In this paper, we propose an unsupervised blind restoration model for images in hybrid degradation scenes. The proposed model encodes the content information and degradation information of images and then uses the attention module to disentangle the two kinds of information. It can improve the ability of disentangled presentation learning for a generative adversarial network (GAN) to restore the images in hybrid degradation scenes, enhance the detailed features of restored image and remove the artifact combining the adversarial loss, cycle-consistency loss, and perception loss. The experimental results on the DIV2K dataset and medical images show that the proposed method outperforms existing unsupervised image restoration algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual evaluation.

Phenotypic plasticity in response to adverse conditions determines plant productivity and survival. The aim of this study was to test if two highly productive Populus genotypes, characterized by different in vitro etiolation patterns, differ also in their responses to hormones gibberellin (GA) and abscisic acid (ABA), and to a GA biosynthesis inhibitor paclobutrazol (PBZ). The experiments on shoot cultures of ‘Hybrida 275’ (abbr. H275; Populus maximowiczii × P. trichocarpa) and IBL 91/78 (Populus tremula × P. alba) were conducted either by modulating the physical in vitro environment or by adding specific chemicals to the nutrient medium. Our results show that there are significant differences between the studied genotypes in environmental and hormonal regulation of growth responses. The genotype H275, which responded to darkness with PBZ-inhibitable shoot elongation, was unable to recover its growth after treatment with ABA. In contrast, the genotype IBL 91/78, whose shoot elongation was not affected either by darkness or PBZ treatment, recovered so well after the ABA treatment that, when rooted subsequently, it developed longer shoots and roots than without ABA treatment. Our results indicate that GA catabolism and repressive signaling provide an important pathway to control growth and physiological adaptation in response to immediate or impending adverse conditions. These observations can help breeders define robust criteria for identifying genotypes with high resistance and productivity and highlight where genotypes exhibit susceptibility to stress.

(1) Background: Although resistance to pathogens and pests has been researched in many potato cultivars and breeding lines with DNA markers, there is scarce evidence as to the efficiency of the marker-assisted selection (MAS) for these traits when applied at the early stages of breeding. A goal of this study was to estimate the potential of affordable DNA markers to track Rpi disease resistance genes, that are effective against the pathogen Phytophthora infestans, as a practical breeding tool on a progeny of 68 clones derived from a cross between the cultivar Sudarynya and 13/11-09. (2) Methods: this population was studied for four years to elucidate the distribution of LB resistance and other agronomical desirable or simple to phenotype traits such as tuber and flower pigmentation, capacity and structure of yield. LB resistance was phenotypically determined through natural and artificial infection and the presence/absence of nine Rpi genes was assessed via 11 sequence-characterized amplified region (SCAR) markers. To aid this analysis, the profile of Rpi genes in the 13/11-09 parent was established using diagnostic resistance gene enrichment sequencing (dRenSeq) as a gold standard. (3) Results: at the early stages of a breeding program, MAS can halve the workload when screening the segregation of F1 offspring and selected SCAR markers for Rpi-genes provide useful tools.

Molecularly imprinted polymers (MIPs) have been widely used in nanomedicine during the last few years. However, their potential is limited by their intrinsic properties resulting, for instance, in lack of control in drug release processes or complex detection for in vivo imaging. Recent attempts in creating hybrid nanomaterials combining MIPs with inorganic nanomaterials succeeded in providing a wide range of new interesting properties suitable for nanomedicine. Through this review, we aim to illustrate how hybrids molecularly imprinted polymers may improve patient care with enhanced imaging, treatments and combination of both.

This research project focuses on the optimization of the hybrid energy system together with the assistance of thin-film coatings aiming to achieve self-sustainable food and crop storage facilities which will run effectively with its own generated energy. An infrastructure will be designed and constructed that will comprise a hybrid power generation system accompanied by thin-film coated semitransparent and non-transparent construction materials for energy saving. Thin-film low emissivity (Low-E) type coatings will assist the transparent or semitransparent construction materials to reflect most of the infrared (IR-mostly heat) and UV spectra of sunlight without interrupting the visible spectrum and will lead to saving energy consumption by reducing the heat and lighting during day time

Alfalfa (Medicago sativa L.) is a perennial, outcrossing legume crop predominantly grown for hay, silage, or pasture. Intensive selection has resulted in dramatic improvement in fitness traits, including winter survival and disease resistance. However, there has been minimal improvement in other economically important traits, such as hay yield, which is still comparable to 30 years ago. Intensive phenotyping costs on this type of trait hinder high selection pressure to identify superior outcross individuals. Severe inbreeding depression inhibits the development of inbred lines with accumulated favorable alleles that exhibit heterosis. This review highlights the outcomes of inbreeding depression as well as the causes, including unmasking deleterious alleles and triggering self-incompatibility. We tracked the research efforts that unveil the genetic bases underlying deleterious alleles and self-incompatibility. The magnitudes of inbreeding depression were compared with the rate of heterozygous halved time in diploid and tetraploid organisms. To fill in the gaps between the controversy and existing hypotheses, we theorized a dosage dominant model of inheritance. The dosage dominant model is similar to the Mendelian dominance model, in which a genotype exhibits a dominant phenotype if there is a dominant allele (alphabet dominant). The difference is that in the dosage dominant model, a genotype will result in a dominant phenotype if the number of dominant alleles is equal to or greater than the number of recessive alleles. This review also includes a discussion on the development of pseudo inbreds and a hypothesis to identify deleterious alleles using bulked segregant analysis and consequently to purge deleterious alleles using marker-assisted selection, to progress toward the successful development of pure inbred lines in alfalfa.

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.

Maize (Zea mays L.) production in West and Central Africa is constrained by drought, low soil-N and Striga infestation. Breeders in the region have developed and commercialized extra-early and early-maturing hybrids (E-EH and EH), which combine high yield potentials with tolerance/resistance to the three stresses. Hybrids of both maturity groups are new to the farmers; thus, the urgent need to recommend appropriate agronomic practices for these hybrids. We investigated the responses of four hybrids belonging to extra-early and early-maturity groups to plant density (PD) and nitrogen (N) application in five agroecologies. The EHs consistently out-yielded the E-EHs in all the five agroecologies. The hybrids showed no response to N-fertilizer application above 90 kg ha-1. All interactions involving N had no significant effect on all traits except in few cases. The E-EHs and EHs had similar response to PD; their grain yield decreased as PD increased. Contrarily, flowering was delayed and expression of some other agronomic traits such as plant and ear aspects became poorer with increased PD. Optimal yield was obtained at approximately 90 kg N ha-1 and 66,666 plants ha-1. Most of the measured traits indicated high repeatability estimates (i.e. ≥ 60) across the N levels, PDs and environments. Evidently, the hybrids were intolerant of high PD.

Hybrid materials of conjugated polymer and titanium(IV) oxide have attracted considerable attention concerning potential benefits, including (i) efficient exploitation of visible light, (ii) high adsorption capacity for organic contaminants, (iii) effective charge carriers separation. The new class of the photocatalysts is promising for the removal of environmental pollutants in both aqueous and gaseous phases. For the first time, in this study, the PANI/TiO₂ hybrid composite was used for the degradation of phenol in water and toluene in the gas phase. Polyaniline-TiO₂ was prepared by in-situ polymerization of aniline on the TiO₂ surface. The obtained hybrid material was characterized by diffuse reflectance spectroscopy (DR/UV-Vis), X-ray diffraction (XRD), fast-Fourier transformation spectroscopy (FTIR), photoluminescence (PL) spectroscopy, microscopy analysis (SEM/TEM) and thermogravimetric analysis (TGA). An insight into the mechanism was shown based on the photodegradation analysis of charge carriers scavengers. Polyaniline is an efficient TiO2 photosensitizer for photodegradation in visible light (λ> 420 nm). The trapping experiments revealed that mainly h+ and ˙OH were reactive oxygen species responsible for phenol degradation. Furthermore, the PANI-TiO₂ hybrid nanocomposite was used in gypsum plaster to study the self-cleaning properties of the obtained building material. The effect of PANI-TiO₂ content on hydrophilic/hydrophobic properties and crystallographic structure of gypsum was studied. The obtained PANI-TiO₂ modified gypsum plaster had improved photocatalytic activity in the reaction of toluene degradation under Vis light.

Overcoming terrain obstacles presents a major problem for people with disabilities or with limited mobility who are dependent on wheelchairs. An engineering solution designed to facilitate the use of wheelchairs are assisted propulsion systems. The objective of the research described in this article is to analyse the impact of the hybrid manual-electric wheelchair propulsion system on the kinematics of the anthropotechnical system when climbing hills. The tests were carried out on a wheelchair ramp with an incline degree of 4°, using a prototype wheelchair with a hybrid manual-electric propulsion system in accordance with the patent application P.427855. The test subjects were three people whose task was to propel the wheelchair in two assistance modes supporting manual propulsion. The first mode is hill climbing assistance, while the second one is assistance with propulsion torque in the propulsive phase. During the tests, a number of kinematic parameters of the wheelchair were monitored. An in-depth analysis was performed for the amplitude of speed during a hill climb and the number of propulsive cycles performed on a hill. The tests performed showed that when propelling the wheelchair only using the hand rims, the subject needed an average of 13 pushes on the uphill slope, and their speed amplitude was 1.8 km/h with an average speed of 1.73 km/h. The climbing assistance mode reduced the speed amplitude to 0.76 km/h, while the torque assisted mode in the propulsive phase reduced the number of cycles required to climb the hill from 13 to 6. The tests were carried out at various values of assistance and assistance amplification coefficient, and the most optimally selected parameters of this coefficient were presented in the results. The tests proved that electric propulsion assistance has a beneficial and significant impact on the kinematics of manual wheelchair propulsion when compared to a classic manual propulsion system when overcoming hills. In addition, assistance and assistance amplification coefficient were proved to be correlated to operating conditions and the user's individual characteristics.

