Search Results (45)

Sustaining rice productivity under the dual constraints of freshwater scarcity and low-temperature stress represents a pressing challenge for high-latitude japonica rice systems worldwide. There is an urgent need to develop coupled irrigation–agronomic management strategies that jointly safeguard yield stability and water use efficiency (WUE) in cold-region rice production. In this study, a two-year field experiment was conducted in 2024–2025 on albic soil (Albic Luvisols, WRB; θfc 38.2% v/v, pH 5.80, clayey texture with poor permeability and a propensity for subsurface waterlogging) in the Sanjiang Plain, Heilongjiang Province, China (47°15′ N, 133°28′ E), with nine coupled “irrigation regime × auxiliary practice” treatments, comprising conventional continuous flooding, four-level controlled irrigation (CI) at lower thresholds of 60%, 70%, 75%, and 80% θfc, and their combinations with film mulching (FM) or a humic-acid-based soil amendment (SA). An interpretable machine-learning diagnostic framework was developed, with elastic net (EN) as the primary analytical model and random forest (RF) as a nonlinear control, to simultaneously identify core yield predictors and outlier treatments. The principal findings were: (i) The soil-amendment-coupled 75% θfc CI treatment (SACI) increased grain yield by 12.3% and reduced water input by 17.0% relative to conventional continuous flooding, with WUE reaching 1.801 kg m⁻³, a 35.3% gain over the control (p < 0.05); these improvements were consistent across both individual years (year × treatment interaction: p = 0.601; inter-year rank correlation ρ = 0.967). Lowering the CI threshold below 75% θfc significantly reduced grain yield through diminished effective-panicle retention. (ii) Multi-method consensus analysis (Kendall’s W = 0.871, p < 0.01) identified root volume at the milk stage as the most strongly and consistently associated statistical predictor of yield formation, with convergent mechanistic support from independent rhizosphere evidence (Eh, TTC reductive activity). Definitive causal validation awaits isotope-tracing experiments. (iii) The film-mulching × continuous-flooding treatment (FMCG) was diagnosed as a yield-response outlier (permutation test p = 0.003), three in situ rhizosphere measurements (redox potential, root TTC-reducing activity, and rhizosphere temperature) supported the proposed mechanism of hot–anoxic rhizospheric inhibition. Methodologically, this study develops a four-level evidence convergence framework that integrates intra-model self-consistency, cross-model (EN vs. RF) consensus, independent rhizosphere evidence, and distribution-free permutation testing, with Jackknife+ conformal prediction and companion Monte Carlo simulations (1000 replicates) used to quantify the reliability boundaries under small-sample conditions (n = 27). These findings provide an evidence-based irrigation–soil co-management strategy for cold-region rice production in Northeast China, and the proposed diagnostic paradigm offers a generalizable, reliability-quantified methodological template for interpretable small-sample modeling in multifactorial coupled field experiments. Full article

This study investigates physics-informed and data-driven hybrid modeling strategies for an automotive-grade electrohydraulic (EH) semi-active damper system. Although deep sequence learning architectures such as Long Short-Term Memory (LSTM) networks and Transformers can provide high predictive accuracy, purely data-driven approaches may struggle to preserve physical consistency and maintain robustness under unseen operating conditions. These limitations become more pronounced for EH dampers, whose hysteretic characteristics exhibit highly nonlinear and non-proportional variations under different current and frequency excitations, unlike the more scalable behavior commonly observed in magnetorheological (MR) dampers. To address these challenges, two physics-informed integration strategies are investigated. The first strategy combines physical and data-driven models through parallel loss-function synthesis. The second strategy introduces a learnable physics layer (PINN-Hybrid), in which the coefficients of the extended hyperbolic tangent formulation are adaptively learned within the neural network architecture. In this framework, the physical model acts as a structural regularization mechanism that guides the learning process while preserving the flexibility of data-driven sequence modeling. The proposed models are evaluated under abrupt valve-control operating conditions. Comparative results indicate that the proposed physics-informed architectures improve hysteresis continuity, physical plausibility, and robustness compared with purely data-driven approaches, particularly in low-velocity and transition regions. The proposed framework therefore demonstrates the potential of physics-informed learning strategies for reliable real-time modeling of nonlinear automotive EH damper systems. Full article

