Stellatolides are natural compounds that have shown promising biological activities, including antitumor, antimicrobial, and anti-inflammatory properties, making them potential candidates for drug development. Chemical Reactivity Theory (CRT) is a branch of chemistry that explains and predicts the behavior of chemical reactions based on the electronic structure of molecules. Conceptual Density Functional Theory (CDFT) and Computational Peptidology (CP) are computational approaches used to study the behavior of atoms, molecules, and peptides. In this study, we present the results of our investigation of the chemical reactivity and ADMET properties of Stellatolides A-H using a novel computational approach called Conceptual DFT-based Computational Peptidology (CDFT-CP). Our study uses CDFT and CP to predict the reactivity and stability of molecules and to understand the behavior of peptides at the molecular level. We also predict the ADMET properties of the Stellatolides A-H to provide insight into their effectiveness, potential side effects, and optimal dosage and route of administration. This study sheds light on the potential of Stellatolides A-H as promising candidates for drug development and highlights the potential of CDFT-CP for the study of other natural compounds and peptides.

In this review, we summarize recent progress in crystal growth and understanding the influence of crystal structure on the superconductivity in pure and Pb-doped Bi2Sr2CuOy (Bi-2201) materials belonging to the overdoped region of high temperature cuprate superconductors. The crystal growth of Bi-2201 superconductors faces challenges due to intricate materials chemistry and lack of knowledge of corresponding phase diagrams. Historically, a crucible-free floating zone method has emerged as the most promising growth approach for these materials, resulting in high-quality single crystals. This review outlines the described in the literature and the authors' synthesis endeavors, encompassing Pb-doped Bi-2201 crystals, detailed structural characterization of as-grown and post-growth annealed samples, and highlights optimal growth conditions that yield large-size, single-phase, and compositionally homogeneous Bi-2201 single crystals.

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

In this study, we evaluated the antibacterial effect against Staphylococcus aureus and Salmonella Typhimurium in fried fish paste using floating electrode-dielectric barrier discharge (FE-DBD) plasma (1.1 kV, 43 kHz, N2 1.5 m/s, 1-60 min). In addition, quality evaluation (pH, VBN) of fried fish paste was conducted after FE-DBD plasma treatment. When FE-DBD plasma was treated for 1, 5, 10, 20, 30 and 60 min, S. aureus decreased by 0.16-1.13 log10 CFU/g, and S. Typhimurium decreased 0.25-1.13 log10 CFU/g. Both decreased > log10 CFU/g at 60 min. The D-value was 58.92 and R2 was 0.97 for S. aureus using first-order kinetics, and the D-value was 43.60 and R2 was 0.97 for S. Typhimurium using the Weibull model. There was no significant difference in pH after FE-DBD plasma treatment (p > 0.05). However, volatile basic nitrogen (VBN) significantly decreased as the treatment time increased (p < 0.05), and it was the lowest at 60 min to 3.46. Therefore, FE-DBD plasma treatment could be considered as a technology for preserving the quality of processed foods.

The Giant Babax, an endemic bird in China and a nationally protected wildlife species of Class Ⅱ, is primarily distributed in southern and southeastern Tibet. To investigate the factors influencing nesting site selection, this study conducted research on nest characteristics and nesting site preferences at four sample sites around Lhasa from March to October 2023y, using systematic search methods, tracking methods, and sampling methods. The research findings are as follows: (1) The average nest depth was (7.70±1.47)cm, nest height was (19.58±2.97)cm, inner diameter was (10.64±1.73)cm, outer diameter was (24.65±4.8)cm, average weight was (162.3±13.5)g, and average nest spacing within the plot was 396.93m. (2) The nests of Giant Babax were predominantly found on vegetation such as Salix longistamina (38%), Hippophae gyantsensis (20%), Rosa sericea Lindl. (14%), and Populus szechuanica Schneid(11%). They showed a preference for artificial secondary forests as nesting sites. (3) Giant Babax exhibited a preference for habitats with robust trees that had high canopies while being distant from houses and roads, but close to water sources; moreover, the canopy of the nesting tree played a crucial role in their choice of nesting sites. (4) The reproductive success rate reached 76%, with key factors affecting it being the canopy coverage of the nesting tree and the number of helpers. These research results provide fundamental data support for population conservation efforts and ecological resource management strategies concerning the Giant Babax.

In this study, we introduce a new class of bi-univalent functions using q-Janowski Function. We derive the Fekete-Szegö functional problems for functions in this new subclass, as well as estimates for the Taylor-Maclaurin coefficients |α2| and |α3|. Furthermore, a collection of fresh outcomes are3 presented by customizing the parameters employed in our initial discoveries.

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

Bicyclic peptides have attracted the interest of pharmaceutical companies because of their remarkable properties putting them on a new path in medicine. Their conformational rigidity that improves proteolytic stability, leads to rapid penetration into tissues via any possible route of administration and elimination, occurring by renal metabolism, is of great importance for people with a history of liver diseases. In addition, each ring can function independently, making them extremely versatile molecules for further optimization. In this paper we compared the potentiometric and spectroscopic properties studied by UV-Vis, MCD, and EPR, of four synthetic analogues of the bi-cyclic peptide c(PKKHP-c(CFWKTC)-PKKH). In particular we correlated the structural and spectral properties of complexes with coordinating abilities toward Cu(II) ions of MCL (Ac-PKKHPc(CFWKTC)PKKH-NH2) that contains the unbinding cycle and N- and C-terminal linear parts, with two histidine residues, one per part; two monocyclic ligands containing one histidine residue, both in the N-terminal position i.e., MCL2 (Ac-PKKHPc(CFWKTC)PKKS-NH2) and in the C-terminal position i.e., MCL3 (Ac-PKKSPc(CFWKTC)PKKH-NH2), respectively, and the linear structure LNL (Ac-PKKHPSFWKTSPKKH-NH2).

This paper describes a stochastic clustering architecture that is used in the paper for making predictions over energy data. The design is discrete, localised optimisations based on similarity, followed by a global aggregating layer, which can be compared with the recent random neural network designs, for example. The topic relates to the IDEAS Smart Home Energy Project, where a client-side Artificial Intelligence component can predict energy consumption for appliances. The proposed data model is essentially a look-up table of the key energy bands that each appliance would use. Each band represents a level of consumption by the appliance. This table can replace disaggregation from more complicated methods, usually constructed from probability theory, for example. Results show that the table can accurately disaggregate a single source to a set of appliances, because each appliance has quite a unique energy footprint. As part of predicting energy consumption, the model could possibly reduce costs by 50% and more than that if the proposed schedules are also included. The hyper-grid has been changed to consider rows as single units, making it more tractable. A second case study considers wind power patterns, where the grid optimises over the dataset columns in a self-similar way to the rows, allowing for some level of feature analysis.

Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

Detection of formaldehyde is very important in terms of life protection, as it can cause serious injury to eyes, skin, mouth and gastrointestinal function if indirectly inhaled and hence researchers are putting effort in developing novel and sensitive device. In this work, we have fabricated an electro-chemical sensor in the form of a field effect transistor (FET) to detect formaldehyde over wide range (10 nM to 1 mM). For this, ZnO nanosheets (NS) were first synthesized by hydrothermal method with in-situ deposition on cleaned SiO₂ coated Si (100) substrate. The synthesized materials were characterized for morphology and purity and surface area (31.718 m²/g). The developed device was tested for formaldehyde detection at room temperature that resulted in a linear response with concentration (96%), sensitivity value of 0.27 mA/M/cm² and a low detection limit of 210 nM, and a high 0.93194 return.

In this study, the effect of NaOCl (10–200 ppm of Cl2) on the inactivation of human norovirus (HuNV) GII.4 and hepatitis A virus (HAV) in groundwater was investigated using PMA/RT­qPCR. Initially, 4.00 log copies/μL of HuNV GII.4 or 5.50 log copies/μL of HAV were artificially inoculated in ground water. The titers of HuNV GII.4 and HAV decreased significantly (p < 0.05) with increasing Cl2 concentrations. Ground water was treated with 10, 30, 50, 100, 150, and 200 ppm of Cl2, and the viable HuNV GII.4 was significantly (p < 0.05) reduced to 3.28 (0.21 log reduction), 3.18 (0.31 log reduction), 3.01 (0.48 log reduction), 2.75 (0.74 log reduction), 2.54 (0.95 log reduction), and 2.34 (1.15 log reduction) log copies/μL, respectively. The viable HAV was also significantly (p < 0.05) reduced to 4.99 (0.23 log reduction), 4.76 (0.46 log reduction), 4.55 (0.67 log reduction), 4.21 (1.01 log reduction), 3.89 (1.33 log reduction), and 3.64 (1.58 log reduction) log copies/μL, respectively. The decimal reduction time (D-value) (1-log reduction) of HuNV GII.4 and HAV infectivity in ground water was predicted as 116.7 and 98.9 ppm of Cl2, respectively using the first-order kinetics model (HuNV GII.4 ; y = - 0.0054x + 3.3585, R2= 0.97, HAV ; y = - 0.0091x + 5.0470, R2= 0.97). The result specifically suggests that 150–200 ppm Cl2 can potentially be used for the inactivation of > 1 log copies/μL HuNV GII.4 and HAV in ground water.

This study developed Makgeolli (Korean traditional alcoholic beverage) by adding raw 20% Codium fragile (Cf), a green seaweed, to a mixed base consisting of rice and oats (1:1 ratio) (COM) and analyzed its quality characteristics and antioxidant activity. The rice based Makgeolli supplemented with raw 20% Cf was designated as control (CRM). The physicochemical (pH, acidity), microbiological (yeast, lactic acid bacteria), antioxidant (DPPH, ABTS), and sensory properties (7-henodonic scale) of CRM and COM were investigated during 7 days of fermentation. The pH/acidity levels of CRM and COM reached 3.66/0.68 and 3.69/0.95 after 7 days of fermentation, respectively. Compared to CRM, COM had lower alcohol and sugar contents (P < 0.05) (CRM: 14.5%/9.8 Brix, COM: 11.2%/7.4 Brix). However, COM contained more yeast and lactic acid bacteria (P < 0.05) (CRM: 1.30/4.28 log, COM: 6.04/6.09 log) compared to CRM. The antioxidant activity of DPPH/ABTS (P < 0.05) in COM was approximately 1.5 times that of CRM (CRM: 40.00/51.57%, COM: 56.76/70.91%). In addition, COM had an excellent sensory preference score (P < 0.05) due to its overall fresh flavor and savory taste (CRM: 4.93, COM: 6.06). Therefore, COM was shown to be Makgeolli with enhanced antioxidant function, with yeast and lactic acid bacteria, and improved flavor, suggesting the possibility of commercializing this Makgeolli.

