The increasing demand for craft beer drives the search for novel ale yeast cultures from brew-ing-related wild environments. The focus of bioprospecting for craft cultures is to identify feral yeasts suitable to imprint unique sensorial attributes to the final product. Here, we integrated phylogenetic, genotypic, genetic, and metabolomic techniques to demonstrate that sour beer during aging in wooden barrel is a source of suitable craft ale yeast candidates. Differently from traditional lambic beer maturation phase, during aging of sour-matured production style beer, different biotypes of Saccharomyces cerevisiae dominated the cultivable in-house mycobiota, fol-lowed by Pichia membranifaciens, Brettanomyces bruxellensis, and Brettanomyces anomalus. In addi-tion, three putative S. cerevisiae x Saccharomyces uvarum hybrids were identified. S. cerevisiae feral strains sporulated, produced viable monosporic progenies, and have STA1 gene as well as the full-length promoter. During hopped wort fermentation four S. cerevisiae strains and the S. cere-visiae x S. uvarum hybrid WY213 overcame non-Saccharomyces strains in fermentative rate and ethanol production, except for P. membranifaciens WY122. This strain consumed maltose after a long lag phase, in contrast with the phenotypic profile descripted for the species. According to STA1+ genotype S. cerevisiae partially consumed dextrin, but no 4-vynil guaiacol was detected among VOCs, suggesting that these strains were phenolic off flavor-negative (POF-). In conclu-sion, the strains characterized here have relevant brewing properties and are exploitable as in-digenous craft beer starters.

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Pre-operative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared localization accuracies between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. Patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

Peptide nucleic acid (PNA) is a synthetic DNA mimic that outperforms the properties of traditional oligonucleotides (ONs). On account of its outstanding features, such as remarkable binding affinity towards complementary DNA or RNA as well as high thermal and chemical stability, PNA has been proposed as a valuable alternative to the ON probe in gene-sensor design. In this study, a hybrid transducer made-up of graphene oxide (GO) nano-sheets covalently grafted onto a porous silicon (PSi) matrix has been investigated for the early detection of a genetic cardiac disorder, the Brugada syndrome (BS). A functionalization strategy towards the realization of a potential PNA-based device is described. A peptide nucleic acid (PNA), able to detect the SCN5A associated with the BS has been properly synthesized and used as a bioprobe for the realization of a proof-of-concept label-free optical PNA-biosensor. PSi reflectance and GO photoluminescence (PL) signals were simultaneously exploited for the monitoring of the device functionalization and response.

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the SST turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

A study on the selection of hyperelastic constitutive model for polymeric materials is performed using a hybrid experimental-computational approach. Bis-GMA polymer is used as a case study of hyperelastic material to describe the polymer characteristics by determining its Poisson’s ratio and its valid range of the hyperelastic stress-strain curves. These two parameters are then used to determine the hyperelastic constitutive model by using the hybrid approach. Several uniaxial compression tests along with their finite element simulations are implemented in a systematic way, to identify the polymer behavior under the compressive loading conditions. Nano-indentation experiments are conducted to verify the hyperelastic behavior of the polymer. The experimental and computational evidences confirm that the Poisson’s ratio of Bis-GMA is 0.40 and the appropriate hyperelastic constitutive model for this polymer is of a second order polynomial. It is shown that, the results can be used to determine the true stress-strain curve of hyperelastic materials.

In order to harness the potential of mobile technologies to enhance the quality of clinical research, it is critical to first understand how to engage patients and research sites when planning and conducting mobile clinical trials. The Clinical Trials Transformation Initiative has developed the first comprehensive, evidence-based set of recommendations for incorporating patient and site perspectives in mobile clinical trials, which can aid in engaging stakeholders, addressing site challenges, and maximizing value for participants.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to currently not provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed `quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Hybrid renewable energy systems (HRES), complemented by pumped hydropower storage (PHS), have become increasingly popular amidst the increase of renewable energy penetration. This con-figuration is even more prosperous in remote regions that are typically not connected to the mainland power grid, where the energy independence challenge intensifies. This research focuses on the design of such systems, from the perspective of establishing an optimal mix of renewable sources that takes advantage of their complementarities and synergies, combined with the versatility of PHS. However, this design is subject to substantial complexities, due to the multiple objectives and constraints to fulfill, on the one hand, and the inherent uncertainties as well, that span over all underlying processes, i.e., external, and internal. In this vein, we utilize a proposed HRES layout for the Aegean Island of Sifnos, Greece, to develop and evaluate a comprehensive simulation-optimization scheme in deterministic and, eventually, stochastic setting, revealing the design problem under the umbrella of uncertainty. In particular, we account for three major uncertain elements, namely the wind velocity (natural process), the energy demand (anthropogenic process), and the wind-to-power conversion (internal process, expressed in terms of a probabilistic power curve). Emphasis is also given to the decision-making procedure, which requires a thorough interpretation of the uncertainty-aware optimization outcomes. Finally, since the proposed PHS uses the sea as the lower reservoir, additional technical challenges are addressed.

The 2020 Elucidating the role of clouds-circulation coupling in climate - Ocean-Atmosphere (EUREC4A-OA) and Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) campaigns sought to improve the knowledge of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign consisted of numerous research technologies, some of which are relatively novel to the scientific community. In this study we used a saildrone uncrewed surface vehicle to validate satellite and modelled sea surface salinity (SSS) products in the Western Tropical Atlantic. These products include the Soil Moisture Active Passive (SMAP) Jet Propulsion Laboratory (JPL), SMAP Remote Sensing Systems (RSS), and Hybrid Coordinate Ocean Model (HYCOM). In addition to the validation, we investigated a fresh tongue south east of Barbados. The saildrones accurately depicted the salinity conditions and all satellite and modelled products performed well in areas that lacked small-scale salinity variability. However, SMAP RSS 70 km outperformed its counterparts in areas with small submesoscale irregularities while RSS 40 km was better at identifying small irregularities in salinity such as a fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product for their application and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

Many industrial processes, several natural processes involving non-living matter, and all the processes occurring within living organisms take place in solution. This means that the molecules playing active roles in the processes are present within another medium, called solvent. The solute molecules are surrounded by solvent molecules and interact with them. Understanding the nature and strength of these interactions, and the way in which they modify the properties of the solute molecules, is important for a better understanding of the chemical processes occurring in solution, including possible roles of the solvent in those processes. Computational studies can provide a wealth of information on solute-solvent interactions and their effects. Two major models have been developed to this purpose: a model viewing the solvent as a polarizable continuum surrounding the solute molecule, and a model considering a certain number of explicit solvent molecules around a solute molecule. Each of them has its advantages and challenges, and one selects the model that is more suitable for the type of information desired for the specific system under consideration. These studies are important in many areas of chemistry research, from the investigation of the processes occurring within a living organism to drug design and to the design of environmentally benign solvents meant to replace less benign ones in the chemical industry, as envisaged by the green chemistry principles. The paper presents a quick overview of the modelling approaches and an overview of concrete studies, with reference to selected crucial investigation themes.

The strength of laser joined metal and plastic hybrids is based purely on adhesion. Unpolar plastics like polypropylene depend solely on the mechanical adhesion while polar plastics like polyamide 6.6 develop covalent bonds with the metal. In this research the influence of different surface metallizations on the lap shear strength of laser joined hybrids was investigated. Sandblasted AISI 304 was used as base metal and reference. Aluminum and titanium coatings were deposited by the means of Arc-PVD. Polypropylene and polyamide 6.6 were utilized as polar und unpolar plastics. The lap shear strength of all hybrids with polyamide 6.6 was higher than the polypropylene samples. For all investigated variations of the laser power, scanning speed and the cohesive and adhesive failure modes, the samples joined with the sandblasted AISI 304 showed the highest lap shear strength. The reduction in lap shear strength for the coated samples was due to the increased difference in thermal conductivity between the plastics and the coating materials. This lead to increased warping and residual stresses. It can be concluded that the thermal properties of the metallization in laser joined metal and plastic hybrids has a high impact on the lap shear strength.

