was developed. The peak stress, critical stress, and steady-state stress could be represented as σ=A+Bln(Z/A). Dynamic recrystallization occurred under the deformation conditions where the values of Z were lower than 10²⁰. Processing maps were established to optimize the processing parameters. The power dissipation efficiency decreased in the high temperature and low strain rate region, increased in the high temperature and high strain rate region, and remained unchanged in other regions with the increase of true strain. Furthermore, the unstable area expanded. The true strain of 0.7 was the optimum reduction according to the processing map. Based on the analysis of surface quality, microstructures, and ordered structures, the optimized processing parameters for the Fe-6.5wt.%Si alloys were the temperature and strain rate of higher than 900 °C and 0.01–10 s^-1, respectively, or 800–900 °C and lower than 0.4 s^-1, respectively.

In this work, we investigated the effects of heat treatment on the microstructure, hydrogen storage characteristics and corrosion rate of a Ti₃₄V₄₀Cr₂₄Fe₂ alloy. The arc melted alloy was divided into three samples, two of which were separately quartz-sealed under vacuum and heated to 1000 °C for 1 h; one of these samples was quenched and the other furnace cooled to ambient temperature. The crystal structures of the samples were studied via X-ray diffractometry and scanning electron microscopy. Absorption/desorption characteristics were investigated using a Sievert apparatus. Potentiostat corrosion tests on the alloys were performed using an AutoLab^® corrosion test apparatus and electrochemical cell. All samples exhibited a mixture of body-center-cubic (BCC) and Laves phase structures. The corrosion rate, maximum absorption, and useful capacities increased after both heat treatments. The annealed sample had the highest absorption and reversible capacity. The plateau pressure of the as-cast alloy increased after quenching. The corrosion rate increased from 0.0004 mm/y in as-cast sample to 0.0009 mm/y after annealing and 0.0017 mm/y after quenching, due to a decrease in the Cr-content of the C14 phase.

Abstract In order to solve the problem that it is difficult to obtain the accurate trigger signal moment when using the time difference of UAV flight acoustic signals collected by acoustic sensors arranged in advance to locate the position of UAVs when they cannot be tracked by radar and are difficult to be observed by the human eye, an acoustic source localisation method with improved empirical modal decomposition (REMD) under an adaptive frequency window is proposed. Firstly, the collected UAV flight signals are smoothed and filtered, then the robust empirical modal decomposition (REMD) is used for IMF modal decomposition and spectral analysis of the collected signals, and a panning frequency window with variable bandwidth is introduced for corresponding frequency locking and extracting the IMFs; the GWO optimisation is used and the sliding metrics are set to determine the position of the panning frequency window automatically; finally, the extracted Finally, the extracted IMF components are reconstructed according to the criterion, and the trigger signal moments in the reconstructed IMF components are resolved and recorded, and the algorithm is used to calculate the difference between the reception moments of different sensors; for the traditional sound source localisation algorithms with a small detection range, which are prone to fall into the no-solution or false-solution, i.e., unable to achieve the localisation, a weighted least-squares-based Chan-Taylor algorithm is proposed for the localisation computation, and a weighted least-squares algorithm is proposed for the localisation computation, through the Inputting the sensor delay parameter and solving the linear equation system to get the target position information; finally, this paper verifies the robustness and performance of the design method with simulated and actual signals. The results show that the maximum positioning error is no more than 5% in an area with a side length of 15m in the measurement area. With the improved accuracy of time delay estimation, the positioning simulation results meet the positioning requirements even though the positioning error is further expanded when the measurement area is further expanded.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

