Články z časopisů s impakt faktorem / Articles from Impact Factor Journals

Permanent URI for this collectionhttp://hdl.handle.net/10084/96217

Kolekce obsahuje články z časopisů (od roku 2008 do současnosti), které v době vydání článku měly impakt faktor (podle databáze InCites Journal Citation Reports společnosti Clarivate Analytics).

Přehled publikační činnosti ze všech časopisů indexovaných v době vydání článků ve Web of Science poskytuje kolekce Publikační činnost VŠB-TUO.

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MXene and polyaniline coated 3D-printed carbon electrode for asymmetric supercapacitor
(Taylor & Francis, 2024) Mappoli, Shidhin; Ghosh, Kalyan; Pumera, Martin
3D printing has emerged as an attractive manufacturing technique in supercapacitor electrodes owing to the precise and customisable fabrication of complex electrode designs, enhancing the performance and efficiency of the device. Despite the advantages, 3D-printed electrodes are limited by their low conductivity and electrochemical properties, predominantly due to the lack of availability of suitable conductive materials. To address this limitation, we modified the 3D-printed nanocarbon (3D-PnC) electrode by activation and surface deposition of Ti3C2Tx MXene. A solid-state asymmetric supercapacitor was fabricated by using 3D-PnC/Ti3C2Tx as the negative electrode and polyaniline (PANI) electrodeposited 3D-printed nanocarbon electrode (3D-PnC@PANI) as the positive electrode. The fabricated symmetric supercapacitor exhibits enhancement in overall voltage window, areal capacitance and energy density. The successful operation of the supercapacitor was demonstrated by the illumination of the red light-emitting diodes. Furthermore, this research opens the possibility of utilising MXene-modified 3D-printed electrodes for various electrochemical applications and devices.
Sustainable refrigeration technology selection: An innovative DEA-TOPSIS hybrid model
(Elsevier, 2024) Arabi, Behrouz; Toloo, Mehdi; Yang, Zaoli; Zhang, Peihao; Xu, Bing
This study proposes a novel multiple criteria decision making (MCDM) framework aimed at selecting refrigeration technologies that are both carbon- and energy-efficient, aligning with the UK's net-zero policies and the UN's Sustainable Development Goals (SDGs). Addressing the challenge of a limited number of competing technologies and the need to incorporate diverse stakeholders' perspectives, we design a hybrid DEA-TOPSIS approach utilizing the Feasible Super-Efficiency Slacks-Based Algorithm (FSESBA). FSESBA proves invaluable, especially in scenarios involving super-efficiency or efficiency trend measurement, where addressing undesirable factors may lead to the well-known infeasibility problem. While we establish the theoretical soundness of the DEA-TOPSIS model, we validate the efficacy of our proposed approach through comparative analysis with conventional methods. Subsequently, we evaluate the choices of present and upcoming refrigeration technologies at a leading UK supermarket. Our findings reveal a shift from prevalent HFO-based technologies in 2020 to CO2-based technologies by 2050, attributed to their lower energy usage and GHG emissions. In addition, maintaining current refrigeration systems could contribute to achieving international and national targets to decrease F-Gas refrigerant usage, although net-zero targets will remain out of reach. In summary, our research findings underscore the potential of the introduced model to reinforce the adoption of novel refrigeration system technology, offering valuable support for the UK SDGs taskforces and net-zero policy formulation.
Performance analysis of a cognitive RIS-NOMA in wireless sensor network
(MDPI, 2024) Thien, Huynh Thanh; Le, Anh-Tu; Minh, Bui Vu; Rejfek, Luboš; Koo, Insoo
The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme.
