Final Hybrid Research Articles

Comparative analysis of stock selection using a hybrid MCDM approach and modern portfolio theory

Abstract The problem of selecting an optimal set of investment stocks is of a huge interest for both individual and institutional investors. This paper compares the hybrid multiple criteria decision making (MCDM) approach to selecting the best stock to invest in, with the stock selection using modern portfolio theory (MPT). When selecting stocks, it is very important to thoroughly analyse stocks, according to multiple criteria, including their equity market indicators, as well as financial indicators. The objective of the research is to compare the stock selection using a hybrid MCDM approach and MPT, which includes only the equity market indicators. The analysed sample includes 18 stocks, which are CROBEX components on the Croatian capital market from January 2017 to January 2019. The rankings of stocks were calculated using five MCDM methods. These were then used to obtain the final hybrid stock ranking, which was compared to the MPT stock selection. The results show that there is a significant difference in the stock rankings. However, the stocks which have not entered any portfolio in MPT selection were ranked as lowest according to the hybrid MCDM approach, which confirms that those stocks are the worst to invest in. The research can serve as a guidance for investors to use all available stock information in their decision making process of investment.

Hybrid Transition Metal Dichalcogenide/Graphene Microspheres for Hydrogen Evolution Reaction.

A peculiar 3D graphene-based architecture, i.e., partial reduced-Graphene Oxide Aerogel Microspheres (prGOAM), having a dandelion-like morphology with divergent microchannels to implement innovative electrocatalysts for the hydrogen evolution reaction (HER) is investigated in this paper. prGOAM was used as a scaffold to incorporate exfoliated transition metals dichalcogenide (TMDC) nanosheets, and the final hybrid materials have been tested for HER and photo-enhanced HER. The aim was to create a hybrid material where electronic contacts among the two pristine materials are established in a 3D architecture, which might increase the final HER activity while maintaining accessible the TMDC catalytic sites. The adopted bottom-up approach, based on combining electrospraying with freeze-casting techniques, successfully provides a route to prepare TMDC/prGOAM hybrid systems where the dandelion-like morphology is retained. Interestingly, the microspherical morphology is also maintained in the tested electrode and after the electrocatalytic experiments, as demonstrated by scanning electron microscopy images. Comparing the HER activity of the TMDC/prGOAM hybrid systems with that of TMDC/partially reduced-Graphene Oxide (prGO) and TMDC/Vulcan was evidenced in the role of the divergent microchannels present in the 3D architecture. HER photoelectron catalytic (PEC) tests have been carried out and demonstrated an interesting increase in HER performance.

Differential Evolutionary and PI Regulated Converter Technology for Loading Analysis in Renewable Energy Systems

Many remote communities around the world cannot be physically or economically connected to an electrical grid. The main objective of the study of designing an inverter control that attains a lower distortion level in the voltage as well as current waveforms by incorporating an optimization algorithm. The controller should reduce the spikes at the transient loading point when the system is subjected to sudden load changes at the power generating units. And the system is to be integrated with the fuel system also to obtain energy efficiency. The fuel system would be connected in parallel to the DC voltage output of the solar/wind hybrid system. The final hybrid system with fuel cell integration was studied for the total harmonic distortion in the voltage and current waveform. The distortion level in the voltage waveform was found to be 0.25% and that in the current waveform was 1.84%. It is under the IEEE acceptable limits.

Static and ultrafast optical response of two metal nanoparticles glued with a semiconductor quantum dot

A hot-spot of high local field can be created between two closely placed metal nanoparticles and by irradiating them with an appropriate wavelength and polarization of incident light. The strength of the field at the gap between the particles is expected to get enhanced by several orders of magnitude higher than that of the applied field. Placing a semiconductor quantum dot or an analyte molecule at the hot-spot is an essential step towards harnessing the enhanced field for various applications. This article shows that it is possible to position a CdTe quantum dot (QD) between two larger silver nanospheres in a colloidal solution. The extinction spectra measured during growth suggests that the final hybrid nanostructure have two touching Ag nanoparticles (NPs) and a CdTe QD in between them close to the point of contact. Using ultrafast transient measurements, it has been shown that the presence of CdTe QD strongly influences the dynamics when the probe excites the gap plasmon. The method demonstrated here to place the semiconductor QD in between the two Ag NPs is an important step in the area of colloidal self-assembly and for the applications of hot-spot in plasmonic sensing, optoelectronics, energy-harvesting, nanolithography and optical nano-antennas.

