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).

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Now showing 1 - 20 out of 6681 results

Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems
(Elsevier, 2024) Pandya, Sundaram B.; Kalita, Kanak; Jangir, Pradeep; Čep, Robert; Migdady, Hazem; Chohan, Jasgurpreet Singh; Abualigah, Laith; Mallik, Saurav
In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with nondominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://github.com/kanak02/MORIME
Calculating torque, back-EMF, inductance, and unbalanced magnetic force for a hybrid electrical vehicle by in-wheel drive application
(Springer Nature, 2024) Hosseinpour, Alireza; Rahideh, Akbar; Abbas, Ahmed; Iqbal, Atif; El-Bayeh, Claude Ziad; Flah, Aymen; Ali, Enas; Ghaly, Ramy N. R.
To use a Hybrid Excitation Synchronous Machine (HESM) in a hybrid electrical vehicle (HEV), its performance indicators such as back-EMF, inductance and unbalanced magnetic force should be computed preferably by an analytical method. First, the back-EMF is calculated by considering alternate-teeth and all-teeth non-overlapping and overlapping windings. The effects of three types of magnetization patterns including the radial, parallel and Halbach magnetizations on the back-EMF waveform have also been investigated. Then, the self-inductance of the stator and rotor windings, the mutual inductance between the stator and rotor windings, and the mutual inductance between the stator phases are computed. Next, the components of the unbalanced magnetic force (UMF) in the direction of the x and y axes and its amplitude are computed. Moreover, the effects of the magnetization patterns on those magnetic pulls are investigated. To minimize the UMFs, symmetry must be implemented in the excitation sources; therefore, first the stator winding then the permanent magnet and rotor winding are modified in such a way that the UMFs are reduced. Increasing the temperature leads to a weakening of the magnet's residual flux density, which strongly affects the performance characteristics of the electric machine such as Back-EMF and UMF. Finally, the ratio of the permanent magnet flux to the rotor flux is determined in such a way that the average torque is maximized. In this section, the effects of three magnetization patterns will be investigated.
Advanced materials for micro/nanorobotics
(Royal Society of Chemistry, 2024) Kim, Jeonghyo; Mayorga-Burrezo, Paula; Song, Su-Jin; Mayorga-Martinez, Carmen C.; Medina-Sánchez, Mariana; Pané, Salvador; Pumera, Martin
Autonomous micro/nanorobots capable of performing programmed missions are at the forefront of next-generation micromachinery. These small robotic systems are predominantly constructed using functional components sourced from micro- and nanoscale materials; therefore, combining them with various advanced materials represents a pivotal direction toward achieving a higher level of intelligence and multifunctionality. This review provides a comprehensive overview of advanced materials for innovative micro/nanorobotics, focusing on the five families of materials that have witnessed the most rapid advancements over the last decade: two-dimensional materials, metal-organic frameworks, semiconductors, polymers, and biological cells. Their unique physicochemical, mechanical, optical, and biological properties have been integrated into micro/nanorobots to achieve greater maneuverability, programmability, intelligence, and multifunctionality in collective behaviors. The design and fabrication methods for hybrid robotic systems are discussed based on the material categories. In addition, their promising potential for powering motion and/or (multi-)functionality is described and the fundamental principles underlying them are explained. Finally, their extensive use in a variety of applications, including environmental remediation, (bio)sensing, therapeutics, etc., and remaining challenges and perspectives for future research are discussed.
Optimal structure to maximize torque per volume for the consequent-pole PMSM and investigating the temperature effect
(IEEE, 2024) Hosseinpour, Alireza; Abbas, Ahmed; Sadegh, Mahmoud Oukati; Iqbal, Atif; Flah, Aymen; Prokop, Lukáš; Ali, Enas; Ghaly, Ramy N. R.
Heat removal, maximizing torque, minimizing losses, volume, cost, and temperature effect play essential roles in electrical vehicle applications. An inner-rotor consequent-pole permanent magnet synchronous machine (CPPMSM) merits suitable losses, cost, and heat rejection. Hence, first, a two-dimensional model of CPPMSM is explained based on solving Maxwell's equations in all regions of the machine. Then, all the components of torque, back-EMF, inductance, and unbalanced magnetic forces in the direction of the X-axis and Y-axis and their magnitudes are calculated. Afterward, the overload capability and the torque-speed characteristic are determined based on the average torque. Therefore, to maximize the torque/volume ratio, four metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Teaching Learn Base Optimization (TLBO), have been implemented, and the mentioned index is optimized. Since the said algorithms usually can minimize, its inverse is minimized instead of the index mentioned above being maximized. At this stage, the effect of three types of magnetization patterns, i.e., radial, parallel, and bar magnet in shifting, is also considered. The flux density of the permanent magnet changes concerning temperature. Finally, the effect of these changes on cogging, reluctance, and instantaneous torque, as well as back-EMF, unbalance magnetic force (UMF), torque-speed characteristic, and overload capability diagram, will be analyzed. The simulation was performed using MATLAB software.