Plasmonic nanocomposites have the ability to enhance light-matter interaction due to the plasmonic effects leading to promising potential applications. In this paper, we theoretically study the group index of the silver nanoparticle-gold nanoparticle-quantum dot plasmonic hybrid system in the optical region of the electromagnetic spectrum using the density matrix approach. The quantum dot is considered to be a three-level atomic system of a lambda configuration, interacting with strong control and weak probe fields. We find that the plasmonic hybrid system exhibits at near resonance a strong switching between slow and fast light, which can be controlled by the inclination angle of the gold nanoparticle and the size of the metals nanoparticles, in addition to the strength and detuning of the control field. Thus, the silver nanoparticle-gold nanoparticle-quantum dot could potentially be used in optoelectronic devices.

Silica aerogels and their derivatives have outstanding thermal properties with exceptional values in the thermal insulation industry. However, the brittle nature restricts its large-scale commercialization. Thus, enhancing their mechanical strength without affecting their thermal insulating properties is essential. Therefore, for the first time, highly thermally stable Poly(acrylamide-Co-acrylic acid) partial sodium salt is used as a reinforcing polymer to synthesize hybrid P(AAm-CO-AAc)-silica aerogels via epoxy ring-opening polymerization in the present study. Functional groups in P(AAm-CO-AAc) partial sodium salts, such as CONH2 and COOH, acted as nucleophiles for the epoxy ring-opening reaction with (3-glycidyloxypropyl)trimethoxysilane, which resulted in a seven-fold enhancement in mechanical strength compared to that of a pristine silica aerogel while maintaining the thermal conductivity at less than 30.6 mW/mK and porosity of more than 93.68%. Moreover, the hybrid P(AAm-CO-AAc)-silica aerogel demonstrated improved thermal stability up to 343 ⁰C owing to the synergetic effect between P(AAm-CO-AAc) and the silica aerogel, corresponding to thermal stability and strong covalent bonding among them. These excellent results illustrate that this new synthetic approach for producing hybrid P(AAm-CO-AAc)-silica aerogels is useful for enhancing the mechanical strength of a pristine silica aerogel without impairing its thermal insulating property and shows potential as an industrial heat insulation material.

A The aim of this study is to analyze the effect of different endodontic irrigants and adhesive systems on resin bond strength of fiber post cementation. 144 single-rooted, unrestored human teeth were endodontically treated and randomly divided into 12 groups according to 4 endodontic irrigants (distilled water as control; EDTA 17%; NaOCl 5%; clorhexidine 2%) and 3 different adhesive/resin cement systems (etch-and-rinse: ortofosforic acid, Parabond® A+B/Paracore®; self-etch: ParaBond® Non-Rinse Conditioner, Parabond® A+B /Paracore®; Universal: ClearfilTM Universal Bond/ ClearfilTM DC Core Plus). 48 h after post cementation ten teeth from each group were cross sectioned into 3 discs (cervical, middle and apical regions). 30 specimens of each group (n=30) were submitted to push-out test at a crosshead speed of 1 mm/min. The remaining 2 teeth of each group were sectioned in the same manner and the resin-dentin interface was evaluated using scanning electron microscopy (SEM). The results were statistically analyzed with the ANOVA and Tukey’s test (< 0,01). The adhesive protocols and post space region showed no significant effect on bond strength (> 0.01). The combination of NaOCl 5% and ClearfilTM Universal Bond reduced the adhesive strength (< 0.01). The NaOCl 5%, in relation with other irrigants, significantly decreased the push-out bond strength.

Generalized Bayes is a Bayesian approach based on the learning rate parameter η. In this study, we examine the effect of parameter η on the estimation results considering joint type-I and type-II hybrid censored samples from k exponential populations. In addition to the learning rate parameter, we consider two loss functions, the Linex and general entropy loss functions in the Bayesian approach. Monte Carlo simulations are performed to compare the performances of the estimation results under losses and different values of η. An illustrative example is performed to study the effect of the learning rate parameter and the different losses with different parameters.

This paper presents a new method of process parameters optimization, adequate for 3D printing of PLA (Polylactic Acid) components. The authors developed a new Hybrid Manufacturing Equipment (HME), suitable for producing complex parts made from a biodegradable thermoplastic polymer, for environmental sustainability. Our new HME equipment is producing PLA parts by both additive and subtractive techniques, with the aim of obtaining accurate PLA components with a good surface quality. A design of experiments has been applied for optimization purposes. The following manufacturing parameters were analyzed: rotation of the spindle, cutting depth, feed rate, layer thickness, nozzle speed, and surface roughness. Linear regression models and neural network models were developed to improve and predict the surface roughness of the manufactured parts. A new test part was designed and manufactured from PLA, to validate the new mathematical models, which can now be applied for producing complex parts made from polymer materials.

We summarize the results on electron transport in hybrid superconducting S/B/S' mesa-structure consisted of the oxide epitaxial S/B heterostructures, where S is the cuprate YBa2Cu3O 7-x superconductor, B – an interlayer with the spin dependent characteristics, and S’ is the Nb top superconductor. Josephson effect was observed manifested in appearance of oscillating Shapiro steps amplitudes with microwave signal power due to synchronization of junction self-generation of electromagnetic radiation and the external microwave probe signal. Triplet component of superconducting correlations emerged when the interlayer comprised two magnetic materials SrRuO3 and La0.7Sr0.3MnO3 with non-collinear magnetizations as well in the case of antiferromagnetic insulator Sr2IrO4 characterized by strong spin-orbit interaction. Thickness of the interlayer in the both cases considerably exceeded the coherence length of the magnetic spin active interlayer hinting on appearance of long-range proximity effect and spin-triplet superconducting current. Magnetic field dependences measured at dc, and the existence of the second harmonic in superconducting current–phase relation revealed by measurements at microwave frequencies will be discussed.

Recently, the demand for electric vehicle is increasing worldwide due to eco-friendly policies and stricter emission regulations. As a traction motor for electric vehicle, interior permanent magnet synchronous motors are mainly used. For the design of the interior permanent magnet synchronous motor, the magnetic equivalent circuit method, which is a method of lumped constant circuit, and the finite element method, which is a method of distributed constant circuit, mainly are used. Magnetic equivalent circuit method is useful for simple design through fast and intuitive parameters, but it cannot derive the distribution of magnetic field. The finite element method can derive an accurate magnetic field distribution, but it takes a long time to analyze and it is difficult to analyze intuitive design parameters. In this paper, magnetic equivalent circuit method and Carter coefficient are mixed for rotor structure design. This design method will be called the hybrid magnetic equivalent circuit method. Intuitive design parameters are derived through this hybrid magnetic equivalent circuit method. We will derive the Air gap flux density distribution according to rotor shape, no-load induced voltage, and cogging torque, and compare and verify it with the finite element method.

The utilisation of the industrial residual products to create new value-added materials and to reduce footprint is a modern challenge of science and industry. Development of the new multifunctional and bio-based composites is an excellent opportunity for complex utilisation of industrial residual products. The study describes the preparation and characterisation of the three-phases bio-based composites. The main components are bio-based binder made of peat, devulcanised crumb rubber (DCR) from used tires and part of the fly ash the cenosphere (CS). Three-phase composite prepared in the form of a block for investigation of the mechanical properties and density and a form of granules for determination of the water and oil products sorption was investigated. This work investigated the dependence of the properties on the main component DCR and CS fraction. Is found, that maximum compression strength (in block form) observed for composition without CS and DCR addition - 79.3 MPa, the second highest value of compression strength is 11.2 MPa for composition with 27.3 wt% of CS. For compositions with bio binder content from 17.4 to 55.8 wt% and with DCR contents in range from 11.0 to 62.0 wt% compression strength is in range 1.1 to 2.0 MPa. Liquid sorption analysis (water and diesel) showed that the maximum saturation of liquids in both cases is set after 35 minutes and ranges from 1.05 to 1.4 g·g -1 for water and 0.77 to 1.25 g·g-1 for diesel. It was noted that 90% of the maximum saturation with diesel fuel comes after 10 minutes and for water after 35 minutes.