The accompanying mechanical behavior during the flow of ore-rock dispersion is an important factor leading to the instability and failure of the orepass in the ore storage section. It is of great significance to accurately understand the instability characteristics of the well wall in the ore section, during the ore drawing process by understanding the flow characteristics and internal mechanical transfer mechanism of ore-rock dispersion. Using the discrete element method to analyze the flow characteristics, contact compactness, stress distribution characteristics and contact force probability distribution of the ore-rock dispersion, it realizes the quantitative characterization of the damage degree of ore-rock flow, and reveals the damage mechanism of mine storage wall. The results show that: (1) The friction between ore-rock particles, between ore-rock and well wall is an important reason for the change of the macroscopic flow form of ore-rock from “one” to “V” type. (2) The compactness and stress concentration of ores are increasing with the increase of storage depth, which is shown that with the increase of storage depth, the greater the probability of well wall instability. (3) The probability distribution of contact force strength between particles decreases exponentially in the whole process of ore drawing, in which the strong chain plays a major role in the stability of the dispersion system. (4) The number of overpressure and the overpressure coefficient can be used to quantitatively characterize the wall damage degree under the action of ore-rock flow. The dynamic side pressure decreases periodically in the form of exponential fluctuations, and the dynamic load formed by the ore-rock flow mainly played a role in the lower area of the ore storage section.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

Band structure calculations using the spin-polarized relativistic Korringa-Kohn-Rostoker (SPRKKR) band structure method have been performed to determine the intrinsic magnetic properties (magnetic moments, magneto-crystalline anisotropy -MAE and Curie temperatures) of the Fe5-x-yCoxMySiB2 (M = Re, W) alloys. The general gradient approximation for ex-change-correlation potential and the atomic sphere approximation (GGA ASA) have been em-ployed. Previous studies showed that the Co doping is turning the in-plane into axial anisotropy for a certain doping range, whilst the 5d doping enables a strong spin-orbit coupling of Fe-3d and M-5d states which is needed to enhance the MAE. The theoretical calculations aim to find the dependence of the anisotropy constant K1 for combined Co and M doping, building a two-dimensional (2D) map of K1 for 0 ≤ x ≤ 2 and 0 ≤ y ≤ 1. Similar theoretical 2D maps for magnetization and Curie temperature vs. Co and M content (M = W and Re) have been built, allowing to select alloy compositions with enhanced values of uniaxial anisotropy, magnetization and Curie temperature. Magnetic properties of Fe4.1W0.9SiB2 alloy that meets the selection criteria for axial anisotropy K1 > 0.2 meV/f.u., Curie temperature Tc > 800 K determined by mean field approach and magnetization µ0Ms > 1 T are discussed.

The separation of rare earth metals (REM) from a neodymium magnet has been widely studied in the last year. We have shown that the waste of computer hard disk contains 25.41 % neodymium, 64.09 % iron, and &lt;&lt;1 % boron. To further isolate rare-earth metals, the magnet was acidically dissolved in open and closed systems. In both methods of dissolution was used concentrated nitric acid. The difference between these methods are the conditions of dissolution of magnet. The magnet was dissolved in a microwave sample preparation system at different temperatures and pressures in a closed system. In the open system, the acid dissolution of the magnet conducted at room temperature. 0.2 g of the neodymium magnet sample was taken under two conditions, and the dissolution process in the closed system lasted 1 hour, and in the open system-30-40 minutes. The open system is a non-laborious, simple and cheap method of dissolving the magnet by comparing both systems. Therefore, an open sample preparation system is used for further work. To remove the iron in the magnet, oxalic acid was used and precipitated as oxalates under both conditions. According to the result of the ICP-MS method, it is shown that the neodymium and iron contents in the precipitate are 24.66 % and 0.06 %, respectively. This shows that the iron has almost completely passed to the filtrate. Thus, it is possible to remove the iron from the sample.

