Rapid Variations Research Articles

Zero and double-knotted droplet-shaped optical fiber sensor for rapid temperature variation monitoring

Compact, simple optical fiber sensors based on a macro-bend fiber configuration were fabricated and proposed for instantaneous temperature measurement. These fiber sensors were manufactured by straightforwardly mechanically bending two different pieces of single-mode fiber (SMF) into zero-knotted droplet-shaped and double-knotted droplet-shaped, separately, with radii of a few millimeters. The sharp bending excites mode interferences among the core mode and the stimulated modes transmitting in the cladding region of the silica SMF. Many resonant interference fringes were perceived in the transmission signal and were noticed to shift to a shorter spectral region with the rise of environmental temperature (ET). The resultant data prove the practicality of the fabricated optical fiber sensors with excellent temperature sensitivities of about −1.128 nm/°C and −1.904 nm/°C, for zero-knotted droplet-shaped and double-knotted droplet-shaped, respectively, which is several times larger than sensors based on straight-transmitted fiber constructions. The proposed sensors’ structures may contribute effectively to temperature variations monitoring for environmental or industrial uses due to their large thermo-optical coefficient of the silica core and the big RI difference between the core and cladding of the bending fiber section.

Molecular basis for loss of virulence in Magnaporthe oryzae strain AM16

The rapid virulence variation of Magnaporthe oryzae (M. oryzae) to rice is a big challenge for rice blast control. Even though many studies have been done by scientists all over the world, the mechanism of virulence variation in M. oryzae remains elusive. AM16, an avirulent M. oryzae strain reported in our previous study, provides an excellent entry point to explore the mechanism of virulence variation in M. oryzae. In this study, we found that the Pmk1 and Mac1 had specific mutations in strain AM16. The AM16 strains overexpressing Pmk1Guy11 or (and) Mac1Guy11 allele from strain Guy11 displayed significantly increasing conidiation, functional appressorium formation, and restoring pathogenicity to rice. Moreover, we observed that the strains overexpressing Mac1Guy11 had stronger conidia forming capacity than that of the strains overexpressing Pmk1Guy11, while the appressorium formation rate of strains overexpressing Pmk1Guy11 was similar to that of strains overexpressing Pmk1Guy11-Mac1Guy11, much higher than that of the strains overexpressing Mac1Guy11. Taken together, our results reveal that the natural mutation of Pmk1 and Mac1 genes are important, but not the sole cause, for the loss of virulence in strain AM16. The functional difference between Pmk1 and Mac1 in the growth and development of M. oryzae was first discovered, providing new insight into the pathogenic mechanism of M. oryzae.

Mass organic matter accumulation induced by rapid redox variations in lakes: Evidence from the Miaoxi area, Bohai Bay Basin, China

The Member 4 of the Paleogene Shahejie Formation (Es4) in the Bohai Bay Basin is interspersed with a set of high-quality source rocks typified as intercalation of red and black mudstones. A large amount of petroleum (crude oil) originates from these source rocks. The Paleocene–Eocene Thermal Maximum (PETM) event occurred during the deposition of Es4 in the Bohai Bay Basin, and the organic matter enrichment model under this event is worth further investigation due to its relationship with and influence on petroleum accumulations. Well LK25-A, as the first oil exploration well drilled into the Es4 in Miaoxi area, serves as a valuable case to study organic matter accumulation. In this study, we integrate total organic carbon (TOC), Rock-Eval pyrolysis, microscopic observation, vitrinite reflectance (VRo), elemental analysis, and gas chromatography–mass spectrometry (GC–MS) to evaluate the hydrocarbon generation potential, organic matter types, thermal maturity, and sedimentary environment of the Es4 in Miaoxi area. The analysis of maceral and rock pyrolysis data reveals that Type I and Type II organic matter make up the majority of Paleogene mudstones in this region. The TOC and rock pyrolysis data show that mudstones in this area have high organic matter abundance and oil-generation potential. The measured vitrinite reflectance distribution of mudstone samples, which ranges from 0.3% to 0.74%, demonstrates that the Paleogene strata are at the immature to mature stage, and the samples from this area contain a sizable amount of bituminite and mineral-bituminous groundmass. The analysis of biomarkers in the mudstone samples indicates that most of the mudstones in this area are in lacustrine and brackish-hypersaline lacustrine environment under a reducing condition, and some of the red mudstones in the Es4 are formed under a suboxic condition. Based on the size and morphology of the pyrite framboid, the redox conditions of the water mass during deposition or diagenesis are further analyzed. The ratio of the size of framboid pyrite (D) to the size of its micrograins (d) suggests that the Es4 black mudstone developed in an anoxic sulfuretted water environment. Both the inorganic and organic geochemical indexes show that the Paleogene paleoclimate has a great influence on the source rocks of Es4 in this area. The distribution of red–black strata in the area is the result of the combined action of the water redox state and the climatic variation during the PETM event. The development models of organic-rich source rocks from the Es3 and Es4 in this area have been created in light of potential connections between rapid redox variation and the PETM event during the Paleogene. These models may offer a theoretical guidance for petroleum exploration in Miaoxi area of Bohai Bay Basin and other contemporaneous continental basins around the world.