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

"Finding fresh water in the ocean of data." is a challenge that all deep learning domains struggle with, especially in the area of hyperspectral image analysis. As hyperspectral remote sensing technology advances by leaps and bounds, there are increasing amounts of hyperspectral images(HSIs) can be available. Whereas in fact, these unlabeled HSIs are powerless to be used as material to driven a supervised learning task due to the extremely expensive labeling costs and some unknown regions. Although learning-based methods have achieved remarkable performance due to their superior ability to represent features, at the cost, these methods are complex, inflexible and tough to carry out transfer learning. In this paper, we propose the "Instructional Mask AutoEncoder"(IMAE), which is a simple and powerful self-supervised learner for HSI classification that uses a transformer-based mask autoencoder to extract the general features of HSIs through a self-reconstructing agent task. Moreover, we utilize the metric learning to perform an instructor which can direct the model focus on the human interested region of the input so that we can alleviate the defects of transformer-based model such as local attention distraction, lack of inductive bias and tremendous training data requirement. In downstream forward propagation, instead of global average pooling, we employ a learnable aggregation to put the tokens into fullplay. The obtained results illustrate that our method effectively accelerates the convergence rate and promotes the performance in downstream task.

Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

Stability considerations play a central role in structural dynamics to determine states that are robust against perturbations during the operation. Linear stability concepts, such as the complex eigenvalue analysis, constitute the core of analysis approaches in engineering reality. However, most stability concepts are limited to local perturbations, i.e. they can only measure a state’s stability against small perturbations. Recently, the concept of basin stability has been proposed as a global stability concept for multi-stable systems. As multi-stability is a well-known property of a range of nonlinear dynamical systems, this work studies the basin stability of bi-stable mechanical oscillators that are affected and self-excited by dry friction. The results indicate how the basin stability complements the classical binary stability concepts for quantifying how stable a state is given a set of permissible perturbations.

South Korea proposed reducing greenhouse gas emissions by 37% compared to the expected emissions by 2030 as the POST-2020 greenhouse gas reduction target. Electric vehicle distribution in the public sector is essential to achieve the carbon dioxide reduction target for transportation. In particular, when buses with internal combustion engines, which travel long distances and contribute substantially to greenhouse gas emissions, are replaced with electric buses, it is expected that greenhouse gas emissions will be significantly reduced. There are three types of electric buses with different power supply systems: a plug-in type in which power is supplied when a plug is inserted, a battery-swapping type in which a battery mounted on top of the vehicle is swapped at a swapping station, and a wireless type in which the battery is wirelessly charged through self-induction at a charging facility installed on the road. Vehicles of each charging type have different advantages and disadvantages. The performance, charging type, battery capacity, and operating environment of electric buses are mutually related parameters that must be considered when introducing such vehicles. Therefore, the optimal charging type must be selected according to the operating environment to enable the widespread use of electric buses. As such, this report proposes the optimal charging type according to the operating environment of public-sector electric vehicles.

The synergistic efficacy of combined treatment mild heat (MH) and dielectric barrier discharge (DBD) plasma in Bacillus cereus-contaminated red pepper powder was tested. A cocktail of three strains of B. cereus (NCCP 10623, NCCP 14579, ATCC 11778) was inoculated onto red pepper powder and then treated with MH (60 ℃ for 5-20 min) and DBD plasma (5-20 min). Treatment with MH and DBD plasma alone for 5~20 min resulted in reductions of 0.23~1.43 and 0.12~0.96 log CFU/g, respectively. Combined treatment with MH and DBD plasma was the most effective at reducing B. cereus counts on red pepper powder and resulted in log-reductions of ≥ 6.0 log CFU/g. The largest synergistic values (4.24-4.42 log) against B. cereus in red pepper powder were obtained by the combination of 20 min MH and 5~15 min DBD plasma. Hunter color ‘‘L’’, ‘‘a’’, and ‘‘b’’ values of the combination-treated samples were not significantly different from those of non-treated samples. Also, no significant (p > 0.05) differences in pH values between samples were observed. Therefore, these results suggest that the combination of MH treatment and DBD plasma can be potentially utilized in the food industry to effectively inactivate B. cereus without incurring quality deterioration of red pepper powder.

This paper presents the automation of the thin-layer chromatography technique whose separation and identification of molecules present in a mixture are currently done manually and laboriously [1]. We have therefore found an interest in automating this technique. In this part, the method implemented comprises 2 steps. First we proceeded to the segmentation of the spots obtained on the chromatographic plate. We then developed a program to identify families of molecules such as coumarins, terpenes, tannins, flavonoids, polyphenols, etc. by calculating segmentation parameters such as standard deviation, entropy, average pixel intensity from an algorithm on the matlab software. Finally our results have been compared to the results obtained by the traditional identification technique in laboratories. Some similarity between the two results obtained shows the reliability and the robustness of our technique.

We have successfully synthesized a series of Bi doped BaFe2As2 high quality single crystals for the first time. The x-ray diffraction (XRD) patterns show an expansion of lattice parameter c with Bi doping, indicating a negative pressure effect. By investigating the resistivity, a Fermi liquid (FL) to non-Fermi liquid (NFL) crossover is observed from normal state to antiferromagnetic order state, accompanied by three superconducting transitions labeled as SC I, SC II and SC III which are supposed to be induced from three superconducting realms with various Bi concentration. Thus, the NFL behavior is proposed to be closely related with the presence of superconductivity. The magnetic susceptibility measurements further speculates that the SC I and SC III phases should be filamentary superconductivity while the SC II be a possible bulk superconductivity with TC 7 K.

Nanopowders are continuously under investigation as they open new perspectives in numerous fields. There are two main challenges to stimulate their development: sufficient low-cost high throughput synthesis methods leading to a production with well-defined and reproducible properties, and for ceramics, conservation of their nanostructure after sintering. In this context, this paper presents the synthesis of a pure nanosized powder of ZnO (dv₅₀ ~ 60 nm, easily redispersable) by using a continuous Segmented Flow Tubular Reactor (SFTR), which has previously shown its versatility and its robustness, ensuring a high powder quality and reproducibility over time. A higher scale of production can be achieved based on a “scale-out” concept by replicating the tubular reactors. The sinterability of ZnO nanopowders synthesized by the SFTR was studied, by natural sintering at 900 °C and 1100 °C, and Spark Plasma Sintering (SPS) at 900 °C. The performances of the synthesized nanopowder were compared to a commercial ZnO nanopowder of high quality. The samples obtained from the synthesized nanopowder could not be densified at low temperature by traditional sintering, whereas SPS led to a fully dense material after only 5 minutes at 900 °C, while limiting the grain growth and thus leading to a nanostructured material.

Abstract: To describe the feeder topology and information exchange between intelligent electronic devices (IEDs) for Internet of Things (IoT), it is necessary to extend IEC 61850 and add a logical model. The feeder model described in IEC 61970 is then extended in IEC 61850 to serve as a container for the equipment along the distribution line. For the physical connections in the electrical equipment, both terminal and connectivity node logical models are added. Taking logical node XCBR as the example, its attributes for the terminals are added to the electrical equipment. The logical node LCNN is then further added in order to describe the connectivity node. By next adding the logical node LTPN to describe the topological node, and FTPA to describe the results of the topology analysis, we can efficiently describe the topology analysis results. Based on these new logical nodes, the exchange of topology information between intelligent terminals no longer relies on configuration files, but only requires logical nodes. Through the above expansion, the topology description and information exchange of medium and low voltage feeders are realized.

With the disruption of digital technologies, customers have emerged as co-producers in order to reduce costs and enhance productivity. Customer-driven business logic recognizes customer capabilities and sustainable innovation as key factors in the performance of customized manufacturing enterprises. The potential relationship between customers and producers has become a new area of inquiry. Existing research rarely delves into the impact of customer predictive ability on the process of customized production activities, particularly in relation to sustainable innovation, which remains inadequately characterized. Ships serve as a typical representation of customized enterprises. This study explores the underlying drivers of sustainable innovation through customer demand orientation by examining 20-year historical patterns in the global shipping and shipbuilding markets, considering the environmental dependency and temporal correlation characteristics of the shipbuilding market (producers) and the shipping market (consumers). By employing time series and panel data in a machine learning algorithm, specifically the random forest model, the study reveals a strong and statistically significant correlation between new ship deliveries and the Baltic Dry Index (BDI), with larger value ships having a more pronounced impact on the consumer market. The correlation analysis confirms that these two variables, in combination, can comprehensively reflect customer predictive ability and serve as crucial decision criteria for customer investment in new ship production. Furthermore, based on principal component analysis of customer predictive ability and ship innovation levels, as well as Granger causality tests, the study demonstrates that customer predictive ability is a Granger cause of sustainable innovation in customized production. Customer predictive ability influences sustainable innovation in customized enterprises to varying degrees. This research provides valuable insights for shipbuilding companies in terms of engaging in sustainable innovation in international markets and understanding the value of international market customers.

Fresh water springs are unique natural resources in Florida, currently under threat from increasing groundwater pumping and pollution resulting from a variety of sources. This paper estimates current recreation benefits from visiting springs using the travel cost method and elicits residents’ willingness to contribute for springs restoration using the contingent valuation method. It further compares the performance of count data models correcting for endogenous stratification and truncation, and finds that the annual consumer surplus per person per trip is between $20 and $43. Furthermore, visitors are willing to contribute $12 to $14 per person per trip for springs restoration without reducing trip demand.

In current study we have obtained Co2FeSi-glass coated microwires with different geometrical aspect ratio, ρ = d/Dtot (diameter of metallic nucleus, d and total diameter, Dtot). The structure and magnetic properties are investigated at a wide range of temperature. XRD analysis illustrates a notable changing in the microstructure by increasing the aspect ratio of Co2FeSi glass coated microwires. Amorphous structure is detected for the sample with the lowest aspect ratio (ρ = 0.23), whereas a growth of crystalline structure is observed in the other samples (aspect ratio ρ = 0.30 and 0.43). This change at the microstructure properties correlates with dramatic changing in magnetic properties. For the sample with the lowest ρ -ratio, non-perfect square loops are obtained with low normalized remanent magnetization. A notable enhancement in the squareness and coercivity are obtained by increasing ρ -ratio. Changing the internal stresses strongly affects the microstructure, resulting in a complex magnetic reversal process. The thermomagnetic curves show large irreversibility for the Co2FeSi with low ρ -ratio. Meanwhile, if we increase the ρ -ratio, the sample shows perfect ferromagnetic behavior without irreversibility. The current result illustrates the ability to control the microstructure and magnetic properties of Co2FeSi-glass-coated microwires by changing only their geometric properties without performing any addition heat treatment. The modification of geometric parameters of Co2FeSi glass-coated microwires allows to obtain microwires which exhibit an unusual magnetization behavior that offers opportunities to understand the phenomena of various types of magnetic domain structures, which is essentially helpful for designing sensing devices based on thermal magnetization switching.

