Search Results (1,389)

Microalgae proteins are increasingly recognized in the food and nutraceutical industries for their functional versatility and high nutritional value. Mild alkaline treatment is commonly used for cell wall degradation and intracellular protein solubilization, consequently enhancing the protein extraction yield. The findings of this study reveal that alkaline treatment alone, even at higher NaOH concentration (up to 0.3 M) and treatment time (up to 90 min), was ineffective (max. 2.4% yield) for the extraction of protein from Auxenochlorella protothecoides biomass. This challenge was significantly reduced through synergistic application of mechanical cell disruption using high-pressure homogenization (HPH) and alkaline solubilization. Single-pass HPH (35 k psi) alone without alkaline treatment led to 52.3% protein solubilization from wet biomass directly harvested from culture broth, while it was only 18.5% for spray-dried biomass. The combined effect of HPH and alkaline (0.1 M NaOH) treatment significantly increased protein extraction yield to 68.0% for a spray-dried biomass loading of 50 g L⁻¹. Through replacing spray-dried biomass with wet biomass, the requirement of NaOH was reduced by 5-fold to 0.02 M to achieve a similar yield of 68.1%. The process integration of HPH with the mild alkaline solubilization and utilization of wet biomass from culture broth showed high potential for industrialization of microalgae protein extraction. This method achieves high extraction yield while reducing alkaline waste and eliminating the need for energy-consuming drying of biomass, thereby minimizing the environmental impact. Full article

The conventional Capon beamforming algorithm can achieve a high gain in the direction of desired signals and zero-trapping in the direction of interfering signals, providing a high output signal-to-interference-plus-noise ratio (SINR). However, when the steering vector of the desired signal is mismatched, the performance of the Capon beamforming algorithm degrades. In addressing this challenge, the present research introduces a refined algorithm. The core of the proposed robust Capon beamforming technique lies in leveraging the orthogonality between the steering vector and the noise space, the estimated expected signal steering vector is corrected. Based on this feature, the proposed algorithm meticulously optimizes the predicted steering vector of the desired signal, which can mitigate the problem of performance degradation caused by the mismatch in the steering vector. Moreover, the covariance matrix is corrected using the desired signal elimination method, which can overcome the problem of signal self-cancelation. Furthermore, through the optimization process, the proposed algorithm can maintain high robustness in complex environments and under the condition of different input signals, its beam pattern performance is more excellent. The results of simulation experiments show that the proposed algorithm demonstrates greater robustness compared to the currently available algorithms, can achieve a higher output SINR, and is insensitive to steering vector mismatch. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

With the rapid advancement of high-precision optical systems, increasingly stringent demands are imposed on the surface figure accuracy of optical components. The magnitude of residual stress in multilayer films directly influences the post-coating surface figure stability of these components, making the control of multilayer film stress a critical factor in enhancing optical surface figure accuracy. In this study, which addresses the process constraints and substrate damage risks associated with conventional annealing-based stress compensation for large-aperture optical components, we introduce an active stress engineering strategy rooted in in situ deposition process optimization. By systematically tailoring film deposition parameters and adjusting the thickness modulation ratio of TiO₂ and SiO₂, we achieve dynamic compensation of residual stress in multilayer structures. This approach demonstrates broad applicability across diverse optical coatings, where it effectively mitigates stress-induced surface distortions. Unlike annealing methods, this intrinsic stress polarity manipulation strategy obviates the need for high-temperature post-processing, eliminating risks of material decomposition or substrate degradation. By enabling precise nanoscale stress regulation in large-aperture films through controlled process parameters, it provides essential technical support for manufacturing ultra-precision optical devices, such as next-generation laser systems and space-based stress wave detection instruments, where minimal stress-induced deformation is paramount to functional performance. Full article

Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery. Full article

The cast nickel-based superalloy K417G exhibits excellent high-temperature strength, but non-equilibrium solidification during casting can cause defects such as irreparable interdendritic microporosity, which significantly degrades its fatigue and creep properties. This study uses hot isostatic pressing (HIP) to eliminate internal flaws such as porosity in the K417G alloy, aiming to improve its mechanical properties. We investigated the microstructure and mechanical properties of K417G under two thermal conditions: solution heat treatment (SHT) and hot isostatic pressing (HIP). The results indicate that HIP significantly reduces microporosity. Compared to SHT, HIP improves the mechanical performance of K417G. The creep fracture mechanism shifts from intergranular brittle fracture (SHT) to ductile fracture (HIP). Consequently, HIP increases the alloy′s creep life approximately threefold and raises its fatigue limit by about 20 MPa. This improvement is attributed to pore density reduction, which decreases stress concentration zones and homogenizes the microstructure, thereby impeding fatigue crack nucleation and extending the crack incubation period. Full article