Quebec is the third-largest wine grape producing province in Canada, and the industry is constantly expanding. Traditionally, 90% of the grapevine cultivars grown in Quebec were rustic or semi-rustic and largely dominated by winter hardy interspecific hybrid Vitis sp. cultivars. Over the years, the winter protection techniques adopted by growers and climate changes have offered an opportunity to establish V. vinifera L. cultivars (e.g. Pinot Noir). We characterized the virome of leafroll-infected interspecific hybrid cultivar and compared it to the virome of V. vinifera cultivar to support and facilitate the transition of the industry. A dsRNA sequencing method was used to sequence symptomatic and asymptomatic grapevine leaves of different cultivars. The results suggested a complex virome in terms of composition, abundance, richness, and phylogenetic diversity. Three viruses, grapevine Rupestris stem pitting-associated virus, grapevine leafroll-associated virus (GLR) 3 and 2 and hop stunt viroid (HSVd) largely dominated the virome. However, their presence and abundance varied among grapevine cultivars. The symptomless grapevine cultivar Vidal was frequently infected by multiple virus and viroid species and different strains of the same virus, including GLR virus 3 and 2. Our data shows that viruses and viroids associated with the highest number of grapevines expressing symptoms included HSVd, GLR3 and GLR2, in gradient order. However, co-occurrence analysis revealed that the presence of GLR species was randomly associated with the development of virus-like symptoms. These findings and their implication for grapevine leafroll disease management were discussed.

Despite advances in algorithms and computational platforms, de-novo genome assembly remains a challenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454, PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This comparative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods.

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.

Star block-copolymers (SBCs) are macromolecules formed by a number of diblock copolymers anchored to a common central core, being the internal monomers solvophilic and the end monomers solvophobic. Recent studies have demonstrated that SBCs constitute a self-assembling building blocks with specific softness, functionalization, shape, and flexibility. Depending on different physical and chemical parameters the SBCs can behave as flexible patchy particles. In this paper, we study the rotational dynamics of isolated SBCs using a hybrid mesoscale simulation technique. We compare three different approaches to analyse the dynamics: the laboratory frame, the non-inertial Eckart's frame, and a geometrical approximation relating the conformation of the SBC to the velocity profile of the solvent. We find that the geometrical approach is adequate when dealing with very soft systems while in the opposite extreme, the dynamics is best explained using the laboratory frame. On the other hand, the Eckart frame is found to be very general and to reproduced well both extreme cases. We also compare the rotational frequency and the kinetic energy with the definitions of the angular momentum and inertia tensor different from recent publications.

This paper considers a hybrid relay network consisting of the source, the amplify-and-forward (AF) relay, the decode-and-forward (DF) relay, and the destination. We propose the optimal power allocation schemes between two different relays which maximize the achievable rate under a sum relay power constraint for given channel gains and transmit power from source. By solving the optimization problem to maximize the achievable rate for each relay network, the transmit power values in closed-form are derived. When the channel gains are the same, the optimal power allocation scheme for AF-DF relay network proves that a more power should be allocated at the first relay to maximize the achievable rate. In case of the DF-AF relay network, we derive the optimal power allocation scheme for the possible four cases. Under the same SNR condition at the first hop, we show that the achievable rate of AF-DF relay network is greater than that of DF-AF relay network when the channel gain between two relays is higher than that between the second relay and destination. Simulation results show that the proposed power allocation schemes provide a higher achievable rate than the equal power allocation schemes.

Mode locked pulse generation has been reported using both active and passive mode locking schemes. Active mode locking technique has been proven to be an effective way to generate high-repetition-rate pulses by incorporating a modulator inside the laser cavity. Compared to actively mode locked lasers, passively mode locked lasers can generate pulse train at ultrashort pulse width but with relatively lower repetition rate. Thus, it is a brilliant idea to build a hybrid mode locked system combining both active and passive mode lockers. In this review, several hybrid mode locked fiber ring laser systems are discussed and summarized. Hybrid mode locking is a promising method to generate high speed ultrashort optical pulses for fiber-optic telecommunication system.

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

In space application, hybrid pixel detectors of the Timepix family have been considered mainly for measurement of radiation levels and radiation dosimetry in low earth orbits. By the example of the Space Application of Timepix Radiation Monitor (SATRAM), we demonstrate the unique capabilities of Timepix-based miniaturized radiation detectors for particle separation. Using a novel method for proton spectrum reconstruction, we were able to measure the spectrum of protons trapped in the inner Van-Allen radiation belt, for the first time with a single-layer detector. We assess the measurement stability and the resiliency of the detector to the space environment demonstrating that, even though degradation is observed, data quality has not been affected significantly over more than 10 years. Based on the SATRAM heritage and the capabilities of the latest-generation Timepix-series chips, we discuss their applicability for use in a compact magnetic spectrometer for deep-space mission or in the high radiation environment of the Jupiter radiation belts, and their capability for use as single-layer X- and γ-ray polarimeter. The latter was supported by measurement of the polarization of scattered radiation in a laboratory experiment, where a modulation of 80% was found.

This report covers the analysis of Intrusion Detection Systems nowadays. Therefore, it focuses on the IDS classification, which includes not only a well-known explanation of Network/Host-Based IDS type but also cloud-based solutions. The paper explores ways of intrusion detection, providing examples of the best detecting tools (OSSEC, Snort or Bro (ZEEK)). Another aspect covered in this report is the juxtaposition of three systems: IDS, IPS and IRS and their response. Finally, there will be presented Intrusion Detection Systems' evasion techniques and challenges, followed by critical conclusions.

The advanced industrial systems are characterized by complex event-driven processes. The specification and computer analysis are illustrated by the common problem of a servo drive with the PWM controller. A mathematical model from the class of Cauchy problems with non-liner control logic is presented to resolve the task discussed. The designed model is performed in two stages: the mathematical model specification (modeling) and implementation (simulation). Two approaches to make a model specification are considered, the first one is the traditional structural technique, and the advanced method is based on the finite state machine paradigm. Structural and finite state software models are built in progressive simulation tools. Computer implementation of the software models of hybrid systems is performed using platform built-in libraries with numerical methods and algorithms for correct event detection. The results of computational experiments for transient processes of phase variables are qualitatively identical, that proves the adequacy of the developed models. The algorithm for asymptotic event detection is considered in detail. This technique demonstrated the best results by the one-sidedness criterion, which is explained by using the author’s algorithm for the integration step determining and the event function dynamics monitoring in the vicinity of the event limits.

The results shown in this paper extend our research group's previous work, which presents the theoretically achievable hydrogen engine-out NOx (H2-NOx) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOx trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing the NOx. In a case study it is shown that the MPC is able to satisfy user-defined NOx limits over the course of various driving missions. Moreover, a comparison to the optimal Pareto front highlights the MPC's ability to achieve close-to-optimal fuel-performance for any desired cumulated NOx target on four realistic routes for passenger cars.

Styryl dyes are generally poor fluorescent molecules inherited from their flexible molecular structures. However, their emissive properties can be boosted by restricting their molecular motions. A tight confinement into inorganic molecular sieves is a good strategy to yield highly fluorescent hybrid systems. In this work, two Mg-aluminophosphate zeotypes (AEL framework, a one-dimensional channeled structure with elliptical pores of 6.5 Å x 4.0 Å; and CHA framework, composed by large cavities of 6.7 Å x 10.0 Å connected by 8-ring narrower windows) were selected for the encapsulation of 4-DASPI dye (trans-4-[4-(Dimethylamino)styryl]-1-methylpyridinium iodide). The synthesis and photophysical properties of 4-DASPI styryl dye occluded within these two types of confinements, AEL and CHA, are optimized and analyzed. The resultant hybrid systems display significantly improved photophysical features compared to 4-DASPI in solution. Molecular simulations reveal a tighter confinement of 4-DASPI in the elliptical channels of AEL, whilst a particular orientation is found within the CHA framework, where one 4-DASPI molecule spans along two adjacent cavities. In the latter scenario, each aromatic ring is positioned on these adjacent cavities and the polymethine chain resides within the 8-ring window, being this confinement slightly less tight than for AEL.

