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Electricity access is often a persistent challenge in remote rural areas of developing countries because of high costs and logistical difficulties in extending the national grid. This work will investigate optimizing a hybrid standalone renewable energy system to provide a sustainable and adequate power supply to dispersed villages in the Chamoli district of Uttarakhand, India. Integrating locally available renewable resources includes solar, wind, biomass, and biogas. In this work, an HSRES model will be developed using HOMER Pro software and Harris Hawk Optimization to optimize the sizing and cost of the system. The proposed HSRES model significantly reduces NPC and COE in traditional fossil fuel-based systems, reducing greenhouse gas emissions and mitigating environmental degradation. The optimized values of NPC and COE are 1,024,323 $ and 0.219 $/kWh. The HHO technique provides better results than the Homer Pro technique, as the NPC of the hybrid system is 0.25% less than that obtained in the case of the HHO technique. The work highlights the economic viability, ecological benefits, and social impacts of the HSRES model as it improves living standards and creates job opportunities, which contribute to economic growth in remote areas. It shows how the complementarity of the different renewable energy sources makes the HSRES model replicable for rural electrification worldwide. [ABSTRACT FROM AUTHOR]

Precise monitoring and diagnosis outwit the faults in generator rotors, preventing malfunction and resurrections. This article, therefore, introduces a hybrid decision support system for wind turbine fault diagnosis. The hybrid system assimilates deep learning functions and fuzzy optimization linearly to prevent faults. First, the deep learning paradigm inputs the rotor speed and vibration for the recurrent identification of abnormal frequency harmonics. The identified frequency harmonics are verified with the output power in the conventional operation routine to compute the fault intensity. This harmonic change varies the training intensity to prevent major faults. Secondly, fuzzy optimization inputs the difference in energy generated between different scenarios and identifies the optimal (maximum) performance outcome. Such identification improves turbine operation and maintenance decisions, mitigating the rotor faults. The decision intervals are variable, abiding by the optimization of learning routines required for smooth rotor operations of the turbine. [ABSTRACT FROM AUTHOR]

This paper addresses the smart management and control of an independent hybrid system based on renewable energies. The suggested system comprises a photovoltaic system (PVS), a wind energy conversion system (WECS), a battery storage system (BSS), and electronic power devices that are controlled to enhance the efficiency of the generated energy. Regarding the load side, the system comprises AC loads, DC loads, and a water pump. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT technique is suggested to enhance the efficiency of the PVS and WECS. This technology provided good performance compared with the Perturb and Observe (P&O) algorithm and MPPT-based fuzzy logic controller (FLC). The use of the ANFIS-PI proposed to control the bidirectional converter accomplished voltage stabilization for the DC bus. This work also came with a fuzzy logic-based algorithm to manage the load side that depends on battery charge ratio, solar radiation, and wind speed. According to results obtained in the MATLAB/Simulink environment, the proposed technologies were found to have performed well. The goal we were also pursuing was achieved through the full use of the energy generated by the proposed algorithm. The proposed study holds great potential for remote regions. [ABSTRACT FROM AUTHOR]

The framework of the methodology presented in this study is an effort to integrate and optimize the agro-industry sector, especially energy in biogas. In this study, the technique of the system in functional analysis is shown systematically to translate various energy requirements in the factory as criteria for performance and functional design to be integrated, optimized, and energy efficient. The case study results indicated that biogas power plants, with a capacity of 1.5 MW, can produce around 13,140 MWh per year. The annual return on investment (ROI) is around 37.13%. With this ROI value, the payback period is 31 months. The overall reduction of greenhouse gases is approximately 77,826 tons CO2 eq/year. The potential value of carbon trading is about USD 3,113,040 per year. This strategic model presents a novel approach by integrating biogas energy production with a customized wastewater treatment system adapted to biodigesters' effluent characteristics. It offers a sustainable, economically feasible, and scalable solution, combining resource recovery, waste minimization, and potential for carbon trading into a unified system. The novelty of this research lies in maximizing the utility of biogas plants by efficiently treating and reusing wastewater, creating a closed-loop, zero-waste process. Future research on hybrid systems integrating Biogas power plants by focusing on efficiency optimization, economic feasibility, environmental impacts, and innovative approaches like AI and blockchain could make the hybrid system a more robust, scalable, and sustainable solution. Thus, the framework based on the results of this study finds tools that can maximize and integrate energy sources, especially biogas, in the agro-industrial sector. [ABSTRACT FROM AUTHOR]

Defining end-to-end (or online) training schemes for the calibration of neural sub-models in hybrid systems requires working with an optimization problem that involves the solver of the physical equations. Online learning methodologies thus require the numerical model to be differentiable, which is not the case for most modeling systems. To overcome this, we present an efficient and practical online learning approach for hybrid systems. The method, called EGA for Euler Gradient Approximation, assumes an additive neural correction to the physical model, and an explicit Euler approximation of the gradients. We demonstrate that the EGA converges to the exact gradients in the limit of infinitely small time steps. Numerical experiments show significant improvements over offline learning, highlighting the potential of end-to-end learning for hybrid modeling. End-to-end learning in hybrid numerical models involves solving an optimization problem that integrates the model's solver. In many fields, these solvers are written in low-abstraction programming languages that lack automatic differentiation. This work presents a practical approach to solving the optimization problem by efficiently approximating the gradient of the end-to-end objective function. [ABSTRACT FROM AUTHOR]

Low Voltage Ride Through (LVRT) is considered one of the main and serious problems facing the electrical grid. It occurs due to three-phase symmetric faults and asymmetric faults such as a double line to ground fault that applies in this system. This paper applies Static Synchronous Compensators (STATCOM) to improve the LVRT capability and dynamic performance of an electrical grid linked to a Photovoltaic (PV)/Wind hybrid system through grid disturbances. A hybrid power system containing a PV station that produces 1 MW and a wind farm from type Doubly Fed Induction Generator (DFIG) that produces 9 MW is connected to STATCOM with 48 pulses at PCC bus and energized load. It compensates reactive power to improve LVRT that occurred due to fault. The applied STATCOM controller adjusts the voltage of the PCC bus during an occuring fault on the grid by compensating reactive power. STATCOM is controlled by a Proportional–Integral–Derivative (PID) and is compared with STATCOM controlled by Artificial Intelligence Control (AIC)-based on Proportional—Integral Fuzzy Logic Control (PI FLC). The Lightning Attachment Procedure Optimization Algorithm (LAPO) optimization method is used to adjust the parameters of the PI controller to reduce error signals. A simulation model of the suggested hybrid power system has been performed using Matlab/Simulink. The simulation results of STATCOM proved powerful and the effectiveness of STATCOM with PI FLC in reducing voltage dip, compensating active power of wind and PV farm, protecting DC-link voltage of PV and wind from overvoltage and oscillation that happens at three-phase fault and double line to ground fault as compared with PID STATCOM in enhancement LVRT capability, and power quality. [ABSTRACT FROM AUTHOR]