Long- and short-term self-attention network for sequential recommendation

With great value in real applications, sequential recommendation aims to recommend users the personalized sequential actions. To achieve better performance, it is essential to consider both long-term preferences and sequential patterns (i.e., short-term dynamics). Compared to widely used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), Self-Attention Network (SAN) obtains a surge of interest due to fewer parameters, highly parallelizable computation, and flexibility in modeling dependencies. However, existing SAN-based models are inadequate in characterizing and distinguishing users’ long-term preferences and short-term demands since they do not emphasize the importance of the current interest and temporal order information of sequences. In this paper, we propose a novel multi-layer long- and short-term self-attention network (LSSA) for sequential recommendation. Specifically, we first split the entire sequence of a user into multiple sub-sequences according to the timespan. Then the first self-attention layer learns the user’s short-term dynamics based on the last sub-sequence, while the second one captures the user’s long-term preferences through the previous sub-sequences and the last one. Finally, we integrate the long- and short-term representations together to form the user’s final hybrid representation. We evaluate the proposed model on three real-world datasets, and our experimental results show that LSSA outperforms state-of-the-art methods with a wide margin.

3D hierarchical porous hybrid nanostructure of carbon nanotubes and N-doped activated carbon

In this work, carbon nanotubes (CNTs)/nitrogen-doped activated carbon (AC) hybrids were designed and fabricated using a facile and one-step synthesis. The synthesis of CNTs is based on the recently discovered phenomenon of thermally-induced polyfurfuryl alcohol (PFA) conversion. Hybrid materials are fabricated through the in-situ free growth of closed carbon nanotubes on low-cost activated carbon substrates which were obtained from green algae or amino acids. Herein, three types of carbon nanotubes were observed to freely grow on an activated carbon background from Chlorella vulgaris or l-lysine, types such as multiwalled carbon and bamboo-like nanotubes, whose structure depends on the background used and conditions of the synthesis. Structure type is identified by analyzing transmission electron microscopy images. HRTEM images reveal the tubes’ outer diameter to be in the range of 20–120 nm. Because the carbon surface for the growth of carbon tubes contains nitrogen, the final hybrid materials also possess pyridinic-N and quaternary-N groups, as indicated by X-ray photoelectron spectra.

Prospective Study of the Impact of Artificial Intelligence Organ-at-risk Dose Prediction on Head and Neck Radiotherapy Plan Quality