Nowadays, the increasingly serious environmental pollution and energy problems urgently require internal combustion engine-based transportation vehicles to upgrade or replace, so the new energy transportation vehicles based on hybrid power and fuel cells have gradually stepped on the stage and continue to innovate. The shipping industry is one of the main sources of global greenhouse gas emissions. The development of clean energy ships represented by fuel cells has attracted wide attention. Fuel cell has the advantages of clean, pollution-free and low noise, but it has some disadvantages such as insufficient dynamic response performance and fast performance decay. Limited by the characteristics of a single energy source, a variety of energy sources and bidirectional DC converters are usually mixed together to form a hybrid ship to improve the flexibility, stability and economy of the ship, and enhance its adaptability to complex sea conditions through the reconfiguration of power system energy. Taking fuel cell ferry "FCS Alsterwasser" as the research object, this study proposed an improved equivalent minimum hydrogen consumption energy management strategy based on fuzzy logic control. First, the power system of the modified mother ship was simulated, and a hybrid power system including fuel cell, lithium iron phosphate battery and supercapacitor was proposed. Then, the dynamic system simulation model and double closed loop PI control model are established in MATLAB/Simulink, and the reliability of the model is verified by the simulation analysis of charge and discharge characteristics. Then, the feasibility of the proposed method is demonstrated by designing simulation experiments based on typical working conditions of the mother ship. The simulation results show that under the premise of meeting the load requirements, the control strategy designed in this paper has a better optimization effect than the S-type penalty function in terms of lithium battery power, lithium battery SOC, bus voltage stability and equivalent hydrogen consumption, which improves the stability and economy of the power system, and has certain engineering practical value.

The stress concentrations have become common phenomenon of steel elements when arresting a fracture by implementing the crack stop hole (CSH) technique. Embedding the CSH with Carbon Fibre Reinforced Polymer (CFRP) enhance the fatigue life by delaying the fractures while achieving a stiffness recovery due to superior mechanical characteristics of CFRP material. Hence, low cyclic fatigue (LCF) behaviour of 162 strengthened and non-strengthened CSH specimens were examined in this context. These specimens were subjected to a range of 0 to10,000 fatigue load cycles with the frequency of 5 Hz. At the end of fatigue exposure, the average tensile strength was measured in each case. The average strength reductions in the range between 13% to 25% was noted in steel elements with CSH subjected to fatigue exposure. The application of a CFRP patch on CSH had effectively recovered the strength losses while enhancing the strength in the range between 32% to 45% with respect to the non-strengthened non specimens. The developed numerical model based on cyclic J-integral technique agrees with test results and is capable of predicting characteristics for this novel hybrid technique.

Process induced defects during thermoforming are widespread problems in laminate manufacturing. The aim of this study is to describe the effects of holding time and pressure on several properties of the manufactured laminate. A design of experiments is performed, followed by an analysis of variance to examine significant effects. Subsequently, a regression model is created to predict the laminate’s properties, which is also validated. The highest values of tensile strength and elongation at break are found for low settings of holding time and pressure. The fibre volume fraction is not affected by the process parameters. As holding time and pressure increase, significant fibre misalignment takes place, leading to a decrease of the mechanical properties. The regression model corresponds well with the validation and should be extended with further variables in subsequent studies.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

This study presents the solution of the second type of a two-dimensional nonlinear integral equation in Banach space. Also, the existence and uniqueness of this equation’s solution are discussed. We utilize a numerical approach involving hybrid and block-pulse functions to obtain the approximate solution of a two-dimensional nonlinear integral equation. Nonlinear integral equation in two dimensions is reduced numerically to a system of nonlinear algebraic equations that can be solved using numerical methods. This study focuses on showing the convergence analysis for the numerical approach and obtaining an error estimate. Some numerical examples have been provided to demonstrate the approach’s viability and efficacy

The discovered light modulation capabilities of diatom silicious valves make them an excellent toolkit for photonic devices and applications. In this work, a reproducible surface-enhanced Raman-scattering (SERS) enhancement was achieved with hybrid substrates employing diatom silica valves coated with an ultrathin uniform gold film. Three structurally different hybrid substrates, based on the valves of three dissimilar diatom species, have been compared to elucidate the structural contribution to SERS enhancement. The comparative analysis of obtained results showed that substrates containing cylindrical Aulacoseira sp. valves achieved the highest enhancement up to 14-fold. Numerical analysis based on the frequency domain finite element method was carried out to supplement the experimental results. Our results demonstrate that diatom valves of different shapes can enhance the SERS signal, offering a toolbox for SERS-based sensors, where the magnitude of the enhancement depends on valve geometry and ultrastructure.

Serial femtosecond crystallography (SFX) using an X-ray free-electron laser (XFEL) enables determination of room-temperature structures without causing radiation damage. Using an optical pump-probe or mix-and-injection, SFX enables the intermediate state visualization of a molecular reaction. In SFX experiments, serial and stable microcrystal delivery to the X-ray interaction point is vital for reasonable data collection and efficient beam time. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) facility established SFX instruments at a nanocrystallography and coherent imaging (NCI) experimental station. Various sample delivery methods, including injection, fixed-target scanning, and hybrid methods, have been developed and applied to collect XFEL diffraction data. Herein, we review the currently available sample delivery methods for SFX at the NCI experimental station at the PAL-XFEL. This article will help PAL-XFEL users access the SFX system for their experiments.

Residual stresses in fiber metal laminates (FML) inevitably develop during the manufacturing process. The main contributor to these stresses is the difference in the coefficients of thermal expansion (CTE) between fibers and metal in combination with high process temperatures. To quantify these stresses, the use of specimens with an asymmetric layup is an easily adaptable method. The curvature that develops after the manufacturing of flat laminates with an asymmetrical layer stack is a measure of the level of residual stresses evolving during cure. However, the accuracy of the curvature evaluation is highly dependent on specimen design and other influencing parameters. In this work a large set of FML specimens is investigated to identify relevant influencing parameters and derive conclusions about specimen design and evaluation techniques. For certain layups and process parameters, there is a good correlation between the curvature and the stress-free temperature, which is further covered by analytical solutions for bimetals. This correlation is the basis to transfer curvature into a stress-free temperature that can consequently be used for the quantification of residual stress levels in more complex FMLs. The transfer is validated by in-situ strain measurements during cure using a strain gage technique. Based on the results, the application of asymmetric specimens for residual stress characterization in more complex laminates is presented in the form of a workflow.

The utilisation of the industrial residual products to develop new value-added materials and to reduce footprint is one of the critical challenges of science and industry. Development of the new multifunctional and bio-based composites materials is an excellent opportunity for the effective utilisation of industrial residual products. Keeping the different issues in mind, in this work, we describe the manufacturing and characterisation of the three-phases bio-based composites. The key components are bio-based binder made of peat, devulcanised crumb rubber (DCR) from used tires and part of the fly ash, i.e. the cenosphere (CS). The three-phase composite prepared in the form of a block were investigated for their mechanical properties and density. The three-phase composite was prepared in the form a) of a block were investigated for their mechanical properties, and density and b) a form of granules for determination of the water and oil products sorption were investigated. We have also investigated the dependence of the properties on the DCR and CS fraction. It has been found, that maximum compression strength (in block form) observed for composition without CS and DCR addition was- 79.3 MPa, while the second-highest value of compression strength is 11.2 MPa for composition with 27.3 wt% of CS. For compositions with bio binder content from 17.4 to 55.8 wt% and with DCR contents in range from 11.0 to 62.0 wt%, the compression strength is in the range from 1.1 to 2.0 MPa. Liquid sorption analysis (water and diesel) showed that the maximum saturation of liquids in both cases is set after 35 minutes and ranges from 1.05 to 1.4 g·g -1 for water and 0.77 to 1.25 g·g-1 for diesel. It was noted that 90% of the maximum saturation with diesel fuel comes after 10 minutes and for water after 35 minutes.

This work studies a novel low voltage (<60 V) hybrid system that supports engine boosting and downsizing in addition to start-stop, regenerative braking, and constrained torque assist/regeneration. The hybrid power split supercharger (PSS) shares a 9 kW motor between supercharging the engine or providing hybrid functionalities through a planetary gear set, a brake and a bypass valve. The PSS operation is limited to only one of the parallel hybrid or boosting modes at a time, necessitating a highly optimized decision making algorithm to select the device mode and power split ratio. In this work an adaptive equivalent consumption minimization strategy (A-ECMS) is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces a vehicle fuel consumption by 18.4% over standard FTP75 cycle compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to baseline.

