[Significance]Against the backdrop of consumption upgrading and growing health awareness, consumer concerns about agricultural products are shifting from mere safety to freshness, taste, and brand reputation, making quality a core target of modern agricultural upgrading and accelerating the transition of production modes toward precise and intelligent control. However, the traditional centralized cloud‑computing architecture dominated by a central cloud platform is increasingly constrained in weak‑network environments, low‑latency control, and local autonomy, and thus can hardly support the closed‑loop requirements of "timely detection-rapid response-continuous optimization" in quality control. Consequently, cloud-edge-device integrated collaborative architectures are regarded as an important development direction, and it is necessary to systematically sort out their key technologies and application foundations in the agricultural domain so as to provide unified theoretical and technical support for the design of integrated middleware oriented to agricultural product quality control.[Progress]Starting from the current application status of cloud-edge-device integration in agriculture, the paper focuses on three typical scenarios—field planting, facility horticulture, and livestock farming—and summarizes the functional division of perception, computation, and control among cloud, edge, and device, as well as the differences in business requirements under various geographical conditions and production modes. On this basis, it identifies multiple technical challenges in practical deployments, including heterogeneous adaptation difficulties caused by the coexistence of numerous industrial and IoT protocols, poor interoperability due to inconsistent data definitions and encodings, resource imbalance arising from the mismatch between edge computing capacity and task loads, and coarse‑grained, inefficient cloud-edge collaborative scheduling. To address these challenges, the paper synthesizes relevant research on artificial intelligence, edge computing, federated learning, and blockchain, and proposes improvement ideas for cloud-edge-device collaboration from the perspectives of resource‑allocation optimization, edge‑intelligent inference, privacy protection, and trusted sharing. Furthermore, targeting the concrete requirements of intelligent quality control for agricultural products, it constructs a cloud-edge-device integrated technical framework: At the data layer, it integrates standardized mapping of quality data and a multi‑source data dictionary system; at the access layer, it realizes heterogeneous device‑protocol conversion and unified access; at the computing layer, it achieves dynamic matching between computing resources and services through intelligent task scheduling and load balancing, multi‑level caching, model hot‑updating, and federated deployment. At the collaboration level, it proposes a "device-crop-model" trinity abstraction that brings physical devices, agronomic objects, and algorithmic models under unified middleware management, thereby masking underlying hardware differences while supporting elastic adaptation and capability reuse across diverse scenarios.[Conclusions and Prospects]The cloud-edge-device integrated collaboration and its adaptive middleware constitute the key infrastructure and connective hub of intelligent quality‑control systems for agricultural products, providing reusable architectural concepts and technical references for the development and engineering implementation of systems for precise quality perception, automatic control, and intelligent decision‑making. Looking ahead, further in‑depth research is needed on cross‑scenario data and protocol standard systems oriented to quality indicators and control actions, on the integrated application of edge intelligence and large models, on the enhancement of privacy‑protection and trusted‑computing mechanisms, and on large‑scale engineering demonstrations and validation, so as to continuously improve the intelligence level, operational reliability, and scalability of agricultural product quality‑control systems and to promote the transition of related technologies from pilot applications to large‑scale deployment.

ABSTRACT Wide‐bandgap (WBG) perovskites crystallization is essential for high‐efficiency perovskite–silicon tandem solar cells (TSCs), yet their fabrication on self‐assembled molecules (SAMs) is often challenged by solvent‐induced damage and uncontrolled packing. Here, we report an interfacial engineering strategy that goes beyond conventional SAM modification by introducing a thiophen‐3‐ylmethanamine hydrochloride (3‐TMA) molecular layer between the SAM and perovskite photo‐active layer. The aromatic thiophene units establish strong π ‑ π stacking interactions with the underlying SAM, forming a solvent‐resistant interlayer that stabilizes the anchored SAM structure during solution processing. Meanwhile, hydrogen‐bonding interactions between 3‐TMA and the perovskite precursors effectively decelerate crystallization, promoting uniform nucleation and high‐quality WBG perovskite films. The resulting interface exhibits improved energy‐level alignment and reduced interfacial stress, facilitating efficient charge transport and enhanced device stability. Consequently, single‐junction WBG perovskite devices with bandgaps of 1.67 and 1.84 eV achieve champion power conversion efficiencies (PCEs) of 23.17% and 19.61%, respectively. When integrated into monolithic perovskite‒silicon TSCs, the strategy enables PCEs of 33.21% (certified 32.13%) for rigid tandems and 31.03% (certified 30.34%) for flexible tandems. Encapsulated devices retain 92.3% of their initial performance after 1000 h of continuous 1 sun illumination at room temperature.