Measuring the energy for the molecular graphs of antiviral agents: Hydroxychloroquine, Chloroquine and Remdesivir
(Elsevier, 2024) Aftab, Muhammad Haroon; Akgül, Ali; Riaz, Muhammad Bilal; Hussain, Muhammad; Jebreen, Kamel; Kanj, Hassan, Hassan
We consider the energy for the molecular graphs of antiviral agents like Hydroxychloroquine, Remdesivir and Chloroquine. These drugs play a vital role in the treatment of COVID-19. Let Gamma(1), Gamma(2) and Gamma(3) be the n-dimensional graphs of the molecular structures of antiviral agents Hydroxychloroquine, Chloroquine and Remdesivir, respectively. We define their energies as E '(Gamma(1)) = Sigma vertical bar lambda(i)'vertical bar, E '(Gamma 2) = Sigma vertical bar lambda(j)'vertical bar and E '(Gamma 3) = Sigma vertical bar lambda(k)'vertical bar, respectively. Where the sets {lambda(1)'(Gamma(1)), lambda(2)'(Gamma(1)), lambda(3)'(Gamma(1)), ..., lambda(n)'(Gamma(1))}, {lambda(1)'(Gamma(2)), lambda(2)'(Gamma(2)), lambda(3)'(Gamma(2)), ..., lambda(n)'(Gamma(2))} and { lambda(1)'(Gamma 3), lambda(2)'(Gamma 3), lambda(3)'(Gamma 3), ..., lambda(n)'(Gamma 3)} depict the eigenvalues for the adjacency matrices of Gamma 1, Gamma 2 and Gamma 3, respectively. We have developed some basic ideas and properties in order to measure the energies for the antiviral agents Hydroxychloroquine, Chloroquine and Remdesivir.
Many-objective grasshopper optimization algorithm (MaOGOA): A new many-objective optimization technique for solving engineering design problems
(Springer Nature, 2024) Kalita, Kanak; Jangir, Pradeep; Čep, Robert; Pandya, Sundaram B.; Abualigah, Laith
In metaheuristic multi-objective optimization, the term effectiveness is used to describe the performance of a metaheuristic algorithm in achieving two main goals-converging its solutions towards the Pareto front and ensuring these solutions are well-spread across the front. Achieving these objectives is particularly challenging in optimization problems with more than three objectives, known as many-objective optimization problems. Multi-objective algorithms often fall short in exerting adequate selection pressure towards the Pareto front in these scenarios and difficult to keep solutions evenly distributed, especially in cases with irregular Pareto fronts. In this study, the focus is on overcoming these challenges by developing an innovative and efficient a novel Many-Objective Grasshopper Optimisation Algorithm (MaOGOA). MaOGOA incorporates reference point, niche preserve and information feedback mechanism (IFM) for superior convergence and diversity. A comprehensive array of quality metrics is utilized to characterize the preferred attributes of Pareto Front approximations, focusing on convergence, uniformity and expansiveness diversity in terms of IGD, HV and RT metrics. It acknowledged that MaOGOA algorithm is efficient for many-objective optimization challenges. These findings confirm the approach effectiveness and competitive performance. The MaOGOA efficiency is thoroughly examined on WFG1-WFG9 benchmark problem with 5, 7 and 9 objectives and five real-world (RWMaOP1- RWMaOP5) problem, contrasting it with MaOSCA, MaOPSO, MOEA/DD, NSGA-III, KnEA, RvEA and GrEA algorithms. The findings demonstrate MaOGOA superior performance against these algorithms.