Directed evolution methods are becoming increasingly popular, as they are extremely powerful toward developing new biomolecules with altered/novel activities, e.g., proteins with new catalytic functions or substrate specificities, and nucleic acids that recognize an intended target. Especially useful are systems that have incorporated continuous evolution, where the protein to be evolved undergoes continuous mutagenesis to evolve a desired trait with little to no input from the researcher once the system is started. However, continuous evolution methods can be challenging to implement in the lab and daunting for researchers to invest time and resources. Our intent is to provide basic information and helpful suggestions that we have gained from our experience with bacterial phage-assisted continuous evolution (PACE). Specifically, we review factors to consider before adopting PACE for a given evolution scheme, different types of selection circuits that can be utilized with particular focus on the PACE-B1H selection system, what optimization of a PACE selection circuit may look like using directed evolution of ME47 as a case study, and additional techniques that may be incorporated into a PACE experiment. With this information, researchers will be better equipped to determine if PACE is a valid strategy to use to evolve their proteins and how to set up a valid selection circuit.

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state–of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Viral integration within the host genome plays a pivotal role in carcinogenesis. Various disruptive mechanisms are involved, leading to genomic instability, mutations, and DNA damage. With next-generation sequencing (NGS), we can now precisely identify viral and host genomic breakpoints and chimeric sequences, which are useful for integration site analysis. In this study, we evaluated a commercial hybrid-capture NGS panel specifically designed for detecting three key viruses: HPV, HBV, and HIV-1. We also tested workflows for Viral Hybrid Capture (VHC) and Viral Integration Site (VIS) analysis, leveraging customized viral databases in CLC Microbial Ge-nomics. By analyzing sequenced data from virally infected cancer cell lines (including SiHa, HeLa, CaSki, C-33A, DoTc2, 2A3, SCC154 for HPV; 3B2, SNU-182 for HBV; and ACH-2 for HIV-1), we precisely pinpointed viral integration sites. The workflow also highlighted disrupted and neigh-boring human genes that may play a crucial role in tumor development. Our results included in-formative virus-host read mappings, genomic breakpoints, and integration circular plots. These visual representations enhance our understanding of the integration process. In conclusion, our seamless end-to-end workflow bridges the gap in understanding viral contributions to cancer development, paving the way for improved diagnostics and treatment strategies.

This paper investigates if the polar groups induced by a plasma treatment can increase the lap shear strength of laser joined metal and plastic hybrids. Optimal laser joining parameters for cold-rolled AISI304 – Polyamide 6.6 and sandblasted AISI304 - Polypropylene hybrids are developed. The OWRK-method was utilized to identify the change in surface free energy of a low-pressure plasma treatment. Plasma treated samples were joined and tested. The fractured surfaces were investigated via light microscopy and SEM. The arithmetic means of the plasma treated hybrids lap shear strength with polyamide 6.6 varied slightly but all measured values were within the range of the untreated samples. The lap shear strengths of the polypropylene hybrids were significantly reduced. This was attributed to an overaging and the development of low molecular weight oxidized materials which lead to a weak boundary layer. No correlation between surface free energy and the lap shear strength could be found.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHM) are a feasible alternative to increase the performance of conventional microgrids (MG) and to increase the penetration of Distributed Energy Resource (DER) at the same time, active and reactive power dispatch on the system. Despite this, MHMs present challenges in managing resources under uncertainties and controlling electronic converters associated with the ST and DER, making it complex to achieve optimal system performance. In this paper, a Data-Driven Distributionally Robust Optimization (DDDRO) for Day-Ahead Operation Planning of an ST-based MHM considering the reactive power dispatch of the DER that minimizes network losses, voltage deviations, and operation cost simultaneously, considering uncertainties of Photovoltaic Generators (PVG) and demand, is proposed. The Column and Constraint Generation (C&amp;CG) algorithm and Duality-Free Decomposition (DFD) method are adopted. Besides, the original mixed-integer non-linear planning problem is converted into a Mixed-Integer (MI) Second-Order Cone Programming (SOCP) problem through second-order cone relaxation and positive octagonal constraint method. Simulation experiments and results with a connected MHM system demonstrate the effectiveness and performance of the proposed model. Finally, we observe the effect of the meshed structure of MG and the positive impact of integrating the ST to form MHM, taking advantage of the degrees of freedom of this multi-stage converter for optimal energy management under uncertain conditions.

This comprehensive review explores the multifaceted landscape of supercapacitors, highlighting their pivotal role in meeting the global demand for efficient energy storage solutions. Supercapacitors, with their high power density, rapid charge-discharge cycles, and extended cycle life, offer a compelling alternative to conventional batteries and capacitors. The review delves into the fundamental mechanisms of energy storage in supercapacitors, categorizing them into Electrochemical Double-Layer Capacitors (EDLCs), pseudocapacitors, and hybrid capacitors. It discusses significant advancements in supercapacitor technology, including nanostructured materials, flexible and wearable supercapacitors, solid-state supercapacitors, and hybrid supercapacitors, highlighting their potential applications across industries such as transportation, renewable energy, consumer electronics, and industrial power management. Through continued research and development, supercapacitors are poised to play a pivotal role in driving the transition towards a greener and more efficient future.

We study the tight focusing properties of generic bright and dark HyOPS beams in the presence of primary coma. The role of the polarization singularity index and handedness of the polarization of the HyOPS beams on the focused structure has been discussed. Results have been presented for the total intensity, component intensities, and component phase distributions for left-, and right-handed bright and dark star and lemon types singularities. The presence of primary coma distorted the focal plane intensity distributions for both positive and negative index generic C-points. Coma is known to disturb the circular symmetry of the focal plane intensity distribution. Similarly in tight focusing polarization is known to disturb the symmetry. Therefore, a beam with structured and inhomogeneous polarization distribution tightly focused under the influence of coma is a fit case to study. It is found that the presence of primary coma aberration in the focusing system produces a positional shift of the high-intensity peaks and a reduction of the intensity on one side of the center. As the strength of the primary coma increases, the focal plane intensity distributions shift more and more toward the right from the initial position. Unlike the scalar vortex case, in the case of HyOPS beams, the focal plane intensity distribution undergoes rotation, as the helicity of the HyOPS beams is inverted, in addition to shifting. All the component phase distributions are found to be embedded with phase vortices of charge ±1.

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

Background: Both natural immunity and vaccine-induced immunity to COVID-19 may be useful to reduce the mortality/morbidity of this disease, but still a lot of controversy exists. Aims: This narrative review analyzes the literature about: a) the duration of natural immunity; b) cellular immunity; c) cross-reactivity; d) the duration of post-vaccination immune protection; e) the probability of reinfection and its clinical manifestations in the recovered patients; f) comparisons between vaccinated and unvaccinated in the possible reinfections; g) the role of hybrid immunity; h) the effectiveness of natural and vaccine-induced immunity against Omicron variant; i) comparative incidence of adverse effects after vaccination in recovered individuals vs. COVID-19-naïve subjects. Material and Methods: through multiple search engines we investigated COVID-19 literature related to the aims of the review, published since April 2020 through July 2022, including also the previous articles pertinent to the investigated topics. Results: nearly 900 studies were collected and 238 pertinent articles were included. It was highlighted that the vast majority of individuals after COVID-19 develop a natural immunity both of cell-mediated and humoral type, which is effective over time and provides protection against both reinfection and serious illness. Vaccine-induced immunity was shown to decay faster than natural immunity. In general, the severity of the symptoms of reinfection is significantly lower than in the primary infection, with a lower degree of hospitalizations (0.06%) and an extremely low mortality. Conclusions: this narrative review regarding a vast number of articles highlighted the valuable protection induced by the natural immunity after COVID-19, which seems comparable or superior to the one induced by anti-SARS-CoV-2 vaccination. Vaccination of the unvaccinated COVID-19-recovered subjects may not be indicated. Further research is needed in order to: a) measure the durability of immunity over time; b) evaluate both the impacts of Omicron-5 on vaccinated and healed subjects and of hybrid immunity.

This work presents an approach for detecting mathematical formulas in scanned document images. The proposed approach is end-to-end trainable. Since many OCR engines cannot reliably work with the formulas, it is essential to isolate them to obtain the clean text for information extraction from the document. Our proposed pipeline comprises a hybrid task cascade network with deformable convolutions and a Resnext101 backbone. Both of these modifications help in better detection. We evaluate the proposed approaches on the ICDAR-2017 POD and Marmot datasets and achieve an overall accuracy of 96% for the ICDAR-2017 POD dataset. We achieve an overall reduction of error of 13%. Furthermore, the results on Marmot datasets are improved for the isolated and embedded formulas. We achieved an accuracy of 98.78% for the isolated formula and 90.21% overall accuracy for embedded formulas. Consequently, it results in an error reduction rate of 43% for isolated and 17.9% for embedded formulas.

