Search Results (6,942)

This study focuses on 19 provinces in the Yangtze River Basin of China. It gathers relevant data indicators from 2010 to 2021 and constructs an evaluation index system centered on agricultural science and technology innovation. The study evaluates the relationship between agricultural “science and technology innovation-economy-ecology” systems and identifies key obstacle factors using the obstacle degree model. The study draws the following conclusions: Firstly, the comprehensive development level index of the agricultural science and technology innovation system shows an overall linear upward trend (values range from 0.121 to 0.382). Secondly, the comprehensive development level index of the agricultural economic system exhibits an upward trend but with a relatively small overall magnitude (values range from 0.248 to 0.322). Thirdly, the comprehensive development level index of the agricultural ecological system demonstrates significant overall fluctuations, with notable regional disparities (values range from 0.384 to 0.414). Fourthly, the overall agricultural SEE (Science and technological innovation, Economy, Ecology) complex system exhibits a characteristic of “high coupling, low coordination”, identifying the main obstacle factors influencing agricultural SEECS based on a formulated approach. Subsequently, the following policy recommendations are proposed: Firstly, enhance the agricultural technological innovation system and promote green and efficient agricultural technology research and development. Secondly, to accelerate the transformation and upgrading of modern agriculture, achieving green and high-quality development of the agricultural economy. Thirdly, to strengthen agricultural ecological environment protection, laying a solid foundation for the healthy and sustainable development of agriculture. Full article

Metal–organic framework materials, as innovative functional materials for nonlinear optical technologies, feature linear and nonlinear optical responses, such as a laser damage threshold, outstanding mechanical properties, thermal stability, and optical transparency. Their non-centrosymmetric crystal structure induces a higher-order nonlinear optical response, which guarantees technological applications. Zn^II– and Ag^I–squarate complexes are attractive templates for these purposes due to their good crystal growth, optical transparency, high thermal stability, etc. However, the space group type of the catena-((μ₂-squarato)-tetra-aqua-zinc(II)) complex ([Zn(C₄O₄)(H₂O)₄]) is debatable, (1) showing centro- and non-centrosymmetric monoclinic C2/c and Cc phases. The same is valid for the catena-((μ₃-squarato)-(μ₂-aqua)-silver(I)) complex (Ag₂C₄O₄), (2) exhibiting, so far, only a C2/c phase. This study is the first to report new crystallographic data on (1) and (2) re-determined at different temperatures (293(2) and 300(2)K) and the non-centrosymmetric Cc phase of (2), having different numbers of molecules per unit cell compared with the C2/c phase. There are high-resolution crystallographic measurements of single crystals, experimental electronic absorption, and vibrational spectroscopic data, together with ultra-high-resolution mass spectrometric ones. The experimental results are supported for theoretical optical and nonlinear optical properties obtained via high-accuracy static computational methods and molecular dynamics, using density functional theory as well as chemometrics. Full article

This study investigated the influence of cultural industry agglomeration on the energy carbon emission performance (CEP). Based on panel data from 276 cities in China, we used the Super-SBM model to measure the CEP. We then used the Tobit regression model to calculate the influence coefficient of cultural industry agglomeration and eight control variables on the CEP and analyzed the complex effects of cultural industry agglomeration on the CEP. The results showed that there is the phenomenon of “diseconomies of agglomeration” in cultural industry agglomeration, which cannot improve the CEP. For each unit of cultural industry agglomeration increase, the CEP decreases by 0.055; however, this phenomenon is not linear. Further research showed that the effects of cultural industry agglomeration showed a trend from good to inferior in the order of east, central, and west and did not improve with time. Finally, we used the panel quantile regression model and found that as the CEP levels rise, the negative impact of cultural industry agglomeration improves. Our research results show that strengthening the technical level to promote the upgrading of the cultural industry is the best way to achieve sustainable development. Governments at all levels should pay attention to the emission reduction potential of cultural industry agglomeration under high CEP levels and strengthen the benign agglomeration of the cultural industry. Full article