Exploring for green and affordable protection of perishable fruit like cherry tomato is of significance. Here the protective efficacy and its underpinnings of coating with oleaster gum, alone or incorporated with cuminal, on cherry tomatoes stored at ambient temperature were investigated. The composite coating of oleaster gum with 0.1% cuminal reduced decay, respiration rate, weight loss, and softening of the fruit and decelerated their decreases of total soluble solids, titratable acidity, and soluble proteins, and therefore maintained their marketability. Furthermore, it reduced the accumulation of O•− 2and H2O2 in fruit, and mitigated cell membrane lipid oxidation and permeabilization, thereby retarding their senescence. Instrumentally, it elevated the activities of superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase and the levels of ascorbic acid and glutathione. This potentiation of the fruit antioxidant system makes the composite coating a promising approach to keeping the postharvest quality of perishable fruit.

To solve the problem of low efficiency of source seeking in implicit information field, this paper presents an autonomous souring method based on balanced searching strategy is enlighten by biological homing behaviors. At the beginning of the research, the source seeking task come down to a multi-objective convergence issue. By taking the feasibility search behaviors as the individual sample of the evolutionary population drawing on the thought of evolutionary algorithms, then combine motion searching with population evolution, so the carrier can be guide to complete source seeking task by solving the multi-objective problem. Furthermore, the distribution entropy is also considerated d to measure the searching bias in the process of source seeking. Combined with the demand of the source seeking process, a new source seeking method of balanced searching strategy is designed. Ultimately, through theoretical analysis and simulation verification confirm the effectiveness and rationality of the proposed method.

Increasing clinical and preclinical evidences implicate gut microbiome (GM) dysbiosis as a key susceptibility factor for neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s diseases (PD). In recent years, neurodegenerative diseases have not been viewed as driven solely by defects in brain, and the role of GM in modulating the central nervous system function via gut-brain axis has attracted considerable interest. Encouraged by GM researches, the next-generation probiotics may lead to tangible impacts on the treatment of neurodegenerative disorders. This review summarizes the current understandings of GM composition and characteristics associated with neurodegenerative diseases, and the demonstrations of molecules from GM affecting neurodegeneration. Particularly, applications of next-generation probiotics such as Clostridium butyricum, Akkermansia muciniphila, Faecalibacterium prausnitzii, and Bacteroides fragilis for remediation of neurodegenerative diseases are discussed.

Phospholipid fatty acids (PLFAs) can be used as biomarkers for qualitative and quantitative analyses of soil microbial community diversity. In this study, we collected soil samples at 10-cm intervals to a depth of 1 m from Robinia pseudoacacia plantations of four different ages (10, 15, 25 and 40 years) in a loess area and analysed the soil microbial community structure by PLFA analysis. A total of 97 PLFAs were detected in soils of R. pseudoacacia plantations of different ages. The individual PLFA contents gradually decreased in the 0- to 40-cm soil layers, with little variation in the 40- to 100-cm soil layers. The individual PLFAs were similarly distributed in the soils of R. pseudoacacia plantations of different ages, and there was a clear variation with stand age and soil depth. The individual PLFA contents in the 0- to 20-cm soil layers were highest for the 25-year-old plantation, while those in the 20- to 40-cm soil layers were relatively high for the 25- and 40-year-old plantations; the 16:0 content was the highest among individual PLFAs. The total PLFA content and the PLFA contents of different microbial groups [bacteria, fungi, Gram-positive bacteria (G⁺), Gram-negative bacteria (G^-) and actinomycetes] initially increased before decreasing in the soils of R. pseudoacacia plantations with increasing stand age, whereas these contents gradually decreased with increasing soil depth; the highest PLFA contents was found in the 25-year-old plantation. The total PLFA content and the contents of fungal, G^- and actinomycete PLFAs in the soils of R. pseudoacacia plantations differed significantly among stands of different ages in the 0- to 10-cm, 10- to 20-cm and 30- to 40-cm soil layers, while no significant differences were found in the 20- to 30-cm soil layers; the G⁺ and bacterial PLFAs contents in each of the 0- to 40-cm soil layers were significantly different. The PLFA ratios between different microbial groups differed among the stands of different ages. The fungi/bacteria ratio showed a “decrease-increase-decrease” trend with stand age, while the G⁺/G^- ratio showed an “increase-decrease” trend. The saturated/monounsaturated PLFA ratio initially decreased before plateauing, while the opposite trend was observed for the cyclopropyl/precursor ratio. The PLFA contents of different microbial groups were ranked as follows: bacteria > G^- > G⁺ > actinomycetes > fungi. In the principle component analysis, 18:1ω9c, 10Me18:0, i17:0, a17:0, 18:1ω7c, 18:1ω5c and 18:0 made the greatest contribution to principal component 1, and a14:0, i14:0 3OH, i14:0, i14:1ω7c and 14:0 made the greatest contribution to principal component 2. In conclusion, soil nutrient status and other soil eco-environmental stress factors should be considered in 10- to 25-year-old (particularly ~15-year-old) plots for the management of R. pseudoacacia plantations to prevent forest soil degradation and improve forest stand quality, thereby achieving better soil and water conservation and environmental improvement in R. pseudoacacia plantations.

THz radiation has assumed a great importance thanks to the efforts in the development of technological tools used in this versatile band of the electromagnetic spectrum. Here we propose a reflecting bi-mirror axicon-like device with wavelength-independent long focusing performances in the THz band, by exploiting the high thermo-mechanical deformation of the elastomer polydimethylsiloxane (PDMS). This deformation permits to achieve significant optical path modulations in the THz band and effective focusing. The surface of a PDMS layer is covered with a gold thin film, acting as heater thanks to its absorption for wavelengths below ~500 nm . An invariance property of the Fresnel integral has been exploited to verify experimentally the THz performances of the device with an ordinary visible laser source, finding excellent agreement with the theoretical predictions at 1 and 3 THz. The same property allowed also to verify experimentally that the axicon focus has a longitudinal extension much greater than that one exhibited by a benchmark cylindrical mirror with the same optical power. The axicon is thermo-mechanically stable up to a heating power of 270 mW, although it might be potentially exploited at higher powers with a minor degradation of the optical performances.

In the present paper we introduce three new classes of bi-univalent functions connected with Gregory coefficients. For functions in each of these three bi-univalent function classes we have derived the estimates of the Taylor--Maclaurin coefficients $\left|a_{2}\right|$ and $\left|a_{3}\right|$ and Fekete-Szeg\H{o} functional problems for functions belonging to these new subclasses. We defined three subclasses of the class of the bi-univalent functions $\Sigma$, namely $\mathfrak{HG}_{\Sigma}$, $\mathfrak{GM}_{\Sigma}(\mu)$ and $\mathfrak{G}_{\Sigma}(\lambda)$ by using the subordinations with the function whose coefficients are Gregory's numbers. First, we proved that these classes are not empty, i.e. contains other functions than the identity one. Using the well-known Carath\'eodory Lemma for the functions with real positive parts in the open unit disk, together with an estimation due to P. Zaprawa (see https://doi.org/10.1155/2014/357480) and another one of Libera and Zlotkiewicz, we gave upper bounds for the above mentioned initial coefficients and for the Fekete-Szeg\H{o} functionals. The main results are followed by some particular cases, and the novelty of the definitions and the proofs could involve further studies for such type of similarly defined subclasses.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

This article puts forward the bi-matrix games with crisp parametric payoffs based on interval value function approach. We conclude that the equilibrium solution of the game model can converted into optimal solutions of the pair of the non-linear optimization problem. Finally, experiment results show the efficiency of the model.

Hepatitis E virus (HEV) is a common cause of acute hepatitis worldwide, accounting for approximately 25% of deaths among pregnant women. We previously reported that pregnancy serum facilitates HEV replication in vitro. However, the differences in host cells with HEV infection induced by pregnancy serum and fetal bovine serum (FBS) are unclear. In this study, differentially expressed proteins were identified in HEV-infected hepatoma cells (HepG2) supplemented with different sera by using isobaric tags for relative and absolute quantitation. Proteomic analysis indicated that HEV infection significantly induced 1014 differentially expressed proteins in HEV-infected HepG2 cells when supplemented with FBS compared with pregnancy serum. Further validation by Western blot confirmed that filamin A, heat-shock proteins 70 and 90, Cytochrome c, and Thioredoxin were associated with HEV infection. This comparative analysis provides an important basis to further investigate HEV pathogenesis in pregnant women and HEV replication.

Cooperative localization under complex urban environments has become a solution to replace the Global Navigation Satellite System (GNSS) positioning. Due to utilizing distance measured and information exchanged between vehicles, cooperative localization results in high computational complexity and heavy communication overhead. This paper proposes a cooperative localization method based on evolutionary coalitional game theory, which implements vehicles' location estimation with less communication cost. We select the neighboring vehicles to form a coalition based on the node's square position error bound and communication cost. The location is obtained via exchanging information between vehicles. It is evident from the simulations and results that the proposed method holds low communication overhead while maintaining localization accuracy.

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

Compared to tin lead alloys, lead-free tin alloys have lower toxicity, making them more widely used. In this study, Sn-20Bi-0.3Al-xY solders alloys were prepared. The effects of the addition of Y on its microstructure, micro-hardness, wettability and mechanical properties was investigated. The result showed that the addition of Y had significant effect on the refinement of Sn-20Bi-0.3Al-xY. From the spreading tests results, Y increased the wettability of the solders, however when the fraction of Y gradually increased, the wettability reduced. The ultimate tensile and hardness of Sn-20Bi-0.3Al-xY solders alloys were increased as the content of Y increasing.

In recent years, the management of health systems is a main concern of governments and decision makers. Home health care is one of the newest methods of providing services to patients in developed societies that can respond to the individual lifestyle of modern age and the increase of life expectancy. The home health care routing and scheduling problem is a generalized version of the vehicle routing problem, which is extended to a complex problem by adding special features and constraints of health care problems. In this problem, there are multiple stakeholders such as nurses for which an increase of their satisfaction level is very important. In this study, a mathematical model is developed to expand traditional home health care routing and scheduling models to downgrading cost aspects by adding the objective of minimizing the difference between the actual and potential skills of the nurses. Downgrading can lead to a dissatisfaction of the nurses. In addition, skillful nurses have higher salaries and high-level services increase equipment costs and need more expensive trainings and nursing certificates. Therefore, downgrading can enforce hidden huge costs to the managers of a company. To solve the bi-objective model, an -constraint based approach is suggested and the model applicability and its ability to solve the problem in various sizes are discussed. A sensitivity analysis on the Epsilon parameter is conducted to analyze the effect of this parameter on the problem. Finally, some managerial insights are presented to help the managers in this field, and some directions for future studies are mentioned as well.