Among the transition metal oxides that have been studied for lithium-ion battery anodes, MnO is very attractive because it has good theoretical capacity, abundant raw materials, and low toxicity. Nevertheless, its practical performance is often limited by low electronic conductivity, the tendency of MnO particles to agglomerate, and substantial volume fluctuations during repeated lithiation/delithiation processes. These factors reduce reaction efficiency and gradually deteriorate cycling stability. To address the above problems, we put forward a simple two-step path: hydrothermal crystallisation and then polydopamine-assisted pyrolysis; it can be observed that MnO nanorods are formed, and these are then conformally covered with a nitrogen-rich carbon shell (N-C@MnO). A carbon shell can be used to provide mechanical flexibility and absorb the cyclic volume variation of the electrode, and at the same time, it has good electron conductivity. These synergistic structural features collectively contribute to the outstanding cycling stability and charge-storage properties delivered by the N-C@MnO electrode. The optimised N-C@MnO electrode delivers an initial discharge capacity of 1004.7 mA h g-1 and a high reversible capacity of 912.9 mA h g-1 subsequent to 100 cycles at 0.1 A g-1; and after 1400 cycles at a high rate of 5 A g-1, it is still 293.8 mA h g-1, demonstrating excellent structural durability under demanding operating conditions. The above results have provided a general basis for the structure of engineering high-energy-density conversion-type anodes, and this strategy offers a practical solution to improve the performance of next-generation lithium-ion batteries.

Herein, a novel type of flexible network hydrogel was formed by cross-linking of chitosan (CS) and acrylamide (AM) monomer through non-covalent bond, and was named CS/PAM/hydrogel (CPH). Subsequent functional modification of the CPH was then undertaken through the implementation of in-situ green growth technology, and the cobalt organic framework (ZIF-67) was successfully grown on the hydrogel. Ultimately, the composite hydrogel CS/PA M/Z IF-67/hydrogel (CPZH) was prepared. Subsequently, an electrochemical sensor was developed by combining a composite hydrogel with a glassy carbon electrode, and it was demonstrated that this combination exhibited excellent performance, especially in the electrochemical detection of the target molecule 2,4.6-trichlorophenol. Excellent linear ranges (0.01-10 and 10-10 0 0 & micro;mol/L) and low detection limit (0.003 & micro;mol/L) were achieved. The excellent electrochemical performance can be attributed to (i) 3D network structure of the hydrogel provides an effective mass transfer channel for the electrochemical reactants; (ii) The MOF layer provides a wealth of catalytic active sites, resulting in electrochemical signal amplification of the CPZH composite, (iii) The growth of ZIF-67 in the original location of the hydrogel network successfully solved the problem that traditional ZIF-67 particles are prone to agglomeration and exhibit weak bonding force with the substrate, which significantly improved the stability and electrochemical performance. (c) 2026 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

In this paper, we investigate the problem of optimal decentralized control for large-scale systems defined over a directed digraph with private information. Unlike previous literature, which primarily focuses on sharing complete or partial historical information, our work aims to bridge a critical gap in prior research by considering scenarios where all subsystems operate to formulate strategies without sharing. The private nature of these information introduces a fundamental challenge, resulting in an underdetermined system and coupling of observers and control, thereby making the calculation more complicated. To tackle this challenge, we introduce novel observers and observer-based controllers tailored to the private information which are shown to effectively stabilize the closed-loop system. Additionally, we formulate a cost function for the proposed observer-based controllers and demonstrate their asymptotic optimality in comparison to state feedback controllers. Finally, we provide a platoon of heavy-duty vehicles to illustrate the effectiveness of our approach.