We have proposed several single-qubit phase gates with two quantum dots (QDs) embedded in a T-type plasmonic waveguides (PWs), wherein binary qubits are encoded by frequency of photons. Our results reveal that in such a hybrid system, an arbitrary single-qubit phase shift gates can be achieved and the gate performance could be controlled by adjusting spacing distance between two QDs and frequency detuning in a proper manner. We show that the phase of outstate can be adequately adjusted by distance between two QDs and the detuning could cause a phase shift. We investigated schemes theoretically via the real-space approach and estimated the feasibilities of them by evaluating fidelities for several parameters. Under the present technology and high fidelities, the proposed one could be utilized for quantum computation and quantum information processing. [ABSTRACT FROM AUTHOR]

The increasing demand for electric vehicles (EVs) has brought new challenges in managing battery thermal conditions, particularly under high-power operations. This paper provides a comprehensive review of battery thermal management systems (BTMSs) for lithium-ion batteries, focusing on conventional and advanced cooling strategies. The primary objective of this study is to assess and compare the effectiveness of various cooling approaches, including air-based, liquid-based, phase change material (PCM)-based, and hybrid systems. This review paper reveals that while traditional air- and liquid-based systems offer certain benefits such as simplicity and cooling efficiency, they are constrained by limitations in thermal conductivity and energy consumption. In contrast, PCM-based systems, despite their poor thermal conductivity, provide stable temperature regulation without requiring additional energy input. To overcome these limitations, the integration of thermal conductivity enhancers (TCEs) like carbon fibers, expanded graphite, and metal foams into PCMs significantly improves their performance. For instance, composite PCM (CPCM) enhanced with expanded graphite shows a marked improvement in thermal conductivity, increasing from 0.2 Wm−1 K−1 to 16.6 Wm−1 K−1, resulting in battery temperature reductions by up to 28%. Additionally, hybrid systems that combine active cooling with CPCMs, particularly when using nanoenhanced PCM with additives like graphene and metallic nanoparticles, demonstrate superior cooling efficiency, with temperature reductions of up to 50% compared to traditional systems. The uniqueness of this paper lies in its detailed comparison of the various BTMS strategies, including a thorough evaluation of hybrid systems that merge passive and active cooling techniques. We also explore the potential of nanoenhanced PCMs and hybrid CPCM systems, which offer significant advantages for high-power battery applications by providing both efficient heat dissipation and improved battery longevity. By synthesizing recent advancements in this field, this review highlights the most promising thermal management strategies, paving the way for future innovation in BTMS design for electric vehicles. [ABSTRACT FROM AUTHOR]

In contemporary artificial intelligence, the challenge is making intricate connectionist systems—comprising millions of parameters—more comprehensible, defensible, and rationally grounded. Two prevailing methodologies address this complexity. The inaugural approach amalgamates symbolic methodologies with connectionist paradigms, culminating in a hybrid system. This strategy systematizes extensive parameters within a limited framework of formal, symbolic rules. Conversely, the latter strategy remains staunchly connectionist, eschewing hybridity. Instead of internal transparency, it fabricates an external, transparent proxy system. This ancillary system's mandate is elucidating the principal system's decisions, essentially approximating its outcomes. Leveraging natural language processing as our analytical lens, this paper elucidates both methodologies: the hybrid method is underscored by the compositional vector semantics, whereas the purely connectionist method evolves as a derivative of neural semantic parsers. This discourse extols the merits of the purely connectionist approach for its inherent flexibility and for a pivotal delineation: segregating the explanatory apparatus from the operational core, thereby rendering artificial intelligence systems reminiscent of human cognition. [ABSTRACT FROM AUTHOR]

In areas where conventional energy sources are unavailable, alternative energy technologies play a crucial role in generating electricity. These technologies offer various benefits, such as reliable energy supply, environmental sustainability, and employment opportunities in rural regions. This study focuses on the development of a novel optimization algorithm called the modified firebug swarm algorithm (mFSO). Its objective is to determine the optimal size of an integrated renewable power system for supplying electricity to a specific remote site in Dehiba town, located in the eastern province of Tataouine, Tunisia. The proposed configuration for the standalone hybrid system involves PV/biomass/battery, and three objective functions are considered: minimizing the total energy cost (COE), reducing the loss of power supply probability (LPSP), and managing excess energy (EXC). The effectiveness of the modified algorithm is evaluated using various tests, including the Wilcoxon test, boxplot analysis, and the ten benchmark functions of the CEC2020 benchmark. Comparative analysis between the mFSO and widely used algorithms like the original Firebug Swarm Optimization (FSO), Slime Mold Algorithm (SMA), and Seagull Optimization Algorithm (SOA) demonstrates that the proposed mFSO technique is efficient and effective in solving the design problem, surpassing other optimization algorithms. [ABSTRACT FROM AUTHOR]

The circular economy requires advanced methods to recycle waste matter such as ammonia, which can be further used as a fuel and a precursor of numerous value-added chemicals. Here, we review methods for the recovery of ammonia from wastewater with emphasis on biological and physicochemical techniques, and their applications. Biological techniques involve nitrification, denitrification, and anammox processes and the use of membrane bioreactors. Physicochemical techniques comprise adsorption, membrane filtration, ion exchange, chemical precipitation, ammonia stripping, electrochemical oxidation, photocatalytic oxidation, bioelectrochemical systems, and membrane hybrid systems. We found that nitrification and anammox processes in membrane bioreactors stand out for their cost-effectiveness, reduced sludge production, and energy efficiency. The use of struvite precipitation is an efficient, environmentally friendly, and recyclable method for ammonia removal. Membrane hybrid systems are promising for ammonia recovery, nutrient concentration, and wastewater treatment, with applications in fertilizer production and water purification. Overall, nitrogen removal ranges from 28 to 100%, and nitrogen recovery ranges from 9 to 100%. [ABSTRACT FROM AUTHOR]