Targeted degradation technologies, primarily referring to targeted protein degradation, have emerged as promising drug discovery strategies. In contrast to traditional “occupancy-driven” inhibition approaches, these technologies ingeniously leverage the cell’s endogenous degradation mechanisms to achieve specific elimination of disease-causing targets. Autophagy, a highly conserved cellular clearance pathway, possesses broad substrate recognition capabilities, enabling degradation of not only individual proteins but also protein aggregates, damaged organelles, and invading pathogens. Given these characteristics, researchers are actively exploring the application of autophagy mechanisms in targeted degradation technologies. Herein, we summarize recent advances in autophagy-dependent degradation approaches, including autophagosome tethering compounds (ATTEC), autophagy-targeting chimeras (AUTAC), autophagy-targeting Chimera (AUTOTAC), chaperone-mediated autophagy (CMA)-based methods, nanotechnology-based strategies, and the newly introduced autophagy-induced antibody (AUTAB) technique, highlighting their mechanisms, advantages, and potential applications in treating tumors, neurodegenerative diseases, and other challenging conditions. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article

Biodegradable metallic implants represent a paradigm shift in implantology, eliminating secondary removal surgeries through predictable controlled degradation. This review systematizes current achievements in selective laser melting (SLM) of biodegradable metals (Mg, Fe, Zn), analyzing how processing parameters influence microstructure, mechanical properties, and degradation kinetics. Key findings demonstrate that SLM-produced Mg alloys achieve bone-matching modulus (40–45 GPa) with moderate degradation (1–3 mm/year); Fe-based systems provide superior strength (400–600 MPa) but slower degradation (0.1–0.5 mm/year); while Zn alloys offer intermediate properties. Design strategies for porous/lattice structures enhancing osseointegration and enabling property gradients are discussed. Major challenges include controlling degradation kinetics, optimizing SLM parameters for reactive metals, standardizing testing methodologies, and regulatory harmonization. This comprehensive analysis provides systematic guidelines for material selection and process optimization, establishing a foundation for developing next-generation personalized biodegradable implants. Full article

The 26S proteasome is a large, ATP-dependent proteolytic complex responsible for degrading ubiquitinated proteins in eukaryotic cells. It plays a crucial role in maintaining cellular protein homeostasis by selectively eliminating misfolded, damaged, or regulatory proteins marked for degradation. In this study, whole-exome sequencing (WES) was performed on an individual presenting with developmental delay and mild intellectual disability, as well as on both of his unaffected parents. This analysis identified a de novo variant, c.959C>G (p.Pro320Arg), in the PSMC5 gene. As predicted, this gene shows a very likely autosomal dominant inheritance pattern. Notably, PSMC5 has not previously been associated with any phenotype in the OMIM database. This variant was recently submitted to the ClinVar database as a variant of uncertain significance (VUS) and remains absent in both gnomAD and dbSNP. Notably, it has been identified in six unrelated individuals presenting with clinical features comparable to those observed in the patient described in this study. Multiple in silico prediction tools classified the variant as pathogenic, and a PhyloP conservation score supports strong evolutionary conservation of the mutated nucleotide. Protein structure predictions using the AlphaFold3 algorithm revealed notable structural differences between the mutant and wild-type PSMC5 proteins. We hypothesize that the p.Pro320Arg substitution alters the structure and function of PSMC5 as a regulatory subunit of the 26S proteasome, potentially impairing the stability and activity of the entire complex. Although functional studies are imperative, this study contributes to a deeper understanding of PSMC5, expands the spectrum of associated neurodevelopmental phenotypes, and highlights its potential as a therapeutic target. Furthermore, this study resulted in the submission of the identified variant to the ClinVar database (SCV006083352), where it was classified as pathogenic. Full article

To improve the accuracy of insulation state online monitoring for capacitive dry-type current transformers (CTs) and to address the limitations of traditional methods relying on potential transformer (PT) voltage signals and reference devices, a virtual voltage-based online monitoring method is proposed. First, the leakage current is collected through a core-type current transformer on the end-screen grounding line. Combined with group measurement data from surge arresters and dry-type CTs on the same busbar, phase constraints are established, and a least mean square (LMS) algorithm is utilized to iteratively train the virtual voltage reference phase. Subsequently, the resistive current and dielectric loss factor (tan δ) are calculated based on the virtual voltage reference phase to achieve online insulation state monitoring. Simulation results demonstrate that the proposed method can accurately obtain the virtual voltage reference phase and effectively identify the degradation trend of dry-type CTs. Field applications validate the feasibility of the method, with monitoring data fluctuations (±20 μA for the resistive current and ±0.002 for the dielectric loss) meeting engineering requirements. This method eliminates the need for PT signals and reference devices, thus providing a novel approach for the online insulation monitoring of dry-type CTs. Full article