A comparison between the quality of two level and three levels bidirectional buck-boost converter using the neural network controller
(IEEE, 2024) Gaied, Hajer; Flah, Aymen; Kraiem, Habib; Prokop, Lukáš
A comparison between two-phase and three-phase interlaced DC converter with parallel MOSFET is presented. PWM is evaluated using a two-way DC-DC converter to charge and discharge a battery. The results show an excellent DC voltage gain without an extremely high cycle load. The interlaced DC-DC converters with MOSFETs in parallel in two and three phases offer distinct advantages and limitations. The two-phase converter has a simpler design and a potentially lower cost due to the reduced number of components. However, it can present challenges in terms of precise voltage regulation and current balancing, due to the limited number of switching phases. On the other hand, the three-phase converter offers more precise voltage regulation and improved current balance thanks to its higher number of phases. While this results in increased design complexity and potentially higher cost, it allows for a more uniform distribution of current load among MOSFETs. The choice between the two will depend on the specific requirements of the application, acceptable trade-offs in terms of complexity, cost and performance, as well as the need for accurate voltage regulation and optimal current balancing.
Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization
(Elsevier, 2024) Molu, Reagan Jean Jacques; Tripathi, Bhaskar; Mbasso, Wulfran Fendzi; Naoussi, Serge Raoul Dzonde; Bajaj, Mohit; Wira, Patrice; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav
The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine -tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.
Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set
(Elsevier, 2024) Saeed, Muhammad Haris; Saeed, Muhammad; Rahman, Atiqe Ur; Ahsan, Muhammad; Mohammed, Mazin Abed; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing longlasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi -criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set -based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub -parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
Computation of soliton structure and analysis of chaotic behaviour in quantum deformed Sinh-Gordon model
(PLOS, 2024) Jhangeer, Adil; Ibraheem, Farheen; Jamal, Tahira; Riaz, Muhammad Bilal; Kader, Atef Abdel
Soliton dynamics and nonlinear phenomena in quantum deformation has been investigated through conformal time differential generalized form of q deformed Sinh-Gordon equation. The underlying equation has recently undergone substantial amount of research. In Phase 1, we employed modified auxiliary and new direct extended algebraic methods. Trigonometric, hyperbolic, exponential and rational solutions are successfully extracted using these techniques, coupled with the best possible constraint requirements implemented on parameters to ensure the existence of solutions. The findings, then, are represented by 2D, 3D and contour plots to highlight the various solitons' propagation patterns such as kink-bright, bright, dark, bright-dark, kink, and kink-peakon solitons and solitary wave solutions. It is worth emphasizing that kink dark, dark peakon, dark and dark bright solitons have not been found earlier in literature. In phase 2, the underlying model is examined under various chaos detecting tools for example lyapunov exponents, multistability and time series analysis and bifurcation diagram. Chaotic behavior is investigated using various initial condition and novel results are obtained.
Single atom catalysts based on earth-abundant metals for energy-related applications
(American Chemical Society, 2024) Kment, Štěpán; Bakandritsos, Aristides; Tantis, Iosif; Kmentová, Hana; Zuo, Yunpeng; Henrotte, Olivier; Naldoni, Alberto; Otyepka, Michal; Varma, Rajender S.; Zbořil, Radek
Anthropogenic activities related to population growth, economic development, technological advances, and changes in lifestyle and climate patterns result in a continuous increase in energy consumption. At the same time, the rare metal elements frequently deployed as catalysts in energy related processes are not only costly in view of their low natural abundance, but their availability is often further limited due to geopolitical reasons. Thus, electrochemical energy storage and conversion with earth-abundant metals, mainly in the form of single-atom catalysts (SACs), are highly relevant and timely technologies. In this review the application of earth-abundant SACs in electrochemical energy storage and electrocatalytic conversion of chemicals to fuels or products with high energy content is discussed. The oxygen reduction reaction is also appraised, which is primarily harnessed in fuel cell technologies and metal-air batteries. The coordination, active sites, and mechanistic aspects of transition metal SACs are analyzed for two-electron and four-electron reaction pathways. Further, the electrochemical water splitting with SACs toward green hydrogen fuel is discussed in terms of not only hydrogen evolution reaction but also oxygen evolution reaction. Similarly, the production of ammonia as a clean fuel via electrocatalytic nitrogen reduction reaction is portrayed, highlighting the potential of earth-abundant single metal species.