This paper presents robust control design conditions for uncertain sampled-data systems. We transform such systems into interval or polytopic uncertain hybrid dynamic models, which we then use to analyse and design H 2 conditions. The numerical examples in this paper demonstrate the efficacy of the proposed methods. [ABSTRACT FROM AUTHOR]

We formally introduce IsaVODEs (Isabelle verification with Ordinary Differential Equations), an open, compositional and extensible framework for the verification of cyber-physical systems. We extend a previous semantic approach with methods and techniques that increase its expressivity, proof automation, and scalability to the level of state-of-the-art deductive verification tools. Our contributions include a user-friendly specification language, a flexible hybrid store model, including vectors and matrices, and separation-logic-style rules for local reasoning with hybrid stores using a novel form of differentiation called framed Fréchet derivatives. The formalisation of correctness specifications with forward predicate transformers, the certification of flows as unique solutions to systems of ordinary differential equations, and invariant reasoning for such systems also contribute to the scalability and usability of our framework. In combination, these features make our framework flexible and adaptable to several verification workflows. A suite of examples and hybrid systems verification benchmarks validate our framework relative to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The upcoming communication systems most provide high data transmission rates. At the same time, they have to guarantee an ultra-low power consumption and low latency. Besides, Orthogonal frequency division multiplexing (OFDM), widely used in 4G and 5G systems, faces significant challenges in high mobility scenarios. Such illness come from channel's spreads named delay and Doppler. Consequently, those cons affect various applications such as vehicular communications and railway networks. To overcome such cons, it was suggested a noval processing modulation named orthogonal time frequency space (OTFS). This solution goes perfectly with high mobility situation. Nevertheless, while OTFS offers similar performance to OFDM in low mobility scenarios, it introduces considerable complexity in equipment design for high mobility scenarios. Given these factors, a promising approach could be the coexistence of OFDM and OTFS technologies, leveraging their respective strengths in 4G and planned 6G systems. This paper proposes an implementation of an hybrid system. This offers to adopt an alternative strategy, offering to choose an available waveform as function of user speed's estimation. This strategy will permit to any base station to effectively retain the waveform having a few complexity. This is done with respect to a threshold fixed speed. This hybrid system offers a promising solution to elevate the capabilities of wireless communication systems in high mobility scenarios. The obtained results confirm the viability of this proposed technique. [ABSTRACT FROM AUTHOR]

Due to the limitations of the model itself, the performance of switched autoregressive exogenous (SARX) models will face potential threats when modeling nonlinear hybrid dynamic systems. To address this problem, a robust identification approach of the switched gated recurrent unit (SGRU) model is developed in this paper. Firstly, all submodels of the SARX model are replaced by gated recurrent unit neural networks. The obtained SGRU model has stronger nonlinear fitting ability than the SARX model. Secondly, this paper departs from the conventional Gaussian distribution assumption for noise, opting instead for a generalized Gaussian distribution. This enables the proposed model to achieve stable prediction performance under the influence of different noises. Notably, no prior assumptions are imposed on the knowledge of operating modes in the proposed switched model. Therefore, the EM algorithm is used to solve the problem of parameter estimation with hidden variables in this paper. Finally, two simulation experiments are performed. By comparing the nonlinear fitting ability of the SGRU model with the SARX model and the prediction performance of the SGRU model under different noise distributions, the effectiveness of the proposed approach is verified. [ABSTRACT FROM AUTHOR]

Renewable energy systems are becoming more and more popular and used these days as a result of environmental, technical, and economic concerns. The reliable and optimal economic size of the system is the primary issue with the renewable energy-based power supply system for rural electrification. A new Zebra optimization algorithm (ZOA) is used for the optimal design and to perform the techno-economic performance analysis of the renewable energy-based off-grid power supply system with the stochastic load profile of Ethiopian rural communities. The components of the power supply system are modeled, the objective function is formulated, and optimization and techno-economic analysis are performed to get the minimum total annual cost of the hybrid system with the consideration of loss of power supply probability (LPSP), stochastic load profile and solar module optimal tilt angle. Three off-grid power supply systems, such as PV-BAT, PV-WT-BAT, and WT-BAT, are proposed to evaluate the optimal configuration for the study site at various LPSP. The study's findings showed that the photovoltaic-battery (PV-BAT) system, with an optimal size of 3483.161 kW of PV, 3668 units of storage batteries (11,444.160 kWh), and 2082 kW of converter at 0.044030% LPSP, is the best configuration for electrifying the rural communities of the study site with the minimum annual total cost of 621,736.056 USD and 0.227063 $/kWh COE. It results in a 3.3% annual total cost reduction and a 1.3% unmet load (kWh/year) improvement as compared to the PV-WT-BAT system. The performance of the proposed ZOA in obtaining the optimal size of the renewable energy-based power supply system for rural communities is evaluated by comparing it with the previous studies, gray wolf optimization (GWO) and HOMER Pro software, and it was found that the proposed algorithm is best at finding the optimal size of the power supply system at the minimum annual cost. The standard deviation for ZOA and GWO, respectively, in determining the optimal configuration value for 25 runs is 14.295 and 36.360 for the PV-BAT configuration, indicating that ZOA is more reliable than GWO in determining the optimal size. Furthermore, ZOA yields a 16.76% reduction in the total net present cost when compared to the HOMER software results. [ABSTRACT FROM AUTHOR]

Countries in Africa rank the highest in low energy accessibility and air pollution globally. Presenting the urgent need to explore renewable energy sources to tackle the power challenge and reduce the carbon footprint for a greener atmosphere. A novel hybrid wind and solar renewable energy power system (HREPS) coupled to a battery that is capable of powering industrial appliances in the Basse district of The Gambia has been proposed. The HREPS size was estimated automatically with the PVsyst software taking into account the PV power and battery capacity based on the load profile and loss of load probability, with all the necessary meteorological, technical, and economic factors. The optimal size was possible with 20 photovoltaic modules (250 W) and 1 Wind generator (1 KW) compatible with the annual load requirement of about 2555 MWh. These results suggested that the HREPS is reliable for the user's load demand in all the tested meteorological conditions of the study area. When the HREPS is linked to any other renewable source, it shows the potential of gaining about 8395 kWh annually in the battery. The continuous operation of the hybrid system for 21 years presents a net gain of > 400% for the standalone systems with a projected gain of > 600% when linked to a small smart grid system. Since high taxes may scuttle these gains, it is recommended that the Government encourage investment in renewable energy power systems by subsidizing the taxes on the purchase of items to make them more affordable. [ABSTRACT FROM AUTHOR]