Experimental investigation on solidification cracking & intergranular corrosion of AISI 321 & AISI 316 L dissimilar weld on pulsed current gas tungsten arc welding (PCGTAW)
(Elsevier, 2024) Patil, Tejas; Bhosale, Ajit; Manikandan, S. G. K.; Jose, Bibin; Naidu, Mithul; Salunkhe, Sachin; Čep, Robert; Nasr, Emad Abouel
Dissimilar metal combinations are frequently employed in the power generation and nuclear industries. Where stainless steel piping systems are connected to pressure vessels made of low- alloy steel, the subsystems of liquid rocket engines also have different, dissimilar material combinations. Dissimilar welding plays a vital role in ensuring the integrity, performance, and reliability of components and structures operating in cryogenic environments, in this study, plates of AISI 316L and AISI 321, each 5 mm thick, were successfully joined using the pulsed current gas tungsten arc welding (PCGTAW) technique with optimized process parameters. These weld joints are mostly present in rocket engines subjected to a cryogenic environment. Due to the low temperature environment, the metallurgical properties of these joints change, which affects their mechanical properties. As it is a structural part, PCGTAW welding is most common method for joining this kind of material. In this work, Microstructural analysis of the weldment revealed a combination of vermicular, lacy, and acicular ferrite morphologies in the fusion zone at the root, mid, and crown locations. Furthermore, no solidification cracking was detected in the weldments based on the optical micrograph and SEM results. Intergranular corrosion (IGC) testing indicated the absence of a ditch structure, suggesting that the heat-affected zone (HAZ) on both sides of the weld joint was not being susceptible to sensitization. However, the HAZ of the AISI 316L side exhibited coarser grains compared to AISI 321. Analysis of tensile properties revealed a significant influence of the testing environment on the tensile strength of the dissimilar welded joints. At room temperature, the average ultimate tensile strength (UTS) was measured as 621 MPa. Remarkably, at cryogenic conditions, the average tensile properties significantly increased to 1319 MPa. Microhardness analysis showed the highest hardness associated with the AISI 321 side. The fusion zone exhibited a large deviation in the hardness profile (205 +/- 10 HV), with the highest average hardness observed in the middle part of the weld. However, the hot cracking behavior of the weld was investigated by using a suutula diagram at various locations of the weld. The investigation revealed that the Creq/Nieq eq /Ni eq ratio exceeded the critical threshold value, effectively diminishing the propensity for hot cracking in the fusion zone. Overall, these findings underscore the effectiveness of the PCGTAW technique in joining dissimilar materials, as well as the importance of microstructural and mechanical property evaluations, especially under extreme operating conditions such as cryogenic temperatures.
Phosphoric acid salts of amino acids as a source of oligopeptides on the early Earth
(Springer Nature, 2024) Šponer, Judit E.; Coulon, Rémi; Otyepka, Michal; Šponer, Jiří; Siegle, Alexander F.; Trapp, Oliver; Ślepokura, Katarzyna; Zdráhal, Zbyněk; Šedo, Ondrej
Because of their unique proton-conductivity, chains of phosphoric acid molecules are excellent proton-transfer catalysts. Here we demonstrate that this property could have been exploited for the prebiotic synthesis of the first oligopeptide sequences on our planet. Our results suggest that drying highly diluted solutions containing amino acids (like glycine, histidine and arginine) and phosphates in comparable concentrations at elevated temperatures (ca. 80 degrees C) in an acidic environment could lead to the accumulation of amino acid:phosphoric acid crystalline salts. Subsequent heating of these materials at 100 degrees C for 1-3 days results in the formation of oligoglycines consisting of up to 24 monomeric units, while arginine and histidine form shorter oligomers (up to trimers) only. Overall, our results suggest that combining the catalytic effect of phosphate chains with the crystalline order present in amino acid:phosphoric acid salts represents a viable solution that could be utilized to generate the first oligopeptide sequences in a mild acidic hydrothermal field scenario. Further, we propose that crystallization could help overcoming cyclic oligomer formation that is a generally known bottleneck of prebiotic polymerization processes preventing further chain growth.
Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications
(Elsevier, 2024) Kalita, Kanak; Pandya, Sundaram B.; Čep, Robert; Jangir, Pradeep; Abualigah, Laith
Many-objective optimization (MaO) is an important aspect of engineering scenarios. In manyobjective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel ManyObjective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.