An activity level based surrogate-assisted evolutionary algorithm for many-objective optimization
(Elsevier, 2024) Pan, Jeng-Shyang; Zhang, An-Ning; Chu, Shu-Chu; Zhao, Jia; Snášel, Václav
Addressing expensive many-objective optimization problems (MaOPs) is a formidable challenge owing to their intricate objective spaces and high computational demands. Surrogate-assisted evolutionary algorithms (SAEAs) have gained prominence because of their ability to tackle MaOPs efficiently. They achieve this by using surrogate models to approximate objective functions, significantly reducing their reliance on costly evaluations. However, the effectiveness of many SAEAs is hampered by their reliance on various surrogate models and optimization strategies, which often result in suboptimal prediction accuracy and optimization performance. This study introduces a novel approach: an activity level based surrogate-assisted reference vector guided evolutionary algorithm specifically designed for expensive MaOPs. Utilizing the Kriging model and an angle penalty distance criterion, this algorithm effectively filters solutions that require evaluation using the original function. It employs a fixed number of training sets,that are updated via a two-screening strategy that leverages activity levels to refine population screening. This process ensures that the reference vector progressively aligns more closely with the Pareto fronts,which is enhanced by the deployment of adjusted adaptive reference vectors, thereby improving the screening precision. The proposed algorithm was tested against six contemporary algorithms using the DTLZ, WFG, and MaF test suites. The experimental results show that the proposed method outperforms other algorithms in most problems. Furthermore, its application to the cloud computing task scheduling problem underscores its practical value, demonstrating its notable effectiveness. The experimental outcomes attest to the robust performance of the algorithm across both test scenarios and real-world applications.
Improved robust model predictive control for PMSM using backstepping control and incorporating integral action with experimental validation
(Elsevier, 2024) Djouadi, Hafidh; Ouari, Kamel; Belkhier, Youcef; Lehouche, Hocine; Bajaj, Mohit; Blažek, Vojtěch
The DC motor is being rapidly replaced in the industry by the permanent magnet synchronous motor (PMSM), which has a number of benefits over it. Nonlinear equations are used to describe the dynamics of the PMSM. It is susceptible to unidentified external disturbances (load), and its properties change over time. These constraints make it more difficult to exercise control. To overcome the non-linearities and the aforementioned shortcomings, non-linear controls are necessary. This manuscript refers to the development of a sturdy high-caliber position tracking controller that incorporates integral action for PMSM. A predictive control law for the speed loop is established, combined with the backstepping control law for the inner loop. The overall strategy can be divided into two distinct elements. The initial stage involves the derivation of a reference electromagnetic torque computed through the generalized non-linear predictive control method. Subsequently, the controller law is formulated utilizing the robust backstepping control technique. One of the cardinal merits of this method lies in its exemption from the requirement of measuring and observing the external disturbances and parametric uncertainties. The efficacy of this cutting-edge control approach is rigorously evaluated in simulation with MATLAB/Simulink environment and experimentally using OPAL-RT, under diverse operating conditions. The findings demonstrate steadfast resilience amidst external disruptions and adjustments to parameters, while ensuring swift convergence, a testament to its robustness and reliability.
