Search Results (26,279)

Varroa destructor is an ectoparasitic mite that causes a true syndrome with complex symptoms in honeybees. For mite control, several synthetic active principles are commercially available. However, these formulations are proving less effective due to the acquisition of resistance mechanisms by the parasite. Naturally derived compounds, such as essential oils, are less prone to generating resistance mechanisms in mite populations and have been proven effective in numerous independent laboratory investigations. In this study, semi-field and field tests were conducted to evaluate the acaricidal efficacy of two essential oils (Citrus bergamia and Citrus limon), which, in our previous residual contact toxicity tests, showed efficacy against Varroa mites. For semi-field cage tests, two-level cages were set up. Filter paper soaked with different concentrations of essential oils was placed in the lower compartment of the cage; 20 honeybees and 10 Varroa mites were allocated in the upper level. Mite detachment from the honeybees was evaluated after 24 and 48 h. For field tests, cardboard strips soaked with solutions of essential oils at different concentrations were inserted in the experimental hives. Weekly, the strips were replaced, and the fall of parasites on the diagnostic bottom board was assessed. Semi-field tests showed that the essential oils, at the concentrations used, were not toxic to bees. In the semi-field tests, an average acaricidal efficacy of 33% and 60% was obtained, at the highest concentration used, for bergamot and lemon essential oils, respectively. During the 4 weeks of exposure to 600 mL of BEO, the average mite mortality was 28.5%. In the experimental group treated with the intermediate concentration of 1200 mL, the average mite mortality at the end of four weeks was 30.2%, while with the highest concentration of 1800 mL, it was 40.3%. During the four weeks of exposure to 600 mL of LEO, the average mite mortality was 34.1%. In the experimental group treated with the 1200 mL concentration, the average mite mortality at the end of the four weeks was 46.6%, while with the highest concentration, it was 50.7%. This study shows that the acaricidal efficacy observed in field tests differs from that in laboratory tests. Innovative formulations are needed that allow the time-controlled and gradual release of essential oil in the field. Full article

In this paper, we introduce waveguide Quantum Electrodynamics (wQED) for the description of tryptophans in microtubules representing data qubits for information storage and, possibly, information processing. We propose a Hamiltonian in wQED and derive Heisenberg equations for qubits and photons. Using the Heisenberg equations, we derive time-evolution equations for the probability of qubits and the distribution of photons both at zero and finite temperature. We then demonstrate the resultant sub-radiance with small decay rates, which is required to achieve robust data qubits for information storage by coupling tryptophan residues containing data qubits with water molecules as Josephson quantum filters (JQFs). We also describe an oscillation processes of qubits in a tubulin dimer through the propagation of excitations with changing decay rates of JQFs. Data qubits are found to retain initial values by adopting sub-radiant states involving entanglement with water degrees of freedom. Full article

This study proposes a novel technique for detecting aerial moving targets using multiple satellite radars. The approach enhances the image contrast of fused local three-dimensional (3D) profiles. Exploiting global navigation satellite system (GNSS) satellites as illuminators of opportunity (IOs) has brought remarkable innovations to multistatic radar. However, target detection is restricted by radiation sources since IOs are often uncontrollable. To address this, we utilize satellite radars operating in an active self-transmitting and self-receiving mode for controllability. The main challenge of multiradar target detection lies in effectively fusing the target echoes from individual radars, as the target ranges and Doppler histories differ. To this end, two periods, namely the integration period and detection period, are precisely designed. In the integration period, we propose a range difference-based positive and negative second-order Keystone transform (SOKT) method to make range compensation accurate. This method compensates for the range difference rather than the target range. In the detection period, we develop two weighting functions, i.e., the Doppler frequency rate (DFR) variance function and smooth spatial filtering function, to extract prominent areas and make efficient detection, respectively. Finally, the results from simulation datasets confirm the effectiveness of our proposed technique. Full article