A pilot study on laser 3D printing of inorganic free-form micro-optics is experimentally validated. Ultrafast laser nanolithography is employed for structuring hybrid organic-inorganic material SZ2080TM followed by high-temperature calcination post-processing. The combination allows production of 3D architectures and the heat-treatment results in converting the material to inorganic substance. The produced miniature optical elements are characterized and their optical performance demonstrated. Finally, the concept is validated for manufacturing compound optical components such as stacked lenses. This is opening for new directions and applications of laser made microoptics under harsh conditions such as high intensity radiation, temperature, acidic environment, pressure variations, which include open space, astrophotonics, and remote sensing.

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common one has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

Early in the middle of the 1800s. Up until about 1900, the automotive land speed record 1 was held by an electrical vehicle. Unfortunately, internal combustion engine vehicles did not do 2 well in the market due to their high cost, limited speed, and short range in the 20th century, which 3 resulted in a global fall in their use as private motor vehicles. Since there are no fuel emissions into 4 the environment, there are important uses for electrical vehicles, Electric cars, or EVs, have a closed 5 circuit, cheap operating costs, no maintenance requirements, and excellent performance. The goal 6 of this project is to assess the current state of electric vehicles and the issues surrounding the global 7 advancement of electric vehicle technology. Because of the changing climate.8

The rapid advancements and the merging of hybrid quantum-classical computing, artificial intelligence (AI), machine learning (ML), and deep learning (DL) pose a potentially unseen and significant threat to encryption that may impact Post-Quantum Cryptography (PQC) transition timelines. There is a direct hybrid quantum-classical computing threat on cryptography, and there is also a direct threat AI/ML on cryptography. However, the synergistic combination of these technologies presents known and unknown threats that need attention, focus action, and research. This paper reviews Grover's Adaptive Search (GAS), which combines Grover's Algorithm with adaptive techniques to optimize search further, potentially making it even more efficient for attacking encryption. This work also examines the quantum-accelerated Harrow-Hassidim-Lloyd (HHL) Algorithm, designed to solve systems of linear equations exponentially faster than classical algorithms in certain conditions. The HHL algorithm can solve some lattice-based problems, which have implications for lattice-based encryption. This technological confluence and its potential impact on cryptography and encryption necessitate a proactive and coordinated approach to developing and implementing quantum-resistant AI/ML cryptographic solutions. This paper reviews the technological confluence and its potential implications for classical cryptography and PQC transition timelines and calls for further research.

Compressive sensing (CS) is a technique that allows a signal vector to be reconstructed with a number of linear measurements far fewer than its size. In this study, optical hybrid cryptography with simultaneous compressive sensing based on orthogonal matching pursuit (OMP) is proposed as an alternative solution to the two important problems in communications, namely, effective / efficient / signal processing and secure encryption/decrypton. The whole system is able to both compress and encrypt data with attack immunity.

Recent measurements in the MRX laboratory plasma [J.Yoo et al, Phys. Rev. Lett. 132, 145101 (2024)] have evidenced the role of lower hybrid drift waves produced during magnetic reconnections in producing an effective anomalous resistivity and heating of electrons. A detailed modelization of the energy transfer from the waves to electrons is missing, though. In this work, we attempt such an exercise using a Hamiltonian test-particle model for wave-electron interaction, valid for low-β weakly collisional plasmas. Quantitative agreement with MRX results is found.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

It was recently studied how to achieve the optimal degrees of freedom (DoF) in a multi-antenna full-duplex system with partial channel state information (CSI). In this paper, we revisit the DoF of a multiple-antenna full-duplex system using opportunistic transmission under the partial CSI, in which a full-duplex base station having M transmit antennas and M receive antennas supports a set of half-duplex mobile stations (MSs) having a single antenna each. Assuming no self-interference, we present a new hybrid opportunistic scheduling method that achieves the optimal sum DoF under an improved user scaling law. It is shown that the optimal sum DoF of 2M is asymptotically achievable provided that the number of MSs scales faster than SNR^M, where SNR denotes the signal-to-noise ratio. This result reveals that in our full-duplex system, better performance on the user scaling law can be obtained without extra CSI, compared to the prior work that showed the required user scaling condition (i.e., the minimum number of MSs for guaranteeing the optimal DoF) of SNR^2M−1.

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of seventeen (17) features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e. global best particles) that represent a pruned down collection of seven (7) features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: The All-features (i.e. classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e. QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach marginally improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

The innate and adaptive immune systems collaborate to detect SARS-CoV-2 infection, minimize the viral spread, and kill infected cells, ultimately leading to the resolution of the infection. The adaptive immune system develops a memory of previous encounters with the virus, providing enhanced responses when rechallenged by the same pathogen. Such immunological memory is the basis of vaccine function. Here, we review the current knowledge on the immune response to SARS-CoV-2 infection and vaccination, focusing on the pivotal role of T cells in establishing protective immunity against the virus. After providing an overview of the immune response to SARS-CoV-2 infection, we describe the main features of SARS-CoV-2-specific CD4+ and CD8+ T cells, including cross-reactive T cells, generated in patients with different degrees of COVID-19 severity, and of Spike-specific CD4+ and CD8+ T cells induced by vaccines. Finally, we discuss T cell responses to SARS-CoV-2 variants and hybrid immunity and conclude by highlighting possible strategies to improve the efficacy of COVID-19 vaccination.

Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

This study investigates the effect of different variables in the optimization of a hybrid air charging systems for marine diesel engines, exploring the potential for energy recovery and assistance using electric machines. The optimization parameters include turbine speed, compressor power, start of injection, turbomachine size, and valve timing. At low engine speeds, the larger improvements come from increasing compressor power, which increases air-to-fuel ratio compared with the baseline turbocharging. As free-floating turbocharged engines are known to have low air-to-fuel ratio at low engine speeds, the relative improvement of the hybrid architecture is large at low engine speeds, being near to 10% less BSFC. At high engine speeds, the largest influence is marked by the SOI, an early injection obtains a faster combustion, which improves gross efficiency. However, the relative improvement compared with baseline turbocharger is less than 1% at high engine speeds. Due to the distribution of relative improvement of BSFC, this architecture has a deeper impact on fuel consumption in vessel operating cycles that focus on low engine speeds and loads, such as those of tugboats.

This paper presents a) the novel hybrid learning method to train the type-1 non-singleton interval type-3 (IT3) fuzzy logic systems (IT3 NSFLS-1) and b) the novel method named enhanced Wagner-Hagras (EWH) IT3 NSFLS-1 fuzzy systems which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the back propagation (BP) method to train the antecedent parameters. This proposal dynamically changes the parameters of only the level alpha 0 minimizing some criterion function as new information becomes available to each level alpha k. The antecedent sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the EWH IT3 NSFLS-1 (OLS-BP) system. The algorithm was tested in a hot strip mill facility to predict the transfer bar surface temperature showing its superior capability to obtain the industrial pyrometer’s knowledge uncertainty for tuning and its better performance when compared with IT2 SFLS, IT2 NSFLS-1, GT2 SFLS, GT2 NSFLS-1, IT3 SFLS, and IT3 NSFLS-1 trained with the BP-BP algorithm.

Background. The course of the COVID-19 epidemic process depends on population immunity which prevents pathogen spread among the population. Aim: to study the evolution of SARS-CoV-2 humoral immunity in the Belarusian population relative to COVID-19 pandemic dynamics. Materials and methods. The work was carried out according to a methodology for assessing population immunity developed by Rospotrebnadzor (Russia) and the Belarusian Ministry of Health with the participation of the St. Petersburg Pasteur Institute (SPPI), taking into account WHO recommendations. The study was approved by the Bioethics Committee of Belarus and the SPPI Bioethics Committee. Participant selection was carried out by questionnaire using a cloud (internet server) service. To monitor population immunity, a cohort of 4,661 people (participating in all stages of seromonitoring) was formed from the overall volunteer group. Volunteers were randomized by age group (1-17, 18-29, 30-39, 40-49, 50-59, 60-69, 70+ years), region, and professional group. For the detection of antibodies (Abs) to nucleocapsid (Nc) and S glycoprotein receptor-binding domain (RBD), corresponding assay systems were used following manufacturer instructions. The study was conducted in 4 stages according to a single scheme. Results. In the 1st stage (pandemic month 15), collective immunity was due mainly to Nc+RBD+ Ab status alone. By the 2nd stage (carried out after 4 months), their share decreased 1.2-fold, while the share of volunteers who had only RBD Abs increased 1.7-fold. In the 3rd and 4th stages (carried out after 9 and 19 months), the share of persons with RBD+Nc‒ compared to the 2nd stage decreased by 3.5%; the proportion of persons with Nc+RBD‒ Abs increased by 1.5-fold. The most important factor in population immunity was vaccination of the population, the coverage of which reached 70% by the 4th stage. Among vaccines, the Sputnik V and Sputnik Light vector designs were used most often. The whole-virion, inactivated BIBP-CorV vaccine was used less often. Conclusion. The evolution of collective SARS-CoV-2 humoral immunity included a set of changes in circulating Ab levels (Nc, RBD). The hybrid immunity formed helped to reduce the incidence to nearly zero.

