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With the acceleration of China’s economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the trend of grouping and large-scale. However, as the scale of the company increases, the establishment of a branch also causes many problems. For example, in order to obtain more benefits, the business performance of the company can generate false growth, resulting in financial and operational risks. This paper analyzed the current situation and needs of enterprise financial control from two aspects of theory and practice, combined with specific engineering projects, taking ZH Group as an example, according to the actual situation of the enterprise. The article first introduces the basic situation of the enterprise; Then, the financial control strategy was designed, and different modules were designed to achieve financial control; Afterwards, use a reverse neural network to evaluate the effectiveness of financial management and risk warning; Relying on particle swarm optimization algorithm to seek the optimal solution and applying it to financial management and risk warning, in order to improve the level of introspection and risk management in decision-making. Finally, the value of computer intelligence algorithms in financial big data management is evaluated by constructing a financial risk indicator system. Through the analysis of enterprise financial management, the total asset turnover rate of ZH Group decreased by 0.39 times in 5 years. After 5 years of adjustment of the company’s business, the company’s overall operational capabilities still needed to be improved, and the company’s comprehensive business capabilities also still needed to be improved. Therefore, the application of intelligent algorithms for financial control is very necessary.

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

Incorporating neuromorphic electronics in bioelectronic interfaces can provide intelligent responsiveness to environments. However, the signal mismatch between the environmental stimuli and driving amplitude in neuromorphic devices has limited the functional versatility and energy sustainability. Here we demonstrate multifunctional, self-sustained neuromorphic interfaces by achieving signal matching at the biological level. The advances rely on the unique properties of microbially produced protein nanowires, which enable both bio-amplitude (e.g., <100 mV) signal processing and energy harvesting from ambient humidity. Integrating protein nanowire-based sensors, energy devices and memristors of bio-amplitude functions yields flexible, self-powered neuromorphic interfaces that can intelligently interpret biologically relevant stimuli for smart responses. These features, coupled with the fact that protein nanowires are a green biomaterial of potential diverse functionalities, take the interfaces a step closer to biological integration. For bio-inspired neuromorphic interfaces to emulate biological signal processing and self-sustainability, the mismatch between sensing and computing signals must be addressed. Here, the authors report sensor-driven, integrated neuromorphic systems with signal matching at the biological level.

Developing efficient artificial photocatalysts for the biomimetic photocatalytic production of molecular materials, including medicines and clean energy carriers, remains a fundamentally and technologically essential challenge. Hydrogen peroxide is widely used in chemical synthesis, medical disinfection, and clean energy. However, the current industrial production, predominantly by anthraquinone oxidation, suffers from hefty energy penalties and toxic byproducts. Herein, we report the efficient photocatalytic production of hydrogen peroxide by protonation-induced dispersible porous polymers with good charge-carrier transport properties. Significant photocatalytic hydrogen peroxide generation occurs under ambient conditions at an unprecedented rate of 23.7 mmol g –1 h –1 and an apparent quantum efficiency of 11.3% at 450 nm. Combined simulations and spectroscopies indicate that sub-picosecond ultrafast electron “localization” from both free carriers and exciton states at the catalytic reaction centers underlie the remarkable photocatalytic performance of the dispersible porous polymers. Current industrial production of hydrogen peroxide suffers from hefty energy penalties and toxic byproducts. Here, the authors report efficient photocatalytic production of hydrogen peroxide by protonation-induced dispersible porous polymers with good charge-carrier transport properties.

Employing renewable materials for fabricating clean energy harvesting devices can further improve sustainability. Microorganisms can be mass produced with renewable feedstocks. Here, we demonstrate that it is possible to engineer microbial biofilms as a cohesive, flexible material for long-term continuous electricity production from evaporating water. Single biofilm sheet (~40 µm thick) serving as the functional component in an electronic device continuously produces power density (~1 μW/cm 2 ) higher than that achieved with thicker engineered materials. The energy output is comparable to that achieved with similar sized biofilms catalyzing current production in microbial fuel cells, without the need for an organic feedstock or maintaining cell viability. The biofilm can be sandwiched between a pair of mesh electrodes for scalable device integration and current production. The devices maintain the energy production in ionic solutions and can be used as skin-patch devices to harvest electricity from sweat and moisture on skin to continuously power wearable devices. Biofilms made from different microbial species show generic current production from water evaporation. These results suggest that we can harness the ubiquity of biofilms in nature as additional sources of biomaterial for evaporation-based electricity generation in diverse aqueous environments. Though water evaporation-driven electricity generation is an attractive sustainable energy production strategy, existing electronic devices suffer from poor performance or is costly. Here, the authors report sustainable biofilms for efficient, low-cost evaporation-based electricity production

Bottom‐up synthesized graphene nanostructures, including 0D graphene quantum dots and 1D graphene nanoribbons, have recently emerged as promising candidates for efficient, green optoelectronic, and energy storage applications. The versatility in their molecular structures offers a large and novel library of nanographenes with excellent and adjustable optical, electronic, and catalytic properties. In this minireview, recent progress on the fundamental understanding of the properties of different graphene nanostructures, and their state‐of‐the‐art applications in optoelectronics and energy storage are summarized. The properties of pristine nanographenes, including high emissivity and intriguing blinking effect in graphene quantum dots, superior charge transport properties in graphene nanoribbons, and edge‐specific electrochemistry in various graphene nanostructures, are highlighted. Furthermore, it is shown that emerging nanographene‐2D material‐based van der Waals heterostructures provide an exciting opportunity for efficient green optoelectronics with tunable characteristics. Finally, challenges and opportunities of the field are highlighted by offering guidelines for future combined efforts in the synthesis, assembly, spectroscopic, and electrical studies as well as (nano)fabrication to boost the progress toward advanced device applications. The recent progress on the fundamental properties of different bottom‐up synthesized nanographenes, and the collective properties upon forming nanographene‐2D material‐based van der Waals heterostructures, toward their state‐of‐the‐art applications in optoelectronics and energy storage is summarized. Forthcoming challenges and opportunities of this emerging field are highlighted, and perspectives in boosting the progress toward advanced device applications are offered.