Drilling fluids are vital in oil and gas well operations, ensuring borehole stability, cutting removal, and pressure control. However, fluid loss into formations during drilling can compromise formation integrity, alter permeability, and risk groundwater contamination. Water-based drilling fluids (WBDFs) are favored for their environmental and cost-effective benefits but often require additives to address filtration and rheological limitations. This study explored the feasibility of using vegetable waste, including pumpkin peel (PP), courgette peel (CP), and butternut squash peel (BSP) in fine (75 μm) and very fine (10 μm) particle sizes as biodegradable WBDF additives. Waste vegetable peels were processed using ball milling and characterized via FTIR, TGA, and EDX. WBDFs, prepared per API SPEC 13A with 3 wt% of added additives, were tested for rheological and filtration properties. Results highlighted that very fine pumpkin peel powder (PP_10) was the most effective additive, reducing fluid loss and filter cake thickness by 43.5% and 50%, respectively. PP_10 WBDF maintained mud density, achieved a pH of 10.52 (preventing corrosion), and enhanced rheological properties, including a 50% rise in plastic viscosity and a 44.2% increase in gel strength. These findings demonstrate the remarkable potential of biodegradable vegetable peels as sustainable WBDF additives. Full article

Inertial navigation systems augmented with visual and wheel odometry measurements have emerged as a robust solution to address uncertainties in robot localization and odometry. This paper introduces a novel data-driven approach to compensate for wheel slippage in visual-inertial-wheel odometry (VIWO). The proposed method leverages Gaussian process regression (GPR) with deep kernel design and long short-term memory (LSTM) layers to model and mitigate slippage-induced errors effectively. Furthermore, a feature confidence estimator is incorporated to address the impact of dynamic feature points on visual measurements, ensuring reliable data integration. By refining these measurements, the system utilizes a multi-state constraint Kalman filter (MSCKF) to achieve accurate state estimation and enhanced navigation performance. The effectiveness of the proposed approach is demonstrated through extensive simulations and experimental validations using real-world datasets. The results highlight the ability of the method to handle challenging terrains and dynamic environments by compensating for wheel slippage and mitigating the influence of dynamic objects. Compared to conventional VIWO systems, the integration of GPR and LSTM layers significantly improves localization accuracy and robustness. This work paves the way for deploying VIWO systems in diverse and unpredictable environments, contributing to advancements in autonomous navigation and multi-sensor fusion technologies. Full article

Nephrin is an essential constituent of the slit diaphragm of the kidney filtering unit. Loss of nephrin expression leads to protein leakage into the urine, one of the hallmarks of kidney damage. Autoantibodies against nephrin have been reported in patients with minimal change disease and recurrent focal segmental glomerulosclerosis. Understanding the mechanism of nephrin loss may help improve or lead to the development of novel treatment strategies. In this study, we demonstrated the important function of miR-204-5p expression on the protection of nephrin from anti-nephrin antibodies present in nephrotoxic serum (NTS). In addition, we identified that aspartyl protease cathepsin D is one enzyme that may be involved in nephrin enzymatic degradation and that cathepsin D is a direct target of miR-204-5p gene regulation. The regulation of miR-204-5p expression was determined to be regulated by the long noncoding RNA Josd1-ps. In an NTS in vivo animal model, treatment with the pan aspartic protease inhibitor Pepstatin A ameliorated renal damage. Finally, we showed that the expression of miR-204-5p had a nephrin-protecting function in vitro. Developing a method of delivery of miR-204-5p specifically to podocytes in vivo may provide a novel method of nephroprotection against nephrin autoantibodies. Full article

Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery’s health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient approach to retrieve real-world field data (voltage, current, and time) under running vehicle conditions. In the first step, noise is removed from the field data using a moving-average filter. Then, first- and second-order derivations are applied to the filtered data to determine specific data set conditions. After that, a new approach based on zero-crossing is implemented to retrieve the field data. A second-order Randle circuit (SORC) is utilized in this study to analyze the selected field data. Further, a particle swarm optimization algorithm is adapted to estimate the parameters of the SORC. Our experiments indicate that the relative errors of the equivalent circuit model (ECM) are less than 2% compared to the model voltage and real voltage, which is consistent with the stable parameters of ECM. Full article