The main aim of this work is to report an alternative energy efficient technique of fabricating flexible thermoelectric generators (TEGs) using printable ink. In this process, we have fabricated thermoelectric (TE) composite thick film and we are experimenting several ways to overcome the challenges of conventional and additive manufacturing methods. Two different mesh sizes of n-type bismuth particle, various binder to thermoelectric (TE) material weight ratio, and two different pressure (200 MPa and 300 MPa) were employed for optimizing the thermoelectric properties of TE composite films. We are also exploring naturally occurring chitosan as a binder. Dimethyl sulfoxide (DMSO) dissolved chitosan was used for the binder and less than 0.2 wt% of chitosan was sufficient for the fabrication of TE inks and composite films. Low energy intensive curing process was employed to evaporate the solvent from the drop casted inks. External uniaxial pressure not only eliminated high energy intensive curing processes but also increased the packing density of the film by removing pores and voids in the chitosan-bismuth composite film. The microstructure analysis reveals that bulk-like structure, which rarely has voids, pores and grain boundaries, was observed in the composite films pressed at sufficiently high pressures. The highest performing composite film was obtained with the conditions of 1:2000 binder to bismuth weight ratio, 100 mesh of particle size, and 300 MPa of pressure. The best performing bismuth chitosan composite film pressed at 300 MPa had the power factor as 4009 ± 391 μW/m·K² with high electrical conductivity value of 7337 ± 522 S/cm. The measured thermal conductivity of the best performing chitosan-bismuth composite film was 4.4 ± 0.7 W/m·K and the figure of merit calculated from the thermal conductivity was 0.27 at room temperature.

Characteristics of artificial waveguide lines composed of finite size artificial cells are analyzed and discussed with relation to designing the travelling wave parametric amplifiers. From this point of view, flux driven amplifier suggested recently can be considered as the most successful amplifier design. To increase dynamic range of the amplifier, dc SQUID cells of the artificial line in use can be substituted for bi-SQUID cells. The 1-dB compression points of the amplifier gain are calculated and compared for both types of the cells.

Sustainable agricultural production is endangered by several ecological factors such as drought, extreme temperatures, excessive salts, parasitic ailments, and insect pest infestation. These challenging environmental factors may have adverse effects on future agriculture production in many countries. In modern agriculture, conventional crop breeding techniques alone are inadequate for achieving the increasing population’s food demand on a sustainable basis. The advancement of molecular genetics and related technologies are promising tools for the selection of new crop species. Gene pyramiding through marker assisted selection (MAS) and other techniques have accelerated the development of durable resistant/tolerant lines with high accuracy in the shortest possible period of time for agricultural sustainability. Gene stacking has not been fully utilized for biotic stress resistance development and quality improvement in most of the major cultivated crops. This review emphasizes on gene pyramiding techniques that are being successfully deployed in modern agriculture for improving crop tolerance to abiotic and biotic stresses for sustainable crop improvement.

The purpose of the present paper is to introduce and investigate new subclasses of analytic function class of bi univalent function defined in open unit disk connected with a linear q-convolution operator, which are associated with the quasi-subordination. We find coefficients estimate |h_2 |,|h_3 | for functions in these subclasses. Several known and new consequences of these results are also pointed out.

Given the significant role of the Ningxia Urban Agglomeration along the Yellow River in reshaping the urban network and promoting coordinated development in the upper Yellow River region of China, this paper takes enterprise flow as the explicit manifestation of the regional urban network. Based on the data of registered enterprises from 2005 to 2021, combined with the interlocking network model, social network analysis method, and spatial panel econometric model, this paper interprets the evolution of the regional urban network structure and its influencing mechanisms through the different types of enterprise flow. The results indicate the following: (1) The external network is primarily focused on outflow investments towards North China, East China, and Northwest China. The overall inflow sources form a multi-origin structure dominated by North China and East China. The Jinfeng and Xingqing serve as core hubs for enterprise exports in the external network and destinations for incoming enterprises. However, in the Productive manufacturing connections, a spatial organizational pattern driven by multiple cities has formed. (2) In the internal network, a concentric connection structure centered around the Jinfeng and Xingqing has formed. The Productive service connections are relatively active, while the Productive manufacturing connections are relatively concentrated between the Jinfeng, Xingqing, Ningdong, and Lingwu. However, there is an imbalance in the spatial connections within the urban agglomeration network and an incomplete hierarchical system of network nodes. (3) In the external network, the main feature is the absorption of external elements to foster development momentum. In the internal network, the Jinfeng and Xingqing serve as contact and radiation sources, influencing various nodes. However, their driving capacity is weak. (4) The market demand and coordinated development both demonstrate significant promoting effects on the connections within the external and internal networks. The sluggish adjustment and transformation of regional industrial structure have resulted in a temporary negative inhibitory effect on the development of transformation. The negative impact of urban investment activities and the positive impact of government management are reflected within the internal network. (5) The improvement of urban management and service functions, as well as external borrowing, can promote the connection in different network. However, the borrowing economic activity can have a negative impact in different networks. (6) Industrial agglomeration can promote enterprises connections in different networks and generate significant spatial spillover effects.

Neuroinflammation and brain oxidative stress are recognized as significant contributors to hypertension including salt sensitive hypertension. Extracellular vesicles (EVs) play an essential role in intercellular communication in various situations, including physiological and pathological ones. Based on this evidence, we hypothesized that EVs derived from the brains of hypertensive rats with salt sensitivity could trigger neuroinflammation and oxidative stress during hypertension development. To test this hypothesis, we compared the impact of EVs isolated from the brains of hypertensive Dahl Salt Sensitive rats (DSS) and normotensive Sprague Dawley (SD) rats on inflammatory factors and mitochondrial reactive oxygen species (mtROS) production in primary neuronal cultures and brain cardiovascular relevant regions, including the hypothalamic paraventricular nucleus (PVN) and lamina terminalis (LT). We found that brain-derived DSS-EVs significantly increased mRNA levels of proinflammatory cytokines (PICs) and chemokines, including TNFα, IL1β, CCL2, CCL5, and CCL12, as well as the transcriptional factor NF-κB in neuronal cultures. DSS-EVs also induced oxidative stress in neuronal cultures, as evidenced by elevated NADPH oxidase subunit CYBA coding gene mRNA levels and persistent mtROS elevation. When DSS-EVs were injected into the brains of normal SD rats, mRNA levels of PICs, chemokines, and the chronic neuronal activity marker FOSL1 were significantly increased in the PVN and LT. Furthermore, DSS-EVs caused mtROS elevation in brain PVN and LT, particularly in neurons. Our study reveals a novel role for brain-derived EVs from hypertensive rats in triggering neuroinflammation, upregulating chemokine expression, and inducing excessive ROS production. These findings provide insight into the complex interactions between EVs and hypertension-associated processes, offering potential therapeutic targets for hypertension-linked neurological complications.

The rapid development of electrochemical CO2 reduction offers a promising route to convert in-termittent renewable energy into products of high value-added fuels or chemical feedstocks. However, low faradaic efficiency, low current density, and a narrow potential range still limited the large-scale application of CO2RR electrocatalyst. Herein, monolith 3D bi-continuous nano-porous Bismuth (np-Bi) electrodes are fabricated via a simple one-step electrochemical dealloying strategy from Pb-Bi binary alloy. The unique bi-continuous porous structure ensures highly effective charge transfer, meanwhile the controllable millimeter-size geometric porous structure enables easy catalyst adjustment to expose highly suitable surface curvatures with abundant reactive sites. This results in a high selectivity of 92.6% and superior potential window (400 mV, selectivity >88%) for electrochemical reduction of carbon dioxide to formate. Our scalable strategy provides a feasible pathway for mass-producing high-performance and versatile CO2 electrocatalysts.

This study aimed to evaluate contrast-enhanced mammography (CEM) and compare breast lesions on CEM and breast magnetic resonance imaging (MRI) using 5 features. We propose to create a flowchart for BI-RADS classification of breast lesions on CEM based on the Kaiser score (KS) flowchart for breast MRI. Included were 68 subjects (women and men; median age 61.4 ± 11.6 years) who were suspected of having a malignant process in the breast based on digital mammography (MG) findings. The patients underwent breast ultrasound (US), CEM, MRI, and biopsy of the suspicious lesion. There were 47 patients with malignant lesions confirmed by biopsy and 21 patients with benign lesions, for each of which a KS was calculated. In the patients with malignant lesions, the MRI-derived KS was 9 (IQR 8-9), its CEM equivalent was 9 (IQR 8-9), and BI-RADS was 5 (IQR 4-5). In patients with benign lesions, MRI-derived KS was 3 (IQR 2-3), its CEM equivalent was 3 (IQR 1.7-5), and BI-RADS was 3 (IQR 0-4). There was no significant difference between the ROC-AUC of CEM and MRI (P=0.749). In conclusion, there were no significant differences in KS results between CEM and breast MRI. The KS flowchart is useful for evaluating breast lesions on CEM.

Transparent conducting oxides (TCO) with high electrical conductivity and at the same time high transparency in the visible spectrum are an important class of materials widely used in many devices requiring a transparent contact such as light-emitting diodes, solar cells and display screens. Since the improvement of electrical conductivity usually leads to degradation of optical transparency, a fine-tuning sample preparation process and a better understanding of the correlation between structural and transport properties is necessary for optimizing the properties of TCO for use in such devices. Here we report a structural and magnetotransport study of tin oxide (SnO₂), a well-known and commonly used TCO, prepared by a simple and relatively cheap Atmospheric Pressure Chemical Vapour Deposition (APCVD) method in the form of thin films deposited on soda-lime glass substrates. The thin films were deposited at two different temperatures (which were previously found to be close to optimum for our setup), 590 °C and 610 °C, and with (doped) or without (undoped) the addition of fluorine dopants. Scanning Electron Microscopy (SEM) and Grazing Incidence X-ray Diffraction (GIXRD) revealed the presence of inhomogeneity in the samples, on a bigger scale in form of grains (80–200 nm), and on a smaller scale in form of crystallites (10–25 nm). Charge carrier density and mobility extracted from DC resistivity and Hall effect measurements were in the ranges 1–3 × 10²⁰ cm⁻³ and 10–20 cm²/Vs, which are typical values for SnO₂ films, and show a negligible temperature dependence from room temperature down to -269 °C. Such behaviour is ascribed to grain boundary scattering, with the interior of the grains degenerately doped (i.e., the Fermi level is situated well above the conduction band minimum) and with negligible electrostatic barriers at the grain boundaries (due to high dopant concentration). The observed difference for factor 2 in mobility among the thin-film SnO₂ samples most likely arises due to the difference in the preferred orientation of crystallites (texture coefficient).