The membrane bioreactor (MBR) process synergistically combines biological treatment with membrane filtration, offering a compact design and enhanced operational flexibility. However, membrane fouling remains a critical bottleneck, limiting its widespread application, particularly in treating high-strength wastewater. Recent advances have demonstrated that integrating MBR systems with auxiliary processes such as adsorption, electrochemical treatments, algal-assisted systems, and others can significantly mitigate fouling and enhance treatment efficacy. This paper critically reviews various MBR hybrid configurations, examining their mechanisms, advantages, and limitations in terms of treatment performance and fouling control, while highlighting their potential to extend conventional MBR's applicability to challenging wastewaters and addressing operational challenges like economic viability and sustainability. Elaborated tables incorporating a wide variety of research studies within the realm of synchronization have been meticulously compiled to generate a comprehensive literature review. [ABSTRACT FROM AUTHOR]

Biaxial nematic liquid crystals are fascinating systems sometimes referred to as the Higgs boson of soft matter because of experimental observation challenges. Here we describe unexpected states of matter that feature biaxial orientational order of colloidal supercritical fluids and gases formed by sparse rodlike particles. Colloidal rods with perpendicular surface boundary conditions exhibit a strong biaxial symmetry breaking when doped into conventional chiral nematic fluids. Minimization of free energy prompts these particles to orient perpendicular to the local molecular director and the helical axis, thereby imparting biaxiality on the hybrid molecular-colloidal system. The ensuing phase diagram features colloidal gas and liquid and supercritical colloidal fluid states with long-range biaxial orientational symmetry, as supported by analytical and numerical modeling at all hierarchical levels of ordering. Unlike for nonchiral hybrid systems, dispersions in chiral nematic hosts display biaxial orientational order at vanishing colloid volume fractions, promising both technological and fundamental research utility. The study of nematic hosts with anisotropic colloidal particles is reported, but not on the chiral counterparts. Here, the authors report on biaxial properties in a system of colloidal rods with designed surface anchoring doped into a chiral nematic host. [ABSTRACT FROM AUTHOR]

The application of fuel cell tractors is expected to drive technological upgrades and sustainable development in agricultural machinery. However, fuel cell hybrid systems face issues such as slow dynamic response, low efficiency, and short lifespan. This paper proposes an energy management strategy based on signal reconstruction methods, including Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to achieve optimal energy utilization and system efficiency based on frequency response characteristics. First, we collected data on the tractor's traction force and operating speed, and calculated the required traction power using a full-machine dynamics model built in MATLAB software. We conducted frequency response characteristic analysis of the fuel cell hybrid system based on EMD and VMD, establishing an energy management controller to sequentially meet the average power demand of the fuel cell under plowing load operations, the instantaneous acceleration power demand of the power battery, and the real-time compensation power demand of the supercapacitor. The results show that the EMD strategy exhibits good stability, while the VMD strategy performs better in terms of hydrogen consumption. Under the VMD strategy, the hybrid system achieves a maximum output efficiency of 55.0% with a total hydrogen consumption of 750 g. Compared to the EMD strategy, the maximum efficiency of the system increases by 27.31%, and hydrogen consumption decreases by 3.49%. This study provides a new theoretical foundation and technological route for the application of fuel cell hybrid systems in the field of agricultural machinery. [ABSTRACT FROM AUTHOR]

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets. [ABSTRACT FROM AUTHOR]

Perovskite quantum dots (pQDs) have gathered a lot of attention because of their outstanding optoelectronic properties. Photoswitchable pQDs have the potential for application in single particle optical memories and bio-imaging. Hybrids of photochromic diarylethenes (DAE) and pQDs show a luminescence photoswitching property, however, the cycle stability in such systems is low because of photoinduced electron transfer (PET) from pQDs to DAE. In this study, various hybrids of DAEs and pQDs with different spacer lengths between the pQD donors and DAE acceptors were synthesized and their stability towards multiple cycles of luminescence photoswitching was evaluated. It was found that the electron transfer pathway can be blocked and very stable switchable hybrids can be produced when the distance between the donors and acceptors was long enough. Furthermore, the effect of softness of the basic ligands and the synthesis method of the pQDs on the cycle stability of the hybrids were investigated. The findings of this study suggest that the photoswitching stability can be improved in hybrid systems by proper molecular design of the photochromic molecule. [ABSTRACT FROM AUTHOR]

The study examines a fascinating combination of ferromagnetic leads and double quantum dots in a hybrid system. The "two coupled quantum dots Anderson model" is employed to model the coupled quantum dots and simulate the energy levels of the double coupled quantum dots, which interact with both left and right ferromagnetic leads to investigate the process of spin tunneling. This study discusses the role of coupling interaction between quantum dots the Coulomb correlation within each quantum dot, and their connection with the ferromagnetic leads. We determine the occupation number of the energy levels in the quantum dots and solve it in a self-consistent manner. These solutions are then used to compute each quantum dot's spin accumulation. The results are used to compute the spin current tunneling and examine the system's transport characteristics. [ABSTRACT FROM AUTHOR]

Materials with low lattice thermal conductivity are essential for various heat-related applications like thermoelectrics, and usual approaches for achieving this rely on specific crystalline structures. Here, we report a strategy for thermal conductivity reduction and regulation via guest rotational dynamics and their couplings with lattice vibrations. By applying pressure to manipulate rotational states, we find the intensified rotor-lattice couplings of compressed methane hydrate MH-III can trigger strong phonon scatterings and phonon localizations, enabling an almost three-fold suppression of thermal conductivity. Besides, the disorder in methane rotational dynamics results in anharmonic interactions and nonlinear pressure-dependent heat transport. The overall guest rotational dynamics and heat conduction changes can be flexibly regulated by the rotor-lattice coupling strength. We further underscore that this reduction mechanism can be extended to a wide range of systems with different structures. The results demonstrate a potentially universal method for reducing or controlling heat transport by developing a hybrid system with tailored molecular rotors. Molecular disorder plays an important role in the thermal conductivity of materials. Using atomistic simulations, the authors show that thermal conductivity can be pressure-regulated in methane hydrates by manipulating disordered guest rotational dynamics. [ABSTRACT FROM AUTHOR]

We first investigate a class of generalized non-homogeneous two-dimensional hybrid systems and derive formulas for their solutions. We then obtain the transfer matrix and characteristic equation specific to this system type. Examples are provided to illustrate our theory. [ABSTRACT FROM AUTHOR]