Radiation treatment planning for head and neck cancer patients is complex, as the target and normal tissue anatomy are intertwined. The lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori. Artificial intelligence (AI) holds promise as an accurate predictor of the expected dose distribution for OARs. We prospectively studied a decision-support tool (DST) that leverages AI to predict OAR doses by identifying closely matched patients previously treated in an institutional database. The DST used an AI-based dose prediction model based on 376 volumetric modulated arc therapy (VMAT) plans previously treated at our institution. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD) without engaging the DST. The treating physician then used the DST to identify the OAR doses from the closest matched patient (AI directive, AD), and the final Hybrid Directive (HD) for each OAR was the lower value of the AD and PD. The HD OAR metrics were used as the goals for treatment planning. A dose difference of 3 Gray (Gy) was considered clinically significant. Out of the 50 patients, the AD and PD estimates were within 3 Gy for 55% of all OARs, ranging from 27% (esophagus) to 78% (inferior constrictor). The AD was significantly lower for 23% of all estimates (ranging from 13% for the superficial parotid gland to 39% for superior constrictor dose), whereas the PD was lower in 22% of all estimates (ranging from 6% for inferior constrictor to 31% of the contralateral submandibular gland). When one directive was more than 3Gy lower than its comparison, the clinical plan typically met the lower constraint and the achieved average OAR dose reductions of 8.0Gy (AD >3Gy low and used) and 10.9Gy (PD >3Gy low and used). The reduction of OAR doses is mostly statistically significant (P<0.05). Use of the DST improved the final plan metrics in a significant percentage of patients (P<0.05, range 0.0001-0.046), but strictly relying on its estimates would have led to an inferior plan in an equal number of cases. While a more robust model informed by additional cases may further improve the accuracy of AI-based prediction tools, physician input is currently still imperative to generate an optimal plan.Abstract 197; TableOARPredictionAchieved PlanAD is LOWER than PD by > 3 Gy (%case)PD is LOWER than AD by > 3 Gy (%case)PD & AD within 3 Gy (%case)AD is LOWER than PD by > 3 GyPD is LOWER than AD by > 3 Gy%case AchievedAvg OAR reduction (Gy)%case AchievedAvg OAR reduction (Gy)Esophagus26%47%27%83%10.3*100%11.2*Larynx21%18%62%100%7.2*83%6.1*Constrictor_Sup39%17%44%100%7.5*100%13.6*Constrictor_Mid27%27%46%100%11.5*100%12.8*Constrictor_Inf16%6%78%60%8.1*50%6.6*Parotid_CL16%16%68%29%5.580%9.2*Sup_Parotid_IL13%10%77%100%6.9*100%12.3*SMG_CL15%31%54%100%7.2*100%19.4*Oral Cavity33%26%41%60%8.3*100%6.4**Statistical significance of the dose comparison, P < .05 Open table in a new tab

Obtaining new resolutions in carnivore tooth pit morphological analyses: A methodological update for digital taphonomy.

Modern day investigation in fields of archaeology and palaeontology can be greatly characterised by an exponential growth of integrated new technologies, nevertheless, while these advances are of great significance to multiple lines of research, their evaluation and update over time is equally as important. Here we present an application of inter and intra-observer analysis in taphonomy based geometric morphometrics, employing robust non-parametric statistical analyses for the study of experimental carnivore tooth pit morphologies. To fully understand the influence of measurement errors in the collection of this data, our statistical assessment was performed on fully superimposed, partially superimposed and raw landmark coordinates collected from 3D surface scanning. Experimental samples used to assess these errors includes wolf and dog tooth pits used in modern day ecological livestock predation analysis. Results obtained from this study highlight the importance of landmark type in the assessment of error, emphasising the value of semi-landmark models over the use of ambiguous Type III landmarks. In addition to this, data also reveals the importance of observer experience for the collection of data alongside an interesting increase in error when working with fully superimposed landmarks due to the “Pinocchio Effect”. Through this study we are able to redefine the geometric morphometric models used for tooth pit morphological analyses. This final hybrid Type II fixed landmark and semi-landmark model presents a significant reduction in human induced error, generating a more metrically reliable and replicable method that can be used for data pooling in future inter-institutional research. These results can be considered a fundamental step forward for carnivore inspired studies, having an impact on archaeological, palaeontological, modern-day ecological research as well as applications in other forensic sciences.

New Porous Heterostructures Based on Organo-Modified Graphene Oxide for CO2 Capture.

In this work, we report on a facile and rapid synthetic procedure to create highly porous heterostructures with tailored properties through the silylation of organically modified graphene oxide. Three silica precursors with various structural characteristics (comprising alkyl or phenyl groups) were employed to create high-yield silica networks as pillars between the organo-modified graphene oxide layers. The removal of organic molecules through the thermal decomposition generates porous heterostructures with very high surface areas (≥ 500 m2/g), which are very attractive for potential use in diverse applications such as catalysis, adsorption and as fillers in polymer nanocomposites. The final hybrid products were characterized by X-ray diffraction, Fourier transform infrared and X-ray photoelectron spectroscopies, thermogravimetric analysis, scanning electron microscopy and porosity measurements. As proof of principle, the porous heterostructure with the maximum surface area was chosen for investigating its CO2 adsorption properties.