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

Body-centered cubic bismuth (Bi) is considered an enticing pressure marker, and therefore it is highly desirable to command its accurate equation of state (EOS). However, significant discrepancies are noted among the previous experimental EOSs. In the present work, the EOS up to 300 GPa is theoretically obtained by direct solving the partition function via a direct integral approach (DIA). The calculated results nearly reproduce the hydrostatic experimental measurements below 75 GPa, and the deviations from the measurements gradually get larger with increasing pressure. Based on the ensemble theory of equilibrium state, DIA works with high precision particularly in high-pressure conditions, so the hydrostatic EOS presented in this work is expected to be a reliable pressure standard.

The dual-station Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of single-station dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration.Unmanned aerial vehicles (UAV) equipped with dual-station InSAR, with relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne dual-station InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne dual-station InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision dual-station InSAR imaging and interferometric processing. Height inversion results based on the proposed method is also provided, demonstrating the effectiveness of the proposed method and improving interferometric accuracy at calibration points from 0.66 meters to 0.42 meters.

In this paper, we give all non splitting bi-unitary superperfect polynomials divisible by one or two irreducible polynomials over the prime field of two elements. We prove the nonexistence of odd bi-unitary superperfect polynomials over F2.

The current COVID19 pandemic has raised huge concerns for outdoor air quality due to expected lungs deterioration. These concerns include the challenges in the scalable prediction of harmful gases like carbon dioxide, iterative/repetitive inhaling due to mask and environmental temperature harshness. Even in the presence of air quality sensing devices, these challenges lead to failed planning and strategy against respiratory diseases, epidemics, and pandemics in severe cases. In this work, a dual time-series with bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and optimization responses that use automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e. multi-variate dual time-series of environmental and US Environmental Protection Agency standard sensor variables for air quality index measured from air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor air quality and their data was collected at Ubidots IoT platform over GSM. Results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 seconds for normal and RMSE of 1.646 in the training time of 28.53 seconds for optimization. The estimated R-Squared error of 0.03 with Mean-Square Error for temperature 1.0084 ᵒC and 293.98 ppm for CO2 was observed. Furthermore, the Mean-Absolute Error (MAE) for temperature 0.66226 ᵒC and 10.252 ppm for CO2 at a prediction speed of ~5100 observations/second for temperature 45000 observations/second for CO2 due to iterative optimization of the training time 469.28 seconds for temperature and 28.53 seconds for CO2 was very promising in forecasting COVID19 countermeasures before time.

The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of Machine Learning (ML) and Deep Learning (DL) models. Here we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 19 out of 21 classification exercises, BiGAMi achieved its results by selecting 6-68% fewer features than the highest performance of a Sequential Forward Feature Selection algorithm. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.

Background: Oral narratives are crucial for evaluating youngsters since they reveal a plethora of information about young children's language development. This study aimed to investigate the development of narrative productivity, lexical diversity, syntactic complexity and in Cypriot-Greek-speaking children during preschool and primary school years. An additional aim was to measure the effect of the children’s gender in their performance on the measures of narratives. Methods: One hundred and twenty-nine typically developing Cypriot-Greek-speaking children were recruited for the study. Results: The results showed that the linguistic structure of narrations reflects a subtle developmental trend. As expected, older children tended to produce longer and syntactically more complex stories than the younger ones, which is in line with previous studies. There were significant differences in all productivity measures between the four-year-olds and those aged seven and ten years old. In addition, the results illustrated that there was not any significant effect of gender in the measures of narrative skills. Specifically, gender did not affect the children’s performance in the sample. Boys and girls had similar narrative measurements in all the variables. Conclusions: This study aims to provide initial evidence in retelling narrative development patterns on typically developing Cypriot-Greek speaking children that could be used by speech and language to compare children’s performance and detect language disorders.

Sporisorium scitamineum, the basidiomycetous fungus causing sugarcane smut that leads to severe losses in sugarcane quantity and quality, undergoes sexual mating to form dikaryotic hyphae capable of invading the host cane. Therefore, suppressing dikaryotic hyphae formation would potentially be an effective way to prevent host infection by the smut fungus, and the following disease symptom development. The phytohormone methyl jasmonate (MeJA) has been showed to induce plant defense against insects and microbial pathogens. In this study, we verified that exogenous addition of MeJA suppressed dikaryotic hyphae formation under in vitro culture condition, and effective in inhibiting maize smut disease symptom caused by Ustilago maydis. We constructed an Escherichia coli strain expressing plant JMT gene, encoding a jasmonic acid carboxyl methyl transferase that catalyzes conversion from jasmonic acid (JA) to MeJA. By GC-MS we confirmed that the transformed E. coli strain, designated as pJMT strain, was able to produce MeJA in the presence of JA and S-adenosylmethionine (SAM; as methyl donor). Furthermore, the pJMT strain was able to suppress S. scitamineum filamentous growth under in vitro culture condition. It waits to further optimize JMT expression under field condition, in order to utilize the pJMT strain as a biocontrol agent (BCA) of sugarcane smut disease. Overall, our study provides a potentially novel method for controlling crop fungal diseases by boosting phytohormone biosynthesis.

Liquid-liquid phase separation (LLPS) of proteins has been found ubiquitously in eukaryotic cells, critical in the controlling of many biological processes through forming a temporary condensed phase with different bimolecular components. TDP-43 is recruited to stress granules in cells and is the main component of TDP-43 granules and proteinaceous amyloid inclusions in patients with amyotrophic lateral sclerosis (ALS). TDP-43 low complexity domain (LCD) is able to demix in solution forming the protein condensed droplets. The molecular interactions regulating its LLPS were investigated at the protein fusion equilibrium stage, where the droplets stopped growing. We found the molecules in the droplet were still liquid-like but with enhanced intermolecular helix-helix interaction in the LCD. The protein would start to aggregate after about 200 minutes of lag time and aggregate slower than at the condition when the protein does not phase separate or the molecules have a reduced intermolecular helical interaction. A structural transition intermediate towards protein aggregation was also discovered involving a decrease of the intermolecular helix-helix interaction and a reduction in the helicity. Therefore, LLPS and the intermolecular helical interaction could help maintain the stability of TDP-43 LCD.

The mutton quality of Chinese Tan lambs (Ovis aries) has declined as feeding patterns have shifted from pasturing to pen rationing. Mutton quality is affected by fatty acids, and rumen microorganisms play a role in the synthesis of short-chain fatty acids, long-chain fatty acids, and conjugated linoleic acids. We used 16S rDNA sequencing to analyze the effects of two feeding patterns on the rumen bacteria of Tan lambs. In a randomized block design with 24 newborn Tan lambs, 12 lambs were fed by ewes in pasture and 12 were fed by pen-fed ewes. At 2 months, the biceps femoris and the longissimus dorsi were analyzed by gas chromatography for intramuscular fat content and fatty acid composition, and DNA in the rumen contents was extracted and used to analyze the structure of the bacterial community. Different feeding patterns had no significant effect on the intramuscular fat content of the biceps femoris and longissimus dorsi of the lambs, but there was a significant effect on fatty acid composition. Rumen bacteria were significantly more diverse in the pasture group than in the pen-ration group (P < 0.01). Twenty-one phyla were identified in the pasture group, and 14 phyla were identified in the pen-ration group. The dominant phyla in the pasture group were Bacteroidetes and Fibrobacteres; the dominant phyla in the pen-ration group were Proteobacteria and Bacteroidetes. The relative abundance of Proteobacteria was significantly higher in the pen-ration group than in the pasture group (P < 0.01). Diversity at the genus level was also higher in the pasture group, with 176 genera in the pasture group and 113 genera in the pen-ration group. The dominant genera in the pasture group were Prevotella_1, Rikenellaceae_RC9_gut_group, and Bacteroidales_BS11_gut_group_Na; the dominant genera in the pen-ration group were Prevotella_1, Prevotella_7, Succinivibrionaceae_UCG-001, and Succinivibrionaceae_NA.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then the reconstructed SSTs from the RBFN method are compared with the results from the optimum interpolation (OI) method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and the average RMSE is 0.48°C for the RBFN method, which is quite smaller than the value of 0.69°C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

The paper discusses the electroreduction of Bi(III) ions in the aspect of expanding the ”cap – pair” effect. The ”cap – pair” rule is associated with the acceleration of the electrode’s processes by organic substances. The interpretation of the ”cap – pair” effect mechanism was expanded to include the effect of supporting electrolyte concentration on the acceleration process and the type of electrochemical active as well as used protonated organic substances. It has also been shown that the phenomena occurring at the electrode/solution interface can influence a change in the dynamics of the electrode’s process according to the ”cap – pair” rule.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

Hydrodechlorination reaction of 3-(benzo-1,3-dioxol-5-yl)-3-chloro-2-methylacrylaldehyde in the presence of different low metal content heterogeneous mono- or bi-metallic catalysts was tested for the synthesis of the fragrance Helional® (3-[3,4-methylendioxyphenyl]-2-methyl-propionaldehyde). In particular, mono Pd/Al2O3, Rh/Al2O3 or bi-metallic Pd-Cu/Al2O3, Rh-Cu/Al2O3 catalysts were tested in different reaction conditions from which it emerged that mono-Rh/Al2O3 was the best performing catalyst allowing to achieve 100% substrate conversion and 99% selectivity towards Helional® in 24h at 80°C, p(H2) 1.0 MPa in the presence of a base. To establish correlations between atomic structure and catalytic activity, catalysts were characterized by Cu, Rh and Pd K-edge XANES, EXAFS analysis. These characterizations allowed to verify that the formation of Pd-Cu alloys and the presence of Cu oxide/hydroxide species on the surface of the Al2O3 support, are responsible for the very low catalytic efficiency of bimetallic species tested.