This paper proposes a hybrid medium access control (MAC) protocol for wireless sensor network (WSN) data gathering, employing unmanned aerial vehicles (UAV). The UAV sends a beacon frame periodically to inform sensor nodes regarding its presence. Afterward, each sensor node which receives beacon frame contends to send registration frame to the UAV. The UAV will transmit the second beacon frame to the registered nodes to notify their transmission schedule. The time-slot scheme is used for the transmission schedule. The transmission schedule of each sensor is determined based on their priority. Specifically, the priority of each sensor is determined during the registration process. Furthermore, the architecture of UAV-WSN data gathering system is introduced in this paper. Simulations are performed, showing that the proposed MAC protocol achieves fairness while enhancing network performance.

Centralized/Cloud Radio Access Networks (C-RAN) are deployed in converged fixed-mobile networks to exploit the flexibility coming from joint application of Network Function Virtualization (NFV) and Software Defined Networking (SDN). In this context, optical links connecting C-RAN nodes, possibly based on the Ethernet standards, may carry traffic with different requirements in terms of latency and throughput. This paper considers the problem of traffic aggregation on C-RAN optical Ethernet links with latency control for fronthaul traffic and throughput capability for backhaul traffic. Integrated hybrid nework technique is applied to show how time transparency can be enforced for Ethernet encapsulated Common Public Radio Interface (CPRI) traffic while allowing statistical multiplexing of backhaul traffic. Simulation results show the effectiveness of segmentation of backhaul traffic to allow exploitation of the available bandwidth even with high capacity CPRI options.

Towards the investigation and further understanding of the development and propagation of Medicanes, this study explores the forecasting capability of WRF model in case of cyclone “Cleopatra” which affected with extreme rainfall and strong winds Sardinia and Calabria, Italy, in November 2013. This cyclone was unusual in that it developed a warm core but did not fulfill its transformation into a tropical-like cyclone because its core did not expand high enough in the tropospnere. The ERA5 reanalysis dataset was dynamically downscaled from 31 km spatial horizontal resolution to 9 km using WRF model. The methodology consists of; firstly, an extensive physical parameterization schemes sensitivity test and consequently, a short-range ensemble forecasting implementation based on the highest statistical scored physics configuration. All simulation results were validated against surface observations and remote sensing products. Subsequently, the modeled cyclone trajectories are compared to satellite imagery derived from EUMETSAT-SEVIRI gridded data. The findings of the conducted analysis illustrate that ensemble average displays significant difference in performance compared to any of the deterministic runs individually, suggesting that ensemble forecasts will be beneficial in studies assessing cyclonic events in the Mediterranean region.

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

Hybrid Power Switches (HPS) combine the advantages of SiC unipolar and Si bipolar devices and therefore can bridge the gap between these technologies. In this paper, the performance of a hybrid power switch configuration based on the latest SiC MOSFET and Si IGBT technologies is presented. The device is evaluated through experimental measurements of its characteristics under various conditions. The results show the HPS can achieve switching losses as low as a SiC MOSFET while offering the high current capability of the IGBT without significant increase in costs.

The existing PDC(Polycrystalline Diamond Compacts) -cone hybrid bit bearing adopts a unilateral support structure, which is prone to stress concentration in the journal area, resulting in fracture and wear failure of the bearing, thus reducing the service life of the hybrid bit. In this paper, a new type of double supported bearing hybrid bit is proposed. The static strength analysis of unilateral and bilateral support bearing structures is carried out by finite element simulation, and the stress and strain distribution of the two structures under the load of 20KN-100KN is obtained. Experimental devices for unilateral and bilateral support bearing structures are designed and manufactured to complete 50KN-100KN static pressure loading experiments. The results showed that the stress and strain of unilateral and bilateral support bearing increased linearly with the increase of load. Compared with unilateral bearing, when the load was 100KN, the maximum Mises stress of bilateral bearing decreased from 358.80MPa to 211.10MPa, with a decrease of 41.16%. The maximum contact stress decreased from 415.20MPa to 378.10MPa, decreased by 8.94%, and the maximum principal strain decreased from 11.01e-4 to 9.71e-4, decreased by 11.81%. The axial strain in the danger zone was reduced by 14.68% and 17.35%, respectively. It is found that the contact stress of the simulation data is highly correlated with the bearing life, and the service life of the bilateral bearing bit is increased by 8.94%. The simulation data and experimental results provide data support for the production of hybrid bits with bilateral bearing support.

The combination of different wood modification technologies to obtain improved performance is increasingly receiving attention in research. In this study, Scots Pine (Pinus sylvestris) sapwood was impregnated with furfuryl alcohol (FFA) in pure aqueous 20, 40 and 60% solution strength without adding any catalyst. In a second step, the FFA was polymerized while simultaneously performing thermal modification in a closed system at 130, 150 or 180 °C. After leaching and ageing tests, the 9 different combinations were tested for use class 4 applications according to CEN/TS 15083-2 (2005) decay lab test. It was noted that even the minimum intensity combination of 20% FFA at 130 °C resulted in maximum durability class (DC) 1 performance. On the contrary, DC 4 was assigned to thermally modified control samples, even at the maximum intensity of thermal modification. Similarly, for FFA modifications, previous research has suggested that an uptake of 35% solution strength is required to obtain an adequate durability performance for use class 4 applications. High levels of resistance against termites were also noted by corresponding termite lab tests. Moisture studies showed the combined treatments resulted in improved stability and reduced moisture uptakes. Thus, the results obtained by this study revealed synergistic performance effects which originate from the combined thermo-chemical modification approach and which were higher than simple accumulation of the individual performance of purely thermally or chemically modified wood. Thus, the presented findings have provided positive implications for industrial applications of thermo-chemical modification techniques and offers an array of new research opportunities.

On return to campus after the pandemic, students were subjected to receiving lectures online, in class, or by the emerging hybrid approach. This study reports students' preference over the three approaches based on the experience gained during the 2022/23 academic session. One hundred and twenty-seven (127) on-campus Education students in one public university in Malaysia participated in the study. Data was collected through an online forum and interviews with the participants. The data collected was analyzed using content and thematic analysis. Preference for the hybrid approach emerged as a finding with three themes describing what makes the wholly online class unpopular (themes one and two); and, the popularity of the hybrid approach (theme three): 1. On-campus learner-environment challenges; 2. Perceived lack of Faculty engagement and support in the online learning environment; and, 3. Student engagement through faculty presence and technology innovation. What the students valued most in the hybrid approach is the face-to-face technology-enhanced classroom interaction that is retained, believing that learning is more effective and interesting in class than online. They considered the additional online learning activities as complementing the classroom learning. The approach provides an opportunity for fundamental learning in class and supplementary learning online. These findings imply that institutions should prioritize technology integration into face-to-face instruction, leveraging innovative approaches to enhance student engagement and learning outcomes. Lecturers should consider adopting hybrid teaching models that combine the benefits of in-person interaction with the flexibility of online learning, catering to diverse student preferences and optimizing the educational experience.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, with a highly variable expression of phenotypes (restricted interest or activity and repetitive behavior in communication and social interactions), genes (mutation), markers (alteration of transcription) and pathways. Loss of function of the CC2D1A gene appears to primarily affect the brain, leading to a range of behavioral problems in humans. In our study published in 2020; we found that the expressions of miR-19a-3p, miR-361-5p, miR-150-5p, miR-3613-3p, miR-126-3p and miR-499a-5p were downregulated in the serum samples of autistic patients and their families. Here, acquired non-Mendelian hereditary character in a genetically defined mouse model of autism (Cc2d1a +/-) correlates with the transcriptional alteration of five miRNAs. We seek to test the hypothesis that miRNA levels are varying by changes in RNA/DNA structure during development, thereby creating transcription alteration. Behavioral tests were conducted on the offspring of Cc2d1a +/- mutant and control mice, such as novel object, social interaction, marble burying and tail suspension behavior. Two RNA fractions were isolated from mouse hippocampal tissues and sperm cells by standard TRIzol extraction: total RNA and fraction of RNA bound to DNA in the form of a DNA/RNA hybrid (R-loop). The expression levels of miR-19a-3p, miR-361-5p, miR-150-5p, miR-126-3p and miR-499a-5p were investigated by quantitative real-time RT-PCR. We report differences in the distribution of five miRNAs in the hippocampus between male and female mice in RNA fractions, particularly in colonies of Cc2d1a +/- mice. Furthermore, the number of miRNAs engaged in the DNA/RNA hybrid fraction is generally higher in the mutant pedigree than in the control group. On the other hand, in sperm both fractions are at lower levels than in controls. R-loops contribute to the physiology and pathology of organisms including human disease. Here we report a variation in five miRNA levels between gender and tissue. Our results suggest that the transcription levels of these five miRNAs are directly regulated by their RNA.

Biosensors are smart devices that convert biochemical responses to electrical signals. Designing biosensor devices with high sensitivity and selectivity is of great interest because of their wide range of functional operations. However, the major obstacles in the practical application of biosensors are their binding affinity toward biomolecules and the conversion and amplification of the interaction to various signals such as electrical, optical, gravimetric, and electrochemical signals. Additionally, enhancement of sensitivity, limit of detection, time of response, reproducibility, and stability are considerable challenges when designing an efficient biosensor. In this regard, hybrid composites have high sensitivity, selectivity, thermal stability, and tunable electrical conductivities. The integration of phthalocyanines (Pcs) with conductive materials such as carbon nanomaterials or metal nanoparticles (MNPs) improves the electrochemical response, signal amplification, and stability of biosensors. This review explores recent advancements in hybrid Pcs for biomolecule detection. Herein, we discuss the synthetic strategies, material properties, working mechanisms, and integration methods for designing electrochemical biosensors. Finally, the challenges and future directions of hybrid Pc composites for biosensor applications are discussed.

Integrating nanomaterials like carbon nanotubes (CNTs) and iron oxide (Fe₃O₄) into epoxy composites has significant interest due to their potential to enhance mechanical properties. This study evaluates the impact of dispersion quality on the mechanical performance of CNT/Fe₃O₄/Epoxy composites, comparing stirring and sonication methods at three different loadings: 0.1, 0.3, and 0.5 wt.%. Tensile testing revealed that sonicated composites consistently outperformed stirred composites, with a significant increase in elastic modulus and ultimate tensile strength (UTS). However, fracture strain decreased in both composite types compared to pure epoxy, with sonicated composites experiencing a more significant reduction than stirred composites. These results underscore the importance of high-quality dispersion for optimizing mechanical properties.