Moisture ingress is a critical concern in buildings, as it may profoundly affect structural integrity, the energy efficiency of a building, and as well the quality of the indoor environment that, in turn, could influence the health and safety of building occupants. Moisture ingress can occur during any phase in the lifecycle of a building component, where environmental loads, such as precipitation, wind, snow, and elevated relative humidity, play a fundamental role in affecting the building structure. Climate change exacerbates the issue of moisture ingress by intensifying these loads. In this review paper, the statistical perspective on publications related to moisture ingress in building envelope materials (BEMs) was first assessed through a scientometric study. All relevant publications were gathered and manually filtered, and the selected papers were categorized based on the topics discussed. The results of the scientometric study, as presented in this paper, include a bar chart in which the number of publications in each category is illustrated; a science journal mapping diagram showing the interdisciplinary connections of the research; a cluster map depicting the network between topics; and an R&D momentum analysis reflecting the rate of growth and publication count in this field. Given the strong focus on material properties, this review also examines experimental methods for characterizing moisture transport properties in building materials used in BEMs. Additionally, the differences between various codes and standards centered on this topic are reviewed and discussed. This combined strategy is intended to comprehensively evaluate available information and approaches to permit identifying the knowledge gaps that need to be addressed. Full article

As traditional federated learning algorithms often fall short in providing privacy protection, a growing body of research integrates local differential privacy methods into federated learning to strengthen privacy guarantees. However, under a fixed privacy budget, with the increase in the dimensionality of model parameters, the privacy budget allocated per parameter diminishes, which means that a larger amount of noise is required to meet privacy requirements. This escalation in noise may adversely affect the final model’s performance. For that, we propose a privacy protection federated learning (PPRD-FL) approach. First, we design a randomized parameter selection strategy that combines randomization with importance-based filtering, effectively addressing the privacy budget dilution problem by selecting only the most crucial parameters for global aggregation. Second, we develop a dynamic local differential privacy-based perturbation mechanism, which adjusts the noise levels according to the training phase, not only providing robustness and security but also optimizing the dynamic allocation of the privacy budget. Finally, our experiments have demonstrated that the proposed approach maintains a high performance while ensuring strong privacy guarantees. Full article

Gain and bias non-uniformities in infrared line-scanning detectors often result in horizontal streak noise, degrading image quality. This paper introduces a novel non-uniformity correction algorithm combining residual guidance and adaptive weighting, which achieves superior denoising and detail preservation compared to existing methods. The method combines residual and original images in a dual-guidance mechanism and significantly enhances denoising performance and detail preservation through iterative compensation strategies and locally weighted linear regression. Additionally, the algorithm employs local variance to adjust weights dynamically, achieving efficient correction in complex scenes while reducing computational complexity to meet real-time application requirements. Experimental results on both simulated and real infrared datasets demonstrate that the proposed method outperforms mainstream algorithms regarding peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, achieving an optimal balance between detail preservation and noise suppression. The algorithm demonstrates robust performance in complex scenes, making it suitable for real-time applications in high-resolution infrared imaging systems. Full article

Bromelain, a natural enzyme derived from pineapple, is known for its antioxidant properties, and its potential as a photoprotective agent has garnered interest in skincare applications. The primary objective of this research was to evaluate and optimize the effectiveness of bromelain-based creams in providing antioxidant and photoprotective protection against ultraviolet (UV) radiation. Antioxidant activity was assessed using the DPPH radical scavenging assay, and the Sun Protection Factor (SPF) was determined in vitro and in vivo to evaluate photoprotective activity. The results revealed that bromelain exhibited strong antioxidant activity. Photoprotection, as measured by SPF, the formulation F3, which combined bromelain with other UV filters, exhibited the highest SPF values of 22.043 ± 0.277 (in vitro) and 21.3 ± 2.901 (in vivo), indicating enhanced photoprotective efficacy. This improvement in SPF was likely due to the synergistic effect of bromelain with the UV filters Octyl Methoxycinnamate (OMC). The findings suggest a positive correlation between antioxidant activity and photoprotection, with bromelain’s antioxidant properties contributing to its overall photoprotective effect. Bromelain may be used on people without causing skin or eye irritation. This study supports the potential of bromelain-based creams as dual-action skincare formulations, offering both antioxidant and UV protection. Full article

The advancement of Vital Pulp Therapy (VPT) in dentistry has shown remarkable progress, with a focus on innovative materials and scaffolds to facilitate reparative dentin formation and tissue regeneration. A comprehensive search strategy was performed across PubMed, Scopus, and Web of Science using keywords such as “vital pulp therapy”, “biomaterials”, “dentin regeneration”, and “growth factors”, with filters for English language studies published in the last 10 years. The inclusion criteria focused on in vitro, in vivo, and clinical studies evaluating traditional and next-generation biomaterials for pulp capping and tissue regeneration. Due to the limitations of calcium-based cements in tissue regeneration, next-generation biomaterials like gelatin, chitosan, alginate, platelet-rich fibrins (PRF), demineralized dentin matrix (DDM), self-assembling peptides, and DNA-based nanomaterials were explored for their enhanced biocompatibility, antibacterial properties, and regenerative potential. These biomaterials hold great potential in enhancing VPT outcomes, but further research is required to understand their efficacy and impact on dentin reparative properties. This review explores the mechanisms and properties of biomaterials in dentin tissue regeneration, emphasizing key features that enhance tissue regeneration. These features include biomaterial sources, physicochemical properties, and biological characteristics that support cells and functions. The discussion also covers the biomaterials’ capability to encapsulate growth factors for dentin repair. The development of innovative biomaterials and next-generation scaffold materials presents exciting opportunities for advancing VPT in dentistry, with the potential to improve clinical outcomes and promote tissue regeneration in a safe and effective manner. Full article

Unmanned aerial vehicles must achieve precise flight maneuvers despite disturbances, parametric uncertainties, modeling inaccuracies, and limitations in onboard sensor information. This paper presents a robust adaptive control for trajectory tracking under nonlinear disturbances. Firstly, parametric and modeling uncertainties are addressed using model reference adaptive control principles to ensure that the dynamics of the aerial vehicle closely follow a reference model. To address the effects of disturbances, a modified nonlinear disturbance observer is designed based on estimated state variables. This observer effectively attenuates constant, nonlinear disturbances with variable frequency and magnitude, and noises. In the next step, a two-stage sliding mode control strategy is introduced, incorporating adaptive laws and a commanded-filter to compute numerical derivatives of the state variables required for control design. An error compensator is integrated into the framework to reduce numerical and computational delays. To address sensor inaccuracies and potential failures, a high-gain observer-based state estimation technique is employed, utilizing the separation principle to incorporate estimated state variables into the control design. Finally, Lyapunov-based stability analysis demonstrates that the system is uniformly ultimately bounded. Numerical simulations on a DJI F450 quadrotor validate the approach’s effectiveness in achieving robust trajectory tracking under disturbances. Full article

Electromagnetic interference (EMI) is a key problem in the design of electric vehicle motor drive systems. Based on the system composition and conducted EMI mechanism, an equivalent circuit model including a motor, inverter, cable, and battery is established, and an optimized double closed-loop control strategy is proposed. Through the joint simulation platform of Simulink and Simplorer, the conduction EMI prediction model of a motor drive system is constructed. On this basis, a filter design method based on Π-type topology is proposed based on the 6 dB safety margin of reducing the interference limit. The simulation results show that the designed filter significantly suppresses the conducted EMI in the frequency band from 150 kHz to 30 MHz, and the interference peak is reduced by approximately 40 dBμV. The effectiveness of the model and filter is verified by experimental tests, and the electromagnetic compatibility (EMC) performance of the system is improved, which provides theoretical support and an engineering reference for the high-frequency interference suppression of the motor drive system. Full article

To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. Subsequently, the rolling bearing failure signals are filtered and denoised using the optimized CYCBD, producing a denoised signal. Ultimately, the noise-reduced signal is fed into the BiLSTM model to realize the classification of faults. The experimental findings demonstrate the suggested approach’s strong noise reduction performance and high diagnostic accuracy. The optimized CYCBD–BiLSTM improves the accuracy by approximately 9.89% compared with other methods when the signal-to-noise ratio (SNR) reaches −9 dB, and it can be effectively used for diagnosing rolling bearing faults under noisy backgrounds. Full article