Frequency-Aware Feature Fusion for Dense Image Prediction.

Dense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In this paper, we observe rapid variations in fused feature values within objects, resulting in intra-category inconsistency due to disturbed high-frequency features. Additionally, blurred boundaries in fused features lack accurate high frequency, leading to boundary displacement. Building upon these observations, we propose Frequency-Aware Feature Fusion (FreqFusion), integrating an Adaptive Low-Pass Filter (ALPF) generator, an offset generator, and an Adaptive High-Pass Filter (AHPF) generator. The ALPF generator predicts spatially-variant low-pass filters to attenuate high-frequency components within objects, reducing intra-class inconsistency during upsampling. The offset generator refines large inconsistent features and thin boundaries by replacing inconsistent features with more consistent ones through resampling, while the AHPF generator enhances high-frequency detailed boundary information lost during downsampling. Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries. Extensive experiments across various dense prediction tasks confirm its effectiveness.

Sedimentary dynamic processes affected by tropical cyclones and anthropogenic forcing in a small-scale estuary: The Sheyang River Estuary, China

Amid the robust landward migration of tropical cyclones (TCs) in the context of global warming, the sedimentary dynamic processes in coastal areas are increasingly affected by TCs. Concurrently, coastal environments face tremendous stress from anthropogenic interventions, such as land reclamation and jetty construction. However, most research puts more emphasis on TC impacts on large-scale estuaries or marginal seas, leaving the sediment dynamics in small estuaries, particularly under the influence of TCs and man-made structures, largely unexplored due to the limited field data. To address this issue, this study took the Sheyang River Estuary (SRE) as an example and explored the sediment dynamic responses to TCs and the jetty construction during Typhoon Lekima in August 2019, utilizing the Finite Volume Coastal Ocean Model (FVCOM). The results showed that the rotating wind field of Lekima induced rapid variations in wave height, residual water level, and currents at different typhoon stages and strengthened the sediment resuspension and alongshore transport. Furthermore, by comparing the effects of Lekima with Typhoon Chan-hom in July 2015, we found that TCs with different tracks produced markedly different dynamic processes, yet all facilitated sediment exchange between the northern and southern areas within the SRE. The study also highlighted the impact of jetty construction on sediment transport during typhoons, noting that jetties can disrupt the typhoon-induced alongshore sediment flux and create eddies on either side by altering sediment advection. In the navigational channel area, the construction of jetties can strengthen the along-channel estuarine circulation during typhoon events, potentially leading to severe channel siltation by trapping riverine sediments and inducing net landward sediment transport. While site-specific, this study provides valuable insights into the combined effects of extreme events and human interventions on sedimentary environments in small estuaries.