Nonthermal plasma (NTP) is an efficient treatment technology for cooking fumes (CFs). However, its practical implementation is hindered due to the low mineralization rate of CFs and high generation of by-products. In this study, a hybrid system coupling NTP and Mn/HZSM-5 catalysts was developed for the deep oxidation of CFs. These catalysts exhibited a remarkable synergistic effect together with NTP in improving the efficiency of CFs removal. When the specific energy density was 282 J·L− 1, the hybrid system had stable reactivity, and the CFs removal efficiency and CO2 yield were 100% and 78.4%, respectively, which were 10% and 61% higher than the values achieved with the NTP system alone. The Mn/HZSM-5 catalysts were also discovered to inhibit the production of O3 and NO2 to a large extent and to achieve a removal efficiency level at > 80%. The Mn/HZSM-5 catalysts' high Mn4+/Mn ratio and the relatively large amount of chemisorbed oxygen on the catalyst surface engendered their remarkable performance. On the basis of the detected active species and organic products, the reaction mechanism governing the destruction of CFs by the NTP-Mn/HZSM-5 catalyst system was also discussed. [ABSTRACT FROM AUTHOR]

This study investigates the effects of incorporating pistachio shell powder and a mixture of Aquilaria agallocha Roxb (AAR) resin with epoxy on the mechanical, dynamic mechanical, thermal, and biodegradability properties of an epoxy composite. Filler loadings ranged from 10 to 35% by volume, in 5% increments. Scanning electron microscopy (SEM) revealed a uniform distribution of the hybrid polymer materials, particularly at 30% natural resin content, enhancing the load-bearing capacity of the composites. The addition of pistachio shell powder and AAR resin significantly improved the flexural modulus and strength of the composites. At a filler volume of 35%, the hybrid polymer exhibited a maximum impact resistance of 2,718 J/m2, demonstrating increased energy absorption. Moreover, the hybrid system enhanced the damping factor by up to 30%, suggesting superior dynamic mechanical performance. Thermogravimetric analysis (TGA) indicated that the hybrid composites displayed better thermal stability compared to pure epoxy resin. These findings suggest that the combination of pistachio shell powder and AAR natural resin offers a sustainable approach to reinforcing epoxy-based composites, providing improved mechanical and thermal performance for potential industrial applications. [ABSTRACT FROM AUTHOR]

Photon-magnon coupling, where electromagnetic waves interact with spin waves, and negative refraction, which bends the direction of electromagnetic waves unnaturally, constitute critical foundations and advancements in the realms of optics, spintronics, and quantum information technology. Here, we explore a magnetic-field-controlled, on-off switchable, non-reciprocal negative refractive index within a non-Hermitian photon-magnon hybrid system. By integrating an yttrium iron garnet film with an inverted split-ring resonator, we discover pronounced negative refractive index driven by the system's non-Hermitian properties. This phenomenon exhibits unique non-reciprocal behavior dependent on the signal's propagation direction. Our analytical model sheds light on the crucial interplay between coherent and dissipative coupling, significantly altering permittivity and permeability's imaginary components, crucial for negative refractive index's emergence. This work pioneers new avenues for employing negative refractive index in photon-magnon hybrid systems, signaling substantial advancements in quantum hybrid systems. The authors demonstrate a magnetic-field-controlled, on-off switchable non-reciprocal negative refractive index in a non-Hermitian photon-magnon hybrid system, highlighting the crucial role of non-Hermitian dynamics in advancing photonics and magnonics in quantum hybrid technologies. [ABSTRACT FROM AUTHOR]

The introduction of hybrid alternating current (AC)/direct current (DC) distribution networks led to several developments in smart grid and decentralized power system technology. The paper concentrates on several topics related to the operation of hybrid AC/DC networks. Such as optimization methods, control strategies, energy management, protection issues, and proposed solutions. The implementation of neural network optimization methods has great importance for the successful integration of multiple energy sources, dynamic energy management, establishment of system stability and reliability, power distribution optimization, management of energy storage, and online fault detection and diagnosis in hybrid networks like the hybrid AC–DC microgrids (MG). Taking advantage of renewable energy generation and cost-cutting through the neural network optimization technique holds the key to these progressions. Besides identifying the challenges in the operation of a hybrid system, the paper also compares this system to conventional MGs and shows the benefits of this type of system over different MG structures. This review compares the different topologies, particularly looking at the AC–DC coupled hybrid MGs, and shows the important role of the interlinking of converters that are used for efficient transmission between AC and DC MGs and generally used to implement the different control and optimization techniques. Overall, this review paper can be regarded as a reference, pointing out the pros and cons of integrating hybrid AC/DC distribution networks for future study and improvement paths in this developing area. [ABSTRACT FROM AUTHOR]

This work models electromagnetically induced grating (EIG) in a metal nanoparticle (MNP)–double quantum dot (DQD) hybrid system considering the Coulomb effect to specify the scattering rates where the strong-coupling case is considered. This work does not take normalized values or values from different structures; it is of the material property by calculating the energy states of DQDs and their transition momenta. The wetting layer (WL) where quantum dots (QDs) are grown is taken in the calculations where both QD–QD and WL–QD transition momenta are considered, and the orthogonalized plane wave is considered for the last transitions. The screening effect is taken when modeling Rabi frequencies. Although complexity faces Coulomb problems in their modeling and computation, one gains the realization of scattering time under this many-body effect, which was not specified earlier. The transmission coefficient is reduced with the probe. Detuning the probe increases the transmission. Tunneling increases the transmission due to an overall increase in scattering rates. A small distance between the MNP–DQD increases the transmission, while a long distance reduces it, where the scattering rates are reduced. Increasing the MNP radius reduced the transmission. The detuning is more effective than other factors. The MNP inhibits the zeroth-order diffraction, while other orders are strengthened due to the modulation of the EIG behavior of the DQD structure by the MNP. The results indicate that the scattering rates control the processes in this system. [ABSTRACT FROM AUTHOR]