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal-oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing QCA irreversible, reversible, and partially reversible gates to establish an optimal balance between power consumption, delay time, and occupied area. The hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We have evaluated the hybrid design method by examining the half-adder circuit as a case study. We have developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level, within a microscopic quantum mechanical model.

Uropathogenic E. coli (UPEC) is the main etiologic agent of urinary tract infections (UTI) and possesses virulence and resistance determinants commonly harbored in mobilizable genetic elements such as plasmids or pathogenicity islands (PAI). UPEC belongs to the group of Ex-traintestinal pathogenic E. coli, however the existence of hybrid strains has been described; these possess both diarrheagenic (DEC) and extraintestinal (ExPEC) E. coli traits and are considered as "hypervirulent strains," which represents a significant health problem. This study aimed to de-termine the prevalence of UPEC PAI, ExPEC sequence types (ST), DEC genes, carbapenemase and extended-spectrum β-lactamase (ESBL) phenotypes, fluoroquinolones/aminoglycosides resistance genotypes and the presence of plasmids in 40 clinical isolates previously characterized as UPEC. A 72.5% of the isolates presented PAI, mainly PAI IV536 (53%). Sixty-five percent of the β-lactam-resistant isolates were positive for ESBL phenotype, and 100% of the car-bapenem-resistant isolates were carbapenemase producers. The most prevalent ESBL-associated gene was blaCTX-M-2 (60%), while among isolates resistant to fluoroquinolones and aminoglycosides the most frequent resistance gene was aac(6')Ib (93%). Plasmid bands were present in 57% of the isolates. Seventy percent belonged to the ST131 clonal group. The presence of molecular markers associated with DEC pathotypes was detected in 20 isolates; 60% were classified as hybrid pathotypes. These data suggest, in addition to an important pathogenic potential, the existence of hybrid pathotypes in clinical isolates of E. coli causing UTI in the Mexican population.

This study delves into Personal Narratives (PN), which encompass both oral and written recounting of individual experiences, encompassing facts, events, people, and thoughts. Traditional emotion recognition and sentiment analysis primarily focus on broader categories like utterances or documents. Our research, however, centers on identifying Emotion Carriers (EC), which are specific segments within speech or text that elucidate the narrator's emotional state (e.g.,"losing a parent", "decision-making moments"). Extracting these ECs enriches the representation of a user's emotional state, thereby enhancing natural language understanding and the sophistication of dialogue models. While previous studies have utilized lexical attributes to identify ECs, we argue that incorporating spoken narratives offers a more nuanced view of the context and emotional state. This paper explores the integration of speech and textual embeddings at the word level, alongside both early and late fusion techniques, for improved EC detection in spoken narratives. We employ Residual Neural Networks (ResNet), initially pre-trained on diverse speech emotion datasets and subsequently fine-tuned for EC detection. Our experimental findings demonstrate that late fusion, in particular, significantly enhances EC detection capabilities.

: The crystallization conditions from the solution play an important role in determining the mor-phology, phase composition, and photovoltaic properties of perovskite films. Post-processing of the obtained films can have a crucial role in increasing the grain size of perovskite and enhancing its crystallinity. It has been shown that the formation of crystal nuclei can be utilized to accelerate crystallization. In this case, crystallization occurs through the growth of seed crystals created in the solution, enabling the formation of relatively large crystals. For the deposition of CH3NH3PbI3 hybrid halide perovskite films from a solution of the perovskite in dimethylformamide the spin coating technique was employed. Pre-crystallization was achieved by annealing the films at a temperature of 100 or 110 °C. The dissolution process involved adding a drop of dimethylformamide onto the substrate surface and allowing it to partially dissolve the perovskite film. Subsequently, residual solvent was removed through spin coating. The mor-phological analysis of the perovskite film surface after recrystallization at temperatures ranging from 80 to 130 °C was performed. The infrared transmission spectra of the obtained perovskite films were investigated, and their light absorption characteristics were studied through transmission spectra. The perovskite structure in the obtained films was confirmed by the peaks observed in the X-ray diffraction patterns.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited because fixed recipes are usually applied to run the processes. Therefore, a digital twin has been developed to optimize an industrial brownfield DCF plant. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation of selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

Thermoplastic pultrusion is a suitable process for fabricating continuous unidirectional thermoplastics with a uniform cross-section, high mechanical properties due to continuous fiber reinforcement, low cost, and suitability for mass production. In this paper, jute and glass fibers were reinforced with a polypropylene matrix and fabricated using the thermoplastic pultrusion process. The volumetric fraction of the composite was designed by controlling the filling ratio of the reinforcing fiber and matrix. The effects of molding parameters were investigated, such as pulling speed and molding temperature, on the mechanical properties and microstructure of the final rectangular profile composite. The pulling speed and molding temperature were varied from 40 to 140 mm/min and 190 to 220°C, respectively. The results show that an increase in molding temperature initially led to an increase in mechanical properties, up to a certain point. Beyond that point, they started to decrease. The increase in molding temperature also led to an increase in void content. Meanwhile, un-impregnation decreased with the increase in molding temperature, and the jute fiber began to degrade at high temperatures. In the next step, with varying pulling speed, the mechanical properties decreased as the pulling speed increased, with a corresponding increase in void content and un-impregnation. The decrease in mechanical properties was influenced by the increase in void content and un-impregnation, as the jute fiber degraded at higher temperatures.

Abstract: The present work evaluates the feasibility of using volcanic fly ash (VFA), generated by the eruption of the Tajogaite volcano on the island of La Palma (Spain) in 2021, as a precursor in the preparation of cementitious materials with different Portland cement (PC) replacement levels (0%, 30%, 70% and 100%), in the absence (Blended Cement, BC) and presence of an alkaline ac-tivator (Hybrid Alkaline Cement, HAC, and Alkaline Cements, AC). Hydration kinetics (isother-mal conduction calorimetry), paste mechanical strengths and reaction products were character-ised by XRD, FTIR, TG/DTG and BSEM/EDX. The results obtained indicate that the strengths de-veloped by the hybrid alkaline cements (HAC) are higher than those of the blended cements (BC), especially at the age of 2 days, where 25 MPa was obtained with the replacement of 70% PC by VFA. Alkaline cements (AC, 100% VFA) prepared with 8M NaOH solution as activator reached 40 MPa after 2 days. It was observed that in all the binders, depending on the initial composition of the binder mixture and the percentage of replacement and/or activator, VFA reacts to form cementitious gels, C-A-S-H and N-A-S-H type, which supports its use as a mineral addition to blended cement or as a precursor in the preparation of alkaline and hybrid alkaline cements.

Glycosaminoglycans (GAGs) with unique structures from marine animals show intriguing pharmacological activities and negligible biological risks, providing more options for us to explore safer agents. Swim bladder is a tonic food and folk medicine, and its GAGs show good anticoag-ulant activity. In this study, two GAGs SJG-1.0 and GMG-1.0 have been extracted and isolated from the swim bladder of Sphyraena jello and Gadus morhua. The physicochemical properties, pre-cise structural characteristics, and anticoagulant activities of these GAGs were determined. The analysis results of the SJG-1.0 and GMG-1.0 showed that they were chondroitin sulfate (CS)/dermatan sulfate (DS) hybrid chains with molecular weights of 109.32 kDa and 123.13 kDa, respectively. They were mainly composed of the repeating disaccharide unit of -{IdoA-α1,3-GalNAc4S-β1,4-}- (DS-A). The DS-B disaccharide unit of -{IdoA2S-α1,3-GalNAc4S-β1,4-}- also existed in both SJG-1.0 and GMG-1.0. SJG-1.0 had a higher proportion of CS-O disaccharide unit -{-GlcA-β1,3-GalNAc-β1,4-}-, while GMG-1.0 contained higher proportion of CS-E disaccha-ride unit -{-GlcA-β1,3-GalNAc4S6S-β1,4-}-. The disaccharide compositions of the GAGs varied in a species-specific manner. Anticoagulant activity assay revealed that both SJG-1.0 and GMG-1.0 had potent anticoagulant activity, which can significantly prolong activated partial thrombosis time. GMG-1.0 also can prolong the thrombin time. SJG-1.0 showed no intrinsic tenase inhibition ac-tivity, while GMG-1.0 can obviously inhibit intrinsic tenase with EC50 of 58 nM. Their significantly different anticoagulant activities may be due to their different disaccharide structural units and proportions. These findings suggested that the swim bladder CS/DS hybrid chains with well-defined structure may be safer anticoagulants with low bleeding risk.

Two new compounds, named aspergilins A (1) and B (2), were isolated from the mangrove endophytic fungus Aspergillus sp. A1E3 associated with the fruit of Rhizophora mucronata, together with averufanin (3). The planar structures and absolute configurations of these compounds were unambiguously established by extensive NMR investigations, quantum-chemical electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Most notably, the absolute configuration of aspergillin A (1) was determined by single-crystal X-ray diffraction analysis of its tri-pivaloyl derivative 4, conducted with Cu Kα radiation, whereas that of averufanin (3) was first clarified by quantum-chemical ECD calculations. Aspergilin A is the first orsellinic acid–ribose–​pyridazinone-Noxide hybrid containing a unique β-oxo-2,3-dihydropyridazine 1-oxide moiety, whereas aspergilin B and averufanin are the sterigmatocystin and anthraquinone derivative, respectively. From the perspective of biosynthesis, aspergilin A could be originated from the combined assembly of three building blocks, viz., orsellinic acid, β-D-ribofuranose, and L-glutamine. It is an unprecedented alkaloid-Noxide involving biosynthetic pathways of polyketide, pentose, and amino acid. Aspergilin B (2) exhibited potent antiproliferative activity against four cancer cell lines. In addition, Aspergilin B (2) dose-dependently induced G2/M phase arrest in HepG2 cells.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

The crystallization conditions from the solution play an important role in determining the mor-phology, phase composition, and photovoltaic properties of perovskite films. Post-processing of the obtained films can have a crucial role in increasing the grain size of perovskite and enhancing its crystallinity. It has been shown that the formation of crystal nuclei can be utilized to accelerate crystallization. In this case, crystallization occurs through the growth of seed crystals created in the solution, enabling the formation of relatively large crystals. For the deposition of CH3NH3PbI3 hybrid halide perovskite films from a solution of the perovskite in dimethylformamide the spin coating echnique was employed. Pre-crystallization was achieved by heating films at a temperature of 100 or 110 °C. The dissolution process involved adding a drop of dimethylformamide onto the substrate surface and allowing it to partially dis-solve the perovskite for 30-40 seconds. Subsequently, residual solvent was removed through spin coating. The recrystallization process was carried out by heating films at the temperature range of 80 to 130 °C. The morphological analysis of the perovskite film surfaces after recrystallization at tem-peratures ranging from 80 to 130 °C was performed. The infrared transmission spectra of the obtained perovskite films were investigated, and their light absorption characteristics were studied through transmission spectra. The perovskite structure in the obtained films was con-firmed by the peaks observed in the X-ray diffraction patterns.