Aberration correction in complex media over multiple isoplanatic patches by exploiting their spatial correlation

Optical imaging in complex media, such as biological tissues, poses significant challenges due to the presence of aberrations and scattering that distort wavefronts and degrade image quality. Wavefront correction has emerged as a crucial technique for mitigating these effects. Recent advances, such as adaptive optics, wavefront shaping, and matrix-based methods, have made significant strides in correcting for wavefront distortions to achieve high-resolution imaging. However, current approaches face challenges when addressing spatially varying distortions and small isoplanatic patches, where the corrections lose reliability due to the rapid spatial variation of distortions. To address this limitation, we introduce a method that exploits the spatial correlation of wavefront distortions across neighboring isoplanatic patches. This approach, termed "inward-outward progression", enables a more reliable wavefront correction across multiple small patches. The process involves two key steps: (1) the inward progression, where a small zone surrounding a pixel is optimized while progressively shrinking the optimized region, and (2) the outward progression, where the wavefront correction phases of the corrected zone serve as the initial guess to optimize adjacent areas, extending the correction from the small zone at the inward progression until the entire field of view is covered. We validate this method by imaging a USAF resolution target under 700-µm-thick chicken breast. Our approach shows a significant improvement in image quality and superior performance over existing techniques, paving the way for noninvasive imaging where conventional methods face substantial limitations due to the spatially varying aberrations and scattering.

Improving Solar PV System Performance with DC-DC Converters with MPPT Technique in Real-Time Simulation

A Photo Voltaic (PV) system turns Solar Energy (SE) into electricity that needs efficient converter topology. One significant obstacle associated with solar systems is the fluctuation in energy production from solar PV modules due to meteorological factors like temperature and irradiance. The advancement of Maximum Power Point Tracking (MPPT) algorithms has enhanced the effectiveness of solar panels. Implementing an effective operational control approach may optimize the performance of PV panels, while utilizing proper semiconductor materials can maximize the efficiency of converters. This work introduces a MPPT method based on Perturb and Observe (P&O) algorithm. The PV system performs with optimal efficiency when it is near the IV curve, where it produces the highest amount of electricity due to its non-linear voltage-current characteristics. The power output is determined by the degree of irradiance and the temperature of the cells, resulting in the maximum achievable power output. The conditions vary according on the season, time of day, and environmental factors. Rapid variations in light can occur due to factors like clouds. Thus, it is imperative to precisely monitor the maximum power point (MPP) in all settings to optimize power generation. This study proposes a method to optimize the power output of a PV system by integrating MPPT with a DC-DC energy converter. This approach ensures that the PV generator operates at its maximum power level, irrespective of external factors such as sunshine intensity and temperature. MPPT algorithms exhibit variations in their implementation and performance, with a range of MPPT control algorithms being accessible. The P&O algorithms are widely used due to their ability to regulate the MPP of PV panels. The method is validated in real-time using Typhoon Hill Simulation.

SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system.

A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.

Reconstructing Signals in Millimeter Wave Channels Using Bayesian-Based Fading Models

Fading in communication channels presents eminently stochastic characteristics and is a significant challenge, especially at millimeter wave (mmW) frequencies, where the need for lines of sight and the high attenuation of obstacles complicate transmission. This article presents a model based on Bayesian fundamentals intended to improve the description and simulation of stochastic fading effects in these channels. It also includes the use of signal processing techniques to simulate and reconstruct the received signal, simulating the communication channel with an FIR filter. The results obtained by simulating the model show its ability to efficiently capture rapid and profound variations in the signal, typical of those that occur in urban and suburban environments and transmissions in the mmW spectrum. It also provides greater uniformity in signal reconstruction compared to the traditional models that are in use. Using Bayesian fundamentals, which allow dynamic adaptation to change in channel behavior, can improve the efficiency and reliability of networks, especially modern smart networks. Compared to traditional models, the proposed model offers improved signal reconstruction and fading mitigation accuracy, with prospects for future integration in smart communication systems. The better capacity in signal reconstruction presents itself as a differentiator of the model, suggesting greater precision in data transmission.

