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The effects of the solution's dielectric properties on the conformation and dynamics of star-shaped polyelectrolytes in shear flow are investigated using a hybrid simulation method coupling multi-particle collision dynamics and molecular dynamics. The simulation results showed that by modulating the dielectric properties of the solution, star-shaped polyelectrolytes showed a three-step dynamic behavior transition from tumbling to tank-treading to tumbling dynamics under shear flow. The analysis indicated that this distinct transition in dynamics could be attributed to the uneven distribution of counterions induced by shear on the chain, resulting in a change in the polyelectrolyte conformation and degree of segmental alignment in arms. These findings contribute to a comprehensive understanding of the non-equilibrium dynamics of star-shaped polyelectrolytes in shear flow and offer a viable approach for controlling the dynamic behavior of star-shaped polyelectrolytes by adjusting the dielectric properties of the solution. [ABSTRACT FROM AUTHOR]

Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements. [ABSTRACT FROM AUTHOR]

The theory of analytic gradients is presented for the projector-based density functional theory (DFT) embedding approach utilizing the Huzinaga-equation. The advantages of the Huzinaga-equation-based formulation are demonstrated. In particular, it is shown that the projector employed does not appear in the Lagrangian, and the potential risk of numerical problems is avoided at the evaluation of the gradients. The efficient implementation of the analytic gradient theory is presented for approaches where hybrid DFT, second-order Møller–Plesset perturbation theory, or double hybrid DFT are embedded in lower-level DFT environments. To demonstrate the applicability of the method and to gain insight into its accuracy, it is applied to equilibrium geometry optimizations, transition state searches, and potential energy surface scans. Our results show that bond lengths and angles converge rapidly with the size of the embedded system. While providing structural parameters close to high-level quality for the embedded atoms, the embedding approach has the potential to relax the coordinates of the environment as well. Our demonstrations on a 171-atom zeolite and a 570-atom protein system show that the Huzinaga-equation-based embedding can accelerate (double) hybrid gradient computations by an order of magnitude with sufficient active regions and enables affordable force evaluations or geometry optimizations for molecules of hundreds of atoms. [ABSTRACT FROM AUTHOR]

Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA. [ABSTRACT FROM AUTHOR]

We propose a theoretical scheme for the generation of nonreciprocal microwave-optical entanglement in a magnon-based hybrid system where an optical photon in a spinning resonator is coupled to a phonon representing the quantum of the mechanical deformation of a yttrium iron garnet crystal via radiation pressure. Meanwhile, the magnon interacts with the phonon and the microwave through the magnetostrictive and magnetic dipole–dipole interactions, respectively. By spinning the resonator, the light driven in opposite directions propagates irreversibly, which leads to the classical nonreciprocity of mean optical photon numbers. Strikingly, the nonreciprocal entanglement between microwave and optical photons can be generated owing to the Fizeau light-dragging effect. Physically, the magnon–phonon entanglement induced by the magnomechanical parametric downconversion interaction is partially transferred to the microwave-optical subsystem through magnon-microwave and optomechanical state-exchange interactions. Moreover, the nonreciprocity of entanglement can be manipulated by properly choosing various system parameters and the ideal nonreciprocal microwave–optical entanglement could be achieved, in which the entanglement depending on the effective optical detuning is present in a chosen direction but disappears in the other direction. Our work could be applied in the multi-task quantum information processing and construction of chiral quantum networks. [ABSTRACT FROM AUTHOR]

To explore potential applications in classical and quantum information transfer, the hybrid systems between yttrium iron garnet (YIG) and cavities have been extensively studied, and four coupling regimes have been defined based on the relative strength between the coupling strength and dissipation rate of each subsystem. Achieving the control of magnon–photon coupling between nano-thick YIG films and cavities remains to be explored. We experimentally measure the microwave transmission spectra of a nano-thick yttrium iron garnet/gadolinium gallium garnet (YIG/GGG) film coupled to a superconducting cavity at different temperatures. The dissipation rate of the superconducting cavity increases significantly with decreasing temperature, which is influenced by the temperature-dependent magnetic susceptibility of the GGG substrate. Accompanied by the temperature-dependent magnon dissipation rate, a continuous transformation of the coupled system in strong coupling, Purcell and weak coupling regimes is achieved. [ABSTRACT FROM AUTHOR]

The hybrid system consisting of microwave photons and ferromagnetic magnons has been studied in the context of quantum sensing and quantum manipulation of magnons. We demonstrate a strong coupling between magnons and photons in a dielectric resonator with a large dielectric constant and high quality factor. The coupling rate between magnons and photons amounts to g / 2 π = 126 MHz, and the corresponding cooperativity reaches C = 1.09 × 10 6 at cryogenic temperature. The high cooperativity is mainly due to the small internal decay rate of the resonator, which is advantageous for various quantum magnonics experiments. [ABSTRACT FROM AUTHOR]