A brief review on quantum computing based drug design
(Wiley, 2024) Das, Poulami; Ray, Avishek; Bhattacharyya, Siddhartha; Platoš, Jan; Snášel, Václav; Mršić, Leo; Huang, Tingwen; Zelinka, Ivan
Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre-existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi-criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state-of-the-art comprehensive review of the quantum computing-based methodologies involved in drug design. A comparative study is made about the different quantum-aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state-of-the-art concept of quantum-based drug design. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care
Microwave pyrolysis-prepared engineering carbons from corn cobs and red mombin seeds towards xylene adsorption
(Elsevier, 2024) Matějová, Lenka; Vaštyl, Michal; Jankovská, Zuzana; Cichoňová, Petra; Peikertová, Pavlína; Šeděnková, Ivana; Cruz, Gerardo Juan Francisco; Veliz, Jose Luis Solis; Kania, Ondřej
High-quality biochars/activated carbons were prepared, optimizing individual parameters of energetically-save microwave pyrolysis (raw material loading - 20 vs. 60 g, nitrogen atmosphere - flow vs. batch, ZnCl2 activation) from two agricultural wastes - corn cobs, red mombin seeds. Most promising carbons were examined for gaseous xylene adsorption and showed higher sorption capacity (similar to 250-475 mg(xylene) g(-1)) than commercial carbon (similar to 214 mg(xylene) g(-1)). ZnCl2 activation of both raw materials reduces the fixed carbon content and increases volatiles in activated carbon, suggesting microwave pyrolysis of activated feedstock should take 25 min. While biochars are microporous materials with inhomogeneous low-surface mesopore/macropore network, activated carbons are highly microporous-mesoporous. ZnCl2 activation of both raw materials contributes to formation of extensive high-surface mesopore network (with pore-size < 20 nm) and enlargement of micropore-size, but does not affect the micropore volume. ZnCl2 activation increases H-2 and decreases CH4 production. Microwave pyrolysis of larger raw material loading with ZnCl2 leads to CO2 increase. Best xylene adsorption capacity (475 mg(xylene) g(-1)) was determined for activated carbon produced from 60 g loading of corn cobs in batch nitrogen atmosphere, showing the largest micropore volume, lowest surface polarity and medium rate of graphitization. Large micropore volume, low surface polarity and high rate of graphitization of carbon are xylene sorption capacity-determining factors.
Static characteristics and energy consumption of the pressure-compensated pump
(MDPI, 2024) Kolář, David; Bureček, Adam; Hružík, Lumír; Ledvoň, Marian; Polášek, Tomáš; Jablonská, Jana; Lenhard, Richard
The motivation of this research was to assess the possibility of speed control for the selected pressure-compensated pump. Measured static characteristics of an axial piston pump with pressure compensation are presented in the paper. Based on these characteristics, the pump efficiencies are determined. The characteristics and efficiencies are determined for the different pump outlet pressures, pump speeds, relative displacements and for the different pressures set at the pressure compensator. In addition, the different methods of pump control were compared. These are displacement control, speed control and both controls. The efficiency of each control method was compared based on the determined mechanical input power at the pump drive shaft. By comparing these control methods, it was found that the combination of both control methods can achieve up to 93% savings of mechanical power in the controlled state (stand-by state). Also, the adverse effects resulting from each control method that reduces pump efficiency were defined.
ZrN coating as a source for the synthesis of a new hybrid ceramic layer
(Elsevier, 2024) Gabor, Roman; Cvrček, Ladislav; Kudrnová, Marie; Hlinka, Josef; Večeř, Marek; Buřil, Matěj; Walter, Jan; Čekada, Miha; Drnovšek, Aljaž; Unucka, Petr; Mamulová Kutláková, Kateřina; Motyka, Oldřich; Seidlerová, Jana
The study focuses on an innovative process for the use of a ZrN coating on Ti6Al4V alloy for orthopaedic bone implants. The preparation process combines the technology of physical vapour deposition (PVD) and micro-arc oxidation (MAO) to achieve hydrophobic properties, improved corrosion resistance and enhanced coating adhesion to Ti6Al4V alloy. An alkaline electrolyte and different microarc discharge intensities were used to prepare MAO coatings. The evaluation of the structure and topography of the coatings was performed using SEM with XRPD, EDX, and XPS analysis. The prepared oxide coatings Zr, ZrSiO4, and ZrTiO4 increase the corrosion potential depending on the applied source frequency and thus increase the corrosion resistance of the hybrid system. At the same time, the formation of oxide phases leads to changes in surface topography associated with increasing friction coefficient and better wear resistance.