ДИНАМІКА ЗМІН ЖИВОЇ МАСИ ТА ОСОБЛИВОСТІ РОСТУ ТА РОЗВИТКУ МОЛОДНЯКУ СВИНЕЙ РІЗНОГО ПОХОДЖЕННЯ

Одержані результати засвідчують, що запропоноване виробництво фінальних гібридів з умовною часткою крові ¾ ландраса + ¼ великої білої за показником живої маси є найбільш вдалим та адаптованим до технології помірного рівня інтенсивності. Використання чистопородних та помісних тварин, що мають відношення до так званих «білих порід» – велика біла та ландрас, відзначається підвищеними показниками інтенсивності формування та індексів напруги росту. Одержані результати додатково підтверджують складність адаптації кольорових порід (дюрок та особливо породи п’єтрен) до технологічних умов вітчизняних господарств з виробництва свинини. Молодняк свиней одержаний у поєднаннях, де батьківською формою були породи: велика біла, ландрас або дюрок відзначався кращими екстер’єрними особливостями, що характеризують розвиток тварин у довжину. У поєднаннях, де батьківською формою була порода п’єтрен покращувалися широтні проміри та індекс м’ясності на фоні зменшення довжини тулубу.

Going beyond free viewpoint: creating animatable volumetric video of human performances

An end‐to‐end pipeline for the creation of high‐quality animatable volumetric video of human performances is presented. Going beyond the application of free‐viewpoint video, the authors allow re‐animation and alteration of an actor's performance through the enrichment of the captured data with semantics and animation properties. Hybrid geometry‐ and video‐based animation methods are applied that allow a direct animation of the high‐quality data itself instead of creating a CG model that resembles the captured data. Semantic enrichment and animation are achieved by establishing temporal consistency followed by automatic rigging of each 3D frame using a parametric human body model. The hybrid approach combines the flexibility of classical CG animation with the realism of real captured data. For the face, coarse movements are modelled in the geometry only, while very fine and subtle details, often lacking in purely geometric methods, are captured in video textures, which can interactively be combined to form new facial expressions. On top of that, regions that are challenging to synthesise, such as the teeth or the eyes, are learned and filled in realistically in an autoencoder‐based approach. This study covers the full pipeline from capturing, volumetric video production, and enrichment with semantics for the final hybrid animation.

Lexicon-based Sentiment Analysis Using the Particle Swarm Optimization

This work belongs to the field of sentiment analysis; in particular, to opinion and emotion classification using a lexicon-based approach. It solves several problems related to increasing the effectiveness of opinion classification. The first problem is related to lexicon labelling. Human labelling in the field of emotions is often too subjective and ambiguous, and so the possibility of replacement by automatic labelling is examined. This paper offers experimental results using a nature-inspired algorithm—particle swarm optimization—for labelling. This optimization method repeatedly labels all words in a lexicon and evaluates the effectiveness of opinion classification using the lexicon until the optimal labels for words in the lexicon are found. The second problem is that the opinion classification of texts which do not contain words from the lexicon cannot be successfully done using the lexicon-based approach. Therefore, an auxiliary approach, based on a machine learning method, is integrated into the method. This hybrid approach is able to classify more than 99% of texts and achieves better results than the original lexicon-based approach. The final hybrid model can be used for emotion analysis in human–robot interactions.