This technical communique studies decentralized state feedback control and stabilization for networked control systems with time delay. Unlike previous studies with conservative conditions - such as lower block triangular systems, uncorrelated process noises, and classical information structures - we investigate a more general coupled system with a non-classical d-step delayed state sharing pattern and correlated process noises. The main challenge lies in that variable measurability of controllers caused by time delay complicates solving forward-backward stochastic difference equations which can be addressed using linear controller forms and the projection theorem. The key contributions are: (1) deriving an explicit form of the optimal linear controllers via Riccati equations, and (2) providing a sufficient condition for mean-square boundedness. A dual-motor drive system is used to validate the theoretical results. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Utilizing the Kirkendall effect to acquire hollow zeolitic imidazole framework (HZIF) allows for the predictable combination of superior guest molecules, which has favorable potential in the field of biomolecular detection. Herein, the ∼42 nm hollow nanocages, HZIF(Au), were constructed through the different diffusion rates of Zn2+ and Au3+. By leveraging the Kirkendall effect at different Au3+ levels and over time (0, 5, 30, and 60 min) a composite with iron porphyrin (TCPP(Fe)) (the HZIF(Au4.5)@TCPP(Fe)) was effectively assembled. The selection of various metals within HZIF(Au4.5)@TCPP(Fe)-M (M = Fe, Co, Ni, and Cu) hybrid nanocomposites was then successfully formed via metallization cross-linking engineering. This metallization and the electron-donating properties of TCPP resulted in an electron-rich state for Au while the cross-linking of the carboxyl groups of TCPP with metal ions also significantly improved the electrocatalytic performance. Together, this allowed HZIF(Au4.5)@TCPP(Fe)-Fe to act as an effective electrochemical sensor with dopamine (DA) as the target molecule. In the optimal electrolyte environment, the linear range of 0.006–1500 μmol L−1 and detection limit of 0.002 μmol L−1 (signal-to-noise ratio, S/N = 3) could be realized for the DA response. This study carried out the fine regulation of ZIF nanocrystals through rational design; the unique construction and assembly strategy offered here will open up new routes for the evaluation of disease markers to effectively identify and monitor disease.

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information. Compared with previous work, a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time. The main challenge lies in three aspects: Firstly, the asymmetric information results in coupling between control and estimation and failure of the separation principle. Secondly, two extra unknown variables are generated by asymmetric information (different information filtration) when solving forward-backward stochastic difference equations. Thirdly, the existence of additive noise makes the study of mean-square boundedness an obstacle. The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise. A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.

Ce–La–V/TiO 2 was prepared using cerium-based catalysts and the effect of the V active component tested. The mechanism of the catalyst's resistance to SO 2 toxicity at high temperatures was elucidated using various physicochemical characterization techniques and reaction kinetics analysis.

As a fundamental thermodynamic parameter, pressure serves as an effective tool to control the structures and properties of functional materials. To date, numerous pressure-engineering methods have been introduced to enhance perovskite structures and devices. This paper comprehensively reviews the advances in understanding the effects of pressure on perovskite materials and devices, encompassing both low and high-pressure influences. These effects are categorized into six distinct groups based on their underlying mechanisms, detailing the evolution of perovskite structures from macroscopic to microscopic levels, and exploring the interplay between these structures and their functional characteristics. Finally, the current challenges and offer insights into the future prospects for harnessing pressure effects to further develop perovskite structures, properties, and devices are assessed. Recent progress on the influence of pressure on perovskite materials and devices is reviewed. Based on the mechanism of influence, the pressure effect can be divided into six categories: crystal densification, crystal orientation, crystal size, bond length, bond angle, bandgap, phase transition, and amorphous phase. Finally, prospects for developing new perovskite structures and devices under pressure are presented. image