Hotspots and trends in global antiviral herbal basic research: A visualization analysis

Introduction: Viral infections can become public health emergencies due to the possibility of the wide transmission of their associated pathogens and their rapid variation. Moreover, most viruses lack effective therapeutic drugs and vaccines. Herbal medicines have been clinically validated for their broad-spectrum antiviral properties and their ability to leverage their complex compositions to target multiple levels, pathways, and channels. In this study, we seek to assess the current global research landscape and identify current and future directions for research on antiviral herbal medicines to guide future pharmacological developments. Methods: Bibliometric and visualization methods were used to analyze 2134 Chinese-language and 4600 English-language journal articles published between 2017 and 2022 from both Chinese and international databases, and the theme words and foci of highly cited papers were analyzed. Results: It was found that coronaviruses (especially severe acute respiratory syndrome coronavirus 2, SARS-CoV-2), the influenza virus, the hepatitis B virus, and the human immunodeficiency virus were the primary targets for antiviral herbal medicines. Key herbs included Glycyrrhiza glabra, Lonicera japonica, Scutellaria baicalensis, Ephedra sinica, Forsythia suspensa, Agastache rugosa, Astragalus membranaceus, and Poria cocos. The primary active compounds known to be responsible for these antiviral effects are alkaloids, bioflavonoids, flavonoids, sterols, and polyphenols, including curcumin, quercetin, kaempferol, wogonin, stigmasterol, β-glutosterol, luteolin, coumarins, naringenin, gallic acid, berberine, and andrographolide. These compounds work through mechanisms such as inhibiting viral replication, blocking virus–receptor interactions, destroying viruses, regulating the immune response, oxidative stress induction, and cytokine response suppression. Conclusions: The research foci included the pharmacodynamic foundations, molecular dynamics simulation, and virtual screening of active components in herbs use for the treatment of viral diseases such as SARS-CoV-2 using bioinformatics, macromolecular docking, and network pharmacology. Significant gaps remain in interdisciplinary collaboration, especially regarding herb cultivation, processing, sustainable harvesting, and potential drug interactions.

Monitoring groundwater vulnerability for sustainable water resource management: A DRASTIC-based comparative assessment in a newly township area of Bangladesh

Groundwater is a vital source of freshwater in our daily lives. Global and local groundwater quality are degrading due to rapid urbanization, human interference, and policy variations, which is prominent in developing nations like Bangladesh. The major purpose of this research is to analyze aquifer vulnerability in Bangladesh's north-central area (Mymensingh) using conventional and modified DRASTIC modeling. Seven influencing hydrogeological factors were employed to develop and integrate conventional DRASTIC modeling: soil media, net recharge, aquifer depth, aquifer media, topography, hydraulic conductivity, and influence of vadose zone, while land use and lineament density were used with them for modified DRASTIC modeling. The findings from four vulnerability analysis detected 29.56% (93.35 sq.km), 22.24% (83.12 sq. km), 28.52 (106.93 sq. km), and 37.6% (140.55 sq.km) of the study area as high to very high vulnerable zones for groundwater pollution. Lower groundwater depth, higher hydraulic conductivity, moderate to high groundwater recharge, dense lineaments, dense settlement, agricultural land, and inland waterbodies together might indicate a high vulnerability in the research area. The validation results based on EC and nitrate levels show that conventional (r = 0.884, p ≤ 0.01; r = 0.951, p ≤ 0.01) and modified DRASTIC models (r = 0.868, p ≤ 0.01; r = 0.840, p ≤ 0.01) have a stronger association with unconfined aquifers, than confined aquifers. Modification with both additional parameters showed more accuracy compared to the conventional one. Frequent monitoring of groundwater quality in high and moderately vulnerable zones is recommended for earlier detection and prevention of potential aquifer degradation.

Enhanced MPPT Based on Zebra Optimization Algorithm for Control of a Partially Shaded Photovoltaic System