In this study, pure and hexagonal boron nitride (hBN)-doped zinc oxide (ZnO) nanoparticles with different doping ratios (1, 5 and 10 wt%) were synthesized and their structural morphological properties were investigated. ZnO-hBN nanocomposite particles were used as reinforcement material in AZ91 magnesium alloy. AZ91 powder reinforced with ZnO-hBN nanocomposite particles was combined by powder metallurgy. Hardness of AZ91 matrix composites was measured at 5 different points on each sample and averaged. The specimens were subjected to wear tests in a pin-on-disk test apparatus. As a result of the tests, the hardness value of pure AZ91 alloy was found to be 62 HB, while the hardness value of AZ91-ZnO-10 hBN sample was found to be up to 85 HB. In the wear tests, it was observed that ZnO-hBN additives resulted in low weight losses in wear losses and also decreased the friction coefficient of the hBN additive in the friction coefficient graphs. A hybrid GA (genetic algorithm)-SVR (support vector regression) model was proposed for the prediction of wear quantities of composites. The purpose of using a hybrid system combining SVR and GA is to improve the wear prediction by optimizing the hyperparameters of t and GA. The results are observed using four different kernel functions in the SVR algorithm, and then, a hybrid GA-SVR structure is proposed and compared. With the proposed method, 98.80% prediction success of the wear quantities of the composite is achieved by the hybridized GA. [ABSTRACT FROM AUTHOR]

The accurate prediction of precipitation is crucial for agricultural management, water resources planning, and drought monitoring. One effective approach involves using a combination of linear and nonlinear models in a hybrid system. This study focuses on enhancing the hybrid model by employing the signal decomposition method, particularly for the complex nonlinear component. The research evaluated the effectiveness of incorporating seasonal autoregressive integrated moving average (SARIMA) with empirical mode decomposition (EMD) and maximal overlap discrete wavelet transform (MODWT) methods in the hybrid model structure using monthly precipitation data from stations in Iran. The procedure involved obtaining error series from the SARIMA model, decomposing the error series into intrinsic mode functions (IMFs) using EMD, and then applying support vector regression to forecast them. The evaluation criteria showed that using EMD in the hybrid model structure enhanced its efficiency by reducing significant error criteria and increasing residual predictive deviation. The proposed model also preserved precipitation forecasts in terms of time, with overestimated forecasts exhibiting high efficiency (RPD values > 2.5). Additionally, incorporating MODWT as a secondary decomposition in the final step of the proposed model further improved precipitation forecasting accuracy compared to the hybrid model solely incorporating EMD. The assimilation of signal decomposition methods in a hybrid model can enhance the accuracy and reliability of precipitation forecasts by revealing important error patterns. [ABSTRACT FROM AUTHOR]

This study proposes a promising candidate for organic solar cells through the creation of a novel nano-hybrid system composed of caffeine molecules encapsulated within a semiconducting single-walled carbon nanotube known as NT14 (14,0). The stability and optoelectronic properties of this hybrid system, designated as CA@NT14, were thoroughly investigated. We determined the optimal diameter for the NT14 nanotube using molecular mechanics, Density Functional Theory, spectral moment's method, and bond polarizability model, finding it to be approximately 1.18 nm. The encapsulation's impact on the Raman-active modes, specifically the radial breathing mode and tangential mode, was analyzed through Raman spectra calculations, revealing structural stability and charge transfer from CA to NT14. Notably, the NT14's Fermi level position increased upon encapsulation, indicating charge transfer and a type-II band alignment. The study further examined the bandgap characteristics of the hybrid system, finding that the encapsulation of CA within semiconducting NT14 nanotubes minimally impacts the nanotube's bandgap, thus retaining its excellent visible light absorption properties. Conversely, encapsulating CA within metallic nanotubes significantly reduces their bandgap, limiting their light absorption range. The nonresonant Raman responses of NT14 pre- and post-encapsulation corroborated the charge transfer between CA and NT14. Future research will focus on computing the electronic transport characteristics, transmission spectra, I-V characteristics, and yield of CA@NT14 using DFT and Non-Equilibrium Green's Function (formalism. An experimental study will be conducted to validate these theoretical findings. These results indicate the potential of CA@NT14 hybrids as effective active layers in organic solar cells, highlighting their significance in advancing optoelectronic applications. [ABSTRACT FROM AUTHOR]

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org Neuromorphic software and hardware solutions vary widely, challenging interoperability and reproducibility. Here, authors establish a representation for neuromorphic computations in continuous time and demonstrate support across 11 platforms. [ABSTRACT FROM AUTHOR]

We consider the initial-boundary value problem describing oscillations of a mechanical hybrid system consisting of an elastic rod and an attached rigid body. We establish the uniqueness of a generalized solution. Based on the Galerkin method, we prove the existence and uniqueness of approximate solutions and derive a priori estimates. Then we prove the weak convergence of approximate solutions to the sought solution. [ABSTRACT FROM AUTHOR]

Competition in sports, unlike cooperation in everyday life, does not involve a single solution because individuals aim to behave unpredictably, thereby preventing others from predicting their actions. This study determined how individuals in court-based sports attempted to control others' unpredictable behaviors, addressing the gap in previous studies regarding the lack of clarity around strategies employed by individuals in competitive situations. We achieved this by applying a switching hybrid dynamics model, considering external inputs to analyze individual behaviors. Consequently, the study indicates that skilled individuals, in contrast to intermediate players, exhibit greater consistency in their behaviors. These skilled individuals lead others to anticipate their consistency and subsequently employ strategies to disrupt these expectations. This strategy exploits the principles of active human inference, implying that competition involves cooperation. This was revealed by an analysis of both human decision-making and behavior in actual matches as discrete and continuous dynamical systems. This interpersonal strategy could assist policymakers in the field of everyday life to enhance competitiveness. This strategy enables policymakers to adopt new policies that promote cooperation with competitors, ultimately increasing competitiveness in various aspects of our daily lives. [ABSTRACT FROM AUTHOR]

This paper considers nonlinear continuous-discrete (hybrid) systems containing two subsystems of differential and difference equations, respectively, and one-dimensional (scalar) or multidimensional (vector) control. The transition from a nonlinear hybrid system with a constant sampling step h > 0 to an equivalent, in a natural sense, nonlinear discrete dynamic system is presented. Sufficient conditions are established, first, for reducing the first approximation systems of nonlinear discrete systems to the Brunovský canonical form and, second, for stabilizing such systems and nonlinear hybrid systems with control of different dimensions. Algorithms for constructing stabilizing control laws for nonlinear hybrid systems are developed. Numerical examples are provided to illustrate the effectiveness of this approach to stabilizing nonlinear hybrid dynamic systems. [ABSTRACT FROM AUTHOR]

This paper proposes a magnetic field measurement scheme based on a hybrid microwave optomechanical-magnetic coupled system. The proposed sensor comprises a yttrium iron garnet sphere and an optomechanical cavity, where the spring coefficient of the cavity is parametrically modulated. The results demonstrate that the system's response to the input signal is significantly enhanced, amplifying the weak input signal while reducing the added noise of measurement below the standard quantum limit. Consequently, this hybrid system serves as an effective amplifier, generating a stronger output signal while maintaining sensitivity nearly identical to that of the bare system. We posit that these findings may offer an efficient method for magnetic field measurement and contribute to the advancement of technology in quantum precision measurements. [ABSTRACT FROM AUTHOR]

The present work intended to explore the synergistic effects of incorporating carbon nanotubes infused with magnesium oxide nanofluid and dragon fruit extract as a natural additive in photovoltaic–thermal collector systems on its performance in terms of both electrical and thermal energy yield. The proposed methodology was meticulously developed and implemented to assess the efficiency of a hybrid system integrating nano-phase change material and nanofluid in a photovoltaic–thermal configuration. This novel setup was subjected to a comprehensive comparative analysis against a traditional liquid-cooled photovoltaic–thermal system and a standalone photovoltaic module. The study also encompassed variations in cooling techniques, including water with different flow rates for the photovoltaic–thermal system, and the incorporation of nano-phase change material and nanofluid with varying concentrations and flow rates within the hybrid collector. The outcomes demonstrated that at these respective flow rates, the maximum thermal efficiency of the photovoltaic–thermal collector stood at 66.28% and 74.02%. Likewise, the peak electrical efficiency at the same flow rates was recorded as 20.79% and 21.96%. The outcomes of the investigation highlight the higher thermal conductivity of the nanofluid compared to the base fluid, leading to a slight augmentation in fluid density and viscosity. The results obtained from this investigation were found to be consistent with those documented in existing scientific literature. [ABSTRACT FROM AUTHOR]

Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing's fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain. [ABSTRACT FROM AUTHOR]

Forecasting mortality is challenging. In general, mortality rate forecasting exercises have been based on the supposition that predictors' residuals are random noise. However, issues regarding model selection, misspecification, or the dynamic behavior of the temporal phenomenon lead to biased or underperformed single models. Residual series might present temporal patterns that can still be used to improve the forecasting system. This paper proposes a new recursive direct multi-step Hybrid System for Mortality Forecasting (HyS-MF) that combines the Autoregressive Integrated Moving Average (ARIMA) with Neural Basis Expansion for Time Series Forecasting (N-BEATS). HyS-MF employs (i) ARIMA to model and forecast the mortality rate time series with a recursive approach and (ii) N-BEATS with the direct multi-step approach to learn and forecast the residuals of the linear predictor. The final output is generated by summing ARIMA with the N-BEATS forecasts in each time horizon. HyS-MF achieved an average Mean Absolute Percentage Error (MAPE) less than 1.34% considering all prediction horizons, beating statistical techniques, machine learning, deep learning models, and hybrid systems considering 101 different time series from the French population mortality rate. [ABSTRACT FROM AUTHOR]

This research introduces a hybrid humidification-dehumidification (HDH) desalination unit and dye-sensitized solar cell (DSSC) system to produce power and drinking water using the hot and humid waste air from buildings equipped with wind towers. The thermodynamic, exergeoeconomic and carbon footprint study of this hybrid system is conducted in five arid and semiarid regions, including Tehran, Isfahan, Birjand, Yazd and Kerman, located in Iran. The proposed hybrid system's thermodynamic, cost and CO2 emission rates are evaluated and compared by varying design parameters. The results indicate that increasing wind tower height and DSSC tilt angle significantly improve the thermodynamic performance of the system while rising water-to-air mass flow rate ratio (MR) and saline water temperature, respectively, lessen the cost and CO2 emission rates of the system in all desired regions. Furthermore, the system's optimum performance in each zone is identified using the multi-objective moth-flame optimization (MOMFO) and decision-making procedure. Optimization results indicate that the highest power and freshwater of 783.4 W and 120.5 kg/month, respectively, can be achieved in Birjand, and the highest energy and exergy efficiencies are obtained by 10.8% and 11.41% in Kerman. Moreover, the system delivers the lowest cost and CO2 emission rates of 0.73 $/month and 4.61 kgCO2/month in Tehran. The obtained theoretical results can be a guide for optimal design and practical application of such a system in arid and semiarid regions from the thermodynamic, economic and environmental perspectives. [ABSTRACT FROM AUTHOR]

A hydrodynamic control model is established in attention to coupling relationships among cable, propellers and robot body of a tethered underwater robot system. In this model the governing equations of umbilical cable and the robot are firstly introduced, then supplementary conditions are coupled into the equations to forming the dynamic mathematical model; finally a hybrid control strategy based on feed-forward negative feedback method for the cable and PID rule for the propellers are integrated in the mathematical model for composing the whole hydrodynamic control model. Both the mathematical model and the control algorithms are proved to be effective and reliable through comparing simulation with the experimental data in existed references. Based on the numerical model constructed in this paper, trajectory tracking of a tethered underwater robot system in different motion combinations are numerically simulated through computational fluid dynamics method. In the numerical simulations, finite difference method is used to solving the kinematic parameters of the mathematical model, while finite volume method is applied on calculating the hydrodynamic forces under a hybrid control manipulations. The robot motion in vertical direction is determined primary by feed-forward negative feedback strategy of adjusting the cable length, while the horizontal movement of the robot is controlled mainly through PID algorithm; The hydrodynamic loading on the robot body are influenced by the flow fields around the robot. Article Highlights: Established the coupling equations with considering dynamic behaviors among propellers, umbilical cable and robot body. Discovered the conversion relation which unrelated to objective factors between the rotating speeds of propellers and the corresponding thrusts. Analyzed the changing rules of hydrodynamic loading on the underwater robot under a hybrid control strategy. [ABSTRACT FROM AUTHOR]

The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO2 emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm's merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index. [ABSTRACT FROM AUTHOR]

An optimal control problem is considered for a hybrid system in which continuous motion alternates with discrete changes (switchings) of the state space and control space. The switching times are determined as a result of minimizing a functional that takes into account the costs of each switching. Sufficient conditions for the optimality of such systems under additional constraints at the switching times are obtained. The application of the optimality conditions is demonstrated using academic examples. [ABSTRACT FROM AUTHOR]

The unsteady airflow will cause the distortion and delay change of the signal propagation path, resulting in the delay and distortion of the received satellite signal. This will lead to increased ranging and direction finding errors in the positioning system, which will affect the precision and accuracy of the positioning results. Therefore, a new multi-satellite anti-interference hybrid positioning method under the influence of unstable airflow is studied. Preprocess the unstable turbulence data at high altitude, and verify the turbulence data authenticity quickly. The flow field description equation is constructed based on the momentum conservation theorem. The multi-satellite motion equation is established by using the carrier phase observations and satellite antenna phase center coordinates, and the anti-interference hybrid positioning model is constructed. Global Navigation Satellite System (GNSS) technology is introduced to solve the multi-satellite hybrid anti-interference model by using the signals of multiple satellites to calculate the receiver's position, speed and time. The experimental results show that, for static targets, the research method has a small deviation in the positioning of the target N, S and E. When using the mixed positioning of near-earth satellite, medium altitude satellite and geosynchronous satellite, the mean square value of positioning error is always lower than 0.002 cm. For the dynamic target, the method is not disturbed by clutter, and the target is stably located. Moreover, the proposed method has lower localization delay and higher localization efficiency. [ABSTRACT FROM AUTHOR]

Recently, the area devoted to fractional calculus has given much attention by researchers. The reason behind such huge attention is the significant applications of the mentioned area in various disciplines. Different problems of real world processes have been investigated by using the concepts of fractional calculus and important and applicable outcomes were obtained. Because, there has been a lot of interest in fractional differential equations. It is brought on by both the extensive development of fractional calculus theory and its applications. The use of linear and quadratic perturbations of nonlinear differential equations in mathematical models of a variety of real-world problems has received a lot of interest. Therefore, motivated by the mentioned importance, this research work is devoted to analyze in detailed, a class of fractal hybrid fractional differential equation under Atangana- Baleanu- Caputo ABC derivative. The qualitative theory of the problem is examined by using tools of non-linear functional analysis. The Ulam-Hyer's (U-H) type stability criteria is also applied to the consider problem. Further, the numerical solution of the model is developed by using powerful numerical technique. Lastly, the Wazewska-Czyzewska and Lasota Model, a well-known biological model, verifies the results. Several graphical representations by using different fractals fractional orders values are presented. The detailed discussion and explanations are given at the end. [ABSTRACT FROM AUTHOR]

Chinese hamster ovary (CHO) cells, widely acknowledged as the preferred host system for industrial recombinant protein manufacturing, play a crucial role in developing pharmaceuticals, including anticancer therapeutics. Nevertheless, mammalian cell-based biopharmaceutical production methods are still beset by cellular constraints such as limited growth and poor productivity. MicroRNA-21 (miR-21) has a major impact on a variety of malignancies, including glioblastoma multiforme (GBM). However, reduced productivity and growth rate have been linked to miR-21 overexpression in CHO cells. The current study aimed to engineer a recombinant CHO (rCHO) cell using the CRISPR-mediated precise integration into target chromosome (CRIS-PITCh) system coupled with the Bxb1 recombinase-mediated cassette exchange (RMCE) to express a circular miR-21 decoy (CM21D) with five bulged binding sites for miR-21 sponging. Implementing the ribonucleoprotein (RNP) delivery method, a landing pad was inserted into the genome utilizing the CRIS-PITCh technique. Subsequently, the CM21D cassette flanked by Bxb1 attB was then retargeted into the integrated landing pad using the RMCE/Bxb1 system. This strategy raised the targeting efficiency by 1.7-fold, and off-target effects were decreased. The miR-21 target genes (Pdcd4 and Atp11b) noticed a significant increase in expression upon the miR-21 sponging through CM21D. Following the expression of CM21D, rCHO cells showed a substantial decrease in doubling time and a 1.3-fold increase in growth rate. Further analysis showed an increased yield of hrsACE2, a secretory recombinant protein, by 2.06-fold. Hence, we can conclude that sponging-induced inhibition of miR-21 may lead to a growth rate increase that could be linked to increased CHO cell productivity. For industrial cell lines, including CHO cells, an increase in productivity is crucial. The results of our research indicate that CM21D is an auspicious CHO engineering approach. Key points: • CHO is an ideal host cell line for producing industrial therapeutics manufacturing, and miR-21 is downregulated in CHO cells, which produce recombinant proteins. • The miR-21 target genes noticed a significant increase in expression upon the miR-21 sponging through CM21D. Additionally, sponging of miR-21 by CM21D enhanced the growth rate of CHO cells. • Productivity and growth rate were increased in CHO cells expressing recombinant hrs-ACE2 protein after CM21D knocking in. [ABSTRACT FROM AUTHOR]

The constructed wetland coupled with a microbial fuel cell (CW-MFC) is a wastewater treatment process that combines contaminant removal with electricity production, making it an environmentally friendly option. This hybrid system primarily relies on anaerobic bioprocesses for wastewater treatment, although other processes such as aerobic bioprocesses, plant uptake, and chemical oxidation also contribute to the removal of organic matter and nutrients. CW-MFCs have been successfully used to treat various types of wastewater, including urban, pharmaceutical, paper and pulp industry, metal-contaminated, and swine wastewater. In CW-MFC, macrophytes such as rice plants, Spartina angalica, Canna indica, and Phragmites australis are used. The treatment process can achieve a chemical oxygen demand removal rate of between 80 and 100%. Initially, research focused on enhancing power generation from CW-MFC, but recent studies have shifted towards resource recovery from wastewater. This review paper provides an overview of the development of constructed wetland microbial fuel cell technology, from its early stages to its current applications. The paper also highlights research gaps and potential directions for future research. [ABSTRACT FROM AUTHOR]

Regression analysis helps predict and understand the complex phenomena exhibited by fluids by examining the impact of independent variables on dependent variables. This article introduces a study that utilizes statistical analysis to explore the flow and heat transport behavior dynamics of a two-dimensional electrically conducting hybrid nanomaterial near the stagnation point. The innovation of this research lies in the novel synthesis method, which involves integrating molybdenum dioxide and aluminum oxide into a water-based fluid. The impacts of viscous dissipation, Joule heating (without an electric field), and convective boundary conditions are considered. This approach shows promise for practical applications across various industries. By comprehending and optimizing the behavior of hybrid nanofluids, potential benefits in the efficiency and performance of the system can be realized in areas such as electronics cooling and industrial heat exchangers. A significant aspect of this research involves the intelligent utilization of an external magnetic field oriented perpendicular to the flow direction. While this enhances energy flow, it also moderates the velocity profile of hybrid nanofluids. During regression analysis, it is observed that the effect of the Eckert number on the Nusselt number is greater than that of the Biot number. [ABSTRACT FROM AUTHOR]