In order to limit the ever-increasing consumption of new resources for material formulations, regulations and legislation require us to move from a linear to a circular economy and to find efficient ways to recycle, reuse and recover materials. Taking into account the principles of material circularity and waste reuse, this research study aims to produce thermoplastic composites using two types of industrial waste from neighbouring companies, namely waste polypropylene (wPP) from household production and carbon-fibre-reinforced epoxy composite scrap from a pultrusion company. The industrial scrap of the carbon-fibre-reinforced epoxy composites was either machined/ground to powder (pCFRC) and used directly as a reinforcement agent or subjected to a chemical digestion process to recover the carbon fibres (rCFs). Both pCFRC and rCF, at different weight ratios, were melt-blended with wPP. Prior to melt blending, both pCFRC and rCF were analysed for morphology by scanning electron microscopy (SEM). The pCFRC powder contains epoxy resin fragments with spherical to ellipsoidal shape and carbon fibre fragments. The rCFs are clean from the matrix, but they are slightly thicker and corrugated after the matrix digestion. Further, the morphologies of wPP/pCFRC and wPP/rCF were also investigated by SEM, while the thermal behaviour, i.e., transitions and changes in crystallinity, and thermal resistance were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The strength of the interaction between the filler (i.e., pCFRC or rCF) and the wPP matrix and the processability of these composites were assessed by rheological studies. Finally, the mechanical properties of the systems were characterised by tensile tests, and as found, both pCFRC and rCF exert reinforcement effects, although better results were obtained using rCF. The wPP/pCFRC results are more heterogeneous than those of the wPP/rCF due to the presence of epoxy and carbon fibre fragments, and this heterogeneity could be considered responsible for the mechanical behaviour. Further, the presence of both pCFRC and rCF leads to a restriction of polymer chain mobility, which leads to an overall reduction in ductility. All the results obtained suggest that both pCFRC and rCF are good candidates as reinforcing fillers for wPP and that these complex systems could potentially be processed by injection or compression moulding. Full article

Fluorescence-based aptasensors have been regarded as innovative analytical tools for the detection and quantification of analytes in many fields, including medicine and therapeutics. Using DNA aptamers as the biosensor recognition component, conventional molecular beacon aptasensor designs utilise target-induced structural switches of the DNA aptamers to generate a measurable fluorescent signal. However, not all DNA aptamers undergo sufficient target-specific conformational changes for significant fluorescence measurements. Here, the use of complementary ‘antisense’ strands is proposed to enable fluorescence measurement through strand displacement upon target binding. Using a published target-specific DNA aptamer against the receptor binding domain of SARS-CoV-2, we designed a streptavidin-aptamer bead complex as a fluorescence displacement assay for target detection. The developed assay demonstrates a linear range from 50 to 800 nanomolar (nM) with a limit of detection calculated at 67.5 nM and a limit of quantification calculated at 204.5 nM. This provides a ‘fit-for-purpose’ model assay for the detection and quantification of any target of interest by adapting and functionalising a suitable target-specific DNA aptamer and its complementary antisense strand. Full article

This paper aims to present a robust computational technique utilizing finite difference schemes for accurately solving time fractional reaction–diffusion models, which are prevalent in chemical and biological phenomena. The time-fractional derivative is treated in the Caputo sense, addressing both linear and nonlinear scenarios. The proposed schemes were rigorously evaluated for stability and convergence. Additionally, the effectiveness of the developed schemes was validated through various linear and nonlinear models, including the Allen–Cahn equation, the KPP–Fisher equation, and the Complex Ginzburg–Landau oscillatory problem. These models were tested in one-, two-, and three-dimensional spaces to investigate the diverse patterns and dynamics that emerge. Comprehensive numerical results were provided, showcasing different cases of the fractional order parameter, highlighting the schemes’ versatility and reliability in capturing complex behaviors in fractional reaction–diffusion dynamics. Full article

Objectives: This study was conceived with the aim of translating the experience and knowledge of the research group into the design and creation of multi-active phytocomplex cocktails from lyophilised winery by-products (Grape Pomace—GP) and weeds (Sambucus ebulus L., Dwarf Elder—DE). Methods: Quantification of bioactive molecules was performed by high-performance liquid chromatography (HPLC) method. Results: In the extract obtained from lyophilised GP, the most dominant component that was quantified was petunidin-3-glucoside. Prominent compounds that were quantified in DE extract were cyanidin derivatives. The total number of microorganisms in lyophilisates is low, but some of them still survive lyophilisation. Antibacterial activity was determined by microdilution, the minimum inhibitory concentration (MIC) of the tested bacteria ranged from 0.78 mg/mL to 25.00 mg/mL. Antibacterial susceptibility testing (AST) revealed that Klebsiella spp. and Acinetobacter baumannii complex are extensively drug-resistant (XDR). Conclusions: The GP + DE cocktail showed very strong AB power against both tested XDR bacteria. The total phenolic content and antioxidative effect (determined spectrophotometrically) indicate their linear correlation. Full article

It is unknown how recurring flooding impacts household diet in Central Java. We aimed to assess how recurrent flooding influenced household food access over 22 years in Central Java by linking the Global Surface Water dataset (GSW) to the Indonesian Family Life Survey. We examined linear and nonlinear relationships and joint effects with indicators of adaptive capacity. We measured recurrent flooding as the fraction of district raster cells with episodic flooding from 1984–2015 using GSW. Food access outcomes were household food expenditure share (FES) and dietary diversity score (DDS). We fit generalized linear mixed models and random forest regression models. We detected joint effects with flooding and adaptive capacity. Wealth and access to credit were associated with improved FES and DDS. The effect of wealth on FES was stronger in households in more flood-affected districts, while access to credit was associated with reduced odds of DDS in more flood-affected districts. Flooding had more predictive importance for FES than for DDS. Access to credit, a factor that ordinarily improves food access, may not be effective in flood-prone areas. Wealthier households may be better able to adapt in terms of food access. Future research should incorporate land use data to understand how different locales are affected and further understand the complexity of these relationships. Full article