The development of platinum group metal-free (PGM-free) electrocatalysts derived from cheap and environmentally friendly biomasses for oxygen reduction reaction (ORR) is a topic of relevant interest, particularly from the point of view of sustainability. Fe-nitrogen-doped carbon materials (Fe-N-C) have attracted particular interest as alternative to Pt-based materials, due to the high activity and selectivity of Fe-Nx active sites, the high availability and good tolerance to poisoning. Recently, many studies focused on developing synthetic strategies, which could transform N-containing biomasses into N-doped carbons. In this paper chitosan was employed as a suitable N-containing biomass for preparing Fe-N-C catalyst in virtue of its high N content (7.1%) and unique chemical structure. Moreover, the major application of chitosan is based on its ability to strongly coordinate metal ions, a precondition for the formation of Fe-Nx active sites. The synthesis of Fe-N-C consists in a double step thermochemical conversion of a dried chitosan hydrogel. In acidic aqueous solution, the preparation of physical cross-linked hydrogel allows to obtain sophisticated organization, which assure an optimal mesoporosity before and after the pyrolysis. After the second thermal treatment at 900 °C, a highly graphitized material was obtained, which has been fully characterized in term of textural, morphological and chemical properties. RRDE technique was used for understanding the activity and the selectivity of the material versus the ORR in 0.5 M H2SO4 electrolyte. Special attention was put in the determination of the active site density according to nitrite electrochemical reduction measurements. It was clearly established that the catalytic activity expressed as half wave potential linearly scales with the number of Fe-Nx sites. It was also established that the addition of the iron precursor after the first pyrolysis step leads to an increased activity because of both an increased number of active sites and of a hierarchical structure, which improves the access to active sites. At the same time, the increased graphitization degree, and a reduced density of pyrrolic nitrogen groups are helpful to increase the selectivity toward the 4e- ORR pathway.

This study investigates the role of oxygen vacancy on Fe-doped CeO₂ catalyst activity for soot oxidation. The oxygen vacancy was assessed through Ce³⁺ content. The Fe content was varied between 0 and 30% for two catalyst preparation methods, co-precipitation (CP) and solution combustion synthesis (SCS). X-ray photoelectron spectroscopy indicates that ceria exists as both Ce₄₊ and Ce₃₊, while iron is present only as Fe₃₊. The catalyst’s activity was evaluated by ignition (T10) and combustion (T50) temperatures using thermogravimetric analysis. Optimum Fe contents yielding the highest activity were found to be 10% and 5% for CP and SCS catalysts, respectively. The surface area and morphology have shown moderate effect on catalyst activity, because catalytic soot oxidation involves solid-solid contact. More importantly, regardless of the fabrication method, it was found that Ce₃₊ content, which is closely related to oxygen vacancies, plays the most important role in affecting the catalyst activity.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

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

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.

The mixing of terrestrial groundwater and seawater creates dynamic reaction zones in intertidal areas, where land-derived Fe(II) is oxidized to Fe(III) and then precipitates as Fe hydroxides at groundwater-seawater interface. These hydrogeochemical processes contribute to the formation of iron curtains beneath soil surface, which in turn influences the evolution of coastal aquifers. This paper provides a comprehensive review of physical and geochemical processes at field scale in coastal areas, explores the impact of mineral precipitation on pore structure at pore scale, and synthesizes reactive transport modeling (RTM) approaches for illustrating continuum-scale soil physio-chemical parameters during the evolution of porous media. Upon this review, knowledge gaps and research needs are identified. Additionally, challenges and opportunities are presented. Therefore, the incorporation of observational data into a comprehensive physico-mathematical model becomes imperative for capturing the pore-scale processes in porous media and their influence on groundwater flow and solute transport at large scales. Furthermore, a synergistic approach integrating pore-scale modeling and non-invasive imaging can provide detailed insights into intricate fluid-pore-solid interactions for future studies, as well as facilitate the development of regional engineering-scale models and physio-chemical coupled models with diverse applications in submarine science and engineering.