Impact of glazing type, window-to-wall ratio, and orientation on building energy savings quality: A parametric analysis in Algerian climatic conditions
(Elsevier, 2024) Cherier, Mohamed Kamal; Hamdani, Maamar; Kamel, Ehsan; Guermoui, Mawloud; Bekkouche, Sidi Mohammed El Amine; Al-Saadi, Saleh; Djeffal, Rachid; Bashir, Maaz Osman; Elshekh, Ali. E. A.; Drozdová, Ľubomíra; Kanan, Mohammad; Flah, Aymen
Opaque surfaces, such as walls, are well-known for their significant contributions to heat loss and energy demands in buildings. However, transparent surfaces, such as windows, are equally critical to a building's energy performance. The design of these transparent elements requires a careful balance of various factors, including window size, glazing type, and orientation, each of which plays a pivotal role in enhancing energy efficiency. This study explores the optimization of these factors during the design process, emphasizing their impact on the overall building performance. This research evaluates the potential energy savings in a building archetype representative of the Algerian building stock. Utilizing the EnergyPlus simulation tool, the study conducted 1152 simulations on a baseline model to generate a comprehensive dataset detailing the building's energy demands for heating and cooling across various climatic conditions. The findings reveal that annual energy savings for this type of housing essentially depend on its climatic zone and can range from 6.92 % for a hot semi-arid climate (Bsh) to reach a maximum of 9.75 % in a cold semiarid climate (Bsk), a window-to-wall ratio (WWR) of 60 % typically maximizes energy efficiency, low-E glazing proved most effective in most cases, although regions needing significant solar protection favored alternative glazing types. Optimal window orientation generally trends Eastward, except in regions where southern exposure better supports solar management, highlighting the complex relationship between architectural design choices and energy efficiency.
Exploring electrospun Nafion nanofibers: Bibliographic insights, emerging possibilities, and diverse applications
(AIP Publishing, 2024) Avvari, Venkata Dinesh, VENKATA DINESH; Sreekanth, P. S. Rama; Shanmugam, Raghavanantham; Salunkhe, Sachin; Čep, Robert; Nasr, Emad Abouel; Kimmer, D.
Over the past several decades, there has been a significant surge in interest regarding the use of organic-inorganic hybrid polymers and nanocomposite membranes. The reasons for this are improved attributes, reduced costs, and the additional stability the influence membrane provides. This Review outlines the various techniques and methodologies used to prepare Nafion and its composites, delineating the promising benefits of the electrospinning process. Electrospinning has emerged as a versatile and promising technique for fabricating nanofibers with unique properties and wide-ranging applications. This study explores the electrospinning of Nafion, a perfluorosulfonic acid polymer widely known for its exceptional proton conductivity and chemical stability, into nanofibrous structures, unlocking new possibilities yet unknown features of its inherent properties. The morphology and chemical structure of the resulting nanofibers is analyzed. A thorough bibliographic analysis of electrospun Nafion was presented using the PRISMA approach for methodically presenting the report. Network visualization of connected authors and categorizing application-specific publications are also discussed. Moreover, the electrospinning parameters and blends are systematically investigated to optimize the production of Nafion nanofibers for various applications in fuel cells, water treatment, actuators, sensors, and energy harvesting. The challenges involved in electrospinning Nafion, Nafion nanocomposites, and their variants are also presented, with a discussion delineating the future scope. This work concludes by emphasizing the interdisciplinary character of the Nafion polymer and its composites, connecting materials science and the intricate issues presented by various sectors.
Assessing the feasibility and quality performance of a renewable Energy-Based hybrid microgrid for electrification of remote communities
(Elsevier, 2024) Islam, Md Ashraful; Ali, M. M. Naushad; Mollick, Tajrian; Islam, Amirul; Benitez, Ian B.; Habib, Sidahmed Sidi; Al Mansur, Ahmed; Lipu, Molla Shahadat Hossain; Flah, Aymen; Kanan, Mohammad
Access to reliable energy is crucial for development, yet many rural areas in southern Bangladesh suffer from electricity shortages, impeding essential services and hindering social and economic progress. This paper proposes integrating renewable energy-based microgrids to provide sustainable and reliable electricity, thereby improving living conditions and boosting economic growth. A detailed survey in Ruma, Bandarban, was conducted for load estimation. Simulation results for on-grid and off-grid microgrids are obtained using HOMER Pro and PVsyst software. The off-grid system includes 21.8 kW of PV, 15 kW of hydro, and 222 kWh of battery storage, while the on-grid system includes a 200 kW PV system and a 15 kW hydro turbine. The levelized cost of energy (LCOE) is 0.15 USD/kWh off-grid and 0.03 USD/kWh on-grid. The on-grid system shows economic sustainability with a 6.8-year break-even point, 13 % IRR, and 8.7 % ROI. Environmental analysis shows significant greenhouse gas reductions, with CO2 emissions decreasing from 227,778 kg/year to 199,016 kg/year. Additionally, a sensitivity analysis is conducted, which underscores the resilience of the proposed hybrid microgrid system to weather variations and cost fluctuations. This paper provides a comprehensive foundation for policymakers to consider renewable microgrids as a solution for rural electrification in southern Bangladesh, utilizing solar and hydropower resources.