Numerical solution to the time-fractional Burgers-Huxley equation involving the Mittag-Leffler function
(MDPI, 2024) Hayat, Afzaal Mubashir; Riaz, Muhammad Bilal; Abbas, Muhammad; Alosaimi, Moataz; Jhangeer, Adil; Nazir, Tahir
Fractional differential equations play a significant role in various scientific and engineering disciplines, offering a more sophisticated framework for modeling complex behaviors and phenomena that involve multiple independent variables and non-integer-order derivatives. In the current research, an effective cubic B-spline collocation method is used to obtain the numerical solution of the nonlinear inhomogeneous time-fractional Burgers-Huxley equation. It is implemented with the help of a theta-weighted scheme to solve the proposed problem. The spatial derivative is interpolated using cubic B-spline functions, whereas the temporal derivative is discretized by the Atangana-Baleanu operator and finite difference scheme. The proposed approach is stable across each temporal direction as well as second-order convergent. The study investigates the convergence order, error norms, and graphical visualization of the solution for various values of the non-integer parameter. The efficacy of the technique is assessed by implementing it on three test examples and we find that it is more efficient than some existing methods in the literature. To our knowledge, no prior application of this approach has been made for the numerical solution of the given problem, making it a first in this regard.
Association of selected adipokines with vitamin D deficiency in children with inflammatory bowel disease
(BMC, 2024) Geryk, Miloš; Kučerová, Veronika; Velgáňová-Véghová, Mária; Foltenová, Hana; Bouchalová, Kateřina; Karásek, David; Radvansky Jr., Martin; Karásková, Eva
Background: Adipose tissue is significantly involved in inflammatory bowel disease (IBD). Vitamin D can affect both adipogenesis and inflammation. The aim of this study was to compare the production of selected adipokines, potentially involved in the pathogenesis of IBD - adiponectin, resistin, retinol binding protein 4 (RBP-4), adipocyte fatty acid binding protein and nesfatin-1 in children with IBD according to the presence of 25-hydroxyvitamin D (25(OH)D) deficiency. Methods: The study was conducted as a case-control study in pediatric patients with IBD and healthy children of the same sex and age. In addition to adipokines and 25(OH)D, anthropometric parameters, markers of inflammation and disease activity were assessed in all participants. Results: Children with IBD had significantly higher resistin levels regardless of 25(OH)D levels. IBD patients with 25(OH)D deficiency only had significantly lower RBP-4 compared to healthy controls and also compared to IBD patients without 25(OH)D deficiency. No other significant differences in adipokines were found in children with IBD with or without 25(OH)D deficiency. 25(OH)D levels in IBD patients corelated with RBP-4 only, and did not correlate with other adipokines. Conclusions: Whether the lower RBP-4 levels in the 25(OH)D-deficient group of IBD patients directly reflect vitamin D deficiency remains uncertain. The production of other adipokines does not appear to be directly related to vitamin D deficiency.
