Abstract In the era of big data, personalised recommendation systems are essential for enhancing user engagement and driving business growth. However, traditional recommendation algorithms, such as collaborative filtering, face significant challenges due to data sparsity, algorithm scalability, and the difficulty of adapting to dynamic user preferences. These limitations hinder the ability of systems to provide highly accurate and personalised recommendations. To address these challenges, this paper proposes a clustering‐based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm (GOA), termed LCGOA, to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment. By combining the K‐means algorithm with the enhanced GOA, which incorporates a Lévy flight mechanism and multi‐strategy co‐evolution, our method overcomes the centroid sensitivity issue, a key limitation in traditional clustering techniques. Experimental results across multiple datasets show that the proposed LCGOA‐based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy, offering more relevant content to users and driving greater customer satisfaction and business growth.
This work proposes a covert transmission scheme for integrated sensing and communication (ISAC) scenario, where the radar with the aid of an intelligent reflecting surface (IRS) is employed to achieve covert communication and target detection simultaneously. Specifically, we jointly design the transmit beamforming vector of radar and the phase shifts of the IRS to maximize the covert rate subject to a covertness constraint, a total power constraint, and a detection mutual information constraint. To solve the non-convex optimization problem, we resort to alternating optimization and the semidefinite relaxation method. Numerical results illustrate the effectiveness of the covert transmission system aided by the IRS and demonstrate the performance trade-off between radar detection and covert communication.
Covert communications can hide the very existence of wireless transmissions and thus are able to address privacy issues in numerous applications of the emerging Internet of Things (IoT). In this article, we adopt channel inversion power control (CIPC) to achieve covert communications in Rayleigh fading wireless networks, where a transmitter can possibly hide itself from a warden while transmitting information to a receiver. The CIPC can guarantee a constant signal power at the receiver, which removes the requirement that the receiver has to know the channel state information in order to coherently decode the transmitter's signal. Specifically, we examine the performance of the achieved covert communications in terms of the effective covert rate (ECR), which quantifies the amount of information that the transmitter can reliably convey to the receiver subject to the warden's total error probability being no less than some specific value. The noise uncertainty at the warden serves as the enabler of covert communications. For fairness, we also consider noise uncertainty at the legitimate receiver. Our examination shows that increasing the noise uncertainty at the warden and the receiver simultaneously may not continuously improve the ECR achieved in the considered system model.
Introduction Controlled-release fertilizers effectively improve crop yield and nitrogen use efficiency (NUE). However, their use increases the cost of crop production. Optimal management modes involving urea replacement with controlled-release N fertilizers to increase rice yield through enhanced NUE are not widely explored. Methods Field experiments were conducted from 2017 to 2018 to determine the effects of different controlled-release N fertilizers combined with urea [urea-N (180 kg ha -1 , N 1 )]. We used controlled-release N (150 kg ha -1 , N 2 ) as the base, and four controlled-release N and urea-N ratio treatments [(80%:0% (N 3 ), 60%:20% (N 4 ), 40%:40% (N 5 ), or 20%:60% (N 6 ) as the base with 20% urea-N as topdressing at the panicle initiation stage under 150 kg ha -1 ] to study their impact on the grain yield and NUE of machine-transplanted rice. Results and discussion Grain yield and NUE were positively correlated with increases in photosynthetic production, flag leaf net photosynthetic rate ( P n ), root activity, N transport, and grain-filling characteristics. The photosynthetic potential and population growth rate from the jointing to the full-heading stage, highly effective leaf area index (LAI) rate and P n at the full-heading stage, root activity at 15 d after the full-heading stage, and N transport in the leaves from the full-heading to mature stage were significantly increased by the N 4 treatment, thereby increasing both grain yield and NUE. Furthermore, compared with the other N treatments, the N 4 treatment promoted the mean filling rate of inferior grains, which is closely related to increased filled grains per spikelet and filled grains rate. These effects ultimately improved the grain yield (5.03-25.75%), N agronomic efficiency (NAE, 3.96-17.58%), and N partial factor productivity (NPP, 3.98-27.13%) under the N 4 treatment. Thus, the N 4 treatment with controlled-release N (60%) and urea-N (20%) as a base and urea-N (20%) as topdressing at the panicle-initiation stage proved effective in improving the grain yield and NUE of machine-transplanted hybrid indica rice. These findings offer a theoretical and practical basis for enhancing rice grain yield, NUE, and saving the cost of fertilizer.
The usefulness of watershed hydrological process models is considerably increased when they can be extrapolated across spatial and temporal scales. This scale transfer problem, meaning the description and prediction of characteristics and processes at a scale different from the one at which observations and measurements are made, and has become the subject of much current research in hydrology and other areas. Quantitative description of fractal scaling behavior of runoff and stream network morphometry in agricultural watersheds has not been previously reported. In the present study, fractal and multifractal scaling of daily runoff rate in four experimental agricultural watersheds and their associated sub-watersheds (32 in total) were investigated. The time series of daily runoff rate were obtained from the database (comprising about 16,600 station years of rainfall and runoff data for small agricultural watersheds across the U.S.) developed by the Hydrological and Remote Sensing Laboratory, Agricultural Research Service, US Department of Agriculture (HRSL/ARS/USDA). Fractal scaling patterns of the Digital Elevation Model (DEM)extracted stream network morphometry for these four watersheds were also examined. The morphometry of stream networks of four watersheds were obtained by Geographic Information System (GIS) manipulation of digital elevation data downloaded from the most recent (July 2004) U.S. Geological Survey (USGS) National Elevation Dataset (NED). Several threshold values of contribution area for stream initiation were used to extract stream networks for each of the four watersheds.
Sludge was used as raw material for fast composting to remove hazardous substance and reduce the content of heavy metals to satisfy the standard of agricultural use.The composted sludge was used in the soil of greenhouse as fertilizer to study the changing of nitrogen and phosphorus content.Results showed that total nitrogen in the mixed soil decreased to certain extent in the process of planting due to the plant absorption and self loss while ammonia nitrogen was depressed rapidly by absorption and transformation.Content of total phosphorus in the mixed soil was unchanging since crops need little phosphorus in growing and phosphorus loss was avoided in the greenhouse.Changing of rapidly available phosphorus was complicated,and the contents were ascended after some time of growing.
This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs. Specifically, a full-duplex (FD) UAV collects data from each scheduled SN on the ground and generates artificial noise (AN) to prevent the scheduled SN's confidential information from being wiretapped by other unscheduled SNs. We first derive the reliability outage probability (ROP) and secrecy outage probability (SOP) of a considered fixed-rate transmission, based on which we formulate an optimization problem that maximizes the minimum average secrecy rate (ASR) subject to some specific constraints. We then transform the formulated optimization problem into a convex problem with the aid of first-order restrictive approximation technique and penalty method. The resultant problem is a generalized nonlinear convex programming (GNCP) and solving it directly still leads to a high complexity, which motivates us to further approximate this problem as a second-order cone program (SOCP) in order to reduce the computational complexity. Finally, we develop an iteration procedure based on penalty successive convex approximation (P-SCA) algorithm to pursue the solution to the formulated optimization problem. Our examination shows that the developed joint design achieves a significant performance gain compared to a benchmark scheme.
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