The heavy transition metal tantalum (Ta) offers a wide range of applications, especially in the field of electronics, e.g. Ta capacitors with a comparatively long life-time. Therefore, electronic scrap containing these components is a potentially valuable secondary source of this metal. However, the recovery of Ta, as other critical raw materials, that are lost in pyrometallurgical slags can be a challenging process. As a way of overcome this, the "engineered artificial minerals" EnAM approach is proposed by first searching for suitable host minerals and characterizing them. A favorable prerequisite is the enrichment of the desired element(s) in compounds that have particularly suitable properties for this purpose, such as morphology, crystal size, surface or magnetic properties. Subsequently, the slag system is adapted (e.g. chemism, solidification curve, cooling and holding times) to optimize this compound. This paper presents the results of mineralogical characterization of Ta-containing iron (Fe)-rich synthetic slags in reducing environment under different cooling rates, and describes and discusses the incorporation of Ta into the different compounds detected by powder X-ray diffraction (PXRD) and electron beam microanalysis (EPMA). Additionally, the speciation of Fe and Ta was accessible through X-Ray absorption near edge structure (XANES) spectroscopy. EPMA also provides a first semi-quantitative assessment of the Ta distribution in these individual compounds. According to this, depending on the cooling rate the enrichment factor is highest in tantalite/perovskite-type oxides (FexTayO6, CaxFeyTazO3) with up to 60 wt.% Ta and ‘tantalomagnetite’ (FeII(FeIII(2-5/3xTax)O4) with a maximum of ~ 30 wt.% Ta (only fast cooling). This is followed by a perovskite-like silicon containing oxide (XYO3) with 12-15 wt.-% Ta (only slow cooling), and a hedenbergite-like compound (XYZ2O6) with varying content of 0.3 - 7 wt.-%. The Ta-concentration in pure Fe, Fe(1-x)O, hercynitic spinel and hematite is negligible. Since a larger fraction of potentially amorphous hedenbergitic matrix is formed at faster cooling, in this case the main Ta is enriched in this compound despite the significantly lower Ta content.

The present study produced and characterised chitosan, chitosan nanoparticle, chitosan n – benzaldehyde Schiff base, chitosan nanoparticle n – benzaldehyde Schiff base, Fe(III) chitosan n – benzaldehyde Schiff base and Fe(III) chitosan nanoparticle n – benzaldehyde Schiff base for biomedical application as antimicrobial agents. The materials were characterized with Fourier Transform Infrared spectroscopy, X-ray Diffractogram and biologically evaluated using disc diffusion method with three gram-positive bacteria. The FTIR absorption peaks were shifted to a lower wave number than the micro materials from which it was modified. These clearly indicate the linkage between phosphate, ammonium ion, Schiff base and Fe(III) metal. The diffracted peaks of Fe(III) chitosan nanoparticle n – benzaldehyde Schiff base were new peaks at 2θ = 24o and 42o when compare to the peak of Fe(III) chitosan n – benzaldehyde Schiff base of 2θ = 22.5o and 34o. The difference in peak shift were attributed to the ionic bonding of the complexation of Fe(III) with the blending of benzaldehyde to chitosan – Tpp backbone structure. Fe(III) chitosan nanoparticle Schiff base has more antimicrobial activity against same bacteria and fungi tested than Fe(III) chitosan n – benzaldehyde Schiff base, chitosan n – benzaldehyde Schiff base and chitosan. The antimicrobial activities of the synthesised six materials shown that the materials have high activities than the above – mentioned standard drugs.

The influence of pore size on the toughness of Fe-Mo-Cu-C sintered and carburized components was investigated. The charpy impact value of the component made from Fe-0.4%Mo diffusion-alloyed steel powder mixed with 2%Cu powder and 0.3% graphite powder significantly increased with a decrease in pore size. The highest impact value of 17 J/cm2 was obtained at the average pore size of 12 µm. This value was 25% higher than that of the sintered and carburized component made from conventional Fe- 4%Ni-1.5%Cu-0.5%Mo alloyed steel powder called 4Ni even though the component was made from a Ni-free alloyed steel powder. The mechanism of the improvement of the impact value by pore size refinement and the influence of microstructural difference between the components made from Fe- 0.4%Mo diffusion-alloyed steel powder and 4Ni on toughness is discussed based on optical microstructural observation and crystal orientation analysis data.