Graphene can support surface plasmons with higher confinement, lower propagation loss, and substantially more tunable response compared to usual metal-based plasmonic structures. Interestingly, plasmons in graphene can strongly couple with nanostructures and gratings placed in its vicinity to form new hybrid systems that can provide a platform to investigate more complicated plasmonic phenomena. In this Perspective, an analysis on the excitation of highly confined graphene plasmons and their strong coupling with metallic or dielectric gratings is performed. We emphasize the flexibility in the efficient control of light–matter interaction by these new hybrid systems, benefiting from the interplay between graphene plasmons and other external resonant modes. The hybrid graphene-plasmon grating systems offer unique tunable plasmonic resonances with enhanced field distributions. They exhibit a novel route to realize practical emerging applications, including nonreciprocal devices, plasmonic switches, perfect absorbers, nonlinear structures, photodetectors, and optical sensors. [ABSTRACT FROM AUTHOR]

Conjugated polymer coatings enhance bacteria with eco-friendly energy use. A new hybrid system boosts hydrogen production by Rhodopseudomonas palustris@polypyrrole (R. palustris@PPy) through interface electron transfer and hydrogel encapsulation. To maximize the output, we studied hydrogen metabolism using various techniques and found that conductive polymer modification facilitated electron transfer, affecting intracellular pathways. This technology offers enhanced green hydrogen production for sustainable energy. [ABSTRACT FROM AUTHOR]

\nHighlightsThis study involved the synthesis of magnetite nanoparticles by co-precipitation method. The synthesized nanoparticles were subsequently evaluated for their catalytic efficacy in the degradation and ozonation of tartrazine dye (TTD), employing sodium persulfate as a catalyst both in the presence and absence of ozone. The material characterization revealed that synthesized Fe3O4 nanoparticles were proven to have a crystalline structure by XRD. While spherical agglomerates with a flower-like morphology were revealed by the SEM, similarly FT-IR detected Fe – O – Fe and O – H bond vibrations, boosting surface area and catalytic activity. The degradation of the TTD was assessed in a laboratory-scale reactor, and its progress was monitored using UV-visible spectrophotometry. A comparative analysis of the two methods revealed that the catalytic ozonation demonstrated superior efficacy compared to the catalytic degradation, achieving a maximum degradation of 94.35% within 25 min of contact time. Optimal degradation parameters included a TTD concentration of 50 ppm, magnetite dosage of 0.75 g, persulfate concentration of 8 mm, and ozone inlet concentration of 5 g/L at pH 3. Evaluation of the degradation kinetics indicated the second-order kinetics model as the most appropriate, suggesting a physicochemical nature of the dye removal process. Furthermore, the study demonstrates enhanced efficiency in TTD decomposition compared to conventional methods. Magnetite catalyst was synthesized by co-precipitationComparative study for catalytic degradation and catalytic ozonation was done94.35% Removal of tartrazine dye by using activated magnetite catalystDetermined effective performance of magnetite using PS and OzoneMagnetite catalyst was synthesized by co-precipitationComparative study for catalytic degradation and catalytic ozonation was done94.35% Removal of tartrazine dye by using activated magnetite catalystDetermined effective performance of magnetite using PS and Ozone [ABSTRACT FROM AUTHOR]