Mechanical design and energy absorption performances of novel dual scale hybrid plate-lattice mechanical metamaterials

In this paper, novel dual scale hybrid mechanical metamaterials consisting of simple cubic structure (SC), body-centered cubic structure (BCC), face-centered cubic structure (FCC) unit cells at different scale levels were designed and proposed for impact energy absorption, where one large type A unit cell and eight (2 × 2 × 2) small periodicspatially architected type B unit cells with half size of type A unit cell along X, Y and Z directions were integrated together for harvesting final hybrid metamaterials. Comparisons of specific energy absorption performances between novel hybrid mechanical metamaterials and constituent simple single type of unit cell lattice during compression process were performed through experimental investigation, where the failure and energy absorption process were recorded using the digital camera. Afterwards, finite element analysis (FEA) was performed and compared with experimental results for investigating the mechanical benefits of novel hybrid Plate-lattice mechanical metamaterials, it was found that the novel hybrid plate-lattice mechanical metamaterials can generate enhanced specific strength, specific stiffness and elevated energy absorption performances indicators, demonstrated promising industrial application potentials as energy-absorbing materials and structures for aerospace, vehicles, transport industrial sections.

Effect of Inclusion of MOF-Polymer Composite onto a Carbon Foam Material for Hydrogen Storage Application

Despite the extensive studies done on the remarkable characteristics of metal–organic frameworks (MOFs) for gas storage applications, several issues still preclude their widespread commercial lightweight applications. In most cases, MOF materials are produced in powdery form and often require shaping to attain application-specific properties. Fabrication of MOF-polymer composites is considered an attractive approach for shaping MOF powders. In most cases, the final hybrid material retains the intrinsic adsorbing properties of the pristine MOF coupled with other interesting synergistic features which are sometimes superior to their pristine counterparts. In this regard, the use of porous polymers of intrinsic microporosity (such as PIM-1) has proved to be of interest. However, most of these polymers lack some other important properties such as conductivity, which is of paramount importance in a hydrogen storage system. It is on this basis that our study aimed at direct anchoring of a PIM-1/MOF viscous solution onto a carbon foam (CF) substrate. The effects of PIM-1/UiO-66(Zr) inclusion into CF to the resulting thermal properties (thermal conductivity, thermal diffusivity and volumetric heat capacity) as well as hydrogen uptake capacity was investigated. Contrary to our expectations, the incorporation of PIM-1/UiO-66(Zr) into CF only offered better handling but did not lead to the enhancement of thermal conductivity.

The role of accumulative roll bonding after stir casting process to fabricate high-strength and nanostructured AA2024-(SiO2+TiO2) hybrid nanocomposite

Major challenges in casting routes for the fabrication of metal matrix nanocomposites are the weak bonding between reinforcement and matrix, porosity and nonuniform distribution of nanoparticles in the matrix alloy following in the presence of agglomerated nanoparticles and reinforcing nanoparticle free zones. Applying accumulative roll bonding as a severe plastic deformation process can improve the microstructure of the matrix alloy and the distribution of reinforcing nanoparticles in the metal matrix after the production of the casting nanocomposite. In this study, the accumulative roll bonding process was used to complete the composite fabrication process on the as cast AA2024-(SiO2+TiO2) hybrid nanocomposites. The microstructural and mechanical examinations of the accumulative roll bonded hybrid nanocomposites were characterized by measurement of density using the Archimedean immersion method, optical metallography, field emission scanning electron microscopy, high resolution transmission electron microscopy, X-ray diffraction, and hardness and tensile tests. The results showed that the microstructure of the accumulative roll bonded hybrid nanocomposite had a great distribution of nanoparticles in the matrix without any significant porosity. In the final nanostructured hybrid nanocomposite, the grain size of 100 nm was obtained. Mechanical examinations also indicated that the hardness, ultimate tensile strength and yield stress of the nanocomposite increased as the number of accumulative roll bonding cycles increased. After five accumulative roll bonding cycles, hardness, ultimate tensile strength and yield stress values of the nanocomposite increased about 160%, 110% and 300% rather than those of non-accumulative roll bonded monolithic product, respectively.