The development of a portable smartphone-based electrochemical sensor for analyzing adrenaline levels in real samples can make a great contribution to the research community worldwide. In order to achieve this goal, the key challenge is to build sensing interfaces with excellent electrocatalytic properties. In this work, microspherical bimetallic metal-organic frameworks (CoNi-MOF) consisting of nanoclusters were first synthesized using a hydrothermal method. On this basis, the catalytic activity of pure chitosan-polyacrylamide hydrogel (CS-PAM) was modulated by adding different amounts of CoNi-MOF during the in-situ synthesis of CS-PAM. Finally, a portable electrochemical detection system based on CS-PAM was established for the detection of adrenaline. A series of resulting composite hydrogels with a large specific surface area, abundant active sites, and unique network structure facilitate the enrichment and catalysis of adrenaline molecules. Under optimal conditions, the analytical platform constructed by using CoNi-MOF-based CS-PAM has the advantages of a wide detection range (0.5-10 and 10-2500 mu M), a low detection limit (0.167 mu M), and high sensitivity (0.182 and 0.133 mu Amu Mcm(-2)). In addition, the sensor maintains selective detection of the target in the presence of many different types of interferences, and the current response is not significantly reduced even after 60 cycles of testing. We strongly believe that the designed smart portable sensing can realize the accurate determination of adrenaline in complex systems, and this study can provide new ideas for the research of MOFs-based hydrogels in electrochemical analysis.

The two‐step sequentially deposition strategy has been widely used to produce high‐performance FAPbI 3 ‐based solar cells. However, due to the rapid reaction between PbI 2 and FAI, a dense perovskite film forms on top of the PbI 2 layer immediately and blocks the FAI diffusion into the bottom of the PbI 2 film for a complete reaction, which results in a low‐efficiency and limited reproducibility of perovskite solar cells (PSCs). Here, high‐quality α‐FAPbI 3 perovskite films by crystal growth regulation with 4‐fluorobenzamide additives is fabricated. The additives can interact with FAI to suppress the fast reaction between the FAI and PbI 2 and effectively passivate the under‐coordinated Pb 2+ or I ‐ defects. As a result, α‐FAPbI 3 perovskite films with low trap density and large grain size are prepared. The modified PSCs present a high‐power conversion efficiency of 24.08%, maintaining 90% of their initial efficiency after 1400 h in high humidity. This study provides an efficient strategy of synergistic crystallization and passivation to form high‐quality α‐FAPbI 3 films for high‐performance PSCs.

Hypertensive disorders complicating pregnancy (HDCP) represent a systemic condition specific to pregnant women. Three-dimensional (3D) power Doppler ultrasonography is a technique that utilizes erythrocyte density, scattered intensity, or energy distribution in the bloodstream for imaging purposes. This study aimed to compare the changes in 3D power Doppler ultrasonography parameters in late pregnancy between patients with HDCP and those without HDCP, and to evaluate the predictive value of these parameters for pregnancy outcomes in patients with HDCP. The study included 160 pregnant women diagnosed with HDCP and 100 pregnant women without HDCP, who served as the control group. 3D power Doppler ultrasonography was performed, and the values of the vascularization index (VI), flow index (FI), and vascularization flow index (VFI) were measured. In the HDCP group, the VI, FI, and VFI were all lower than those observed in patients without HDCP. In HDCP patients with positive outcomes, these three parameters were higher than those recorded in patients with negative outcomes. The area under the predicted curve (AUC) for VI, FI, VFI, and the combination of these three parameters were 0.69, 0.63, 0.66, and 0.75, respectively. The parameters of 3D power Doppler ultrasonography can reflect the perfusion status of the placenta and predict the outcome of pregnancy in patients with HDCP. By monitoring these relevant hemodynamic parameters, valuable information can be provided for the clinical diagnosis, objective evaluation, and treatment of HDCP.

How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis.