In recent years, antimicrobial resistance in many human pathogens has become a serious health concern. Since infections with resistant pathogens cannot be treated with traditional antimicro-bial drugs, new strategies are necessary to fight bacterial infections. Hybrid nano-systems may provide a solution to this problem, by combining multiple mechanisms for killing bacteria to synergistically increase the effectiveness of the antimicrobial treatment. In this review, we high-light recent advances in the development of hybrid nano-systems for the treatment of bacterial infections. We discuss the use of hybrid nano-systems for combinational therapy, focusing on various triggering mechanisms for drug release and the development of biomimetic nanomateri-als. We also examine inherently antimicrobial nano-systems and their uses in preventing infec-tions due to wounds and medical implants. This review summarizes recent advances and pro-vides insight into the future development of antimicrobial treatments using hybrid nanomateri-als.

Navigation is the most challenging issue in autonomous vehicles. Researchers in the current era have developed many Artificial Intelligence techniques to navigate, generate paths, and avoid obstacles for optimum path planning for autonomous vehicles. Different studies have investigated bio-inspired techniques to overcome the navigation issue, including obstacle avoidance. This paper uses new meta-heuristic optimization techniques called Dragonfly Algorithm (DA) to set the goal by detecting and avoiding obstacles with minimum human interference. For effective results, the Dragonfly-Fuzzy hybrid algorithm is analyzed over the unstructured environment because individual techniques may not be sure of an optimal solution over all configurations. The main advantage of the proposed hybrid controller is that it combines the multiple features of different approaches into a single controller. This paper compares simulation and experimental findings over various environmental conditions to the individual algorithm. Regarding time and path optimization, the hybrid Dragonfly-Fuzzy controller performs better than the respective controller.

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid engine, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICEV) and the electric engine (BEV). The paper also presents the assumptions of the DEVS model, which allows for the estimation of vehicle’s energy consumption. The study described herein also presents the influence of factors such as ambient temperature and constant speed on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in every-day driving conditions. When analyzing the energy consumption based on the vehicle’s unit energy consumption model, attention was paid to values that are strongly correlated with the traffic volume and weather conditions affecting the moving vehicle. In addition, the authors emphasized that the estimated total energy consumption of an electric (BEV) or hybrid (PHEV) vehicle, relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. In extreme cases, the actual range of a hybrid vehicle is 75% shorter.

In this nano era, nanomaterials and nanostructures are popular in developing novel functional materials. However, the combinations of nanoproducts with materials at micro and macro scale can open new routes for conceiving novel materials with an improved or even new functional performances. In this study, a brand-new hybrid, containing both nanofibers and particles having a microsize, was fabricated using a sequential electrohydrodynamic atomization (EHDA) process. Firstly, the microparticles loaded with drug (berberine hydrochloride, BH) molecules in the cellulose acetate (CA) were fabricated using a solution electrospraying process. Later, these microparticles were suspended into a co-dissolved solution containned BH and a hydrophilic polymer (polypyrrolidone, PVP), and were co-electrospun into the nanofiber/microparticle hybrids. The EHDA processes were clearly recorded, and the resultant products showed a typical hybrid topography, with microparticles distributed all over the nanofibers, which was demonstrated by SEM assessments. FTIR and XRD demonstrated that the components within the hybrids were presented in an amorphous state and have fine compatibility with each other. In vitro dissolution tests verified that the hybrids were able to provide the designed dula-step drug release profiles, a combination of the fast release step of BH from the hydrophilic PVP nanofibers through an erosion mechanism and the sustained release step of BH from the insoluble CA microparticles via a typical Fickian diffusion mechanism. The present protocols pave a new way for developing trans-scale functional materials.

Nowadays Air-Source Heat Pumps (ASHP) in combination with a Photovoltaic (PV) installation are a very promising option for a necessary and urgent energy transformation in European Union (EU). It is extremely important to develop solutions that will help maximize the use of energy generated from renewable energy sources (RES). Such issues include the problem of insufficient use of generated electricity in PV on-grid microinstallations in residential buildings. This paper's aim is to analyze the results of one-year-round operation of a PV array grid-connected hybrid installation with ASHP for domestic hot water (DHW) preparation in a residential building in Cracow, Poland in the context of increasing self-consumption (SC) of PV energy. Models of systems are built and simulated in Transient System Simulation Tool 18 (TRNSYS) software. Simulations were carried out for different scenarios involving different building electricity consumption profiles, PV system capacity and specified runtime management of ASHP. The novelty of this study lies in the evaluation of the impact of a certain range of conditions on the energy performance of the system, in particular on SC. The results showed that the use of ASHP, with specified runtime management, results in an increase in monthly SC values from 7 to 18%, and annual SC values up to 13%. Also determining the appropriate size of the used PV system depending on whether it is present ASHP in the installation is crucial to increase the value of the SC parameter. Overall, this study provides valuable insights into the potential benefits of PV panels and ASHP operating together, in particular on SC values.

Leishmania parasites present astonishing adaptative abilities that represent a matter of life or death within disparate environments during the heteroxenous parasite life cycle. From an evolutionary perspective, organisms develop methods of overcoming such challenges. Strategies that extend beyond the genetic diversity have been discussed and include variability between parasite cells during the infections of their hosts. The occurrence of Leishmania subpopulation fluctuations with variable structural genomic contents demonstrates that a single strain might shelter the variability required to overcome inconsistent environments. Such intrastrain variability provides parasites with an extraordinary ability to adapt and thus survive and propagate. However, different perspectives on this evolution have been proposed. Strains or species living in the same environment can cooperate but also compete. These interactions might increase the replication rate of some parasites but cause the loss of more aggressive competitors for others. Adaptive responses to intra- and interspecific competition can evolve as a fixed strategy (replication is adapted to the average genetic complexity of infections) or an optional strategy (replication varies according to the genetic complexity of the current infection). This review highlights the complexity of interspecies and intrastrain interactions among Leishmania parasites as well as the different factors that influence this interplay.

Electron-hole recombination and narrow range utilization of sunlight limit the photocatalytic efficiency of TiO2. We synthesized a carbon dots (CDs) modified TiO2 nanoparticles (NPs) with flower-like mesoporous structure, i.e., porous TiO2/CDs nanoflowers. Among such hybrid parti-cles, CDs worked as photosensitizers for the mesoporous TiO2 and enabled the resultant TiO2/CDs nanoflowers with a wide-range light absorption. Rhodamine B (Rh-B) was employed as a model organic pollutant to investigate the photocatalytic activity of the TiO2/CDs nanoflowers. The results demonstrated that the decoration of CDs on both TiO2 nanoflowers and P25 NPs enabled a significant improvement of the photocatalytic degradation efficiency compared with the pristine TiO2. TiO2/CDs nanoflowers with porous structure and larger surface areas than P25 showed a higher efficiency owing to prevent local aggregation of carbon materials. All the results revealed that the introduced CDs and the unique mesoporous structure, large surface areas and loads of pore channels of the prepared TiO2 NPs played important roles in the enhancement of the photocatalytic efficiency of the TiO2/CDs hybrid nanoflowers. Such TiO2/CDs composite NPs also opens a door for photodegradation, photocatalytic water splitting and enhanced solar sun-light as light source.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

: Microbiology has long been a keystone in fermentation and the utilization of yeast biology rein-forces molecular biotechnology as the pioneering frontier in brewing science. Consequently, modern understanding of the brewer’s yeast has faced significant refinement over the last few decades. This publication presents a condensed summation of Saccharomyces species dynamics with an emphasis on the relationship between traditional ale yeast, Saccharomyces cerevisiae, and the interspecific hybrids used in lager beer production, S. pastorianus. Introgression from other Sac-charomyces species is also touched on. The unique history of Saccharomyces cerevisiae and Saccharo-myces hybrids are exemplified by recent genomic sequencing studies aimed at categorizing brewing strains through phylogeny and redefining Saccharomyces species boundaries. Phylogenetic investigations highlight the genomic diversity of Saccharomyces cerevisiae ale strains long known to brewers by their fermentation characteristics and phenotypes. Discoveries of genomic contribu-tions from interspecific Saccharomyces species into the genome of S. cerevisiae strains is ever more apparent with increased investigations on the hybrid nature of modern industrial and historical fermentation yeast.

The genus Rhodotorula includes basidiomycetous oleaginous yeast species. R. babjevae can produce compounds of biotechnological interest such as lipids, carotenoids and biosurfactants from low value substrates such as lignocellulose hydrolysate. High-quality genome assemblies are needed to develop genetic tools and to understand fungal evolution and genetics. Here, we combined short- and long-read sequencing to resolve the genomes of two R. babjevae strains, CBS 7808 (type strain) and DBVPG 8058 at chromosomal level. Both genomes have a size of 21 Mbp and a GC content of 68.2%. Allele frequency analysis indicated tetraploidy in both strains. They harbor 21 putative chromosomes with sizes ranging from 0.4 to 2.4 Mb. In both assemblies, the mitochondrial genome was recovered in a single contig, which shared 97% pairwise identity. The pairwise identity between the majority of chromosomes ranges from 82% to 87%. We found indications for strain-specific extrachromosomal endogenous DNA. 7,591 protein-coding genes and 7,607 associated transcripts were annotated in CBS 7808 and 7,481 protein-coding genes and 7,516 associated transcripts in DBVPG 8058. CBS 7808 has accumulated a higher number of tandem duplications than DBVPG 8058. We identified large translocation events between putative chromosomes and a high genetic divergence between the two strains.

Over the years, leaked fluids from the aircraft caused severe deterioration of the airfield pavement. The combined effect of hot exhaust from the auxiliary power unit of military aircraft and spilt aviation oils caused rapid pavement spalling. If the disintegrated concreted pieces caused by spalling is sucked into the jet engine, it may cause catastrophic damage to the aircraft engine or physical injury to maintenance crews. This study investigates the effectiveness of incorporating hybrid fibres into ordinary concrete to improve the residual mechanical and thermal properties to prevent spalling damage of pavement. Three fibre reinforced concrete samples made with micro steel fibre and polyvinyl alcohol fibre with a fibre content of zero, 0.3%, 0.5% and 0.7% by volume fraction. These samples were exposed to recurring high temperature and aviation oils. Tests were conducted to measure the effects of repeated exposure on the concrete's mechanical, thermal and chemical characteristics. The results showed that polyvinyl alcohol fibre reinforced concrete suffered a significant loss of thermal properties and residual mechanical strength than the micro steel fibre reinforced concrete. However, hybrid fibre reinforced concrete performed better in retaining higher residual properties, and no spalling of concrete was observed.

In this paper, Hybrid Automata, which is a formal model for hybrid systems, has been introduced. A summary of its theory is presented and some of its special and important classes are listed and some properties that can be studied and checked for it are mentioned. Finally, the purposes of use, the most widely used areas, and the tools that provide H.A. Support are addressed.

COVID-19 and new concept, lockdown, change social life of all classes of humans. Children partially feel the changes of daily life and this situation has been children’s free mind. Children are under a new type of restriction imposed on them by their parents. Normally they prefer play with their their friends than study and always waiting for holidays. They heard a new jargon i.e. lockdown where everything stands still. Very often they see peoples in the roads and few vehicles are moving in the roads. However, a peculiar thing happens now that they sit in front of computer to hear the virtual classes that are taken by the teachers. This also happens when there is no lockdown since COVID-19 still affects people. The environment is totally changed and they do not find any proper answers from the parents about the scenario.This study has been made an attempt to carry out the mental affairs of children in West Bengal, India. Several families are surveyed for collecting responses mostly from rural areas as well as urban areas for the time-period from April, 2020 to July, 2020. An effort has been given in this paper to predict the stress, depression and anxiety faced by children during the COVID-19. A Deep Learning Neural Network (DLNN) based method is applied to understand the stress level, depression level and anxiety level amongst the children. A hybrid DLNN has been presented in this research that combines both Convolutional Layer and Gated-Recurrent Unit (GRU) for obtaining the prediction of the mental health of children. The model obtains an accuracy of 89.57% for defeminizing mental anxiety of children.

Chemistry has been viewed as one of the most fruitful near-term applications to science of quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Much less effort has been placed on how one finishes these calculations by using the results from the quantum computer (on the active region of the molecule) and embeds them back into the remainder of the molecule in order to determine the properties of the entire molecule. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with classical algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

This work attempted to fabricate superhydrophobic fabric via simple immersion technique. Textile fabrics were coated with silica nanoparticles prepared from tetraethoxysilane (TEOS) to obtain sufficient roughness with hydrophobic surface chemistry. Then the coated fabrics were treated with polydimethylsiloxane (PDMS) and aminopropyltriethoxysilane (APTES) to reduce the surface energy. The effects of PDMS concentration on the surface morphology and superhydrophobicity of as-prepared fabric were investigated. The morphology and the composition of superhydrophobic fabric was characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDS) and Fourier transform infrared (FTIR) spectroscopy. The results revealed the formation of spherical silica nanoparticles with average particle size of 250 nm throughout the fabric surface. The possible interactions between silica nanoparticles and APTES, as well as the fabrics were elucidated. Investigating the hydrophobicity of fabrics via water contact angle (WCA) measurement showed that the treated fabric exhibits excellent water repellency with a water contact angle as high as 151° and a very low water sliding angle. It also found that the treated fabric maintained most of its hydrophobicity against repeated washing. The comfort properties of the obtained superhydrophobic fabrics in term of air permeability and bending length did not reveal any significant changes.

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control the renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh and Deylaman) to increase the accuracy of the subsequent hybrid model. Capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI and KGE parameters. It was concluded that WOA optimization algorithm can improve prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.

Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

New hybrid nanostructured electrodes for supercapacitors made by combination of electrical double layer and faradaic supercapacitors based-nanomaterials within a single hybrid composite has a great potential on expanding the range of use of these devices and increase their electrochemical performance. In this work, we developed several hybrid nanostructured composites with combinations of such types of materials with potential applicability as electrodes in supercapacitors. In particular, these composites were obtained by easy, cost-effective and scalable procedures, and were composed by NiCo2O4 nanocores as the main faradaic-based nanomaterial and either the conductive polymer polyaniline (PANI), multiwall carbon nanotubes (MWCNTs), or reduced graphene oxide (r-GO) as the electrical double layer-based carbonaceous-based nanomaterials in order to enable the combination of both type of energy storage processes within a single nanostructured device. These constructions allowed us to obtain specific capacitance as large as 1760 F/g, 900 F/g and 734 F/g at a current density of 1 A/g for NiCo2O4/PANI, NiCo2O4/MWCNT, and NiCo2O4/r-GO hybrid nanocomposite electrodes, respectively. Besides, the stability of NiCo2O4/MWCNTs and NiCo2O4/r-GO-based electrodes was outstanding, with capacity losses below 10% after long periods of operation (> 500 cycles).

We report here the synthesis of uniform nanospheres-like silver nanoparticles (AgNPs, 5-10 nm) and the dumbbell-like Fe3O4-Ag hybrid nanoparticles (FeAgNPs, 8-16 nm) by the use of seeding growth method in the presence of oleic acid (OA)/oleylamine (OLA) as surfactants. The antibacterial activity of pure nanoparticles and nanocomposites by monitoring the bacterial lag–log growth has been investigated. The electron transfer from AgNPs to Fe3O4NPs which enhances the biological of silver nanoparticles has been proven by nanoscale Raman spectroscopy. The lamellae structure in the spherulite of FeAgNPs/PE nanocomposites seems play the key role to the antibacterial activity of nanocomposites, which has been proven by nanoscale AFM-IR. An atomic force microscopy coupled with nanoscale infrared microscopy (AFM-IR) is use to highlight the distribution of nanoparticles on the surface of nanocomposite at the nanoscale. The presence of FeAgNPs in PE nanocomposites has a better antibacterial activity than that reinforced by AgNPs due to the faster Ag+ release rate from the Fe3O4-Ag hybrid nanoparticles and the ionization of AgNPs in hybrid nanostructure.

The NCO functional group of 3-isocyanatoproply triethoxysilane (IPTS) and the OH functional group of DOPO-BQ were used to conduct an addition reaction. Following completion of the reaction, triglycidyl isocyanurate (TGIC) was introduced to conduct a ring-opening reaction. Subsequently, a sol-gel method was used to take place a hydrolysis- condensation reaction on TGIC-IPTS-DOPO-BQ to form a hyperbranched nitrogen–phosphorous–silicon (HBNPSi) flame retardant. This flame retardant was incorporated into a polyurethane (PU) matrix to prepare a hybrid material. Fourier-transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), limiting oxygen index (LOI), UV-VIS spectrophotometry, and Raman analysis were conducted to structure characterization and analyzed transparency, thermal stability, flame retardancy, and residual char to understand the flame retardant mechanism of prepared hybrid materials. After the flame retardant was added, the maximum degradation rate decreased from −36 wt%/min to −17 wt%/min, the integral procedure decomposition temperature (IPDT) increased from 348 ℃ to 488 ℃, and the char yield increased from 0.7 to 8.1 wt%. The aforementioned results verified that thermal stability of PU can be improved after adding HBNPSi. The LOI analysis indicated that the pristine PU was flammable because the LOI of pristine PU was only 19. When the content of added HBNPSi was 40%, the LOI value was 26; thus the PU hybrid became nonflammable.

Several challenges stand in the way of production of Metal Matrix Composites (MMCs) such as higher processing temperatures, particulate mixing, particulate-matrix interface bonding issues and ability to process into desired geometrical shapes. Although there are many literatures showing composites with single particulate reinforcements, the studies on composites with multiple reinforcing agents (hybrid composites) are found to be limited. Development of a hybrid particulate composite with optimized mechanical and tribological properties is very significant to suit modern engineering applications. In this study, Al-Si hypereutectic alloy (A390) is used as the matrix and Silicon Carbide (SiC) and Graphite (Gr) and Molybdenum di-Sulphide (MoS₂) are used as particulates. Particulate volume (wt%) is varied and sample test castings are made using squeeze casting process through stir casting processing route. The evaluation of mechanical properties indicates that presence of both the hard phase (SiC) and the soft phase has distinct effect on the properties of Hybrid Composite. Composite samples were characterized to understand the performance and to meet the tribological applications. Fractography study indicated an intra-granular brittle fracture for hybrid composites. Wear study shows that Hybrid MMCs has better tribological performance compared to that of A390 alloy.

This paper deals with the techno-economic study of the hybrid renewable energy system based on energy storage aspect under the form of hydrogen and methane. Indeed, with the intermittency of the renewable energy sources such as photovoltaic and wind energy, several problems of produced energy injection to the power system network can be encountered due to the shortage or the excess of these sources. This situation appeals the use of systems that ensure the stability of network based on the storage of energy surplus into gas using electrolyzer systems, which will be used afterward to cover the eventual shortage. In the present paper, the study of performance of each pathway of methane and hydrogen storage has been performed by the treatment of multiple scenarios via different architecture case studies in an Algerian location. Whereas, the energy produced by the photovoltaic system, the wind energy and the gas micro turbine sources are considered similar in each case. The modeling and simulation of the studied system operation under optimization criteria has been performed in this work, where the main aim is to define the appropriate configuration taking into account the different with low costs of investment, maintenance operation and immediate reactivity with a big storage capacity.

Pigeonpea (Cajanus cajan (L.) of Fabaceae family belongs to genus Cajanus usually grown in semi-arid tropics of Asia and Oceania, Africa and America. This crop has been a best source for improving food and soil quality amongst farmers. However, its seed have been always questioned for purity. This problem is managed by using polymorphic SSR markers. In present study, a DNA fingerprints generated by seven SSR markers and hybrid testing is performed on Pigeonpea test samples along with parental lines. The seed samples of pigeonpea were germinated in laboratory and three week old leaves samples were used for DNA isolation by CTAB method. A total of 9 alleles were observed in three test samples using three primers out of seven primers. The screening of the allelic data associated with the three cultivated varieties, revealed markers (CcM0246) displayed unique allelic profiles for one variety. Yet, the genetic fingerprinting data is not well resolved to potentially distinguished two bands of hybrid that are merely of 4–8 bp to confirm hybrid testing of seed. Hybrid testing of may be confirmed including more SSR primers prepared from genomic DNA of pigeonpea.

Antimicrobial peptides and structurally related peptoids offer potential for the development of new antibiotics. However, progress has been hindered by challenges presented by poor in vivo stability (peptides) or lack of selectivity (peptoids). Herein, we have developed a process to prepare novel hybrid antibacterial agents that combine both linear peptoids (increased in vivo stability compared to peptides) and a nisin fragment (lipid II targeting domain). The hybrid nisin-peptoids prepared were shown to have low µM activity (comparable to natural nisin) against methicillin-resistant Staphylococcus aureus.

Hybrid reverse osmosis electrodialysis systems make it possible to obtain solutions with a higher concentration of components compared to single electrodialysis treatment, i.e. more efficient separation of brine and pure water. The article proposes experimental and pilot-industrial studies of a hybrid membrane system operation using industrial wastewater for the disposal of liquid waste from an ion-exchange chemical-desalting water treatment plant of a thermal power plant, followed by a calculation of economic efficiency and an analysis of the environmental feasibility of their use. The developed technological scheme offers separate processing of acidic and alkaline waste regeneration solutions using calcium carbonate reagent and desalination on baromembrane and electromembrane units to obtain clean water and dry residue. The hybrid system includes a booster filter press and an evaporator. The hybrid system makes it possible to provide a thermal power plant with a "zero discharge" with a minimum consumption of reagents and electricity, as well as return all water from wastewater to the power plant cycle.

Automatic speech recognition of native languages plays a crucial role in fostering inclusivity and preserving linguistic diversity. The Kashmiri language, an underrepresented Indo-Aryan dialect primarily spoken in the Kashmir Valley, poses substantial challenges for existing AI models due to its phonetic diversity and scant linguistic resources. This study addresses these hurdles by developing a robust spoken Kashmiri recognition system that employs dual feature extraction with spectrogram augmentation and a hybrid Convolutional Neural Network (CNN) and Gated Multi-Layer Perceptron (gMLP) model. Key to this endeavour is the creation of a high-fidelity dataset that captures the phonetic variations across Kashmiri dialects, focusing on twelve specific words. Through the integration of dual feature extraction, spectrogram augmentation, and the innovative hybrid modelling approach, our system attains an impressive 96% accuracy on the test dataset for classifying these twelve spoken words. This research not only enhances the generalization and resilience of spoken Kashmiri recognition systems but also represents a critical step towards advancing technology and safeguarding the Kashmiri language within this underrepresented linguistic domain.

Background/Objectives: Different approaches are required in treating patients with multiple pulmonary lesions. A multistage procedure may increase the risk of complications and patient discomfort. This study provides an initial experience with single-stage management of multiple lung lesions using percutaneous ablation with thoracoscopic resection in a hybrid operating room (HOR). Methods: We retrospectively evaluated patients who underwent combined ablation and resection in a HOR between May 2022 and July 2024. All patients received a single anesthesia via endotracheal tube intubation. Clinical data, operative findings, and pathological characteristics of the lung nodules were recorded. Results: A total of 23 patients were enrolled in this study. Twenty patients underwent unilateral procedures, and the other three patients underwent bilateral procedures. Ablations were performed before lung resection in 22 patients; only one patient underwent surgery first. The median global operating room time was 227.0 min. The median total radiation dose (dose area product) was 14076 μGym2. The median hospital postoperative length of stay was 2 days. Conclusion: The single-stage procedure of percutaneous ablation with thoracoscopic resection under general anesthesia in a HOR is feasible and safe. This procedure is an alternative method for managing multiple pulmonary lesions.

In this work, we investigate how to obtain the optimal active elements in Hybrid Relay-Reflecting Intelligent Surface (HR-RIS) systems while considering enhancing the sum rate of the communication system in the presence of malevolent eavesdropper. Therefore, when dealing with numerous elements in the HR-RIS, the complicated optimization task of activating and deactivating the active elements of RIS renders typical exhaustive search methods impracticable. Standard techniques, such as alternating optimization, often frequently converge and unfortunately become trapped in local optima, leading to less-than-ideal results. To address these issues, we suggest performing the Jaya algorithm, which modifies its search strategy in response to historical data to find a compromise between investigating novel avenues and taking advantage of well-established answers. Our simulation findings demonstrate that this strategy outperforms the most recent techniques, yielding higher sum rates with the best possible user location and robust security against eavesdropping. It’s interesting to observe that even at lower total power levels, the Jaya algorithm performs incredibly well and efficiently identifies the optimal amount of active elements. This demonstrates how it can decrease interference, increase signal quality, improve Signal-to-Interference-plus-Noise Ratio (SINR), and increase secrecy rates, opening the door for more efficient and secure wireless communication systems.

Dental implantology has evolved greatly as a result of the application of biomechanical engineering concepts, notably the utilization of additive manufacturing technologies that recreate the intricate architecture of real bone. This technique intends to improve functional compatibility with bone tissue while also addressing continuing problems of stress distribution inside dental implants. The research examines two types of dental implants: fully gyroid latticed and hybrid gyroid latticed with a solid neck, comprising three different cell sizes—FI-111, FI-222, FI-333, and their hybrid equivalents HI-111, HI-222, and HI-333. Traditional solid implants often cause stress shielding, which reduces long-term osseointegration and implant durability. Developed using nTopology for lattice configuration and meshing, this design undergoes a comprehensive stress analysis using finite element analysis (FEA). The study seeks to determine the most efficient implant design by maximizing stress distribution and mechanical stiffness. Analyses suggest that hybrid latticed implants, notably the HI-222 type, possess superior mechanical characteristics. These implants successfully balance stiffness and flexibility, reducing stress concentrations and improving stress distribution throughout the implant structure. This balance is critical for decreasing micromotions at the bone-implant interface, boosting osseointegration, and, ultimately, increasing implant longevity and success rates in clinical settings. The paper describes a substantial redesign of dental implants by combining biomechanical engineering and modern additive manufacturing. The novel proposed hybrid structure not only addresses typical challenges such as stress shielding, but it also establishes new standards in implantology, potentially leading to better clinical results and patient quality of life. This complete approach emphasizes the ability of personalized implant designs to fulfill individual clinical demands, indicating a significant improvement in dental treatment technology.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

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

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

This paper presents a new algorithm to solve the optimal reconfiguration problem in distribution networks, using the algorithm called Improved Simulated Annealing combined with Hybrid Cooling (ISA-HC) and Selective Space Search, which leverages the capabilities of the Open Distribution System Simulator (OpenDSS) software and the selective space search concept to enhance performance and reduce the search space. The ISA-HC algorithm determines an adequate starting point for the temperature and initial solution according to the size of the system. For adequate cooling, a three-stage cooling approach was employed to achieve effective cooling, combining two methods widely used in the literature. Overall, the ISA-HC algorithm is a promising method for solving the optimal reconfiguration problem in distribution networks. The algorithm was tested on the systems of 5, 33, 69, and 94 buses and compared to other existing methods in the literature. The results show that the proposed method is more robust and efficient, providing better convergence and reliably achieving good-quality global solutions

The main motive of this study was to find an auxiliary application of the discarded agricultural remnants and poultry waste. Rice straw and chicken feathers are disposed off as landfills or incinerated causing pollution and environmental threats after their primary consumption. In this study epoxy composites were made using varying proportions of these raw and alkali treated materials. Flexural strength of the composites decreased till 35% fiber loading, followed by an increase. Chicken feather composites showed increased impact strength with fiber addition. Alkali treatment improved the mechanical properties of rice straw composites. Alkali treated chicken feather composites showed better impact strength but no significant change in flexural strength. Hybrid composites explicated enhanced properties both before and after alkali treatment corroborating noteworthy interfacial adhesion and synergistic effect of these fibers as ascertained from SEM images. The study evinced a better option for averting environmental problems by recycling these disposed materials.

Convolutional neural networks (CNNs) face challenges in capturing long-distance text correlations, and Bidirectional Long Short-Term Memory (BiLSTM) networks exhibit limited feature extraction capabilities for text classification of work order. To address the abovementioned problems, this work utilizes an ensemble learning approach to integrate model elements efficiently. This study presents a method for classifying work order texts using a hybrid neural network model called BERT-BiLSTM-CNN. First, use Bert for preprocessing to obtain text vector representations. Then, capture context and process sequence information through BiLSTM. Next, capture local features in the text through CNN. Finally, obtain classification results through Softmax. Through comparative analysis, the method of fusing these three models is superior to other hybrid neural network model architectures in multiple classification tasks. It has a significant effect on work order classification.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

This paper integrates carrier manifolds from control theory and hybrid systems modeling to develop a novel mathematical framework for Artificial General Intelligence (AGI). Carrier manifolds represent the state space of complex AGI systems, while hybrid systems model discrete and continuous dynamics together. The proposed architecture embeds carrier manifold into GPTProX, a hybrid AI solver combining conversational AI, logic programming, and optimization.

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

Background: The commonly known assumption that combinations of several herbs in one formulation can have better efficacy due to additive or synergistic effects has not been systematically studied despite some evidence supporting the synergy concept. Study aim: The study aimed to reveal the molecular interactions in situ of host cells in response to the intervention of BHP and justify the benefits of implementing BHP in clinical practice. Results: This overview provides the results of recent clinical and network pharmacology studies of botanical herbal preparations (BHP) of Rhodiola with other plants, including Ashwagandha, Green Tea, Eleutherococcus, Schisandra, Eleutherococcus, Leuzea, Caffeine, Cordyceps, Gingko, Black Cohosh, saffron, and L-carnosine. Conclusions: The most important finding from network pharmacology studies of BHP was the evidence supporting the synergistic interaction of BHP ingredients, revealing unexpected new pharmacological activities unique and specific to the new BHP. Some studies show the superior efficacy of BHP compared with mono-drugs. At the same time, some a priori-designed combinations can fail, presumably due to antagonistic interactions and crosstalk between molecular targets within molecular networks involved in the cellular and overall response of organisms on the intervention. Network pharmacology studies help predict the results of studies to discover new indications and unpredicted adverse events.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

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

The open hole tensile and flexural test of composites holds immense importance in aerospace and structural engineering. These tests provide critical insights into how composites perform under real-world conditions, allowing for the development of materials that meet rigorous standards, thereby ensuring the safety and durability of aircraft and other applications. This study investigates the influence of hole size on the open hole tensile (OHT) and flexural properties (OHF) of plane woven aramid/epoxy (AFRP), basalt/epoxy (BFRP), and aramid-basalt/epoxy (ABFRP) sandwiched laminates. Specimens with three different hole sizes viz., 4 mm, 6 mm, 8 mm were prepared and tested according to ASTM D5766 and ASTM D7264 for OHT and OHF performance respectively. Results indicated that the hybridization of aramid-basalt layers in a sandwich lay up significantly improved the mechanical performance when compared to only aramid laminate. However, with the increase in hole size the mechanical properties declined irrespective of the material. Basalt fibres showed higher hole sensitivity under tensile loading due to its brittle fracture mechanism while aramid fibres exhibited higher hole sensitivity under flexural loading which can be attributed to its the poor compressive strength. The effect of hole size on the damage mechanism has also been highlighted through scanning electron microscope (SEM). Major failure modes observed are delamination around the hole edges, ductile and brittle fibre fractures, and delamination.

Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict the prices. In this research, we have explored the power of usage of hybrid ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid models using different algorithms. The methodology presented in the paper involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM (long short-term memory networks) + GRU (Gated recurrent units) + Conv1D (one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market analytics. The harmonious integration of algorithms not only underscores the effectiveness of hybrid modeling but also beckons for further exploration within the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.

Although Sn has been intensively studied as one of the most promising anode materials to replace commercialized graphite, its cycling performance and rate performance are still unsatisfactory owing to insufficient control of its large volume change during cycling and poor electrochemical kinetics. Herein, we propose a Sn-TiO2-C ternary composite as a promising anode material to overcome these limitations. The hybrid TiO2-C matrix synthesized via two-step high-energy ball milling effectively regulated the irreversible lithiation/delithiation of the active Sn electrode and facilitated Li-ion diffusion. At the appropriate C concentration, Sn-TiO2-C exhibited significantly enhanced cycling performance and rate capability compared to its counterparts (Sn-TiO2 and Sn-C). Sn-TiO2-C delivers good reversible specific capacities (669 mAh g-1 after 100 cycles at 200 mA g-1 and 651 mAh g-1 after 500 cycles at 500 mA g-1) and rate performance (446 mAh g-1 at 3000 mA g-1). The superiority of Sn-TiO2-C over Sn-TiO2 and Sn-C was corroborated by electrochemical impedance spectroscopy, which revealed faster Li-ion diffusion kinetics in the presence of the hybrid TiO2-C matrix than in the presence of TiO2 or C alone. Therefore, Sn-TiO2-C is a potential anode for next-generation Li-ion batteries.

Germanium (Ge) nanostrip is embedded in polymer and studied as a waveguide. Measurement reveals that this new type of semiconductor/polymer heterogeneous waveguide exhibits strong absorption for the TE mode from 1500 nm to 2004 nm, while the propagation loss for the TM mode declines from 20.56 dB/cm at 1500 nm to 4.89 dB/cm at 2004 nm. The transmission characteristics serves as an essential tool to verify the optical parameters (n-κ) of the thin strips, addressing to the ambiguity raised by spectroscopic ellipsometry regarding highly absorbing materials. Furthermore, the observed strong absorption for the TE mode at 2004 nm is well beyond the cut-off wavelength of the crystalline bulk Ge (~1850 nm at room temperature). This redshift is modeled to manifest the narrowing of the Tauc-fitted bandgap due to the grain order effect in the amorphous Ge layer. The accurate measurement of the nanometer-scale light-absorbing strips in a waveguide form is a crucial step toward the accurate design of integrated photonic devices that utilize such components.

We designed a bidirectional reflection distribution function (BRDF) measurement system to research the characteristics of space target materials. This system measures the BRDF data of an aluminum plate and gold foil. A hybrid biogeography-based optimization-firefly (BBO-Firefly) algorithm was proposed to optimize the five-parameter BRDF model. To check the performance of the hybrid BBO-Firefly algorithm, we set up a contrast experiment to optimize the BRDF parameters using the BBO algorithm, Firefly algorithm, and hybrid BBO-Firefly algorithm. The experimental results prove that the hybrid BBO-Firefly algorithm surpasses the BBO algorithm and Firefly algorithm in convergence speed and precision in the same situation and has a better optimization effect. Finally, we substitute the optimization results of the hybrid BPO-Firefly algorithm into the BRDF model to analyze the reflection characteristics of an aluminum plate and gold foil.

The article introduces the reader to the new thermodynamic potential of classical thermodynamics, called the J-potential. For the case of a homogeneous system, it vanishes, but it perfectly serves multiphase (heterogeneous) systems. The J-potential is especially important for the thermodynamics of capillary systems in colloidal science. The J-potential simplifies the derivation of the Neumann and Young equations, and has found application in the thermodynamics of thin films and the theory of strength of materials. In this work, for example, it was used to study the dependence of the chemical potential of a substance of a small bubble on the bubble size.

Waste management is an unavoidable technological operation in the process of raw material extraction. The main characteristic of this technological operation is the handling of large quantities of waste material, which can amount to several hundred million cubic metres. Working with this amount of material usually requires high-capacity systems for excavation and loading, a large fleet of trucks for haulage, construction, and maintenance of a complex roads network, use of a significant area of land in order to achieve the required capacities, etc. At the same time, this operation must comply with all administrative and environmental standards. Therefore, optimising waste rock management (particularly haulage and dumping) has the potential to significantly improve the overall value of the project. This paper presents a hybrid model for the optimisation of waste dump design and site selection. The model is based on different mathematical methods (genetic algorithm, analytic hierarchy process and heuristic methods) adapted to different aspects of the problem. The main objective of the model is to provide a solution (in analytical and graphical form) for the draft waste dump design, on the basis of which the final waste dump design can be defined.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

Deep learning is improving by leaps and bounds in remote sensing images (RSIs) analysis, pre-trained convolutional neural networks (CNNs) have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, pre-trained CNNs require massive annotated data as samples for data training. When labeled samples are not sufficient, the most common solution is to the pre-trained CNNs using a great deal of natural image dataset (e.g. ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proved that the proposed algorithm improves the classification accuracy while greatly decreasing sum of model parameters and sum of training data.

Many engineering systems are described by using differential models whose solutions, generally obtained after discretization, can exhibit a noticeable deviation with respect to the response of the physical systems that those models are expected to represent. In those circumstances one possibility consists of enriching the model in order to reproduce the physical system behavior. The present paper considers a dynamical system, and proposes enriching the model solution by learning the dynamical model of the gap between the system response and the model-based prediction, while ensuring that the time integration of the learnt model remains stable. The proposed methodology is applied on the simulation of the top-oil temperature evolution of a power transformer, whose data is provided by RTE, the French Electricity Transmission System Operator.

The development of hybrid unmanned aerial underwater vehicles (HAUVs) compatible with the advantages of the aerial vehicles and the underwater vehicles is of great significance. This paper presents the first study on a new HAUV layout using four rotors to realize the medium crossing motion of a transverse slender body similar to the fuselage of a missile or a submarine, that is the hybrid aerial underwater quadrotor (HAUQ). Then a robust control strategy is proposed for the take-off HAUQ on the water in the presence of unknown disturbances and complex model dynamic uncertainties. As a semi-submersible HAUQ rises straightly from the water, the inside of the slender fuselage placed horizontally is filled with water. The center of the mass, the moment of inertia, and the arm of force of the HAUQ will change rapidly in the takeoff phase from the water since the rapid non-uniform change of mass caused by the passive fast drainage. It is difficult to establish a accurate mathematical model of the complex dynamic changes caused by the multi-media dynamics, the fast changing buoyancy, and the added mass crossing air–water surface. Therefore, an uncertain kinematic and dynamic model is established through the passive fast nonuniform change and the complex dynamics are considered as the unknown terms, and the external disturbances of gust and other factors are assumed as the bounded disturbance input. A robust design approach is introduced to deal with the fast time-varying mass disturbance based on the input-to-state stability (ISS) theorem. The complex dynamics are estimated using the basis function and the unknown weight parameters, and the adaptive laws are adopted for the on-line estimation of the unknown weight parameters. Consider the residual disturbance of the uncertain nonlinear system as a total disturbance term, a disturbance observer is introduced for disturbance observation. The numerical simulation shows the feasibility and robustness of the proposed algorithm.

All solar thermal systems, due to their thermal nature, can be hybridised or operated with both fossil fuel and solar energy. By enhancing availability and dispatch ability, hybridization has the potential to boost the value of concentrating solar thermal technology. Hybrid photovoltaic thermal systems (PV/T) offer a solution to improve on the limited capacity growth seen with standard solar panels. The analysis focuses on the creation of alternative structural and design so-lutions that may be used to boost lacking efficiency that was previously hampered by limited spectrum absorption of sun light. As a result, the spectrum is separated into two spectrum ranges, one that directly matches the bandgap of PV materials and the other that is absorbed for thermal absorption outputs. Most two channel systems are used to capture or reflect sunlight, with the layer specifically located right above the PV cell. Another layer of nanofluid is employed to evacuate heat from the PV cell, acting as the second channel, to specifically eliminate overheating difficulties. To address concerns that have arisen as a result of years of observation, the most recent study involves nano-particle-based fluids employed in PV/T, which uses a cooling medium to provide an alternate heat transfer option. Aside from nanofluids, there are phase change materials, channel alterations that result in a higher flow rate and hence improve overall performance.

The development of technology that combines supercapacitors and lithium-ion batteries by externally connecting them in parallel is ongoing. This study examines the correlation between the volume ratio and electrical characteristics of a cell made by internally connecting a battery capacitor with Li4Ti5O12 as the anode active material and a supercapacitor in parallel. It was found that increasing the volume occupied by the battery capacitor in the cell led to increased cell energy and resistance, resulting in decreased output characteristics. Conversely, increasing the volume occupied by the supercapacitor in the cell led to a decrease in the IR drop during discharge and cell temperature when evaluating cycle characteristics with a current of 20C. The study also examined the behavior of the current distributed during the charging and discharging process based on the volume ratio of the supercapacitor and the battery capacitor. Analyzing the correlation between the volume ratio and electrical characteristics of supercapacitors and battery capacitors could potentially lead to the development of a new type of energy storage device.

The present work is focused on functionalizing Montmorillonite (Mt) with poly dimethyl siloxane (PDMS) to develop a hybrid membrane reactor for the removal of Co (II) and Ni (II) ions from model wastewater samples. The specific surface area of the novel material was found to be 444 m2 /g. X-Ray Diffraction (XRD) and Fourier Transform Infra-Red (FTIR) spectra reveal the formation of exfoliated composite material. The efficiency of this membrane material was determined by subjecting it to continuous filtration experiments. It was able to remove 50% of Co (II) and 75% of Ni (II) ions, respectively, in one cycle of filtration in less than three minutes of operation with a permeate flux rate of 675 L/m2/H and 975 L/m2/H respectively, for both the ions.

The transportation sector is a significant source of greenhouse gas emissions. Electric vehicles (EVs) have gained popularity as a solution to reduce emissions, but the high load of charging stations poses a challenge to the power grid. Nuclear-Renewable Hybrid Energy Systems (N-RHES) present a promising alternative to support fast charging stations, reduce grid dependency, and decrease emissions. However, the intermittent problem of renewable energy sources (RESs) limits their application, and the synergies among different technologies have not been fully exploited. This paper proposes a predictive and adaptive control strategy to optimize the energy management of N-RHES for fast charging stations, considering the integration of nuclear, photovoltaics, and wind turbine energy with a hydrogen storage fuel cell system. The proposed dynamic model of a fast-charging station predicts electricity consumption behavior during charging processes, generating probabilistic forecasting of electricity consumption time-series profiling. Key performance indicators and sensitivity analyses illustrate the practicability of the suggested system, which offers a comprehensive solution to provide reliable, sustainable, and low-emission energy to fast-charging stations while reducing emissions and dependency on the power grid.

In Lubumbashi, the capital of Haut Katanga in the Democratic Republic of the Congo (DR Congo), diesel power plants are a common source of electricity. The need to utilise local renewable energy sources in DR Congo has increased due to the unreliability of the state grid and the rising cost of running Diesel generators. Solar photovoltaic (PV) panels and batteries, in particular, have recently seen significant price drops. Therefore, it is important for operators and suppliers to choose optimal generators together with a renewable energy system to lessen the energy deficit. Diesel generators are still widely used in DRC, but their efficiency pales in contrast to that of more recent power facilities. Consuming fossil fuels results in high expenses for upkeep and operation, in addition to severe environmental damage. This study assessed the feasibility of using local weather and technical data to evaluate the efficiency of a diesel power plant hybridized with a PV system. Hybrid Optimization Model for Electric Renewable (HOMER) simulations suggest that the hybrid system schedule is preferable due to its many economic and environmental advantages for the local community and its inhabitants. The promotion of such a hybrid system, then, may encourage the sustainable economic development of a stable source of electricity for the Congo Region.

Time series data having low signal-to-noise ratio, non-stationarity and non-linearity are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time series data. In this paper, we develop an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future time, we use only the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing involves updating only the current data into the process. We evaluate the performance of O-LGT by analyzing the high-frequency Limit Order Book (LOB) data from the China market. It shows that our model in most cases achieves similar speed with much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 40 times faster than the existing high-accuracy neural network models for the LOB data in China market.

Among the various complex systems that we experience each day, there is the physical phenomenon of infiltration. Infiltration is considered as the dynamics of water flow in the subsurface soil that in this work is framed for the context of civil construction. This issue is approached with the use of mathematical models and stochastic techniques, however, there are hardships in collecting the samples in the field, the adoption of a scale type, the influence in soil layers interfaces, the effects of soil anisotropic characteristics and so on. In this study, methodology is proposed to deal with the soil sampling input such as the retro model, constitutive model and optimization algorithms. Additionally, an automatic calibration is set forth to fix parameters from the mathematical model submitted. Especially among the existing Optimization Algorithms there are Genetic Algorithms and the Generalized Simulated Annealing Algorithm (GSA). This work presents an overview of these optimization methods and a proposal for a more efficient and faster algorithm called GSA-HYBRID based on convergence gradient technique. The applied strategy depends on the type of case study considering its physical properties and constraints. In this sense, it is found that while Genetic Algorithms are able to replicate the optimization surface, Generalized Simulated Annealing is much more adequate in characterizing the system at an extremely low computational cost. Nevertheless, the hybrid technique GSA-HYBRID performed the fastest. Further research is necessary to implement the novel GSA-HYBRID algorithm due to its flexibility and higher speed, also, studying its application at different case studies.

Recently, hybrid logic circuits based on magnetic tunnel junctions (MTJs) have been widely investigated to realize zero standby power. However, such hybrid CMOS/MTJ logic circuits suffer from a severe sensing reliability due to the limited tunnel magnetoresistance ratio (TMR≤150%) of the MTJ and the large process variation in the deep sub-micrometer technology node. In this paper, a novel differential sensing amplifier (DSA) is proposed, in which two PMOS transistors are added to connect the discharging branches and evaluation branches. Owing to the positive feedback realized by these two added PMOS transistors, it can achieve a large sensing margin. By using an industrial CMOS 40 nm design kit and a physics-based MTJ compact model, hybrid CMOS/MTJ simulations have been performed to demonstrate its functionality and evaluate its performance. Simulation results show that it can achieve a smaller sensing error rate of 9% in comparison with the previously proposed DSAs with the TMR ratio of 100% and process variation of 10%, while maintaining almost the same sensing delay of 74.5 ps and sensing energy of 1.92 fJ/bit.

Smart devices such as smartphones, smartwatches, etc. are promising platforms that are being used for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities due to inter-class pattern similarity, which occurs when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on the traditional sensors such as accelerometer and gyroscope, which are inbuilt in these devices. Therefore, apart from using information from the traditional sensors, these systems lack contextual information to support automatic activity recognition. In this article, we explore environment contexts such as illumination(light conditions) and noise level to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Networks and Long Short Time Memory(CNN_LSTM) learning models. The models performed sensor fusion by augmenting the low-level sensor signals with rich contextual data to improve recognition and generalisation ability of the proposed solution. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used inertial sensing data whilst the second set of extensive experiments combined inertial signals with contextual information from environment sensing data. Obtained results demonstrate that contextual information such as environment noise level and illumination using hybrid deep learning models achieved better recognition accuracy than the traditional activity recognition models without contextual information.

Digital skills are essential in today’s digital age, which means that students must gain technology-enhanced skills from higher education for their future careers. Studies in Art & Design (A&D) programs in this university surveyed three faculties’ perspectives and nineteen students’ experiences. During the COVID-19 pandemic, this university changed its teaching and learning strategies by offering courses online during autumn 2020 and spring 2021 during mandatory quarantine. However, the A&D program was not entirely based online. As a result, it is important to take a closer look at the A&D programs offered in order to assess the faculties’ perspectives and students’ experiences during the two online semesters. The study included online surveys from instructors’ perspectives and with regard to students’ experiences about the quality of studio learning, traditional studio learning opportunities, and online studio learning opportunities via either live (on-campus) or online studios. Using relationship-based research design, posttest data surveys were collected to ascertain the differences in the mean scores, standard deviations, and percentages of some forms of agreement between the faculties’ perspectives and students’ experiences of the quality of studio learning, traditional studio learning opportunities, and online studio learning opportunities in these Art & Design (A&D) programs. This quantitative research aimed to develop formative assessments and suggestions while establishing whether it would be possible to hold all A&D courses online in a higher education setting.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

This study used the sol–gel method to synthesize a non-halogenated hyperbranched flame retardant containing nitrogen, phosphorus and silicon, HBNPSi, which was then added to a polyurethane (PU) matrix to form an organic–inorganic hybrid material. Using 29Si nuclear magnetic resonance, energy-dispersive X-ray spectroscopy of P- and Si-mapping, scanning electron microscopy, and X-ray photoelectron spectroscopy, this study determined the organic and inorganic dispersity, morphology, and flame retardance mechanism of the hybrid material. The condensation density of the hybrid material PU/HBNPSi was found to be 74.4%. High condensation density indicates a dense network structure of the material. The P- and Si-mapping showed that adding inorganic additives in quantities of either 20% or 40% results in homogeneous dispersion of the inorganic fillers in the polymer matrix without agglomeration, indicating that the organic and inorganic phases had excellent compatibility. In the burning test, adding HBNPSi to PU resulted in the material passing the UL-94 standard at the V2 level, unlike the pristine PU, which did not meet the standard. The results demonstrated that after non-halogenated flame retardant was added to PU, the material’s flammability and dripping were lower, thereby proving that flame retardants containing elements such as nitrogen, phosphorus, and silicon exert an excellent flame retardant synergistic effect.

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.