Designing superhydrophobic and flame retardant photo-cured hybrid coatings

The study investigates the effect of the addition of phenyl phosphine oxide (DAPPO) with acrylate functionality, polyhedral oligomeric silsesquioxane (POSS) (acr-POSS) with acrylate functionality and 1H,1H,2H,2H-Perfluorodecyl acrylate (PFOA) on the superhydrophobicity and fire-resistance of photo-curable urethane-acrylate coatings. The addition of DAPPO to urethane-acrylate (50 wt.%) improved flame resistance. The additions of acr-POSS and PFOA improved surface hydrophobicity through increasing surface roughness by the addition of acr-POSS and decreasing surface energy by the addition of PFOA. The flammability and surface properties of the photo-curable hybrid coatings were evaluated by measuring limiting oxygen index (LOI) and water contact angle (WCA), respectively. Compared with the only urethane-acrylate-containing coating, the final hybrid coating had an increased water contact angle from 72.7°±2 to 147.2°±2 and increased LOI from 17,0 ± 0.08–24,3 ± 0.08. In the final hybrid coating, extinguishing occurred in less than 10 s.

Parental forms and broilers selected by the Center for Genetics &amp; Selection “Smena”

Relevance. The systemic search for the new genetic combinations for the use as parental forms or final hybrids allows for the faster and more cost-effective advancement of the existing poultry crosses and/or development of new ones. Methods. The productive performance in newly developed parental forms (paternal X12, autosexing maternal X34) of broiler breeders and final hybrids (X1234) was assessed on-farm during 2016-2020 in the Center for Genetics &amp; Selection “Smena”. Results. The selection of preparental broiler chicken lines X1 and X2 (Cornish breed) resulted in the improvements in egg fertility percentage and hatch of chicks by 3.6%; live bodyweight at 28 days of age in their hybrid (paternal form X12) was increased by 3.1%. The targeted selection of autosexing maternal form X34 (Plymouth Rock breed) for reproductive performance resulted in the improvements in egg production by 1.3%, the percentage of eggs suitable for incubation by 0.4%, number of chicks per hen housed by 2.1%. The accuracy of sexing with the use of marker genes K-k is 99.5%. The European production efficiency index (EPEF) in the final hybrid broilers X1234 was improved by 11.5%. The conclusion was made that the newly developed parental forms and final hybrid broilers have high genetic productivity potential and could be effectively used in the commercial broiler production.

Two-step weighting method for constructing fourth-order hybrid central WENO scheme

The hybridization of the fourth-order central scheme and a third-order WENO (weighted essentially non-oscillatory) scheme aims to obtain fourth-order accuracy in smooth regions and keep ENO property near shock waves. However, according to our numerical tests, such existing hybrid schemes may generate spurious waves near shocks. To overcome this problem, in this paper, a new two-step method is proposed for constructing a fourth-order hybrid central WENO scheme. The first step is using the traditional weighting method to get the third-order WENO scheme. The second step is to get a hybrid scheme by weighting the third-order upwind scheme and the fourth-order central scheme. Then, the final hybrid WENO scheme is obtained by combining the two weighted schemes through a discontinuity-detecting method for a four-point stencil. Various one- and two-dimensional numerical tests are presented to show that the new scheme can avoid the generation of spurious waves near shock waves, keep the ENO property, and get almost fourth-order accuracy in smooth regions including critical points.

A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys.

This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Optimum Controller Design Go Overall Quality Enhancement of System with Hybrid Renewable Energy Sources

Grid-tied photovoltaic systems are power-generating systems that are connected with grids. Designing of a grid integrated solar wind hybrid energy system for driving loads for improving its reliability and efficiency. This study of designing an inverter control that attains lower distortion level in the voltage as well as current waveforms. The controller should reduce the spikes at the transient loading point when the system is subjected to sudden load changes. The system is to be integrated with the fuel system also to obtain the energy efficiency. The fuel system would be connected in parallel to the DC voltage output of the solar/wind hybrid system. And Improvement in the reactive power output from the system by the inverter control by designed hybrid system that can compensate the reactive power requirement when required. This project should attain the hybrid solar/wind/fuel system with proposed controller to improve the output parameters. The final hybrid system with fuel cell integration was studied for the total harmonic distortion in the voltage and current waveform. The distortion level in the voltage waveform was found to be 0.39% and that in the current waveform was 1.95%. It is under the IEEE acceptable limits.