Background: Affective states play a role in dietary behaviors. Yet, little research has studied within-subjects associations between affect and diet during pregnancy. We examined the acute bidirectional relationships between affect and food intake and moderation by pre-pregnancy body mass index (BMI) in low-income, Hispanic pregnant women using ecological momentary assessment (EMA). Methods: Women (N=57) completed four days of EMA during their first trimester. Women responded to five random prompts per day about their current affect and past two-hour food intake. Results: Higher positive affect (PA) or lower negative affect (NA) predicted greater likelihood of fruit/vegetable consumption in the next two hours in women with lower pre-pregnancy BMI and lower likelihood in women with higher pre-pregnancy BMI. Higher PA predicted less likelihood of fast food consumption in the next two hours in women with lower pre-pregnancy BMI and slightly higher likelihood in women with higher pre-pregnancy BMI. Women with lower pre-pregnancy BMI had higher PA when they reported consuming chips/fries in the past two hours, and women with higher pre-pregnancy BMI had lower PA when they reported consumption of chips/fries in the past two hours. Conclusions: Results showed differential relationships between affect and food intake as a function of pre-pregnancy BMI.

Background: Pro-golfers are increasingly trained scientifically, but swing technique characteristics have not yet been conclusively determined for golf performance. We investigated the relationship between the player's thorax, pelvis and club based on a wireless biofeedback system. We also determined whether thoracic and pelvic changes during different swing phases have different effects on ball striking. Methods: Eight male professionals performed 10 successful full-swing shots using a 5-iron and applied the K-vest wireless biofeedback system for analysis. Results: Peak angular velocities between the pelvis, torso, and clubs were all significantly different (p&lt;0.05) in the men's professional players. All players deviated more from the PGA players range metrics during the preparation stance. Excessive lateral pelvic bending angle, over-rotation, and hyperextension of the trunk were prevalent at the top of the backswing. Conclusions: Most players have an irrational sequence of pelvic, trunk and club rotation times, which is not conducive to improving club head speed and distance. Excessive pelvic rotation in the closed direction at the apex of the backswing is not conducive to maximizing the X-factor. At the moment of striking the ball, maintain a moderate forward trunk lean and full trunk rotation to avoid sports injuries.

The evaluation of suitability for bioclimatic viticulture indices (BVIs) zones, as with any other crop, requires a well-founded knowledge of the spatial variability in climate data, which is also used to assess different grapevine cultivars and to delimit appropriate regions for vineyard production under the current climate and projected climate change. BVIs, on the other hand, are typically calculated using a small number of locations linked by climatic station sites or applied using a coarsely-resolution distributed dataset. Furthermore, often the methods applied to precisely delineate symmetrical regions are not appropriate to generate accurate maps. To provide an analysis using three temperature-based indices and the hydrothermal coefficient (HTC), quantifying their spatial variation, and representing the spatial patterns of each BVI throughout the Jabal Al Arab viticulture—one of the most important Syrian viticulture regions in the Eastern Mediterranean—daily temperature data from 15 meteorological stations and 57 rain gauges, considering the 1984–2014 period, and downscaled future scenarios (RCPs based on CanESM2), considering the 2016–2100 period, were utilized. The statistical method of each BVI was analyzed, and, later, they were mapped by using GIS and a hybrid interpolation (regression-kriging) approach. The regression fitting method revealed that the sum accumulation of heat and the hydro-thermal index during the growing season were highly related to elevation and distance to the seacoast variables; however, the viticulture zones varied spatially depending on which index was used and under suggested future scenarios compared to the current climate. The spatial distribution of climate in the Jabal Al Arab regions exhibits significant variability. The findings suggest that climate change projections indicate a prevalence of warmer conditions in the future. Under the RCP scenarios, the territory can be categorized into up to three bioclimatic classes for both the Heat Index (HI) and the Winter Index-Growing Degree Days (WI-GDD), in contrast to the current climate which has six classes. These results provide valuable insights into the suitability of viticulture within each climatic region and facilitate the identification of homogeneous zones. The utilization of consistent bioclimatic indices and an advanced hybrid interpolation method has enabled the delineation and comparison of bioclimatic variables of Jabal Al Arab with other regions worldwide. Such comparisons should be considered when selecting grapevine varieties and assessing the potential for grape production.

Introduction: Oral Nicotine Pouches (ONPs) are the new form of nicotine pouches that have become a type of emerging smokeless tobacco product sold by various tobacco companies. These smokeless tobacco products are being marketed for usage all over as Snus containing tobacco-derived nicotine (Natural) or as tobacco-free nicotine (Synthetic) as substitutes for other tobacco products that are limited in public places. ONPs have become popular tobacco products among adolescents/young adults and over 50% of young adult users of ONP use flavored ONP, such as menthol/mint, tobacco, dessert/candy, and fruity, being the most popular flavors. Various new ONP flavors are currently popular locally as well in the online market. Tobacco, Menthol, and fruit-flavored ONPs could motivate cigarette smokers to alteration to ONPs. Methods: We expanded our knowledge on natural/synthetic ONP flavors wheels to available data on ONPs, describing in detail their flavors and brands (US and Europe) in both natural/Synthetic ONP categories. We classified over 152 snus and 228 synthetic ONPs into the following flavor categories: “Tobacco”, “Menthol/Mint”, “Fruity”, “Candy/Deserts”, “Drink”, “Aroma”, “Spices” and “Mixed Flavors”. Results: Based on total numbers, wefound that the most popular ONPs flavor sold as tobacco and menthol to be among natural ONPs, among synthetic ONPs fruity and menthol as the most prominent flavors with varying concentrations of nicotine and other flavoring chemicals including coolant WS-23. We also illustrated possible molecular targets and toxicities due to exposure to ONPs activating several signaling cascades like AKT and NF-B might possibly lead to apoptosis and epithelial mesenchymal transition (EMT). Conclusions: Considering the marketing of ONP products with various flavor profiles and most of these products containing tobacco/menthol/fruit flavor, it is likely to have regulation and marketing disclaimer on some of these products. Further, it would be logical to determine how the market reacts in terms of compliance and non-compliance with flavor restrictions by the regulatory agencies.

Tellurium exhibits exceptional intrinsic electronic properties. However, investigations into the modulation of tellurium's electronic properties through physical modification are notably scarce. Here, we present a comprehensive study focused on the evolution of the electronic properties of tellurium crystal flakes under plasma irradiation treatment by employing conductive atomic force microscopy and Raman spectroscopy. The plasma-treated tellurium experienced a process of defect generation through lattice broken. Prior to the degradation of electronic transport performance due to plasma irradiation treatment, a remarkable observation emerged: in the low-energy region of hydrogen plasma-treated tellurium, a notable enhancement in conductivity was unexpectedly detected. The mechanism underlying this enhancement in electronic transport performance was thoroughly elucidated by comparing it with the electronic structure induced by argon plasma irradiation. This study not only fundamentally uncovers the effects of plasma irradiation on tellurium crystal flakes, but also unearths an unprecedented trend of enhanced electronic transport performance at low irradiation energies when utilizing hydrogen plasma. This abnormal trend bears significant implications for guiding the prospective application of tellurium-based 2D materials in the realm of electronic devices.

Mutations in the “guardian of the genome” TP53 predominate in solid tumors. In addition to loss of tumor suppressor activity, a specific subset of missense mutations confers additional oncogenic properties. These “gain-of-function” (GOF) mutations portend poor prognosis across cancer types regardless of treatment. Our objective in this study was to identify novel therapeutic opportunities to overcome the deleterious effects of GOF TP53 mutants. Using gynecologic cancer cell lines with known TP53 mutational status, we established that treatment with a proteasome inhibitor induced cell death in cells with two recurrent GOF TP53 mutations (R175H and R248Q), and addition of a histone deacetylase inhibitor (HDACi) enhanced this effect. By contrast, p53-null cancer cells were relatively resistant to the combination. Towards understanding the mechanism, we found that proteasome inhibition promotes apoptosis of cells with TP53 GOF mutations, potentially through induction of the unfolded protein response. In line with the reported hyperstabilization of GOF p53 protein, cells treated with HDACi exhibited reduced levels of p53 protein. Together, these data form the basis for future clinical studies examining therapeutic efficacy in a preselected patient population with GOF TP53 mutations.

Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by motor and non-motor impairment with various implications on patients’ quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HC) was utilized. The data were pre-processed to extract relevant time, frequency and energy-related features, and a Bidirectional Long Short-Term Memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using 5-fold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HC. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

Transition delaying is of great importance for the drag and heat flux reduction of hypersonic flight vehicles. The first mode within low frequency and the second mode within high frequency exist simultaneously during the transition of hypersonic boundary layer. This paper proposes a novel bi-frequency synthetic jet to suppress low- and high-frequency disturbances at the same time. Orthogonal table and variance analysis are used to compare the control effects of jet with different frequencies, amplitudes and positions. Linear stability analysis results show that, low frequency synthetic jet can suppress the first mode when it is arranged upstream of synchronization point, while the second mode control effect is relatively weak. The higher the high frequency is, the stronger the suppression effect is on the first mode. For the second mode, the suppression effect is only at f2=89.09kHz. The larger the amplitude, the weaker the promoting effect for the first mode and the second mode, and the more obvious the suppressing effect. For the cases with synthetic jet downstream of synchronization point, all levels of the three parameters promote the unstable mode. In terms of the growth rate with the spanwise wave number, the control effect of the same factor and level under different spanwise wave number is different. In order to obtain the optimal control effect on transition, the three factors and the arrangement position of the synthetic jet should be selected as follows: the position is arranged in the upstream, with f1 = 17.82kHz, f2 = 89.9kHz, a =0.007, so that the maximum growth rate of the first mode is reduced by 9.06% and that of the second mode is reduced by 1.28% compared with the uncontrolled state.

Controlling functional properties of matter and combine them for engineering a functional device is nowadays a common direction of scientific community. For instance, heterogeneous magnetic nanostructures can make use of different types of geometrical and compositional modulations to achieve the control of the magnetization reversal along with the nano-entities and thus enabling the fabrication of spintronic, magnetic data storage and sensing devices, among others. In this work, diameter modulated FeNi nanowires are fabricated paying special effort to obtain sharp transition regions between two segments of different diameters (from about 450 nm to 120 nm), enabling precise control over the magnetic behavior of the sample. Micromagnetic simulations performed on single bi-segmented nanowires predict a double step magnetization reversal where the wide segment magnetization switches near 200 Oe through a vortex domain wall, while at 500 Oe the magnetization of the narrow one is reversed through a corkscrew like mechanism. Finally, these results are confirmed with magneto-optic Kerr effect measurements at the transition of isolated bi-segmented nanowires. Furthermore, macroscopic vibrating sample magnetometry is used to demonstrate that the magnetic decoupling of nanowire segments is the main phenomenon occurring over the entire fabricated nanowires.

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information based change detection methods have been proposed during past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. While the whole bi-temporal images are scanned pixel-by-pixel, the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by two land cover change cases with Landsat bi-temporal remote sensing images. In comparison to several widely used change detection methods, the proposed approach can achieve a land cover change inventory map with a competitive accuracy.

Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the propagation patterns and trends of drought characteristics over a long-time scale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC database was employed for data correction and gap filling. Long-term drought evolution was assessed using both the SPI-12 and SPEI-12 indices to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK analysis method along with the Theil-Sen slope estimator were utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line. Indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and broadly distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country's area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events.

Directional solidification experiments were carried out to investigate the effect of micro-alloying element Sn and magnetic field on the solidification of Al-Bi immiscible alloys. Experimental results show that the size distribution of the dispersed particles in the low-speed solidified Al-3.4wt%Bi alloy presents two peaks, while it only shows one peak when solidified at a relatively high speed. The addition of Sn not only can enhance the nucleation rate and the number density of the Bi-rich droplets in the sample, but also decrease the Marangoni migration velocity and the axial resultant velocity of minority phase droplets in front of the solidification interface. Thereby it promotes the formation of Al-Bi alloys with a well-dispersed microstructure. A static magnetic with the strength of 0.2T increase the number density of the dispersed particles and decrease the average size and the size distribution width of the dispersed particles. Under the effect of Sn addition and static magnetic field, the average radius of the dispersed particles <R> and the solidification velocity V_0 satisfy <R>∝〖V_0〗^(-1/3) when the alloy was solidified at a relatively low velocity, <R> and V_0 satisfy <R>∝〖V_0〗^(-1/2) when the alloy is solidified at a high velocity.

Saccharomyces cerevisiae (S. cerevisiae) ethanol fermentation has left an indelible trace in human history. It is regarded as the original beverage synthesis, and has developed into an essential energy supply for mankind today. However, the history of ethanol synthesis using S. cerevisiae, including the mechanism analysis process, has not been carefully and succinctly reported. In this paper, we review the history of ethanol synthesis using S. cerevisiae. In addition, we also review the substrates that drive S. cerevisiae ethanol synthesis, including the utilization of glucose, sucrose, starch and molasses, in order to summarize the current development status and to contribute to the further improvement of this field.

Although the Slicing Method (SM) is effective for calculating the volume of point cloud objects (PCOs), it is restricted in terms of applicability and practicability because of a certain contingency and directional defects. The Co-Opposite-Direction Slicing Method (CODSM) proposed in this paper is an improved method for calculating PCO volume by increasing parallel (co-opposite-direction) observation and considering the two-way mean as the result. This method takes full advantage of the mutual offsetting of random errors and the compensation of systematic directional errors, which can effectively overcome (or mitigate) the effect of random errors and reduce the effect of systematic errors in SM. In this paper, two typical objects, a cone model and a stone lion base, are the examples for calculating PCO volume using CODSM. The results show that CODSM has all the inherent advantages of SM and effectively weakens the volatility of random errors and the directionality of systematic errors from SM. Therefore, CODSM is a robust configuration upgrade of SM.

Ischemia/reperfusion (I/R) injury results in cell death by inducing apoptosis. During I/R, early generation of mitochondrial reactive oxygen species (mtROS) can induce neighboring mitochondria to release additional ROS, a toxic cycle resulting in significant mitochondrial and cellular injury. Oxidative damage in the mitochondria contributes to various pathologies, including I/R injury. Accordingly, preventing mitochondrial oxidative damage should be therapeutically relevant for many disorders, including cardiovascular diseases. We recently discovered an Indole-Peptide-Tempo Conjugate (IPTC) that served as a novel bifunctional agent with both antioxidant and autophagy-modulating capacity. Here, we demonstrate that IPTC can protect H9C2 cardiomyocytes from hypoxia/reoxygenation (H/R) injury that results from mtROS overproduction due to impaired mitophagy and resultant mitochondrial dysfunction. We hypothesize that the mechanism of action of IPTC involves the capacity to decrease mtROS combined with induction of mitophagy.

Although Cable driven rehabilitation devices (CDRD) have advantages over traditional link-driven devices, including lightweight, ease of reconfiguration, remote actuation, etc, the majority of existing lower-limb CDRD is limited to rehabilitation in the sagittal plane. In this work, we extend our previous sagittal plane model (2DOF) to accommodate hip motion in the frontal plane (abduction/adduction) toward studying the feasibility of tracking bi-planar motion (combined frontal and sagittal plane motion) via intra-planar cable routing. Two optimization problems have been formulated to estimate an optimal location of the hip cuffs, first to estimate the optimal cuff location at each time step to identify a single ‘averaged’ optimal cuff location and second to calculate a single optimal cuff location for the entire gait cycle. The optimization solutions identified for the 3DOF model revealed that optimization of the location of cuffs on the anterior and posterior side of the hip joint for 4 cable configuration is not sufficient to generate the desired bi-planar motion. For simultaneous tracking of the bi-planar motion, 2 additional cables have been added at hip joints and are routed in an intra-planar manner. The simulation result with the 3DOF model confirmed successful bi-planar trajectory tracking. The various number of cables and cable routings for tracking bi-planar motion will be studied in future work.

Since 2007, at the Electron Science Research Institute (ESRI) nano-fabrication laboratories, Edith Cowan University, Australia, we have devoted research efforts to the synthesis and characterization of bismuth-containing ferrite-garnet-type thin-film magneto-optic (MO) materials of different compositions. We report on the development and properties of highly bismuth-substituted iron-garnet thin films prepared by using radio frequency (RF) magnetron sputtering. We study the process parameters associated with the RF magnetron sputter deposition technique and investigate the results of optimizing process parameters and implementing several special techniques including the fabrication of co-sputtered nanocomposite films, all-garnet multilayer structures, applying oxygen plasma treatment on amorphous garnet layers just after the deposition process, and designing modifications of the annealing crystallization process and regimes for achieving the best MO properties. We demonstrated significant improvement in MO properties of Bi-containing ferrite-type garnet thin-film materials, including record-high MO figures of merit and improved conventional and un-conventional hysteresis loops of Faraday rotation. The attractive optical, magnetic, and magneto-optic properties obtained in highly bismuth-substituted iron garnet thin-film materials of multiple composition types are relevant in the context of manufacturing next-generation ultra-fast optoelectronic devices, such as light intensity switches and modulators, high-speed flat panel displays, and high-sensitivity sensors.

The cycling of soil phosphorus (P) is inherently linked with soil organic carbon-iron (C-Fe) cycling, yet empirical integration of these processes within paddy soils remains scarce. In this study, we conducted a microcosm experiment using paddy soils subjected to six distinct fertilization regimes involving varying P inputs for five years. In addition to evaluating P activation under reflooding conditions, we assessed the Fe reduction process and characterized the properties of dissolved organic matter (DOM) at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), alongside profiling the composition of soil microbial communities with high-throughput sequencing. Our findings revealed that after 25 days of reflooding, soil Olsen-P content increased by an average of 73% compared to its initial state, showing a strong correlation with the Fe reduction process. Specifically, treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation, highlighting the role of organic matter in facilitating Fe reduction. Investigations on the relative abundance of typical iron-reducing microbes further supported their importance in P activation, but the rate of iron reduction is limited by soil organic matter content. Delving deeper into DOM properties, soil DOM composition profiling and network analysis suggested that high-molecular-weight DOM, particularly lignins, served as the primary resources driving Fe reduction by iron-reducing microbes, consequently promoting Fe reduction and P release. Taken together, our study assembled the C-Fe-P cycling dynamics in paddy soils, emphasizing the pivotal role of microbial-driven Fe reduction facilitated by soil DOM in P availability and subsequently sustainable agricultural practices.

Breast cancer (BC) affects millions of women; it is the leading cause of death by neoplasia in wom-en worldwide, with at least 500 thousand deaths each year, 70% of which occur in developing countries. Elastography evaluates tissue stiffness, making this a promising non-invasive real-time technique. BI-RADS is recognized as a standard and helpful technique for establishing BC risk. This study aimed to determine, by strain elastography (SE), the fat-to-lesion index (F/L index), sen-sitivity, specificity, and optimal cut-off to diagnose BC. This prospective study included 216 wom-en who underwent SE, ultrasound, mammography, and breast biopsy; according to mammogra-phy, we recruited BI-RADS category 2 (n= 5), category 3 (n=29), category 4 (n= 94), category 5 (n= 71) and category 6 (n=17); after biopsy, 108 women were classified as negative, and 108 were pos-itive for BC. Three expert BC radiologists performed all imaging interpretations and biopsies. Our results show mean values of the F/L index for negative biopsies of 3.70±2.57 and positive biopsies of 18.10±17.01; we developed two models for the prediction of BC, a logistic regression model with an AUC of 0.893 and a neural network with an AUC of 0.902. The Youden index yielded a F/L index cut-off >5.76, with a sensitivity and specificity of 84.26%. Positive Spearman's Rho cor-relation coefficient was observed between the F/L index and BI-RADS, r=.073 (p< 0.001). ANOVA test showed a difference between F/L index vs. molecular subtypes (p=0.002). SE is helpful as a di-agnostic complement to mammography, or in cases where the latter is contraindicated, elas-tography has an adequate predictive capacity and is a fast and minimally invasive procedure.

This paper presents a proposal for a non-isolated bidirectional converter (NIBC) controlled by a deep neural network (DNN) to enable vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) charging, which contributes to the development of more efficient and sustainable transportation and energy systems. The DNN controller manages power flow in both directions, making it possible to charge electric vehicles (EVs) and discharge power from EVs to the grid with improved efficiency and performance compared to traditional control methods. The non-isolated topology used in this proposal offers several benefits, including reduced cost, smaller size, and higher efficiency. To train the DNN controller, a large dataset of simulations was used, and the results were validated with a hardware setup. The real-time performance of the DNN controller was compared to a proportional-integral (PI) based controller through simulated results. The findings of the study show that the DNN controller outperforms traditional PI controllers.

Membrane gas absorption technology has been considered as a promising approach to mitigate CO2 emissions from power plants. The aim of this study is to evaluate the environmental impacts of CO2 absorption and desorption processes by hollow fiber membrane contactors using life cycle assessment methodology. On the basis of ReCipe 2016 Midpoint and ReCipe 2016 Endpoint methods, the research results show that, membrane gas absorption system exhibits the lowest environmental impacts across the majority assessed categories in comparison with chemical absorption and membrane gas separation systems. The CO2 capture process via membrane gas absorption has the most significant impact on METP category, with heat consumption as the primary contributing factor accounting for 55%, followed by electricity consumption accounting for 43.1%. According to the sensitivity analysis, heating by natural gas shows a better performance than other heat supply sources in improving overall environmental impacts. In addition, the increasing utilization of renewable energy in electricity supply reduces the global warming potential, fossil resource consumption and ozone formation.