The UAV landing process has higher requirements for automatic flight control systems due to factors such as wind disturbances and strong constraints. Considering the proven effective adaptation of the out-of-loop L1 adaptive control (OLAC) system proposed in previous studies, this paper applies it to landing control to enhance robustness and control accuracy in the presence of complex uncertainties. Based on modern control theory, an LQR-based OLAC algorithm for multi-input–multi-output (MIMO) systems is proposed, which is conducive to the coupling control of the flight attitude mode. To evaluate the robustness of the designed system, an equivalence stability margin analysis method for nonlinear systems is proposed based on parameter linearization. Along with a detailed autonomous landing strategy, including trajectory planning, control, and guidance, the effectiveness of the proposed methods is verified on a high-fidelity simulation platform. The Monte–Carlo simulation is implemented in the time domain, and the results demonstrate that OLAC exhibits strong robustness and ensures the state variables strictly meet the flight safety constraints. Full article

The growing interest in data privacy and anonymization presents challenges, as traditional methods such as ordinal discretization often result in information loss by coarsening metric data. Current research suggests that modeling the latent distributions of ordinal classes can reduce the effectiveness of anonymization and increase traceability. In fact, combining probability distributions with a small training sample can effectively infer true metric values from discrete information, depending on the model and data complexity. Our method uses metric values and ordinal classes to model latent normal distributions for each discrete class. This approach, applied with both linear and Bayesian linear regression, aims to enhance supervised learning models. Evaluated with synthetic datasets and real-world datasets from UCI and Kaggle, our method shows improved mean point estimation and narrower prediction intervals compared to the baseline. With 5–10% training data randomly split from each dataset population, it achieves an average 10% reduction in MSE and a ~5–10% increase in R² on out-of-sample test data overall. Full article

The Direction of Arrival (DOA) parameter is a key parameter in directional channel modeling for GNSS systems and multipath suppression. However, achieving high-precision, low-complexity DOA estimation of multiple signal sources without requiring a known source number is still a challenge. This paper introduces a satellite navigation DOA parameter estimation method based on deconvolution beamforming. By exploiting the translational invariance property of the uniform linear array pattern, the deconvolution process is applied to the de-spread array pattern of satellite navigation signals, achieving high-precision estimation of DOA parameters. This method can achieve high-precision blind DOA estimation of multiple signal sources while significantly reducing the estimation complexity. Compared with traditional methods, precise DOA estimation can be achieved even in low-signal-to-noise-ratio conditions and with a small number of elements in the array. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm. Full article

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45$°$ sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel. Full article

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss. Full article

The N-Queens problem is a fundamental challenge in combinatorial optimization, commonly used as a benchmark for assessing the efficiency of algorithms. Traditional algorithms, such as Backtracking with Forward Checking (BFC), constraint satisfaction problem (CSP) techniques, Lookahead algorithms, and heuristic-based methods, often face challenges with exponential time complexity, making them less practical for large-scale instances. This paper introduces a novel algorithm, non-sequential conflict resolution (NSCR), which improves performance over traditional algorithms through dynamic conflict resolution. The NSCR algorithm iteratively resolves conflicts among queens by adjusting their positions, aiming to optimize both time complexity and memory usage. While NSCR also operates within exponential time bounds, it demonstrates improved scalability and efficiency compared to traditional methods. A significant strength of the NSCR algorithm lies in its space complexity, which is O(n), and a time complexity that, while typically lower than traditional methods, can reach O(n³) in the worst-case scenario. This linear space complexity is highly advantageous, particularly when dealing with large problem sizes, as it ensures efficient use of memory resources. Comparative analysis with the aforementioned algorithms shows that NSCR offers superior resource management, using up to 60% less memory and reducing runtime by approximately 50%, making it an efficient option for large-scale instances of the N-Queens problem. The algorithm’s performance, evaluated on problem sizes ranging from 8 to 1000 queens, highlights its ability to manage computational resources effectively, despite the inherent challenges of exponential time complexity. Full article

Using adaptive filtering to estimate the frequency of power systems has become a popular trend. In recent years, however, few studies have been performed on adaptive frequency estimations in non-stationary noise environments. In this paper, we propose the distributed complex inverse square root algorithm and distributed augmented complex inverse square root algorithm for the frequency estimation of power systems based on the widely linear model and the inverse square root cost function, where the function can restrain both positive and negative large errors, based on its symmetry. Moreover, the wireless sensor networks support monitoring and adaptation for the frequency estimation in the distributed networks, and the proposed approach can ensure good robustness of the balanced or unbalanced three-phase power system with the help of a local complex-value voltage signal generated by Clark’s transformation. In addition, the bound of step size is driven by the global vectors, and that low computation complexity do not hinder those performances. The results of several experiments demonstrate that our algorithms can effectively estimate the frequency in impulsive noise environments. Full article