Objective: The objective of this paper is to adapt the Zebra Optimization Algorithm (ZOA) to track the Global Maximum Power Point (GMPP) of a PV system when subjected to partial shading conditions. Theoretical Framework: Photovoltaic (PV) systems are increasingly being used due to their clean nature and low maintenance cost. However, solar cells exhibit a nonlinear relationship between current and voltage, which necessitates the use of Maximum Power Point Tracking (MPPT) algorithms to ensure the system operates at its full potential. Bypass diodes are integrated into PV modules to protect solar cells from damage during partial shading, but this leads to a distorted multi-peak Power-Voltage (P-V) curve. This distortion presents a challenge for algorithms to distinguish between Global Maximum Power Point (GMPP) and Local Maximum Power Points (LMPPs). Method: The Zebra Optimization Algorithm (ZOA) is applied to track the GMPP of a PV system under partial shading conditions. Simulations were conducted using the MATLAB/Simulink platform to evaluate the algorithm’s performance. The focus was on ensuring fast and accurate tracking of the GMPPT under rapid changes in solar irradiance levels. Results and Discussion: Simulation results demonstrated that the ZOA-based MPPT algorithm effectively tracked the GMPPT of the PV system, even under rapid variations in solar irradiance. The algorithm proved its accuracy and speed in distinguishing the GMPP from LMPPs, addressing the challenges posed by the distorted P-V curve. Research Implications: The successful application of the ZOA for tracking the GMPP in partial shading conditions offers a significant improvement for PV systems. This can lead to enhanced efficiency in energy production and more reliable performance in real-world scenarios, where shading and other environmental factors frequently affect solar panels. Originality/Value: This study introduces the adaptation of the Zebra Optimization Algorithm (ZOA) in MPPT for PV systems, addressing the issue of distinguishing between GMPP and LMPPs under partial shading. The approach demonstrates both innovation and practical value in improving the efficiency of PV systems in challenging environmental conditions.

A methodology for assessing multiple hazards applied to Sweden

Despite extensive efforts through various international initiatives to reduce global warming, it has been determined that human induced climate change is here, that the present scale of disruption of the climate is unprecedented and will continue. Increasing weather volatility can be expected, which will most likely increase exposure to weather related hazards, e.g. wildfires, flooding. The aim of this paper is to present an index-based multi-hazard risk assessment method to assess wildfires and flooding hazard for two municipalities within Sweden. The method is designed to be used by the Fire and Rescue Service (FRS) for planning purposes and can be modified to take the local FRS's capabilities and local conditions into account, thereby improving hazard preparedness at a local level. The analysis presented indicates that, while the frequency of multi-hazard overlap from wildfires and flooding is greatest in more northern parts of Sweden, the method provides important information even when applied to areas with limited overlap. A variation of the hazard assessment using a box kernel sliding window was studied to investigate the sensitivity of the model for rapid variations of an individual hazard level. Given that resource needs will typically spread over several days for large scale natural hazards, the box kernel approach is valuable in helping to identify a span of days when resources associated with incident response might be needed. In the future, the model should be expanded to include additional single hazards, the application to additional municipalities and extension to FRS planning exercises for natural hazards.

Relativistic reflection modeling in AGN and related variability from PCA: a brief review

X-ray observations of active galactic nuclei (AGNs) reveal relativistic reflections from the innermost regions of accretion disks, which contain general-relativistic footprints caused by spinning supermassive black holes (SMBH). We anticipate the spin of a SMBH to be stable over the human timeframe, so brightness changes in the high-energy corona above the SMBH should slightly alter relativistic reflection. In this brief review, we discuss the latest developments in modeling relativistic reflection, as well as the rapid small variation in relativistic emission disclosed by the principal component analysis (PCA) of X-ray variability in AGN. PCA studies of X-ray spectra from AGNs have shown that relativistically blurred reflection has negligible fluctuations over the course of observations, which could originate from rapid (intrahour) intrinsic variations in near-horizon accretion flows and photon rings. The PCA technique is an effective way to disclose relativistic reflection from X-ray observations of AGNs, simplifying the complexity of largely variable X-ray data for automated spectral analysis with machine learning algorithms.

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

RKFD Scheme for Quantum Reflection Model of Bose-Einstein Condensates (BEC) from Silicon Surface

We applied the numerical combination of Runge-Kutta and Finite Difference (RKFD) scheme for a quantum reflection model of Bose-Einstein condensate (BEC) from a silicon surface. It is by the time-dependent Gross-Pitaevskii equation (GPE), a non-linear Schrödinger equation (NLSE) in the context of quantum mechanics. The role of cut-off potential δ and negative imaginary potential Vim is essential to estimating non-interacting BEC reflection models. Relying on these features, we performed a numerical simulation of the BEC quantum reflection model and calculated the effect of reflection probability R versus incident speed vx. The model is based on the three rapid potential variations: positive-step potential +Vstep, negative-step potential -Vstep, and Casimir-Polder potential VCP. As a result, the RKFP numerical scheme was successfully set up and applied to the quantum reflection model of BEC from the silicon surface. The numerical simulations results show that the reflection probability R decays exponentially to the incident speed vx.