As AI increasingly enters classrooms, educational designers have begun investigating students' learning processes vis‐à‐vis simultaneous feedback from active sources—AI and the teacher. Nevertheless, there is a need to delve into a more comprehensive understanding of the orchestration of interactions between teachers and AI systems in educational settings. The research objective of this paper is to identify the challenges and opportunities when AI intertwines with instruction and examine how this hybrid teaching intelligence is being perceived by the students. The insights of this paper are extracted by analysing a case study that utilizes an AI‐driven system (MOVES‐NL) in the context of learning integer arithmetic. MOVES‐NL is an advanced interactive tool that deploys whole‐body movement and immediate formative feedback in a room‐scale environment designed to enhance students' learning of integer arithmetic. In this paper, we present an in‐situ study where 29 students in grades 6–8 interacted individually with MOVES‐NL for approximately 1 hour each with the support of a facilitator/instructor. Mixed‐methods analyses of multimodal data sources enabled a systematic multifaceted account of students' cognitive–affective experiences as they engaged with MOVES‐NL while receiving human support (eg, by asking students to elaborate on their digital actions/decisions). Finally, we propose design insights for instructional and technology design in support of student hybrid learning. The findings of this research contribute to the ongoing discourse on the role of hybrid intelligence in supporting education by offering practical insights and recommendations for educators and designers seeking to optimize the integration of technology in classrooms. Practitioner notes What is already known about this topic Students and teachers develop different relations with and through AI, beyond just interacting with it. AI can support and augment the teachers' capabilities. Hybrid intelligence (HI) has already demonstrated promising potential to advance current educational theories and practices. What this paper adds This research identifies the important learning opportunities and adversities emerging when AI intertwines with instruction and examines how learners perceive those moments. The results show that the system and the facilitator's feedback were complementary to the success of the learning experience. AI‐enabled students to reflect upon and test their previous knowledge and guided teachers to work with students to consolidate challenging topics. Findings provide insights into how the teacher–AI collaboration could engage and motivate students to reflect conceptually upon mathematical rules. Implications for practice and/or policy This study encourages practitioners and scholars to consider hybrid teaching intelligence when designing student‐centred AI learning tools, focusing on supporting the development of effective teacher–AI collaborative technologies. What is already known about this topic Students and teachers develop different relations with and through AI, beyond just interacting with it. AI can support and augment the teachers' capabilities. Hybrid intelligence (HI) has already demonstrated promising potential to advance current educational theories and practices. What this paper adds This research identifies the important learning opportunities and adversities emerging when AI intertwines with instruction and examines how learners perceive those moments. The results show that the system and the facilitator's feedback were complementary to the success of the learning experience. AI‐enabled students to reflect upon and test their previous knowledge and guided teachers to work with students to consolidate challenging topics. Findings provide insights into how the teacher–AI collaboration could engage and motivate students to reflect conceptually upon mathematical rules. Implications for practice and/or policy This study encourages practitioners and scholars to consider hybrid teaching intelligence when designing student‐centred AI learning tools, focusing on supporting the development of effective teacher–AI collaborative technologies. [ABSTRACT FROM AUTHOR]

Providing quality electricity to improve the living conditions of rural and urban populations in Africa, particularly in Cameroon, represents a challenging but achievable task. Indeed, Cameroon has the second largest hydrographic network in Central Africa, and thanks to this network, over 90% of electricity comes from aging hydroelectric dams that cause permanent power outages. The desire to take advantage of other benefits that nature has given this country has directed this work towards renewable energies. Thanks to the HOMER Pro software, a renewable energy hybrid system with hydrogen gas serving as storage and energy vector was adopted. Photovoltaic panels and wind turbines are proposed as primary energy sources for the permanent, low-cost supply of the IRAD laboratory at the University of Maroua. The software proposes three systems, one with the wind turbine as the only primary source, and considered as the most optimal. It has an energy cost equal to $0.0633, an NPC of $20330, and an initial capital of $8500. The other two systems are hybrids with a renewable fraction equal to 100%, and a load distribution strategy that is the charge cycle (CC) like the first system. The first less expensive hybrid system has an energy cost and an NPC respectively of $0.0778 and $24986. As for the second hybrid system, it has an NPC and an energy cost respectively of $26195 and $0.0778. Hydrogen fully plays its role as an energy vector and storage in all three systems. A sensitivity analysis was conducted in this work to ensure that the system proposed for the laboratory can also be adapted in the 9 other regions of the country. It appears that the values of wind resources, solar radiation, and temperature have a significant impact on the energy cost. The cities of Yaoundé and Ebolowa, displaying respective wind speeds of 2.07 m/s and 1.76 m/s, are recognized for having the lowest wind speeds in the country, which hinders the overall energy production and profitability of the system. The hybrid system suggested for these cities proves to be expensive, with energy costs exceeding the standard electricity price set by the energy distribution and marketing organization in the country. • Hydrogen as a storage system and energy vector. • Sensitivity analysis on nine regions of Cameroon. • Techno-economic analysis on the proposed system in each region. • Environmental impacts of the proposed system. • Policy recommendations. [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]

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]

Providing sustainable electricity access to remote areas is critical for economic development and environmental preservation. This study investigates the performance of single‐source and hybrid renewable energy systems for the town of Zahedan, Iran, which has significant solar and wind energy potential. Using TRNSYS software, eight configurations were simulated and analyzed, comprising two single‐source (photovoltaic [PV] and wind turbine [WT]) and six hybrid systems incorporating combinations of PV panels, WTs, alkaline fuel cells, and diesel generators. The analysis revealed that hybrid systems, particularly those combining PV and WT, outperformed single‐source configurations. For instance, a hybrid system with 800 kW of PV and a 50 kW WT reduced diesel consumption by 35% and CO2 emissions by 45% compared to a system relying solely on a diesel generator. Conversely, the configuration involving WTs, fuel cells, and diesel generators showed high energy dumping (1,821,776 kWh) and considerable diesel usage, underscoring the challenges of maintaining energy balance without solar integration. Overall, hybrid renewable systems generally provide enhanced reliability and environmental benefits, although their performance heavily depends on the specific energy source mix. This study offers insights into optimizing renewable energy systems for remote locations, highlighting the necessity of a balanced solar‐wind combination to achieve optimal sustainability and cost‐effectiveness. The findings are applicable to regions with similar climatic conditions and contribute to global sustainable energy solutions, providing crucial information for policymakers and investors focused on supporting sustainable energy projects in isolated areas. [ABSTRACT FROM AUTHOR]