Evolution of beryllium minerals in granitic pegmatite Maršíkov D6e, Czech Republic: Complex breakdown of primary beryl by internal and external hydrothermal-metamorphic fluids
(Elsevier, 2024) Chládek, Štěpán; Novák, Milan; Uher, Pavel; Gadas, Petr; Matýsek, Dalibor; Bačík, Peter; Škoda, Radek
Beryllium mineralization was studied by EPMA and XRD techniques in the beryl-columbite pegmatite D6e from the Marsikov District, Bohemian Massif, Czech Republic. A detailed study of microtextures in BSE images revealed a complex formation of fine-grained secondary Be-silicates at the expense of primary beryl and earlier secondary Be-minerals in the following proximal and distal assemblages: (A) primary magmatic beryl; (B) proximal secondary beryl; (C) proximal bertrandite + K-feldspar and minor muscovite, chamosite, gismondine-Ca and quartz; (D1) proximal assemblages of milarite + gismondine-Ca and bavenite-bohseite + epidote; (D2) distal assemblages on brittle tectonic cracks including milarite, bavenite-bohseite, albite, K-feldspar, quartz and rare phenakite, and (D3) epidote, bavenite-bohseite, quartz, albite, K-feldspar and minor milarite. A formation of secondary Mg,Fe,V,Na-enriched beryl (B) is connected with a mixing of residual (pegmatite) and external Ca,Mg,Fe,V-enriched fluids from the host amphibole gneiss at T similar to 300-400 degrees C and P similar to 200-400 MPa. The assemblage (C) formed due to an income of K,Mg,Ca-enriched fluids (residual + external) at T similar to 150-300 degrees C. The subsequent proximal (D1) and distal (D2, D3) assemblages formed during an moderate to strong income of Ca-rich external fluids from the host rocks related to retrograde hydrothermal-metamorphic overprint manifested by the Alpine-type hydrothermal veins. A common presence of epidote in the assemblages with bavenite-bohseite suggests crystallization at T < similar to 200-300 degrees C. Detailed textural and paragenetic study of primary and secondary Be-minerals is a useful tool to recognize and study various processes proceeded during subsolidus evolution of granitic pegmatites.
Investigating pseudo parabolic dynamics through phase portraits, sensitivity, chaos and soliton behavior
(Springer Nature, 2024) Jhangeer, Adil; Ibraheem, Farheen; Jamal, Tahira; Rahimzai, Ariana Abdul; Khan, Ilyas
This research examines pseudoparabolic nonlinear Oskolkov-Benjamin-Bona-Mahony-Burgers (OBBMB) equation, widely applicable in fields like optical fiber, soil consolidation, thermodynamics, nonlinear networks, wave propagation, and fluid flow in rock discontinuities. Wave transformation and the generalized Kudryashov method is utilized to derive ordinary differential equations (ODE) and obtain analytical solutions, including bright, anti-kink, dark, and kink solitons. The system of ODE, has been then examined by means of bifurcation analysis at the equilibrium points taking parameter variation into account. Furthermore, in order to get insight into the influence of some external force perturbation theory has been employed. For this purpose, a variety of chaos detecting techniques, for instance poincar & eacute; diagram, time series profile, 3D phase portraits, multistability investigation, lyapounov exponents and bifurcation diagram are implemented to identify the quasi periodic and chaotic motions of the perturbed dynamical model. These techniques enabled to analyze how perturbed dynamical system behaves chaotically and departs from regular patterns. Moreover, it is observed that the underlying model is quite sensitivity, as it changing dramatically even with slight changes to the initial condition. The findings are intriguing, novel and theoretically useful in mathematical and physical models. These provide a valuable mechanism to scientists and researchers to investigate how these perturbations influence the system's behavior and the extent to which it deviates from the unperturbed case.
Retinal image dataset of infants and retinopathy of prematurity
(Springer Nature, 2024) Timkovič, Juraj; Nowaková, Jana; Kubíček, Jan; Hasal, Martin; Varyšová, Alice; Kolarčík, Lukáš; Maršolková, Kristýna; Augustynek, Martin; Snášel, Václav
Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients' information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.
Dual resource constrained flexible job shop scheduling with sequence-dependent setup time
(Wiley, 2024) Barak, Sasan; Javanmard, Shima; Moghdani, Reza
This study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence-dependent setup times (DRCFJS-SDSTs). We introduce a pioneering tri-objective mixed-integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi-skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence-dependent setup time-related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP-hard nature of the proposed DRCFJS-SDST, we employ the epsilon constraint method to derive exact optimal solutions for small-scale problems. For larger instances, we develop a modified variant of the multi-objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi-objective particle swarm optimization (MOPSO) and non-dominated sorted genetic algorithm (NSGA-II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA-II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies.

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