Precise control of energy migration between sensitizer ions and activator ions in lanthanide-doped upconversion nanoparticles (UCNPs) nowadays has been extensively investigated to achieve efficient photon upconversion. However, these UCNPs generally emit blue, green or red light only under fixed excitation conditions. In this work, regulation of the photon transition process between different energy levels of a single activator ion to obtain tunable upconversion fluorescence under different excitation conditions is achieved by introducing a modulator ion. The cross-relaxation process between modulator ion and activator ion can be controlled to generate tunable luminescence from the same lanthanide activator ion under excitation at different wavelengths or with different laser power density and pulse frequency. This strategy has been tested and proven effective in two different nanocrystal systems and its usefulness has been demonstrated for high-level optical encryption. Here, the authors report tunable luminescence from a single lanthanide ion upon changing excitation conditions through co-doping an energy-modulator ion, thus adjusting the photon transition process of the lanthanide activator ion. Optical encryption has also been demonstrated as an application of this universal strategy.

With the advent of IoT and its immense possibilities through cloud randomization, connectivity has matured almost out of proportion; yet this factor has equally opened various attack surfaces thereby rendering IoT-cloud environments vulnerable to multiple attacks. To address the issue, we introduce a versatile approach for hybrid intrusion diagnosis made possible through ResNeXt, a DCNN architecture noted for high efficiency and scalability in extracting features, and the Improved Ebola Optimization Search Algorithm (IEOSA), a novel metaheuristic optimizer fashioned after the spread dynamics of the Ebola virus, yet enhanced to offer speed and reliability in search performance. The proposed approach uses the comprehensive feature extraction ability of ResNeXt combined with the improved searching efficiency of the IEOSA to provide a superior method for the detection of anomalies and intrusions in an IoT-cloud environment. The network attained a detection accuracy of 98.3% and above 97% for recall, F1 score, and precision on standard datasets, including CICIDS 2017 and NSL-KDD. The hybrid framework enhances the performance of traditional and even a few deep-learning techniques, to provide a more secure IoT-cloud ecosystem. The results highlight the opportunity for integrating deep learning with a robust metaheuristic optimization approach for better efficiency and effectiveness in intrusion detection within digital infrastructures.

Granite is a common construction material that is widely used in the various types of structures, but its dynamic behavior is not clearly understood. To investigate the uniaxial compressive behavior of the granite under high strain rates compression, three groups of specimens with the same aspect ratio (0.5) but different diameters were tested with two large split Hopkinson pressure bar systems with respective diameters of 60 and 155 mm. The latter allowed the large specimen with diameter up to 150 mm to be employed in the compressive tests. Brittle fracture was the main failure pattern in the dynamic tests. The fragment size decreased with the increasing strain rate, ranging from 21 to 286 s−1. The percentage of the fragments with small sizes (< 5 mm) increased with the increasing strain rate, while the percentage with larger sizes (> 20 mm) decreased. The incomplete stress–strain curves corresponded to the incomplete fragmentation of the specimens. The representative stress–strain curve of each impact velocity was developed using a mathematical model. The dynamic strength of the 50-mm- and 60-mm-diameter specimens were identical, while that of the 150-mm specimens was larger at the same strain rate level. The dynamic increase factor was < 1.0 at the low strain rates (< 55 s−1 for the 150-mm-diameter specimens, and < 100 s−1 for the 50-mm- and 60-mm-diameter specimens), while it was > 1.0 at higher strain rates. The energy absorption density increased with the impact velocity, dynamic strength, and strain rate. Two groups of representative curves were developed to describe the relationships between the energy absorption density, strain rates, and dynamic strength.

Two-dimensional conjugated polymers (2DCPs), composed of multiple strands of linear conjugated polymers with extended in-plane π-conjugation, are emerging crystalline semiconducting polymers for organic (opto)electronics. They are represented by two-dimensional π-conjugated covalent organic frameworks, which typically suffer from poor π-conjugation and thus low charge carrier mobilities. Here we overcome this limitation by demonstrating two semiconducting phthalocyanine-based poly(benzimidazobenzophenanthroline)-ladder-type 2DCPs (2DCP-MPc, with M = Cu or Ni), which are constructed from octaaminophthalocyaninato metal(ii) and naphthalenetetracarboxylic dianhydride by polycondensation under solvothermal conditions. The 2DCP-MPcs exhibit optical bandgaps of ~1.3 eV with highly delocalized π-electrons. Density functional theory calculations unveil strongly dispersive energy bands with small electron–hole reduced effective masses of ~0.15m0 for the layer-stacked 2DCP-MPcs. Terahertz spectroscopy reveals the band transport of Drude-type free carriers in 2DCP-MPcs with exceptionally high sum mobility of electrons and holes of ~970 cm2 V−1 s−1 at room temperature, surpassing that of the reported linear conjugated polymers and 2DCPs. This work highlights the critical role of effective conjugation in enhancing the charge transport properties of 2DCPs and the great potential of high-mobility 2DCPs for future (opto)electronics.Linear π-conjugated polymers have attracted great attention as semiconductors for (opto)electronic devices, but charge transfer is only effective along polymer chains. Here poly(benzimidazobenzophenanthroline)-ladder-type two-dimensional conjugated polymers are presented with high charge carrier mobilities.