Formation of Fe- and Mg-rich smectite and zeolite under alkaline conditions is concerned as secondary minerals after alkaline alteration of bentonite in a repository of radioactive wastes. It might be crucial for safety assessment whether smectite will be formed or not as secondary minerals after alkaline alteration of bentonite. In present paper, Fe- and Mg-rich smectite which are currently interacting with hyperalkaline groundwater was found at Narra in Palawan, Philippines. Mineralogical and geochemical investigation was conducted to understand formation process of smectite and factors determined secondary mineral species. Our study revealed a certain amount of smectite may have been produced under hyperalkaline conditions, altered from amorphous or poorly crystalline material such as M-S-H and F-S-H. Key factor which decides smectite or zeolite as secondary minerals after alkaline alteration of bentonite might be whether nuclei of M-S-H and/or F-S-H will be formed or not. This might be decided by the presence of dissolved Mg2+ and Fe2+ in the system. Our suggested formation process of smectite under alkaline conditions is analogue with generally-accepted model of smectite formation that might have been occurred on early Mars.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Dynamic systems which underly controlled systems are expected to increase in complexity as robots, devices, and connected networks become more intelligent. While classical stable systems converge to a stable point (a sink), another type of stability is to consider a stable path rather than a single point. Such stable paths can be made of saddles which draw in trajectories from certain regions, and then push the trajectory toward the next saddle point. These are chains of saddles are called stable heteroclinic channels (SHCs), and can be used in robotic control to represent time sequences. While we have previously shown that each saddle is visualizable as a trajectory waypoint in phase space, how to increase the fidelity of the trajectory was unclear. In this paper, we hypothesized that the waypoints can be individually modified to locally vary fidelity. Specifically, we expected that increasing the saddle value (ratio of saddle eigenvalues) causes the trajectory to slow to more closely approach a particular saddle. Combined with other parameters that control speed and magnitude, a system expressed with an SHC can be modified locally, point by point, without disrupting the rest of the path, supporting their use in motion primitives. However, even more complex trajectory shape modifications are possible. While some combinations can enable a trajectory to better reach into corners, other combinations can rotate, distort and round the trajectory in the region of a corner. In our example, modifying select saddle values produced a 32% decrease in the trajectory error of a complex trajectory produced using this system. This is an effect not visible in previous 1D studies, that can lead to different learnable and tunable representations of dynamic systems.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% [email protected], which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

Despite the efforts made to stop the tuberculosis (TB) epidemic, it still remains one of the leading causes of death from an infectious disease. Previous work in the group uncovered a new family of amides which showed promising activities against Mycobacterium tuberculosis. A closer look at the literature showed that these compounds are structurally related to the DNB family of DprE1 inhibitors, so, we decided to study a wide range of substituted amides and determine their activity, focusing on unexplored structures related to the dinitrobenzamides (DNB) found in the literature. To synthesize our library of compounds we started from 3,5-dinitrobenzoic acid to form the nitroaromatic core that is characteristic of the DNB’s, to which we then added linear or cyclic amine moieties. Additionally, the impact of terminal aromatic moieties was also assessed for some derivatives, via an ether, ester, or amide bond. In order to obtain the desired derivatives, multiple synthetic approaches were used, mainly focused on nucleophilic addition/elimination reactions, SN2 reactions and Mitsunobu reactions. The activity was impacted mainly by two structural features, the addition of an aromatic moiety as a terminal group and the type of the linker. The most interesting compounds exhibited activities comparable to isoniazid and DNB1. Computational studies were also performed aimed at understanding their possible interactions with DprE1. The most active compounds are a good starting point for further development, and we plan to study an extended family of compounds based in those structures.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression was replaced by the noise addition process in the attack layer of the conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by Ahmadi et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.