CsPbBr3 perovskite quantum dots/polymethyl methacrylate composites were prepared by a ball milling method, using PMMA as polymer matrix and cesium stearate, lead stearate, and KBr as perovskite sources. We investigated the stability of the composites against UV light, heat, ethanol, and water vapor and evaluated the luminescence performance of the products. The results show that the 1.5% CsPbBr3 PQDs/PMMA composite prepared by the present method exhibited excellent performance, with a PLQY up to similar to 78% and a high stability. The luminous intensity remained at 80%, 87%, 53%, 59%, and 64% of its initial value under ultraviolet light irradiation for 12 days, 467.5 nm LED irradiation for 10 days, heating at 60 degrees C for 10 days, erosion in water vapor at 95% relative humidity and 25 degrees C for 90 h, and soaking in ethanol for 3 h, respectively. The luminous efficiency of a white light-emitting diode fabricated with the CsPbBr3 PQDs/PMMA composite was 76.73 lm center dot W-1, and the RhB degradation percentage under visible light reached 92.2% within 60 min. The preparation method has the advantages of simple processing, solvent-and ligand-free nature, low synthesis temperature, and easy scalability; the as-prepared PQDs composites have high quantum yields and good environmental stability, which highlights the great application prospects of the present method.

article: Intelligent polysomnography to monitor the effect of sleep performance on neurological mechanism in patients with Parkinson's disease - Panminerva Medica 2021 Oct 25 - Minerva Medica - Journals

Aiming at the problems of incomplete audit mechanism and low degree of automation and intelligence in data sharing service of surveying and mapping results, this paper puts forward a method of compiling and sharing metadata of surveying and mapping results based on intelligent contract technology. Firstly, the metadata of surveying and mapping results is compiled, and the metadata system of surveying and mapping results oriented to smart contract is constructed. Then, according to different conditions of open data sharing, the intelligent sharing method of surveying and mapping results data based on metadata is designed. Finally, the metadata function of smart contract is expanded, and the right of sharing data based on metadata is realized. This method improves the automation and intelligence of resource sharing and exchange of surveying and mapping results catalogue, and lays a technical foundation for the realization of sharing and exchange system based on blockchain.

This paper concerns the control problem for networked control systems (NCSs) with local and remote controllers (LRC) subject to one-step delay and intermittent observations. The main contributions lie in two aspects. Firstly, for the finite-horizon case, applying the maximum principle and the completing square, a necessary and sufficient condition is derived for the solvability of the optimal control problem. Meanwhile, we obtain the explicit controller which is a linear function of the optimal state estimator, with the feedback gain based on the coupled Riccati equations. Secondly, for the infinite-horizon case, a necessary and sufficient condition of the stabilization in the mean square sense for the system without additive noise is derived and a sufficient condition is developed for the boundedness in the mean square sense of the system with the additive noise. Numerical examples are given to show that the proposed algorithms are valid.

Objectives To explore the effect of dexmedetomidine (DEX) on postoperative nausea and vomiting (PONV) in adult patients after general anaesthesia. Design Systematic review and meta-analysis. Eligibility criteria for selecting studies Randomised controlled trials (RCTs) comparing the efficacy of DEX with placebo or a single drug on PONV in adult patients after general anaesthesia. Data sources We searched the PubMed, the Web of Science, the Cochrane Library and Embase (1 January 2000 to 30 June 2022) to select the relevant RCTs. Data analysis All the relevant data were analysed by using RevMan V.5.4. Heterogeneity was tested for each outcome, and random-effect or fixed-effect models was selected according to the level of heterogeneity. The primary outcome was the incidence of PONV. The secondary outcomes were the incidence of bradycardia, perioperative opioid consumption, extubation time and the length of hospitalisation. Results A total of 18 trials involving 2018 patients were included in this meta-analysis. Notably, 15 updated studies were not involved in the previous meta-analysis. The incidence of PONV in DEX group was lower than that in the control group (OR=0.49, 95% CI: 0.36 to 0.67) and the perioperative opioid consumption in DEX group was also decreased significantly (standard mean difference (SMD)=−1.04, 95% CI: −1.53 to −0.54). Moreover, the length of hospitalisation (SMD=−2.29, 95% CI: −4.31 to −0.28) and the extubation time (SMD=−0.75, 95% CI: −1.26 to −0.25) in DEX group were shorter. Whereas, more number of patients receiving DEX might increase the occurrence of bradycardia (OR=1.60, 95% CI: 1.13 to 2.27). Conclusions DEX could decrease the occurrence of PONV in adult patients under general anaesthesia and promote the recovery after surgery. However, DEX might increase the occurrence of bradycardia. PROSPERO registration number CRD 42022341548.