Acid hydrolysis can be more efficient than water hydrolysis, particularly in breaking down cured adhesives found in waste panels within a shorter reaction time, which could benefit large-scale industrial processes. This study evaluates the effects of various acid hydrolysis conditions on the thermal, physical, and chemical properties of recycled particles intended for particleboard production. Particleboards were recycled using oxalic acid and ammonium chloride at different concentrations and reaction times at 122 °C. The thermal stability of the particles was determined by thermogravimetric analysis. Particle size distribution, particle morphology, nitrogen content, pH and acid/base buffer capacity were analyzed. The effect of the recycled particles on the urea-formaldehyde (UF) curing was assessed using differential scanning calorimetry and the gel time method. The recycled particles exhibited a higher thermal degradation beyond 200 °C, indicating their thermal stability for manufacturing new panels. The acid treatments did not damage the anatomical structure of the particles, preserving the prosenchymatous elements. The nitrogen content of recycled particles decreased by up to 90% when oxalic acid was used, compared to raw board particles. Recycled particles exhibited a lower pH, with a maximum reduction of 44%. They also showed a decreased acid buffer capacity and an increased base buffer capacity compared to raw board particles. This effect was particularly pronounced in treatments that included ammonium chloride. The recycled particles did not significantly affect the peak polymerization temperature of the UF adhesive. However, some treatments affected the gel time of the adhesive, particularly those using 30% ammonium chloride. The results indicate that particleboards can be effectively recycled through acid hydrolysis, mainly with oxalic acid, which provides better results than hydrolysis using water alone. Oxalic acid showed increased selectivity in eliminating the cured UF adhesive, resulting in recycled particles suitable for manufacturing new panels. Full article

Electromyography (EMG) signals have diverse applications, ranging from prosthetic hands and assistive suits to rehabilitation devices. Nonetheless, their performance suffers from cross-subject generalization issues, electrode shifts, and daily variability. In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. To address this issue in real-time prediction, we investigated a sliding-window normalization (SWN) technique that merges z-score normalization with sliding-window processing to align the EMG amplitude across channels and mitigate the performance degradation caused by electrode displacement. We validated SWN using experimental data from a right-arm trajectory-tracking task involving three motion classes (rest, flexion, and extension of the elbow). Offline analysis revealed that SWN mitigated accuracy degradation to −1.0% without additional data for re-training or multi-condition training, a 6.6% improvement compared with the −7.6% baseline without normalization. The advantage of SWN is that it operates with data from a single electrode position for training, which eliminates both the collection of multi-position training data and the calibration of deep learning models before practical use in EMG applications. Moreover, combining SWN with multi-position training exceeded the classification accuracy of the no-shift condition by 2.4%. Full article

Inconsistencies in lithium-ion battery packs pose significant challenges for both electric vehicles and energy storage systems, causing diminished energy utilization and accelerated battery aging. This study investigates the characteristics and aging processes of 32 batteries, creating simulation models for cells and packs based on experimental data. Through a controlled single-variable approach, the decoupled analysis of multi-parameter inconsistencies is carried out. Simulation results demonstrate that parallel-connected packs can maintain charge consistency without the need for external balancing systems, thanks to their self-balancing mechanisms. On the other hand, series-connected packs experience accelerated capacity degradation primarily due to charge inconsistencies linked to differences in Coulombic efficiency (CE) and the initial state of charge (SOC). For packs with minor capacity variations and temperature inconsistencies, a passive balancing current of 0.001 C can effectively eliminate up to 3.8% of capacity loss caused by charge inconsistencies within 15 cycles. Active balancing systems outperform passive ones primarily when there is significant capacity inconsistency. However, for packs that have undergone capacity screening before assembly, both active and passive balancing systems prove to be equally effective. Additionally, inconsistencies in internal resistance have a minimal impact on overall pack capacity but limit the power of both series-connected and parallel-connected packs. These findings offer essential insights for the development of balancing systems within battery management systems. Full article

Background/Objectives: Chronic illness after COVID-19 vaccination (longvax) lacks a therapeutic protocol anchored in pathophysiology. Persistent vaccine derived spike protein appears to trigger microvascular fibrin amyloid microclots, immune dysfunction, pathogen reactivation and multisystem injury. This article proposes an integrative approach, Vaxtherapy, to tackle these mechanisms. Methods: A narrative synthesis of peer reviewed literature from 2021 to 2025 on spike related injury and vaccine adverse events was conducted, supplemented by clinical case series and mechanistic observations from long COVID. The findings were arranged into a four stage therapeutic sequence ordered by pathophysiological precedence. Results: Stage one aims to reopen hypoperfused tissue through oral fibrinolytics that degrade fibrin amyloid resistant microclots; stage two intends to neutralise circulating or tissue bound spike via a receptor binding domain monoclonal antibody cocktail; stage three seeks to eliminate reactivated viral or microbial reservoirs with targeted antivirals or antimicrobials once perfusion is improved; and stage four aspires to support tissue repair with mitochondrial supplements and, when indicated, cell based therapies. Omitting or reordering stages may reduce efficacy or foster resistance. Conclusions: This hypothesis driven framework outlines a biologically plausible roadmap for longvax research. By matching interventions to specific mechanisms (fibrinolysis, spike neutralisation, pathogen clearance and regeneration), it aims to guide controlled trials and compassionate pilot programs directed at durable recovery rather than chronic symptom management. Full article