Optical damage thresholds of single-mode fiber-tip spintronic terahertz emitters
(Optica Publishing Group, 2024) Paries, Felix; Selz, Felix; Santos, Cristiane N.; Lampin, Jean-Francois; Koleják, Pierre; Lezier, Geoffrey; Troadec, David; Tiercelin, Nicolas; Vanwolleghem, Mathias; Addda, Ahmed; Kampfrath, Tobias; Seifert, Tom S.; Von Freymann, Georg; Molter, Daniel
Spintronic terahertz emitters (STEs) are gapless, ultrabroadband terahertz sources that can be driven within a wide pump-wavelength and repetition-rate range. While STEs driven by strong pump lasers operating at kilohertz repetition rates excel in generating high electric field strengths for terahertz spectroscopy or ellipsometry, newly advancing technologies such as ultrafast modulation of terahertz polarization, scanning tunneling microscopy, laser terahertz emission nanoscopy, and fully fiber-coupled integrated systems demand an STE pumping at megahertz repetition rates. In all these applications the available terahertz power is ultimately limited by the STE's optical damage threshold. However, to date, only very few publications have targeted this crucial topic and investigations beyond the kilohertz repetition-rate regime are missing. Here, we present a complete study of our single-mode fiber-tip STEs' optical damage thresholds covering the kilohertz, megahertz, and gigahertz repetition-rate regimes as well as continuous-wave irradiation. As a very important finding, we introduce the necessity of classifying the optical damage threshold into two regimes: a low-repetition-rate regime characterized by a nearly constant fluence threshold, and a high-repetition-rate regime characterized by an antiproportional fluence dependence ("average-power threshold"). For our single-mode fiber-tip STEs, the transition between these regimes occurs around 4 MHz. Moreover, we present a cohesive theory of the damaging thermodynamical processes at play and identify temperature-driven inter-layer diffusion as the primary cause of the STE failure. These findings are substantiated by atomic force microscopy, infrared scattering-type scanning near-field optical microscopy, and scanning of spintronic terahertz emission.
MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals
(Elsevier, 2024) Karnati, Mohan; Sahu, Geet; Yadav, Akanksha; Seal, Ayan; Jaworek-Korjakowska, Joanna; Penhaker, Marek; Krejcar, Ondřej
Approximately 65 million individuals experience epilepsy globally. Surgery or medication cannot cure more than 30% of epilepsy patients.However, through therapeutic intervention, anticipating a seizure can help us avoid it. According to previous studies, aberrant activity inside the brain begins a few minutes before the onset of a seizure, known as a pre-ictal state. Many researchers have attempted to anticipate the pre-ictal condition of a seizure; however, achieving high sensitivity and specificity remains challenging. Therefore, deep learning-based early diagnostic tools for epilepsy therapies using electroencephalogram (EEG) signals are urgently needed. Traditional methods perform well in binary epilepsy scenarios, such as normal vs. ictal, but poorly in ternary situations, such as ictal vs. normal vs. inter-ictal. This study proposes a multi-scale dilated convolution-based network (MD-DCNN) to predict seizures or epilepsy. Traditional DCNNs for epilepsy classification overfit due to insufficient training data (fewer subjects). Windowing 2-sec EEG recordings and extracting the frequency sub-band from each window prevents overfitting in deep networks, which lack training data. We convert each segmented window and its sub-bands into scalogram images and input them into MD-DCNN. The proposed MD-DCNN combines data from several scales without narrowing the acquisition domain. Integrating detailed information into high-level semantic features improves network interpretation and classification. The proposed MD-DCNN is evaluated for two-class, three-class, and cross-database strategy problems using three publicly accessible databases. Experiments show that the MD-DCNN statistically performs better than 13 other current approaches. This demonstrates its potential for developing equipment capable of measuring, monitoring, and recording EEG signals to diagnose epilepsy.