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system. Full article

Achieving coordinated development among social equity (SE), economic development (ED), and ecosystem health (EH) is central to resolving the sustainability trilemma. This study investigated the spatiotemporal evolution and driving forces of SE–ED–EH coordinated development in Hebei Province, China, from 2005 to 2020 using a 1 km grid dataset. A comprehensive analytical framework integrating the Coupling Coordination Degree (CCD) model, fuzzy C-means clustering, and interpretable machine learning (XGBoost–SHAP) was developed to quantify changes in coupling and coordination (CC) levels and reveal nonlinear threshold effects. Results show pronounced spatial heterogeneity: urban cores exhibit “high coupling degree (C)–high coordination degree (T)–high CC level,” southeastern plains show “high C–low T–medium CC level,” and northwestern mountainous areas present “low C–medium/high T–low CC level.” Six dominant temporal evolution types were identified. XGBoost–SHAP reveals that nighttime lights (NL), population density (POP), and elevation (DEM) are the dominant drivers, with clear threshold ranges (NL 500–1500 nits; POP threshold near 40 persons km⁻² with diminishing returns beyond 100 persons km⁻²; DEM constraint at 1000–1250 m) and strong interaction effects. The results suggest that Hebei is entering a quality- and structure-oriented rebalancing stage, where threshold-based management is critical for avoiding marginal loss of coordinated development. This study demonstrates that interpretable machine learning provides a transferable paradigm for threshold calibration, spatial zoning, and policy optimization aligned with SDGs, particularly applicable for resource-constrained regions undergoing late industrial transition. Full article

Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput maximization in an EHSR network with a nonlinear EH model. To solve this non-convex problem, we designed a two-layered optimization algorithm combining convex optimization with a deep reinforcement learning (DRL) framework. The derived optimal power, time allocation factor, and the time-varying environment state are fed into the proposed long short-term memory (LSTM) attention mechanism combined Deep Deterministic Policy Gradient, named the LAMDDPG algorithm to achieve the optimal long-term throughput. Simulation results demonstrate that by equipping the Actor with LSTM to capture temporal state and enhancing the Critic with channel-wise attention mechanism, namely Squeeze-and-Excitation Block, for precise Q-evaluation, the LAMDDPG algorithm achieves a faster convergence rate and optimal long-term throughput compared to the baseline algorithms. Moreover, we find the optimal number of PDs to maintain efficient network performance under NLPM, which is highly significant for guiding practical EHSR applications. Full article

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

Integrated sensing and communication (ISAC) can improve the energy harvesting (EH) efficiency of simultaneous wireless information and power transfer (SWIPT)-assisted IoT networks by enabling precise energy harvest. However, the transmit power is increased in the hybrid system due to the fact that the sensing signals are required to be transferred in addition to the communication data. This paper aims to tackle this issue by formulating an optimization problem to minimize the transmit power of the base station (BS) under a nonlinear EH model, considering the coexistence of power-splitting users (PSUs) and time-switching users (TSUs), as well as the beamforming vector associated with PSUs and TSUs. A two-layer algorithm based on semi-definite relaxation is proposed to tackle the complexity issue of the non-convex optimization problem. The global optimality is theoretically analyzed, and the impact of each parameter on system performance is also discussed. Numerical results indicate that TSUs are more prone to saturation compared to PSUs under identical EH requirements. The minimal required transmit power under the nonlinear EH model is much lower than that under the linear EH model. Moreover, it is observed that the number of TSUs is the primary limiting factor for the minimization of transmit power, which can be effectively mitigated by the proposed algorithm. Full article