The papaya mealybug Paracoccus marginatus is an invasive, polyphagous pest and poses a quarantine threat to tropical and subtropical countries. Infested commodity internationally should be undergoing phytosanitary treatment, while irradiation is recommended to replace methyl bromide fumigation. Dose-response tests were conducted on the 2-, 4-, and 6-d-old eggs and gravid females of P. marginatus using the X-ray radiation doses of 15–105 Gy with an interval of 15 Gy, respectively. Radiotolerance was compared using fiducial overlapping and lethal dose ratio (LDR) test, resulting in insignificant difference except for LDR test at LD90 (leading 90% mortality at 95% confidence level (CL)); the estimated dose for 99.9968% mortality at 95% CL was 176.5–185.2 Gy, which was validated in the confirmatory tests using gamma irradiation because gammy and X-rays have equal biological effect on pests. Therefore, a total of estimating 60,386 gravid females of papaya mealybug irradiated with 146.8–185.0 Gy resulted in no F1 nymphs developing. Eggs can therefore be used as an alternative to gravid females for dose-response tests; the recommended minimum dose for phytosanitary treatment of P. marginatus on infested commodity is 185 Gy, with treatment efficacy ≥ 99.9950% at 95% CL.

Relapsed leukemia following initial therapeutic response and remission is difficult to treat and causes high patient mortality. Leukemia relapse is due to residual quiescent leukemia cells that escape conventional therapies and later reemerge. Eliminating not only growing but quiescent leukemia cells is critical to effectively treating leukemia and preventing its recurrence. Such therapeutic agents, however, are lacking in the clinic. Here we report that a 4-arylmethyl derivative of the natural anticancer compound curcumin demonstrates a dual effect in eliminating both growing and quiescent leukemia cells. This curcumin derivative, C212, on the one hand, inhibits growing leukemia cells at a higher efficacy than curcumin by inducing apoptosis and cell cycle arrest; it, on the other hand, kills quiescent leukemia cells that are resistant to conventional chemotherapy drugs. Furthermore, C212 drives leukemia cells into and kills them at deep quiescence, avoiding the potential risk associated with awaking therapy-resistant subpopulation of quiescent leukemia cells. Lastly, we show that C212 induces apoptosis and drives cells into deep dormancy at least partially by binding to and inhibiting Hsp90, leading to client protein degradation and protein aggregation. Further elucidating the molecular mechanisms underlying the dual function of C212 in eliminating both growing and quiescent leukemia cells will aid the development of novel therapies against leukemia relapse.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

A new di-mannitol adipate ester-based zinc metal alkoxide (DMAE-Zn) was synthesized as a bi-functional PVC thermal stabilizer for the first time. The materials were characterized with Fourier-transform Infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA). Characterization results confirmed the formation of Zn-O bonds in DMAE-Zn; and that DMAE-Zn had a high decomposition temperature and a low melting point. The thermal stability of DMAE-Zn on PVC also was tested by conductivity test, thermal aging test, and UV-visible spectroscopy (UV-VIS) test. PVC stabilized by DMAE-Zn had a good initial color and excellent long-term stability. UV-VIS also showed that the conjugated structure in PVC stabilized by DMAE-Zn was almost all of the triene, suggesting that the addition of DMAE-Zn would suppress the formation of conjugated structures above tetraene. Dynamic processing performance of PVC samples tested by torque rheometer indicated that, having a good compatibility with PVC chains in the amorphous regions, DMAE-Zn contributed good plasticizing effect to PVC. DMAE-Zn thus effectively demonstrates bi-functional roles, e.g., thermal stabilizers and plasticizers to PVC. Furthermore, FT-IR, HCl absorption capacity test, and complex ZnCl₂ test were also used to verify the thermal stability mechanism of DMAE-Zn for PVC.

This paper models the factors explaining households members economic well-being in Burkina Faso, with a focus on the relative influence of gender inequality in literacy status. It does so, using data from the 2014 survey on household living conditions and a semi-parametric bi-variate sample selection modeling approach. This approach compared to the classic Heckman two-step estimator is methodologically innovative because it deals simultaneously with non-random sample selection using conventional systems of two equations, non-linear covariates' effects using spline approach, and the non-normal bivariate distribution using copula functions. The graphical results from the Lorenz curves combined with the numerical Atkinson and Gini coefficients suggest that inequality in overall per-capita consumption spending among households headed by literates is higher than that of their illiterate counterparts in 2009 and 2014. However, independently of the head of household’s literacy status, the level of inequality in total economic well-being decreased from 2009 to 2014. Using the poverty indices of Watts, Sen, Foster ( alpha= 1)) we found that poverty among households headed by literates is lower than that of their illiterates counterparts for both years, although overall poverty decreased nationally between 2009 and 2014. The results also show that although the gender inequality in literacy status does not translate into inequalities in non-food wellness, it does however for food-wellness as female headed households have 38.9% less per-capita food consumption spending than their men counterparts. Combining both food and non-food consumption spending, total economic well-being also seems to exhibit gender inequality as female headed households now have relatively 26.7% less combined per-capita consumption spending.

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of MRI sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material, and prolongs scanning time. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasively management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx+SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade >=2 (<2) was judged as CsPCa (CiPCa). Linear and Logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R~0.17). Patients assessed with combined MRBx+SysBx and from individual MRI scanners achieved higher R (R=0.207+/-0.118) than using all patients in the fits. Conclusions: In the first study to date to apply and test hyperspectral approaches for assessing tumor aggressiveness on SRBP-MRI, high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA were comparable or better than those from artificial intelligence in other studies.

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

Small molecule kinase inhibitors (SMKIs) are one of the heightened concerns in the field of drug research and development. There are seventy-nine (up to July 2023) small molecule kinase inhibitors have been approved by FDA and hundreds kinase inhibitors candidates in clinical trials, which shed light on the treatment of some major diseases. As an important strategy of drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods, such as docking, molecular dynamic, pharmacophore have been applied to the design and optimization of small molecule kinase inhibitors. In the review, we provide an overview of recent advances in CADD and SMKIs; and the application of CADD for discovery of SMKIs.

Traditional camera sensors rely on human eyes for observation. However, the human eye 1 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3 Target recognition technology is an important technology to judge the target category in camera 4 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8 preserve the context feature information. The feature fusion network was improved to effectively 9 combine shallow information and deep information. A new network structure was proposed to 10 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15 more accurate than YOLOv8.

In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.

Visible light communication (VLC) can offer the advantages of license and electromagnetic interference (EMI) free wireless transmission. As optical signal does not interference with the radio-frequency (RF) signal, VLC can be used to augment RF wireless communication to provide extra communication capacity while without degrading the performance of both signals. In order to achieve high performance VLC transmission, optical alignment between the optical transmit (Tx) and receiver (Rx) is very critical to enhance the received signal-to-noise ratio (SNR). Optical beam-steering at the Tx can be utilized to ensure narrow optical beam can reach the Rx; however, complicated and active tracking are required. Lenses or compound parabolic concentrators (CPCs) can be install in front of the Rx for focusing to enhance the SNR. However, these will limit the Rx field-of-view (FOV) and making the VLC transmission more subjected to misalignment issue. Hence, many creative optical antennas have been proposed and demonstrated using special optical materials as well as special Rx to enhance the FOV of VLC systems. However, they have their limitations, such as data rates and FOVs. In this work, we put forward and demonstrate a bi-direction free-space VLC system supporting multiple moveable Rxs using a light-diffusing optical fiber (LDOF). The downlink (DL) signal is launched from an head-end or central office (CO) far away to the LDOF at the client side via a free-space transmission. When the DL signal is launched to the LDOF, which acts as an optical antenna to re-transmit the DL signal to different moveable Rxs. The uplink (UL) signal is sent via the LDOF towards the CO. In a proof-of-concept demonstration, the LDOF is 100 cm long and the free space VLC transmission between the CO and the LDOF is 100 cm. 210 Mbit/s DL and 850 Mbit/s UL transmissions, meeting the pre-forward-error-correction bit error rate (pre-FEC BER = 3.8 × 10−3) threshold are achieved.

Public transport systems are considered to be a crucial aspect of a sustainable urban development, as they allow more passengers to efficiently travel across an urban area at low environmental and economic costs. Multiple factors can influence the public transport level of service. All take roots in the network structure and the operating regime, i.e. how bus lines are arranged atop the street network and how the service frequency is adjusted to meet urban mobility patterns. This is known as the bus network design problem and has been the subject of several studies. The problem is so challenging that most studies until now resort to strong assumptions such as a static description of the peak hour demand, homogeneous user behavior, and equal trip lengths. Potential effects of different types of user behavior and trip lengths patterns on the user and/or operator cost function have not been investigated whatsoever. Moreover, the existing studies have not considered the effects of the bus network structure on private car users, the level of interactions between the two modes, and the passenger mode choice that depends on the traffic conditions. This paper aims to close this gap and provide a general framework considering multiple trip length patterns, two types of user behavior, and the effects that the bus network structure might have on the traffic performance and passenger mode choice. For modeling different trip length patterns, the proposed approach combines all origin-destination pairs with the same trip length and uses the trip length distribution as an intermediate level of abstraction. As such, it allows to solve the optimal bus network design problem in an analytical way, while considering a more realistic setting including network congestion, mixed traffic, and different mode choice decisions depending on trip lengths and walking preferences. Numerical analysis reveals that both, the user behavior and the trip length patterns, have significant effects on the operator and user cost function. Results show that the probability of choosing any given mode is not constant across the user trip lengths, but follows certain distribution. This distribution is not unique, but varies across the trip length patterns, indicating the importance of modeling the mode choice at the trip length level. Finally, the analysis demonstrates the significance of addressing simplifications made in previous studies.

This work is devoted to physical vapor deposition synthesis, and characterisation of bismuth and lutetium-substituted ferrite-garnet thin-film materials for magneto-optic (MO) applications. The properties of garnet thin films sputtered using a target of nominal composition type Bi_0.9Lu_1.85Y_0.25Fe_4.0Ga₁O₁₂ are studied. By measuring the optical transmission spectra at room temperature, the optical constants and the accurate film thicknesses can be evaluated using Swanepoel’s envelope method. The refractive index data are found to be matching very closely to these derived from Cauchy’s dispersion formula for the entire spectral range between 300-2500 nm. The optical absorption coefficient and the extinction coefficient data are studied for both the as-deposited and annealed garnet thin-film samples. A new approach is applied for accurately deriving the optical constants data simultaneously with the physical layer thickness, using a combination approach employing custom-built spectrum-fitting software in conjunction with Swanepoel’s envelope method. MO properties, such as specific Faraday rotation, MO figure of merit and MO swing factor are also investigated for several annealed garnet-phase films.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.