Object of this work is the design and the simulation of a hybrid UAV with VTOL capabilities that has been designed for landing on naval moving platforms. The work is focused on the innovative propulsion layout adopted on the UAV. Authors discuss how adopted low level control strategies can exploit the innovative features of the proposed system assuring a good rejection of transversal wind disturbances. Particular attention is dedicated to low level modelling and control of the system emphasizing how choices regarding low level actuation and control should improve performances and robustness of the system.

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

Many stable water dispersions of various chemical compositions containing bioactive chemical compounds: copper silicate, titanium dioxide and zinc oxide (and other auxiliary substances) were developed and fabricated - they were used for preparation of thin hybrid coatings having very good antimicrobial properties against gram negative bacteria (Escherichia coli), gram positive bacteria (Staphylococcus aureus) and yeast fungus (Candida albicans). Polyester (PES) nonwovens were modified by dip-coating, and PES and cotton fabrics - by dip-coating and coating methods.

During the Covid-19 pandemic, although the experience of workers and managers was examined, the role, experiences and functions of supervisors was relatively underexplored, with no investigation into their changing health and safety responsibilities. This project attempted to fill this gap. Twenty supervisors across Canada were interviewed for an hour. A Framework Method guided the study. We used a conceptual framework of 10 supervisor functions to help direct the data collection, identify codes, manage and organize the data analysis, and identify major themes which were highlighted in the findings. What was found was that since supervisors did not have access to workers’ homes, they could not execute most of their OHS functions. They were obliged to give workers more control over how and when they worked. They Increased their communications with their workers in response to workers’ psychological health concerns. Notably, supervisors reported that they were under extreme stress. A hybrid work environment, with workers sometimes working at home, has become the new norm. Supervisor stress will continue to escalate unless upper management provides more support, supervisors get training on how to deal with the psychological health and safety of workers, and supervisors’ responsibilities are re-defined for at-home workers.

In bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guides on how models for QbD are validated are still missing. In this review we provide a comprehensive overview about validation methods, mathematical approaches and metrics currently applied in bioprocess modeling. The methods cover analytics for data used for modeling, model training and selection, measures for predictiveness and model uncertainties. We point out general issues in model validation and calibration for different types of models and put this into context of existing health authority recommendations. This review provides the start-point for developing a guidance for model validation approaches. There is no one-fits-all approach but this review shall help to identify the best fitting validation method or combination of methods for the specific task and type of bioprocess models that is developed.

With the rapid surge in technological advancements, an equal amount of investment in technology-embedded teaching has become vital to pace up with the ongoing educational needs. Distance education has evolved from the era of postal services to the use of ICT tools in current times. With the aid of globally updated content across the board, technology usage ensures all students receive equal attention without any discrimination. Importantly, web-based teaching allows all kind of students to learn at their own pace, without the fear of being judged, including professionals who can learn remotely without disturbing their job schedules. Having web-based content allows low-cost and robust implementation of the content upgradation. An improved, yet effective, version of the education using such tools is Hybrid Learning (HL). This learning mode aims to provide luxurious reinforcement to its legitimate candidates while maintaining the quality standards of various elements. Incorporated with both traditional and distance learning methods, along with exploiting social media tools for increased comfort level and peer-to-peer collaboration, HL ultimately facilitates the end user and educational setup. The structure of such a hybrid model is realized by delivering the study material via a learning management system (LMS) designed in compliance with quality standards, which is one of the fundamental tackling techniques for controlling quality constraints. In this paper, we present the recently piloted project by COMSATS University Islamabad (previously known as COMSATS Institute of Information Technology) which is driven by technology-embedded teaching model. This model is an amalgam of the traditional class room model with the aid of state-of-the-art online learning technologies. The students are enrolled as full-time students, with all the courses in traditional classroom mode, except one course offered as hybrid course. This globally adapted model helps the students to benefit from both face-to-face learning as well as gaining hands-on experience on technology-enriched education model providing flexibility of timings, learning pace, and boundaries. Our HL model is equipped with two major synchronous and asynchronous blocks. The synchronous block delivers real-time live interaction scenarios using discussion boards, thereby providing a face-to-face environment. Interactions via social network has witnessed equally surging improvement in the output performance. The asynchronous block refers to the lecture videos, slides and handouts, prepared by imminent professors, available 24/7 for students. To ensure quality output, our HL model follows the course learning outcomes (CLOs), and program learning outcomes (PLOs) as per international standards. As a proof of concept, we have deployed a mechanism at the end of each semester to verify the effectiveness of our model. This mechanism fundamentally surveys the satisfaction levels of all the students enrolled in the HL courses. With the surveys already conducted, a significant level of satisfaction has been noted. Extensive results from these surveys are presented in the paper to further validate the efficiency and robustness of our proposed HL model.

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

Electrical microgrids are deemed to be the future of modern power systems. Microgrids are sophisticated, decentralized, and self-sufficient small-scale power systems consisting of various resources ranging from wind turbines, solar photovoltaics, electric vehicles, smart energy storage, and complex communication infrastructure. However, renewable energy sources such as solar photovoltaics and wind-based generators are highly intermittent, and if not appropriately planned, they can compromise the stability of the grid. Formal methods can define and analyze the functionality and behavior of any system and show if the system design is correct before the actual system is implemented. Although formal methods have been around for many years, it is surprising that little to none are utilized in the design of safety-critical electrical power systems. Currently, in modeling microgrids, few to no attempts of formalization are being used to improve the design reliability and reduce system operating costs and time. This work demonstrates how complex systems such as microgrids can be modeled elegantly using a formal specification method. In this work, the Z state-based formal specification language (Z-Method) is used to model and verify microgrid designs. In this work, 3-interconnected microgrid systems with a high penetration level of solar and wind-based renewable energy sources with plugin hybrid electric vehicles (PHEV) as battery energy storage systems (BESS) are modeled using the Z-method. To the best of authors, the knowledge presented formal method is one of the first reported attempts in modeling microgrid communities using Software Engineering formalism.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

The goal of this study was to evaluate the feasibility of methods based on the Worldwide Harmonized Light-Duty Vehicles Test Procedure Brake Cycle to measure brake wear particle emissions for currently used electrified vehicles. Our results indicated that reducing brake friction work reduced brake wear particle emissions. Commercially available, plug-in hybrid electric vehicles reduced emissions by 85% for PM₁₀, 78% for PM_2.5, and 87% for particle numbers (PNs) compared to vehicles with internal combustion engines. Brake friction work showed a linear relationship with PM₁₀ and PM_2.5. Nanoparticle PM emissions tended to increase slightly with regenerative braking, but did not contribute significantly to the overall PM percentage. the emission events of high number concentrations of nuclei mode particles (<20 nm in diameter) in electric vehicle brake assemblies designed for regenerative braking use under high-temperature, high-load braking conditions with full-friction brakes. The nuclei mode particles amplified the PN emissions and led to a high variability. In strict regulatory certification tests, where measurement reproducibility and stability are required, it is appropriate to measure PNs under brake conditions appropriate for the actual use of electric vehicles rather than under full-friction brake conditions or to remove particle measurements smaller than 20 nm.