Wireless Sensor Nodes (WSNs) are becoming increasingly popular in various industrial sectors due to their capability of real-time remote monitoring of assets. Powering these devices with vibrational energy harvesters (EHs) provides multiple benefits, such as minimal maintenance and ideally infinite lifespan. Among the vibrational harvesters, translational electromagnetic ones (TEMEHs) are a promising solution due to their simple and reliable architecture and their ability to harvest energy at low frequencies. However, a major challenge is achieving a high power density. In this paper, recent literature about this typology of harvesters is reviewed. Different techniques to tune the resonance frequencies to the fundamental frequencies of the ambient vibrations are analyzed, such as non-linearities and multi-DOF configurations. The harvesters are classified on the basis of the suspension type, highlighting advantages and disadvantages. A final comparison is carried out in terms of NPD and FoMv, two indexes that evaluate power density in relation to size and excitation amplitudes. Full article

Background: This study aimed to investigate the association between abdominal aortic calcification (AAC) and coronary heart disease (CHD) in essential hypertension (EH). Methods: This study included patients diagnosed with EH during the 2013–2014 NHANES survey cycle. The study cohort was categorized into the following four groups based on their AAC-24 score: no AAC (0); mild AAC (1–4); moderate AAC (5–15); and severe AAC (16–24). Logistic regression models were used to assess the association between AAC and CHD. Restricted cubic spline curves (RCS) were used to explore possible nonlinear relationships between AAC and CHD. Results: The prevalence of CHD was found to be higher in the moderate AAC and severe AAC groups than in the group without AAC (40.1% versus 30.9%, 47.7% versus 30.9%). On a continuous scale, the fully adjusted model showed a 7% increase in the risk of CHD prevalence per score increase in AAC [OR (95% CI) = 1.07 (1.03–1.11)]. On a categorical scale, the fully adjusted model showed the risk of CHD prevalence in EH patients with moderate AAC and severe AAC was 2.06 (95%CI, 1.23–3.45) and 2.18 (1.09–5.25) times higher than that in patients without AAC, respectively. The RCS curve suggested a dose-response linear relationship between AAC and CHD. Conclusion: These findings highlight that in patients with EH, a higher severity of AAC is associated with a higher risk of CHD prevalence. Full article

Changes in the activity of the renin–angiotensin–aldosterone system are responsible for a stable shift in the regulation of the cardiovascular system in essential hypertension (EH). They can be characterized as hemodynamic allostasis. The purpose of our study was to determine the role of hemodynamic parameters in allostatic load in patients with EH without metabolic syndrome. Twenty-four hours of ambulatory blood pressure monitoring was performed, followed by linear and non-linear rhythm analysis. Based on the daily index, patients with EH were divided into two groups: group 1—patients with no significant nighttime decrease in blood pressure (BP); group 2—patients who had a nocturnal decrease in BP. The control group included healthy persons aged 25 to 69 years. A linear analysis was used to determine the mean values of systolic and diastolic BP, heart rate (HR), time load of BP, circadian index, and structural point of BP. Non-linear analysis was applied to determine the mesor, amplitude, range of oscillations and % rhythm of BP and HR. The allostatic load index (ALI) was also calculated on the basis of the corresponding biomarkers. It was found that ALI was significantly higher in groups 1 and 2 in comparison with the control group. The hemodynamic mechanisms of this increase were different. Full article

This paper presents an optimal resource allocation scheme based on time slot switching (TS) for point-to-point single-input single-output (SISO) simultaneous wireless information and power transfer (SWIPT) systems, aiming to maximize the average achievable rate. The proposed scheme considers the nonlinear energy harvesting (EH) characteristic, and thus, the problem is formulated as a nonconvex optimization problem in the presence of a binary TS ratio. Hence, solving the problem is performed using the time-sharing strong duality theorem and Lagrange dual method. Simulations showed that the proposed scheme improves energy efficiency with respect to different transmission powers by 20%, 10%, and 3% for high-SNR, medium-SNR, and low-SNR regions, respectively. Improvement with respect to average energy efficiency versus other system performance metrics has also been noted for the proposed scheme. Full article

Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided. Full article

In the present work, we describe the synthesis of new tetrahydroisoquinoline derivatives and the crystal structures of two of them. Density functional theory (DFT) investigations at the B3LYP/6-31+G(d,p) level provided their structural reactivity and nonlinear optical properties. The low HOMO-LUMO gaps (E_H-L) suggest a soft nature and higher reactivity, while calculated global reactivity descriptors provide assessments of their reactivity and electronic stability. The calculated natural bonding molecular orbital (NBO) charges show excellent charge separation (charge transfer) and identify the donor and acceptor parts of the molecules. Density of states (DOS) analyses show the newly generated energy states and reduced band gaps, which impart higher conductive properties. For surface reactivity, 3D MESP surfaces are plotted and show electron-rich sites near the nitrogen atoms of the tetrahydroisoquinoline rings. Nonlinear optical (NLO) properties of the crystals are predicted from calculated polarizability (α_o) and hyperpolarizability (β_o) values. For IVb, the α_o and β_o values are 415.53 and 1003.44 au. The remarkable value (1003.44 au) of the hyperpolarizability (β_o) shows IVb has excellent NLO properties. Structural activity relationship analysis suggests that nitric oxide synthases are better targets for both compounds, and they were further subjected to molecular docking simulations to understand the binding efficiency. In addition, ADMET analyses were carried out to understand the potential activity of the molecules as drug candidates. Full article

Wireless power transmission (WPT) is expected to play a crucial role in supporting the perpetual operations of Internet of Things (IoT) devices, thereby contributing significantly to IoT services. However, the development of efficient power allocation algorithms has remained a longstanding challenge. This paper addresses the aforementioned challenge by proposing a novel strategy, called energy poverty-based device selection (EPDS), in conjunction with energy beamforming, where orthogonal frequency bands are allocated to energy harvesting IoT devices (EHIs). To solve two power allocation problems, a logarithmic-based nonlinear energy harvesting model (NEHM) is introduced. The first problem tackled is the total received power maximization (TRPM), which is initially presented and, then, solved optimally in closed-form by incorporating Karush–Kuhn–Tucker (KKT) conditions with the modified water-filling algorithm. The second problem formulated is the common received power maximization (CRPM), which takes into account energy fairness considerations. To assess the proposed algorithms and gain insights into the effects of mobility, the mobility of EHIs is modeled as a one-dimensional random walk. Extensive numerical results are provided to validate the advantages of the proposed algorithms. Both the TRPM and CRPM algorithms exhibit exceptional performance in terms of total and minimum received energy, respectively. Furthermore, in comparison to round-robin scheduling, the EPDS demonstrates superior performance in terms of minimum received energy. This paper highlights the impact of the proposed energy harvesting (EH) model, demonstrating 12.68% and 3.69% higher values than the linear model for the minimum and total received energy, respectively. Full article

In this work, we explore a multi-UAV-assisted wireless power transfer (WPT) network, where multiple UAVs are deployed to provide WPT services to multiple ground devices (GDs) in order to extend their lifespan. To enhance the WPT efficiency while considering fairness, we investigate the joint trajectories and transmit power design. For fairness-aware consideration, our objective is to maximize the harvested energy of the GD with the worst condition, taking into account UAV mobility, anti-collision, and power budget constraints. Unlike previous works that focus on the simplified linear energy harvesting (EH) model, a more accurate multi-source nonlinear EH model is, for the first time, adopted to formulate the problem. Given the highly non-convex nature of the original problem due to the presence of coupled variables, we leverage the convexity of the multi-source nonlinear EH model and introduce a convex approximation method, which enables us to construct a tightly convex problem in each iteration for the original joint design problem, thereby obtaining a high-quality solution. Finally, we present numerical results to showcase the convergence of our algorithm and validate the performance advantages of the proposed multi-UAV WPT scheme with a nonlinear EH model versus benchmarks. Full article