Improved ammonia synthesis and energy output from zinc-nitrate batteries by spin-state regulation in perovskite oxides
(American Chemical Society, 2025) Guo, Hele; Zhou, Yazhou; Chu, Kaibin; Cao, Xueying; Qin, Jingjing; Zhang, Nan; Roeffaers, Maarten B. J.; Zbořil, Radek; Hofkens, Johan; Müllen, Klaus; Lai, Feili; Liu, Tianxi
Electrocatalytic nitrate reduction to ammonia (eNRA) is a promising route toward environmental sustainability and clean energy. However, its efficiency is often limited by the slow conversion of intermediates due to spin-forbidden processes. Here, we introduce a novel A-site high-entropy strategy to develop a new perovskite oxide (La0.2Pr0.2Nd0.2Ba0.2Sr0.2)CoO3-delta (LPNBSC) for eNRA. The LPNBSC possesses a higher concentration of high-spin (HS) cobalt-active centers, resulting from an increased concentration of [CoO5] structural motifs compared to conventional LaCoO3. Consequently, this material exhibits a significantly improved electrocatalytic performance toward ammonia (NH3) production, resulting in a 3-fold increase in yield rate (129 mu mol h-1 mgcat. -1) and a 2-fold increase in Faradaic efficiency (FE, 76%) compared to LaCoO3 at the optimal potential. Furthermore, the LPNBSC-based Zn-nitrate battery reaches a maximum FE of 82% and an NH3 yield rate of 57 mu mol h-1 cm-2. Density functional theory calculations reveal that A-site high-entropy management in perovskites facilitates nitrate activation and potentially optimizes the thermodynamic rate-determining step of the eNRA process, namely, *HNO3 + H+ + e- -> *NO2 + H2O. This work presents an efficient concept for modulating the spin state of the B-site metal in perovskites and offers valuable insights for the design of high-performance eNRA catalysts.
Biosensors for detection of pesticide residue, mycotoxins and heavy metals in fruits and vegetables: A concise review
(Elsevier, 2024) Balkrishna, Acharya; Kumari, Amita; Kumar, Ashwani; Arya, Vedpriya; Chauhan, Ankush; Upadhyay, Navneet Kumar; Guleria, Ishita, Ishita; Amarowicz, Ryszard, Ryszard; Kumar, Dinesh; Kuča, Kamil
Consumer concerns and government regulations focused on the safety of fruits and vegetables dictate the need to analyze various food contaminants of concern. Major contaminants include pesticide residues, mycotoxins, and heavy metals. The most significant global challenge is their prompt detection in fruits and vegetables (conventionally grown/organic produce). Foodborne outbreaks are detrimental to the economy and public health both nationally and on a global scale. The scope of the study is to analyze and summarize advanced techniques like immunoassay and advances in biosensors for the detection of food contaminants so that the impact of the latter can be minimized. The preferable techniques for pesticide residues, mycotoxins, and heavy metals detections are outlined, along with their merits, demerits, and future recommendations to ensure adequate quality control measures. The Ag and Au-based biosensors and quantum-dot-based biosensors, especially lateral flow immunoassay, have shown fast and on-spot detection of pesticides and mycotoxins, respectively. Whereas, for heavy metals, electrochemical biosensors are recommended. Biosensors are found highly sensitive, specific, simple, and user-friendly. The higher cost of advanced biosensors, single-time use, and specificity to few contaminants limit their use. Nanotechnology interventions can increase biosensor performance, leading to more economical and productive detection of food contaminants. A comprehensive and efficient approach that can quickly identify multiple food contaminants while being cost-effective and userfriendly is the need of the hour.
Symbiotic communication systems in the Internet of Things: A framework for double adaptive performance analysis
(IEEE, 2026) Vu, Thai-Hoc; Nguyen, Tien-Tung; Nguyen, Tan N; Tu, Lam-Thanh; Vozňák, Miroslav
This letter studies the performance enhancement of symbiotic systems, which begins by formulating a closed-form adaptive mutualism symbiotic strategy for the backscatter coefficient to achieve minimal decoding errors for both primary and secondary signals. Then, we analyze two scenarios: First, the primary source adapts the modulation scheme based on the channel conditions of the primary signal to meet the target bit error rate (BER), evaluating metrics: mode selection probability, outage mode probability (OMP), BER, and spectral efficiency. Second, the primary source adapts its transmission rate and/or power according to three channel policies: constant power with optimal rate adaptation, optimal simultaneous power and rate adaptation, and truncated channel inversion with a fixed rate. Results show that for the first scenario, our proposed approach significantly improves the OMP and BER of the adaptive symbiotic system in the moderate and high signal-to-noise ratio (SNR) regimes compared to the fixed one, while the second scenario shows a promising choice for balancing capacity between backscatter and cellular rates in the low SNR regime.