Nowadays, according to spectacular improvement in health care and biomedical level, a tremendous amount of data is recorded by hospitals. In addition, the most effective approach to reduce disease mortality is to diagnose it as soon as possible. As a result, data mining by applying machine learning in the field of diseases provides good opportunities to examine the hidden patterns of this collection. An exact forecast of the mortality after heart surgery will cause Successful medical treatment and fewer costs. This research wants to recommend a new stacking predictive model after utilizing the random forest feature importance method to foresee the mortality after heart surgery on a highly unbalanced dataset by using the most practical features. To solve the unbalanced data problem, a combination of the SVM-SMOTE over-sampling algorithm and the Edited-Nearest-Neighbor under-sampling algorithm is used. This research compares the introduced model with some other machine learning classifiers to ensure efficiency through shuffle hold-out and 10-fold cross-validation strategies. In order to validate the performance of the implemented machine learning methods in this research, both shuffle hold-out, and 10-fold cross-validation results indicated that our model had the highest efficiency compared to the other models. Furthermore, the Friedman statistical test is applied to survey the differences between models. The result demonstrates that the introduced stacking model reached the most accurate predicting performance after Logistic Regression.

We present a MATLAB-based, open source reconstruction software for Hybrid Imaging problem arising from Current Density Impedance Imaging (CDII) and Acoustic-Electrical Impedance Tomography (AEIT), utilizing mixed finite element method solver of Darcy toolbox {\tt DarcyLite}~. For a formulation of hybrid inverse problems in impedance tomography the successive substitution iterative schemes are adapted and an reconstruction algorithm is developed. The problem formulation is a class of hybrid imaging modality which is current density impedance imaging or Acoustic-Electrical Impedance Tomography. The proposed algorithm is implemented numerically in two dimensions.

Malware detection is a crucial pillar of cybersecurity and aims to identify and neutralize malware threats. This study investigates how three deep learning algorithms - Convolutional Neural Net-works with One-Dimensional Architecture (CNN1D), Neural Networks (NN), and a hybrid model of one dimensional convolutional neural networks (CNN1D) and Long Short-Term Memory (LSTM) - help detect malware in Portable Executable working (.exe) files. Using a large dataset of tagged malware and benign samples, the effectiveness of these algorithms in distinguishing dangerous from harmless executables is extensively tested. In addition, the study evaluates the computational efficiency of the proposed methods for a variety of hardware configurations, including Central Processing Units (CPU), Graphics Processing Units (GPU), Raspberry Pi (IoT device), and Tensor Processing Units (TPU). Performance metrics provide insight into the scalability and effectiveness of the model across different hardware configurations. The results of this study have significant impact on the development of malware detection, particularly in the context of IoT security. By demonstrating the capabilities and limitations of CNN1D, Neural Networks, and CNN1D with LSTM models, the study provides practitioners with efficient and scalable solutions to real-world malware detection difficulties. In addition, insights from performance tests on various hardware platforms provide important assistance in developing robust security measures in IoT contexts, strengthening cybersecurity defenses in an ever-changing digital landscape.

Tables in the document image are one of the most important entities since they contain crucial information. Therefore, accurate table detection can significantly improve information extraction from tables. In this work, we present a novel end-to-end trainable pipeline, HybridTabNet, for table detection in scanned document images. Our two-stage table detector uses the ResNeXt-101 backbone for feature extraction and Hybrid Task Cascade (HTC) to localize the tables in scanned document images. Moreover, we replace conventional convolutions with deformable convolutions in the backbone network. This enables our network to detect tables of arbitrary layouts precisely. We evaluate our approach comprehensively on ICDAR-13, ICDAR-17 POD, ICDAR-19, TableBank, Marmot, and UNLV. Apart from the ICDAR-17 POD dataset, our proposed HybridTabNet outperforms earlier state-of-the-art results without depending on pre and post-processing steps. Furthermore, to investigate how the proposed method generalizes unseen data, we conduct an exhaustive leave-one-out-evaluation. In comparison to prior state-of-the-art results, our method reduces the relative error by 27.57% on ICDAR-2019-TrackA-Modern, 42.64% on TableBank (Latex), 41.33% on TableBank (Word), 55.73% on TableBank (Latex + Word), 10% on Marmot, and 9.67% on UNLV dataset. The achieved results reflect the superior performance of the proposed method.

Ultrafast laser 3D lithography based on non-linear light-matter interactions, widely known as multi-photon lithography (MPL), offers unrivaled precision rapid prototyping and flexible additive manufacturing options. 3D printing equipment based on MPL are already commercially available, yet there is still no comprehensive understanding of factors determining spatial resolution, accuracy, fabrication throughput, repeatability, and standardized metrology methods for the accurate characterization of the produced 3D objects and their functionalities. The photoexcitation mechanisms, spatial-control or photo-modified volumes, and the variety of processable materials are topics actively investigated. The complexity of the research field is underlined by limited understanding and fragmented knowledge of light-excitation and material response. Research to date has only provided case-specific findings on photoexcitation, chemical modification, and material characterization of the experimental data. In this review, we aim to provide a consistent and comprehensive summary of the existing literature on photopolymerization mechanisms under highly confined spatial and temporal conditions, where, besides the excitation and cross-linking, parameters such as diffusion, temperature accumulation, and the finite amount of monomer molecules start to become of critical importance. Key parameters such as, photoexcitation, polymerization kinetics, and the properties of the additively manufactured materials at the nanoscale in 3D are examined, whereas, the perspectives for future research and as well as emerging applications are outlined.

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled colour correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields better edge and contrast enhancement results without halo effect, colour degradation and is faster and more adaptive than all other algorithms from the literature.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement friction performance rating across a statewide road network. A large, safety-oriented dataset is first generated by integrating network friction and vehicle crash data based on the attributes contributing possibly to friction related crashes. One-, two-, and multi-dimensional clustering analyses, respectively, are then performed to rate pavement friction. The Chi-square test is further employed to validate and identify the practical ratings. It is shown that by effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

The increasing global demand for efficient and sustainable energy storage solutions has driven the exploration of sulfur-based battery technologies. Sulfur’s abundance, low cost, and high theoretical energy density make it a promising candidate for next-generation energy storage, particularly in electric vehicles (EVs) and renewable energy systems. This paper reviews the development of sulfur-based batteries, including lithium-sulfur (Li-S), sodium-sulfur (Na-S), magnesium-sulfur (Mg-S), solid-state sulfur, sulfur-carbon nanocomposites, and hybrid sulfur flow batteries. Each technology is analyzed based on its advantages, challenges, and potential applications. Key research areas such as advanced electrolytes, nanomaterial integration, and sustainable manufacturing are highlighted, along with the current technological barriers to large-scale adoption. This review underscores the importance of continued research and innovation in sulfur-based battery technology to meet future energy storage demands.

Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior.

Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus 13 voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ 14 constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources 15 on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar 16 PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed 17 using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of 18 simulation and statistical results of EO with other optimization techniques showed that EO is more effective 19 and superior.

Abstract: This review addresses the complexities of type 1 diabetes (T1D)and its associated complications, with a par-ticular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of the review is devoted to technological advances in diabetes management, particu-larly the use of Hybrid Closed Loop systems (HCLS) and to the potential of open-source HCLS systems, which could be easily adapted to different patiens’needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

While Spinal Cord Stimulation (SCS) provides satisfaction to almost 2/3 of Persistent Spinal Pain Syndrome-Type 2 (PSPS-T2) patients implanted for refractory chronic back and/or leg pain when not adequately addressed the back pain component, leaves patients in a therapeutic cul-de-sac. Peripheral Nerve field Stimulation (PNfS) has shown interesting results addressing back pain in the same population. Far from placing these two techniques in opposition, we suggest that these approaches could be combined to better treat PSPS-T2 patients. We designed a RCT (CUMPNS), with a 12-month follow-up, to assess the potential added value of PNfS, as a salvage therapy, in PSPS-T2 patients experiencing a “Failed SCS Syndrome” in the back pain component. Fourteen patients were included in this study and randomized into 2 groups (“SCS + PNfS” group/n=6 vs “SCS only” group/n=8). The primary objective of the study was to compare the percentage of back pain surface decrease after 3 months, using a computerized interface to obtain quantitative pain mappings, combined with multi-dimensional SCS outcomes. Back pain surface decreased significantly greater for the ”SCS+PNfS” group (80.2% ± 21.3%) compared to the “SCS only” group (13.2% ± 94.8%) (p=0.012), highlighting the clinical interest of SCS+PNfS, in cases where SCS fails to address back pain.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques