High Fluctuations Research Articles

Neuronal basis of high frequency fMRI fluctuation: direct evidence from simultaneous recording

Resting-state functional magnetic resonance imaging (RS-fMRI) has been extensively utilized for noninvasive investigation of human brain activity. While studies employing simultaneous recordings of fMRI and electrophysiology have established a connection between the low-frequency fluctuation (< 0.1 Hz) observed in RS-fMRI and the local field potential (LFP), it remains unclear whether the RS-fMRI signal exhibits frequency-dependent modulation, which is a well-documented phenomenon in LFP. The present study concurrently recorded resting-state functional magnetic resonance imaging (RS-fMRI) and local field potentials (LFP) in the striatum of 8 rats before and after a pharmacological manipulation. We observed a highly similar frequency-dependent pattern of amplitude changes in both RS-fMRI and LFP following the manipulation, specifically an increase in high-frequency band amplitudes accompanied by a decrease in low-frequency band amplitudes. These findings provide direct evidence that the enhanced high-frequency fluctuations and reduced low-frequency fluctuations observed in RS-fMRI may reflect heightened neuronal activity.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Thermal Performance of Novel Eco-Friendly Prefabricated Walls for Thermal Comfort in Temperate Climates

The global threat of climate change has become increasingly severe, with the efficiency of buildings and the environment being significantly impacted. It is necessary to develop bioclimatic architectural systems that can effectively reduce energy consumption while bringing thermal comfort, reducing the impact of external temperatures. This study presents the results of a real-scale experimental house prototype, MHTITCA, using a unique design composed of novel eco-friendly prefabricated channel walls filled with a blend of soil, sawdust, and cement for walls and roofs. The experimental analysis performed in this study was based on dynamic climatology. A solar orientation chart of the place was constructed to identify the solar radiation intensity acting on the house. Measurements of roof surface temperatures were conducted to determine temperature damping and temperature wave lag. Monthly average temperature and direct solar radiation data of the site were considered. Compared to other alternative house prototypes, the system maximizes thermal comfort in high-oscillation temperate climates. Temperature measurements were taken inside and outside to evaluate the thermal performance. A thermal insulating layer was added outside the wall and the envelope to improve the prototype’s thermal comfort and reduce the decrement factor even more. This MHTITCA house prototype had 85% thermal comfort, 0% overheating, and 15% low heating. This eco-friendly prototype design had the best thermal performance, achieving a thermal lag of twelve hours, a reduced decrement factor of 0.109, and preventing overheating in areas with high thermal fluctuations. Comparatively, the other prototypes examined provided inferior thermal comfort. The suggested MHTITCA system can be an energy-saving and passive cooling option for thermal comfort in low-cost houses in temperate climates with high thermal oscillations in urban or rural settings.

Rancang Bangun Aplikasi Untuk Prediksi Harga Bitcoin Menggunakan Algoritma Long Short-Term Memory

Bitcoin, as the first cryptocurrency launched in 2009, has experienced rapid growth and significant volatility.The high price fluctuations of Bitcoin have attracted the attention of investors and traders worldwide.Predicting Bitcoin prices poses a challenge due to its inherent complexity and volatility. Various methodsare employed to predict Bitcoin prices, including fundamental analysis, technical analysis, and deeplearning techniques. This study explores the use of neural networks, specifically Long Short-Term Memory(LSTM), as a tool for predicting Bitcoin prices. Long Short-Term Memory is a type of recurrent neuralnetwork (RNN) designed to address the vanishing and exploding gradient problems present in traditionalRNNs and can learn long-term dependencies in data. The LSTM model used in this research comprises 50neurons, 400 epochs, a batch size of 32, and utilizes Adam optimization. Model evaluation indicates thatLSTM performs well, with a Mean Square Error (MSE) of 2688. Additionally, the model achieves a MeanAbsolute Percentage Error (MAPE) accuracy of 97.77%. Based on these predictions, Bitcoin's price isexpected to decrease over the next month, with an estimated price of approximately 66,191.00 on June 15,2024, and around 64,271.64 on July 15, 2024

Moisture dynamics during high‐load fluctuations in transformers: Localised accumulation and interfacial transfer within oil/pressboard insulation

AbstractPower systems grapple with the challenges of high load rates and intermittent new energy sources integration. Transformers, as vital equipment, employ oil/pressboard (oil/PB) insulation. Uneven moisture distribution in this insulation can jeopardise safety thresholds, necessitating precise moisture assessment for grid stability. A novel mathematical model, adsorption–desorption and porous media moisture transfer (ADP‐MoT), is presented. This model incorporates adsorption and desorption processes within the porous pressboard, enabling a description of the dynamic moisture transfer between the oil and pressboard. Using this mathematical model, simulations for moisture dynamics were performed on a 750‐kV transformer across four typical days. The results indicate that temperature fluctuations are the primary driving factor for moisture migration at the oil/PB interface. Convection and diffusion contribute to moisture movement towards cooler regions. Fluid properties and structural characteristics induce a distinctive streamline‐shaped moisture flow within horizontal oil channels, with localised moisture accumulation in specific areas. Moreover, the analysis of 96 transient results uncovers potential free‐state moisture formation during severe conditions, underscoring the importance of monitoring the pressboard at winding bases during high load fluctuations. In conclusion, this study significantly contributes to scientifically identifying and addressing risks tied to new energy sources integration in power systems.

Species composition and distribution of birds in the area of Kyzyl airport (Republic of Tyva, Russia) and their impact on the ornithological situation

Aim. Study and identification of species composition and actual location of birds on the territory of the Kyzyl airport and in the airspace above it throughout the year and assessment of the intensity of the ornithological situation in order to ensure flight safety.The study of avifauna within the airport area within a radius of 15 km was carried out throughout a calendar year using the method of integrated route accounting and stationary observations on the runway. Statistical processing of the field materials was carried out using the Microsoft Excel program.In total, a fairly high species diversity was revealed within the airport territory – 76 species in zone I – R =2,5 km and 199 species in zone II – R= 15 km. The population density of birds points was calculated in points, the status of stay, seasonal fluctuations in the number and species diversity of avifauna and the intensity of the ornithological situation were revealed.Despite the rather high species diversity and significant fluctuations in the number of birds, ornithological intensity in zone II during the year is ‘calm’ and reaches the upper limit of the ‘satisfactory’ level occasionally on certain days of late summer – early autumn. A place of increased concentration of birds within airport area II is an urban landfill. The decision to move the landfill beyond a 50 km radius from the runway, the prevention of nesting of aircraft‐dangerous bird species within zone II and the use of visual repellents will allow maintenance of a low level of ornithological intensity throughout the year.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Constraints in the Production and Marketing of Soybean

The production pattern as well as the consumption pattern of India has dramatically changed in the recent past. Soybean, a miracle crop with 40 per cent protein and 20 per cent oil has gained demand for consumption as well as oil extraction leading to increase in area under production. This paper has made an attempt to decode the constraints faced by farmers in the production and marketing of soybean in the study region so as to draw the attention of concerned authorities to come up with possible solution. The data related to agricultural year 2021-22 is extracted for present research. the primary data is collected by the personal interview method with the pre-tested schedule. Garrett’s ranking technique is used to interpret the data. It is inferred from the results that among production constraints non-availability or Inadequacy of required improved variety seeds in adequate quantities at the right time is the major constraint with the highest mean Garrett score of 74.34 followed by scarcity of labour during peak periods (Garrett score 51.12). Among various marketing constraints encountered by farmer respondents in soybean marketing in the study region. It was inferred that high price fluctuation was the major constraint with the highest mean Garrett score of 92.29 followed by untimely payment of sale proceeds (Garrett score 72.82). This article highlights the important constraints to be addressed to help farmers prosper in soybean production and marketing.

Quantum Variational Autoencoders for Predictive Analytics in High Frequency Trading Enhancing Market Anomaly Detection

High-frequency trading (HFT) markets, characterized by high and frequent price fluctuations, necessitate the use of anomaly detection mechanisms to monitor the market and ensure the efficacy of the trading system. This paper aims to discuss the possibility of improving predictive analytics in HFT using quantum computing with the help of the Quantum Variational Autoencoder (QL-VAE). As a result, we propose a new direction for further research on quantum VAEs in HFT that involves their direct comparison with classical VAEs. The application of quantum models for mastering the intensive data flow of HFT is conditioned by the advantages of quantum computation in comparison to classical ones, which are more suitable for handling multidimensional data arrangements and intricate topologies. Our detailed study methodology involved examining various aspects of HFT data, such as order book features and stock price characteristics. We normalized all the data and reduced some of its dimensions. We established quantum VAEs using Pennylane, and configured the classical VAEs using TensorFlow. When it comes to market anomalies, the results of the comparative analysis showed higher accuracy, recall, and F1 rate in quantum VAEs compared to classical models when it comes to the analysis of market anomalies. Therefore, the quantum model's ability to handle high-dimensional data makes it a better fit for HFT than classical methods. These studies suggest that quantum VAEs could significantly improve anomaly detection in the financial market.

Effect of Gas Exchange Rate, Vessel Type, Planting Density, and Genotype on Growth, Photosynthetic Activity, and Ion Uptake of In Vitro Potato Plants.