Proactive Scheduling for mmWave Wireless LANs

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential.

A novel energy-bounded Boussinesq model and a well balanced and stable numerical discretisation

Many Boussinesq models suffer from nonlinear instabilities, especially in the context of rapid variations in the bed topography. In this work, a Boussinesq system is put forward which is derived in such a way as to be both linearly and nonlinearly energy-stable.The proposed system is designed to be robust for coastal simulations with sharply varying bathymetric features while maintaining the dispersive accuracy at any constant depth. For constant bathymetries, the system has the same linear dispersion relation as Peregrine's system ([22]). Furthermore, the system transitions smoothly to the shallow-water system as the depth goes to zero.In the one-dimensional case, we design a stable finite-volume scheme and demonstrate its robustness, accuracy and stability under grid refinement in a suite of test problems including Dingemans's wave experiment.Finally, we generalise the system to the two-dimensional case.

High dynamic performance based by sliding mod control of quadrotor MAV

The paper focuses on strong control of high dynamic performance based on the sliding mode in order to control the quadrotor MAV. The model used in the simulation is the Newton-Euler model, and also choose the Mambo Parrot quadrotor parameters, to improve the latter's performance in terms of accuracy and robustness in case the impedance control chosen by the manufacturer needs to be changed. The results of which gave effectiveness and efficiency compared to classical controls PID and sliding mode. High dynamic performance control (HDP) is a type of control system designed to provide the highest level of performance over a wide range of operating conditions. This control is not a regulator but an organizing technique. It uses a prediction-based approach to anticipate rapid variations in system states and disturbances and to generate real-time commands to correct tracking errors and maintain system stability. This approach allows HDP control to achieve high performance while maintaining system stability, even under harsh operating conditions.

Temperature-Driven Stopped-Flow Experiments for Investigating the Initial Aggregation of the α-Synuclein Amyloid Protein, Focusing on Active and Inactive Phases.

The primary objective of this research is to further examine the events occurring during the active or burst phase by focusing on the aggregation of the Syn amyloid protein. Regarding this aspect, it was initially conducted rapid temperature variations using stopped-flow spectrometry and tyrosyl group fluorescence emission detection, within the initial 500 milliseconds in buffered Syn solutions at pH 7, exploring various temperature ranges to investigate protein aggregation. The results obtained were contrasted with results obtained for the Nα-acetyl-L-tyrosinamide (NAYA) parent compound in the same conditions. The utilization of the NAYA compound is suitable as it mimics the peptide bonds in proteins and contains a tyrosyl group resembling the four tyrosyl groups found in the Syn protein structure (the protein has no tryptophan residues). Furthermore, the NAYA compound adopts an intramolecularly hydrogen-bonded structure even in an aqueous solution, similar to the interactions seen in the hydrophilic face of β-sheets. Additionally, the Syn protein system can exhibit the presence of β-sheets as a result of the existence of very low abundant Syn amyloid precursor forms or nuclei during the initial stages of the protein aggregation. Thus, a relationship is present between the molecular processes in the NAYA and Syn protein systems, making the NAYA's application crucial in this research. Moreover, to aid in understanding the results, it was also compared the events during the quiescent or inactive phase (30-500 milliseconds) with those in the burst phase (up to 10 milliseconds) using stopped-flow spectrometry conditions. Steady-state measurements were beneficial in comprehending the occurrences in both the quiescent and burst phases examined. Although protein aggregation and disaggregation were observed during the quiescent phase, determining these processes in the burst phase was more challenging. In the latter case, the aggregation of the Syn protein is actually initiated by the interaction of the intrinsically disordered Syn monomers. In the quiescent phase, first-order rate constants were measured and analysis showed that Syn protein aggregation and disaggregation occur simultaneously. At lower temperatures, early protein disaggregation outweighs protein aggregation whereas at higher temperatures protein disaggregation and aggregation are rather similar. It is also need to highlight that the burst phase, while distinct from the quiescent phase, can be considered as a possible structural phase for obtaining details about the aggregation of this specific disordered protein in solution on a very short timescale.