The structure‐properties relationships of sustainable materials derived from biomass‐based monomers are investigated, focusing on hybrid styrene/terpene‐based copolymers with blocky microstructures, such as β‐myrcene– and β‐ocimene–styrene copolymers. The samples show complex glass transition dynamics, as evidenced by the physical aging experienced by the amorphous phase in styrene‐rich copolymers. The tendency of styrene‐ and terpene‐rich sequences to give heterogeneous morphologies with correlation strength extending over 10–40 nm is outlined, through small‐angle X‐ray scattering analysis. A new class of terpene‐based hybrid systems, holding promise for applications in surface coating technologies, is identified. [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]

The document from the journal "Molecules" issues a correction notice for a study on TiO2-based hybrid systems, clarifying the use of lipopeptides derived from bacterial fermentation instead of the commercial biosurfactant 'Surfactin'. The correction includes changes to the title, text, and figures/tables to accurately reflect the use of lipopeptides in the study, highlighting their diverse properties and potential applications. Detailed results of antibacterial tests on TiO2@LP_S samples obtained through different synthesis methods are provided, along with information on particle size distribution, SAED patterns, and adsorption properties. Revised references focus on the use of lipopeptides as surfactants, and a conflict of interest statement has been added for authors Giovanni Baldi and Valentina Dami. [Extracted from the article]

In the context of the electricity sector's liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model's ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. [ABSTRACT FROM AUTHOR]

A flue gas condenser (FGC) system recovers heat from exhaust flue gases in energy production and chemical plants, reducing air pollution due to dust, SOx, and HCl. An FGC system is divided into indirect contact condenser (ICC) and direct contact condenser (DCC) types. In an ICC, the exhaust gases do not mix with the working fluid, and a water film is formed during flue gas condensation for partial SOx removal. In a DCC, direct mixing of the exhaust flue gas with the cooling fluid (mainly water) occurs, with simultaneous absorption of SOx. In this study, we investigated the SO2 removal efficiency and heat recovery of an ICC, a DCC, and a DCC–ICC hybrid system, and compared the results of the hybrid system with those obtained for a single DCC type at the same liquid-to-gas (L/G) ratio. The SO2 removal characteristics of the hybrid system were examined based on the L/G ratio and absorbent-to-SO2 molar ratio. In the reference ICC-type FGC system, the exit temperature of the mixed gas was 28 °C, with the condensed water ratio and heat recovery efficiency being 80.9% and 93.4%, respectively. At an L/G ratio of 1.5–3.5, the SO2 removal efficiency of a single DCC was 31.5–65.9%, whereas that of the hybrid FGC system (with packing material) increased from 47.1% to 72.3%, which further increased to ~90% upon the addition of NaOH at a molar ratio of 0.7 and an L/G ratio of 1.5. [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 liberal world order is showing signs of disarray: two recent catastrophes in adjoining parts of the world (the Middle East and South Caucasus) have opened the fissures of the international governance gap; and the continuing ramifications of COVID-19 have already paralysed the world. On top of this post-pandemic disquietude, multiple conflicts this year are either in danger of precipitating or wars have already re-ignited over long-standing continuing conflicts, highlighting the failure of international governance institutions, including the UN system. In recent years, one of the central responses to the multilateral failures at multiple levels has been for states to form ‘narrower’ and ‘more flexible’ frameworks called ‘minilaterals’. Thus minilateralism has been contrasted through the lens of receding multilateralism, which has been a long-standing hallmark of the American-led liberal international order. This paper examines how far global governance would be dependent on minilateralism by exploring this growing trend in the Indo-Pacific and explores how minilateralism models can enhance international governance structures. This paper also attempts to analyse whether minilateralism is a product of regional fragmentation, or whether it can reinvigorate the comatose multilateralism. Then this paper posits that minilateralism helps rejig the chaotic multipolar order and thus re-invent global governance. [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]

The influence of carbon nano fiber (CNF) along with organically modified nanoclay (OMMT) on the final properties of high density polyethylene (HDPE) hybrid nanocomposites has been investigated. The hybridized nanocomposites were prepared in a twin screw extruder by melt mixing to achieve better dispersion. The effect of nano fillers on the mechanical, thermal, rheological, and morphological properties has been reported. The incorporation of OMMT along with CNF slightly improved the mechanical properties of the resultant hybrid nanocomposite due to the good adhesion between the filler and matrix. On the other hand, as shown in DSC results, increasing reinforcing filler quantity has no significant influence on the thermal properties such as melting and crystallization temperatures. Thermogravimetric analysis (TGA) results have shown that increasing filler content in the hybrid nanocomposite matrix has enhanced drastically the thermal stability of the neat HDPE. Similarly, the rheological behavior of the hybrid system showed significant increase in the viscosity due to the synergetic effect. A marginal increase in moduli and complex viscosity was observed in the hybrid system, while loss tangent was found to be decreased due to the increase in the stiffness. The morphological features of nanocomposites were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The nanoscale images showed the well dispersion of filler nanoparticles without any prominent aggregation, which further indicates the compatibilizing ability of nanofillers within the HDPE matrix. [ABSTRACT FROM AUTHOR]

A series of the new 1,2,3-triazole-acetophenone hybrid system are synthesized from the click reaction between 1-(2-(prop-2-yn-1-yloxy)phenyl)ethan-1-one 1 and different azido benzene derivatives 2-7. All compounds are characterized by 1H NMR, 13C NMR, mass spectra, and elemental analyses. Field Emission Scanning Electron Microscope (FESEM) detected highly ordered aggregations, and the morphology of the compounds was determined, directed by van der Waals (vdW) forces and π-π stacking interactions. The Gaussian 09 and Gaussian view 6.0 softwares are used for all Density Functional Theory (DFT) calculations at the B3LYP. Band gap, molecular softness, electronegativity, and electrophilicity measurements show that molecule 9 is a soft molecule with higher polarizability, lower kinetic stability, and more chemical reactivity than the other molecules, which tend to be the most stable and least reactive. The molecules can be divided into two categories based on their chemical potential values: Although compound 9 looks to have more activity, compounds 8, 10, 11, 12, and 13 often have the best stability and least reactivity. According to the MEPs, the nitrogen and oxygen atoms are the electronegative potential sites in all compounds, while the hydrogen atoms in the alkyl and aromatic cyclic groups are the electropositive potential sites. [ABSTRACT FROM AUTHOR]

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

Our research is focused on achieving consensus for non-linear singular switched multi-agent systems on arbitrary time scales. The hybrid control strategy that combines an impulsive controller with centralised and distributed event-triggered controllers is proposed. First, the Drazin-inverse approach is applied to transform the singular multi-agent switched system into a differential algebraic switched system. Then event-triggered schemes are applied to study the leader–follower consensus conditions. In the end, some simulation-based examples are given to verify the proposed results. [ABSTRACT FROM AUTHOR]

An enriched hybrid high-order method is designed for the Stokes equations of fluid flow and is fully applicable to generic curved meshes. Minimal regularity requirements of the enrichment spaces are given, and an abstract error analysis of the scheme is provided. The method achieves consistency in the enrichment space and is proven to converge optimally in energy error. The scheme is applied to 2D flow around circular cylinders, for which the local behaviour of the velocity and pressure fields is known. By enriching the local spaces with these solutions, superior numerical results near the submerged cylinders are achieved. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) possesses a simple architecture and high energy efficiency for time‐series data analysis through machine learning algorithms. To date, RC has evolved into several innovative variants. The next generation reservoir computing (NGRC) variant, founded on nonlinear vector autoregression (NVAR) distinguishes itself due to its fewer hyperparameters and independence from physical random connection matrices, while yielding comparable results. However, NGRC networks struggle with massive Kronecker product calculations and matrix‐vector multiplications within the read out layer, leading to substantial efficiency challenges for traditional von Neumann architectures. In this work, a hybrid digital‐analog hardware system tailored for NGRC is developed. The digital part is a Kronecker product calculation unit with data filtering, which realizes transformation of nonlinear vector of the input linear vector. For matrix‐vector multiplication, a computing‐in‐memory architecture based on resistive random access memory array offers an energy‐efficient hardware solution, which markedly reduces data transfer and greatly improve computational parallelism and energy efficiency. The predictive capabilities of this hybrid NGRC system are validated through the Lorenz63 model, achieving a normalized root mean square error (NRMSE) of 0.00098 and an energy efficiency of 19.42TOPS W−1. [ABSTRACT FROM AUTHOR]

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Copyright of Current Retina Journal / Güncel Retina Dergisi is the property of Anadolu Kitabevi Basim Yayim Medikal Turizm Kirtasiye Tic. Ltd. Sti. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In order to address the limitation of fixed-ratio transmission (FRT), which compromises the attainment of both optimal main rotor speed and optimal power turbine speed, an optimal speed control method based on hybrid variable speed (HVS) is proposed. Firstly, based on the integrated performance calculation model of helicopter/turboshaft engine system, the distribution factors of variable speed are applied, and the integrated optimization method of optimal speed is proposed based on the minimum engine fuel flow. Subsequently, an online estimation technique employing a high-order filter is devised and engineered to achieve superior cascaded control of turboshaft engines. Finally, a novel real-time optimal speed control method based on hybrid variable speed is proposed. The simulation results under different operation conditions demonstrate that regardless of whether it is FRT or HVS, the optimal main rotor speed increases with forward velocity. In the case of HVS, turboshaft engine degradations have a significant impact on the optimal power turbine speed rather than optimal main rotor speed. Adopting an estimation method based on high-order filtering for gas turbine rotational acceleration proves more advantageous in mitigating high-frequency oscillation and continuous saltation of estimated values. Moreover, in comparison with the optimal speed control method of FRT, HVS-based approach enables simultaneous attainment of the optimal main rotor speed and power turbine speed, thereby enhancing the overall efficiency of the integrated helicopter/turboshaft engine system and significantly decreasing engine fuel consumption by over 2%. Consequently, there has been a remarkable enhancement in the overall performance of the integrated helicopter/turboshaft engine system. [ABSTRACT FROM AUTHOR]

Renewable energy technologies offer the promise of clean and abundant energy harvested from natural resources, self-renewable sources such as sun, wind, water, earth, and plants. In this work, we optimize the hybrid system using Homer power program, where the hybrid system is composed by solar panels, wind turbines with batteries to supply 20 homes that are not equipped with electricity in Ouzera area (Medea, Algeria), and by taking the results presented by the Homer program for Ouzera region, we obtained the cost of each day, of each season and the cost of energy ($/kWh), as well as the optimal number and characteristics for each solar panels and wind turbines with storage batteries. The homer software allows us to obtain real results in taking into account the constraints cost and variations in off-grid weather data. The most important criterion of this technique for optimizing renewable energy systems was the cost, seeking to minimize the expenses, while ensuring optimum quality and continuity of electricity supply. [ABSTRACT FROM AUTHOR]

Background and objective: One of the most prevalent and significant causes of cancer-related mortality worldwide is considered to be liver cancer. Techniques for automatically segmenting and classifying liver tumors are crucial for supporting medical professionals during the tumor diagnostic process. Classifying liver tumors is challenging due to noise, nonhomogeneity, and the significant appearance diversity observed in tumor tissue. Also, in recent years, most of the research has performed binary classification, but there is still a lack of research on multi-class liver cancer classification. Therefore, we perform a multi-class liver cancer classification and segmentation in this research. Methods: We propose a hybrid deep learning-based multi-class liver cancer classification and segmentation system in this research. The collected CT images are pre-processed in four stages: contrast enhancement, noise filtering, smoothing and sharpening, and liver region segmentation. Next, the binary, texture, histogram, and rotational, scalability, and translational (RST) features are extracted from the pre-processed images. Then, the average correlation (AC) and probability of error (POE) approaches are applied to selecting relevant features and excluding less significant features. After feature selection, a modified AlexNet (MAlexNet) model is used to classify the multi-class classification of liver tumors. Finally, the identified liver tumor regions are segmented using the enhanced U-Net (EUNet) model. Results: Identified liver tumors are accurately classified into three different categories using the proposed classification model: hemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET), with an average accuracy of 99.19%. We have collected the multi-class liver cancer CT images from real time patients. The results of the experiments show that proposed hybrid system provides adequate overall accuracy, is less noise-sensitive, and outperforms other state-of-the-art techniques such as SVM, Faster RCNN, Mask RCNN, and SAR-U-Net on a wide range of CT images. Conclusion: Radiologists and doctors can identify liver tumors more accurately using the suggested innovative framework. [ABSTRACT FROM AUTHOR]

The harmonic balance (HB) method is efficient in predicting the limit cycle oscillations for many dynamic systems including very high dimensional systems in fluid dynamics. Recently, some variations and extensions of the HB method have been developed and employed in the computation of large systems. This study focuses on a new development of the HB method. The new approach, the hybrid harmonic balance method (HHB), is developed based on the ideas in the conventional HB approach and the high dimensional harmonic balance (HDHB) approach. To demonstrate the applications and advantages, the new method is employed on a prototypical dynamic system in comparison with the results from both the HB and HDHB approaches. It is theoretically proved that when twice the number of harmonics in HB are included in the HHB derivation the results from HHB achieve the accuracy of the HB method for the cubic nonlinearity. Numerical simulations reveal that HHB combines the advantages of both the conventional HB and HDHB methods: Ease of implementation for very high dimensional systems regardless of the complexity of the nonlinearities and generation of meaningful solutions while the spurious solutions (from HDHB) are diminished. This new method is more accurate when compared with the HDHB method and much more computationally efficient when compared with the conventional HB method. [ABSTRACT FROM AUTHOR]

The introduction of time‐varying factors makes it difficult and complex to study the stability of hybrid systems. How to improve the traditional stability analysis method to study the stability of time‐varying hybrid systems is a great challenge. In this paper, the fixed‐time stability (FxTS) of time‐varying hybrid systems is investigated. Starting with time‐varying nonlinear systems, two relaxed stability conditions are proposed to verify the FxTS of systems by using the time‐varying Lyapunov functions. Then, the obtained results are extended to study the FxTS of time‐varying hybrid systems, some improved stability conditions are given by combining the methods of minimum dwell time and multiple time‐varying Lyapunov functions. Compared with the existing results, the obtained conditions are less conservative and provide the more accurate estimations for the convergence time of systems. Two numerical examples are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

AbstractQuantum dots (QDs) have emerged as transformative nanomaterials in drug delivery due to their unique optical and electronic properties, such as tunable fluorescence, high surface area and size-dependent emission in pharmaceutical origin. When integrated with polymers, this hybrid system offers enhanced functionality for targeted, controlled and stimuli-responsive drug delivery. This review focuses on the synthesis and application of quantum dot-polymer composites in drug delivery, emphasizing their ability to improve drug targeting, sustained release and real-time imaging in therapeutic applications. The multifunctionality of these systems enables personalized medicine approaches, particularly in cancer therapy and neurological disorders. Despite their promising potential, concerns related to toxicity and scalability pose significant challenges for their widespread clinical use. Ongoing research efforts are focused on developing more biocompatible QDs, optimizing the design of polymer-QD hybrids and addressing regulatory hurdles. This review, we believe, provides a comprehensive analysis of current advancements in quantum dot-based drug delivery systems and outlines future directions to overcome the existing limitations and enhance clinical applicability. The integration of quantum dots with polymers represents a significant leap forward in the field of nanomedicine, offering the potential for highly efficient, precise and safe drug delivery platforms. [ABSTRACT FROM AUTHOR]

Quantum electronics operating in the microwave domain are burgeoning and becoming essential building blocks of quantum computers, sensors, and communication devices. However, the field of microwave quantum electronics has long been dominated by the need for cryogenic conditions to maintain delicate quantum characteristics. Here, a solid‐state hybrid system, constituted by a photo‐excited pentacene triplet spin ensemble coupled to a dielectric resonator, is reported for the first time capable of both coherent microwave quantum amplification and oscillation at X band via the masing process at room temperature. By incorporating external driving and active dissipation control into the hybrid system, efficient tuning of the maser emission characteristics at ≈9.4 GHz is achieved, which is key to optimizing the performance of the maser device. The work not only pushes the boundaries of the operating frequency and functionality of the existing pentacene masers but also demonstrates a universal route for controlling the masing process at room temperature, highlighting opportunities for optimizing emerging solid‐state masers for quantum information processing and communication. [ABSTRACT FROM AUTHOR]

The phase engineering of the polarization interface is of great significance in modifying dielectric loss in electromagnetic wave (EMW) attenuation process, but hard to conduct in a complex hybrid system. Herein, a twin‐phase of β/γ‐MoCx@CN with matched Fermi level and closed work function properties in lightweight MoCx nanoflower is constructed, facilitating electron transport withdraw enhanced conductivity and polarization. Moreover, the EMW multiple dissipations among the β/γ‐MoCx@CN polarization interface is promoted, displaying better matched impedance. It delivered a remarkable minimum reflection loss (RLmin) of −74.2 dB at the thickness of 1.5 mm, which far beyond the single phased β‐MoCx@CN, γ‐MoCx@CN and reported MoCx‐based EMW absorbers. The radar cross‐section (RCS) map of β/γ‐MoCx@CN is simulated, showing a brilliant maximum reduction value of 13.6 dB m2 at the theta angle of 30°. This work presented an excellent sample of atomic‐level manipulation of interfacial polarization in dielectric MoCx EMW absorption materials. [ABSTRACT FROM AUTHOR]

The uniform stabilization of a coupled system arising in the active control of noise in a cavity with a flexible boundary (strings under thermal effects) is considered. Unlike most articles on this subject, which employ the scalar wave equation when analyzing the asymptotic behavior of structural acoustic models, in this paper, we consider classical equations in terms of flow velocity and pressure to describe the acoustic vibrations of the fluid which fills the cavity. This allows to consider, for example, more realistic boundary conditions to model the coupling on the interface between the acoustic chamber and the wall. The main result of this paper, concerning the exponential stability of the model, is established by means of the frequency domain method and the semigroup theory. This method can be adapted to other first‐order hyperbolic dissipative systems as well. [ABSTRACT FROM AUTHOR]

This study addresses cost-optimal sizing and energy management of a grid-integrated solar photovoltaic, wind turbine hybrid renewable energy system integrated with electrolyzer and hydrogen storage tank to simultaneously meet electricity and hydrogen demands considering the case study of Dijon, France. Mixed Integer Linear Programming optimization problem is formulated to evaluate two objective case scenarios: single objective and multi-objective, minimizing total annual costs and grid carbon emission footprint. The study incorporates various technical, economic, and environmental indicators focusing on the impact of sensitivity lying on various grid electricity purchase rates within the French electricity market prices. The results highlight that rising grid prices drive increased integration of renewable sources, while lower prices favor ultimate grid dependency. Constant hydrogen demand necessitates the installation of two electrolyzers. Notably, grid electricity prices above 60 €/MWh, result increase in the size of the hydrogen tank and electrolyzer operation to prevent renewable energy losses. Grid prices above 140 €/MWh depict 70% of electrical and 80% of electrolyzer demand provided by the renewable generation resulting in a carbon emission below 0.0416 Mt of CO 2 and 0.643 kgCO 2 /kgH 2. Conversely, grid prices below 20 €/MWh lead ultimately to 100% grid dependency with a higher carbon emission of approximately 0.14 Mt of CO 2 and 4.13 kgCO 2 /kgH 2 reducing the total annual cost to 41.63 Million €. Increase in grid prices from 20€/MWh to 180 €/MWh resulted in increase of hydrogen specific costs from 1.23 to 3.58 €/kgH 2. Finally, the Pareto front diagram is employed to illustrate the trade-off between total annual cost and carbon emission due to grid imports, aiding in informed decision-making. [Display omitted] • Sizing and energy management for the production of decarbonized H 2 for urban public transportation. • Maximization of renewable share for H 2 production and electric demand. • Sensitivity analysis of the grid electricity price on sizing and total annual cost of ownership. • MILP optimization formulation and low computing effort resolution. • Multi-objective resolution with total costs and CO 2 emissions. [ABSTRACT FROM AUTHOR]

The promising possibility of an organic photodetector (OPD) is emerging in the field of sensing applications for its tunable absorption range, flexibility, and large-scale fabrication abilities. In this work, we fabricated a bulk heterojunction OPD with a device structure of glass/ITO/PEDOT:PSS/P3HT:PC61BM/Al using the spin-coating process and characterized the dark and photocurrent densities at different applied bias conditions for red, green, and blue incident LEDs. The OPD photocurrent density exhibited a magnitude up to 2.5–3 orders higher compared to the dark current density at a −1 V bias while it increased by up to 3–4 orders at zero bias conditions for red, green, and blue lights, showing an increasing trend when a higher voltage is applied in the negative direction. Different OPD inner periphery shapes, the OPD to LED distance, and OPD area were also considered to bring the variation in the OPD dark and photocurrent densities, which can affect the on/off ratio of the OPD–LED hybrid system and is a critical phenomenon for any sensing application. [ABSTRACT FROM AUTHOR]

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell. [ABSTRACT FROM AUTHOR]

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems. [ABSTRACT FROM AUTHOR]

This paper addresses the potential of integrating a concentrating photovoltaic thermal (CPV/T) system with an absorption chiller for the purpose of space cooling in residential buildings in the United Arab Emirates (UAE). The proposed system consists of a low concentrating photovoltaic thermal (CPV/T) collector that utilizes mono-crystalline silicon photovoltaic (PV) cells integrated with a single-effect absorption chiller. The integrated system was modeled using the Transient System Simulation (TRNSYS v17) software. The obtained model was implemented in a case study represented by a villa situated in Abu Dhabi having a peak cooling load of 366 kW. The hybrid system was proposed to have a contribution of 60% renewable energy and 40% conventional nonrenewable energy. A feasibility study was carried out that demonstrated that the system could save approximately 670,700 kWh annually and reduce carbon dioxide emissions by 461 tons per year. The reduction in carbon dioxide emissions is equivalent of removing approximately 98 cars off the road. The payback period for the system was estimated to be 3.12 years. [ABSTRACT FROM AUTHOR]

A new green water treatment agent, a poly(aspartic acid)-modified polymer (PASP/5–AVA), was synthesized using polysuccinimide and 5-aminovaleric acid (5-AVA) in a hybrid system. The structure was characterized, and the scale and corrosion inhibition performance were carried out with standard static scale inhibition and electrochemical methods, respectively. The mechanism was explored using XRD, XPS, SEM, and quantum chemistry calculations. The results indicated that PASP/5–AVA exhibited better scale and corrosion inhibition performance than PASP and maintained efficacy and thermal stability of the scale inhibition effect for a long time. Mechanistic studies indicated that PASP/5–AVA interferes with the normal generation of CaCO3 and CaSO4 scales through lattice distortion and dispersion, respectively; the combined effect of an alkaline environment and terminal electron-withdrawing -COOH groups can induce the stable C- ionic state formation in -CH2- of the extended side chain, thus enhancing its chelating ability for Ca2+ ions. At the same time, the extension of the side chain length also enhances the adsorption ability of the agent on the metal surface, forming a thick film and delaying the corrosion of the metal surface. This study provides the necessary theoretical reference for the design of green scale and corrosion agents. [ABSTRACT FROM AUTHOR]