Self-reverse labelings of distance magic graphs
(Springer Nature, 2026) Kovář, Petr; Rozman, Ksenija; Šparl, Primož
A graph is distance magic if it admits a bijective labeling of its vertices by integers from 1 up to the order of the graph in such a way that the sum of the labels of all the neighbors of a vertex is independent of a given vertex. We introduce the concept of a self-reverse distance magic labeling of a regular graph which allows for a more compact description of the graph and the labeling in terms of the corresponding quotient graph. We show that the members of several known infinite families of tetravalent distance magic graphs admit such labelings. We present a novel general construction producing a new distance magic graph from two existing ones. Using it we show that for each integer , except for the odd integers up to 19, there exists a connected tetravalent graph of order n admitting a self-reverse distance magic labeling. We also determine all connected tetravalent graphs up to order 30 admitting a self-reverse distance magic labeling. The obtained data suggests a number of natural interesting questions giving several possibilities for future research.
The role of nanofluids in enhancing thermal management and biomedical applications: A review
(Elsevier, 2026) Sharma, Aman; Khanal, Sonali; Suvedi, Divyesh; Yadav, Neelesh; Sharma, Shivam; Verma, Rachna; Kumar, Dinesh; Peter, Lukáš; Kalová, Martina
Nanofluids have emerged as next-generation heat transfer fluids (HTFs) with extraordinary multifunctionality in industries, biomedicine and pharmaceutics. This review provides a detailed and quantitative analysis of recent advances, showing that graphene oxide-based nanofluids enhance thermal conductivity by up to 76.8 %, while Fe2O3 water and Al2O3 Nanofluids deliver 9-40 % enhancements in heat transfer coefficients under practical conditions. Beyond thermal performance, nanofluids demonstrate antimicrobial and anti-biofilm properties critical for medicinal devices sterilisation and drug delivery. Moreover, a key novelty of this review lies in its integration of thermal performance metrics with advanced computational innovations, comprising AI-enhanced CFD models that achieve R-2 similar to 0.99 predictive accuracy and similar to 98 % reduction in computational time. Further, it addresses green synthesis approaches, stimuli-responsive formulations, and remaining challenges in the realm of biocompatibility and toxicity; it uniquely bridges thermal engineering, biomedical nanotechnology and intelligent modelling. Overall, it offers a forward-looking roadmap for designing sustainable, efficient and clinically relevant multifunctional nanofluids, as a valuable resource for both industrial and biomedical advancement.
Multi-molecular logic framework based on Morse code, ASCII logic, and Beale's cipher for advanced crypto-steganography
(Wiley, 2026) Mattath, Mohamed Nabeel; Lu, Yingying; Parambil, Ajith Manayil; Gao, Yan; Yao, Tian-Ming; Li, Jing-Jing; Zang, Rui-Min; Hu, Song; Shi, Shuo
Molecular information coding (MIC) involves biomolecules to encrypt and transmit messages, remains in its early stages of development. This work presents a versatile molecular integration framework and a proof-of-concept multi-level security system that combines Morse code, ASCII code, and Beale's cipher through molecular logic computing, using a molecular dye-oligonucleotide platform (single-stranded DNA, duplex DNA, stem-loop, and G-quadruplex (G-4) structures). This study demonstrates the integration of nanotechnology with crypto-steganographic methods to visualize and decipher codes, embedding elementary logic operations into molecular signal transduction. Additionally, a graphical user interface (GUI) is developed for classifying elementary logic gates using a decision tree algorithm, providing researchers with an accessible tool for rapid prediction. The Morse code-mediated strategy enables static key generation using dots, dashes, and intervals, and dynamic key generation through a polyalphabetic cipher framework. In parallel, ASCII-based logic gate operations facilitate multi-key decryption of decimal values to recover hidden information. Furthermore, a multilayered hybrid cryptographic technique combining Beale's cipher with Morse code implemented via a pangramic codebook, establishes an exceptionally resistant system against brute-force attacks. These methods provide insights into the evolution of communication and highlight the importance of encryption without relying on highly complex materials or sophisticated instruments.

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