The growth of high-quality in vitro potato plants (Solanum stenotomum subsp. stenotomum, Solanum stenotomum subsp. goniocalyx, and Solanum tuberosum subsp. andigena) is affected by multiple biological, operational, and environmental factors. Research on in vitro culture is frequently focused on the species, explant, composition of the culture medium, and incubation conditions, but only limited information is available on the effect of the gas exchange rate and volume of in vitro culture vessels under variable planting densities. In the present study, these factors were evaluated with a set of seven diverse potato landraces. The results were compared to the plants' responses in routinely used in vitro culture vessels, i.e., 13 × 100 mm and 25 × 150 mm test tubes, and GA7® magenta vessels. In vitro potato plants grown in plastic vessels equipped with a HEPA filter delivering a high gas exchange rate developed thicker stems (0.95 mm), a higher total average leaf area (2.51 cm2), increased chlorophyll content in leaves (32.2 ppm), and lower moisture content in their tissues (90.1%) compared to filter systems with lower gas exchange rates. A high planting density of 10 × 10 plants per vessel (360 and 870 mL) negatively affected the average stem width and root length but increased the plant height (3.4 cm). High fluctuations of ion-uptake of NO3-, Ca++, K+, and Na+ were observed between genotypes, with some accessions having a 4.6-times higher Ca++-ion concentration in their tissues (190-234 ppm). The in vitro plants developed more robust stems, longer roots, and larger leaves within in vitro culture vessels equipped with a HEPA filter (high gas exchange rate) compared to the control vessels, in contrast to the chlorophyll content in leaves, which was higher in plants grown in narrow test tubes. Depending on the purpose of the subculture of in vitro plants, their growth and development can be molded using different gas exchange rates, planting densities, and vessel volumes.

Numerical investigations into the comparison of hydrogen and gas mixtures storage within salt caverns

Salt caverns, long used for natural gas storage to manage peak loads, are being considered for hydrogen storage as part of the shift towards greener fuels. This transition necessitates re-evaluating heat and fluid transport to ensure the suitability of storage sites. A comparative analysis between current (natural gas and compressed air) and prospective (hydrogen and gas mixtures) storage systems was conducted using a standardized historical cycle over thirty days to identify trends. Hydrogen exhibited the lowest cumulative temperature and pressure increase (17 °C, 0.40 MPa), but the greatest variability per cycle, with an average range of 26 °C and 1.6 MPa. This higher fluctuation could potentially limit its use in short-term cycles compared to natural gas. Moreover, hydrogen's storage capacity was found to be a third of natural gas's, at only 6.6 GWh for the cavern design and specified pressure limits. These findings indicate that while hydrogen presents a greener alternative, its high variability and lower storage capacity pose challenges for its use in existing infrastructure, highlighting the need for further research to optimize its storage and utilisation in energy systems.

Spatial and Temporal Characteristics and Driving Force Analysis of Ecological Environmental Quality in Fengfeng Mining Area with Remote Sensing Ecological Index of PM2.5 Concentration

The Fengfeng mining area is an important coal-producing area in China and crucial environmental problems have been caused by large-scale exploitation of coal mines. The spatio-temporal evolution and driving factors of the ecological environment quality in this area must be explored for promoting the transformation of coal-based cities. Based on Landsat data of the Google earth engine （GEE） platform, this study constructed a new remote sensing-based ecological index （RSEInew） for the Fengfeng mining area from 2000 to 2020. The spatial and temporal evolution of RSEInew and its driving factors were evaluated by using trend analysis and geographic detector methods. The results showed that： ① From 2000 to 2020, the RSEInew of the Fengfeng mining area presented a fluctuating increasing trend （trend = 0.002 2）, and the proportion of good and excellent ecological environmental quality showed an increasing trend, rising from 24.80% in 2000 to 65.54% in 2020. ② The change in the RSEInew grade indicated that the proportion of significant improvement （3 and 4） of ecological environment quality grade in the Fengfeng Mining area from 2000 to 2020 was 10.21%, which was mainly distributed in Hexun town and Yijing town in the northwest of the Fengfeng mining area. The proportion of significant degradation （-3 and -4） was only 1.58%, mainly scattered in Linshui town and Dashe town. ③ RSEInew values increased significantly during 2000-2020 in the area accounting for 18.29%, mainly distributed in the central and northern areas and the western fringe of the Fengfeng mining area. The significantly reduced area accounted for 9.25%, mainly concentrated in the eastern area of the Fengfeng mining area. The coefficient of variation results showed that the areas with high fluctuation of RSEInew were mainly concentrated in Pengcheng town and Linshui town in the middle and eastern Fengfeng mining area. ④ From the perspective of influencing factors, the average q value of land use type （X6） during 2000-2020 was 0.290, which was much higher than other factors. The q value of social and economic factors showed an increasing trend, indicating that the spatial distribution of ecological environment quality in this region was increasingly strongly influenced by human activities. The interaction results showed that land use change was the key factor influencing ecological environment quality in the Fengfeng mining area.

Dose-dependent effects of transcranial photobiomodulation on brain temperature in patients with major depressive disorder: a spectroscopy study.

This study aimed to evaluate the dose-dependent brain temperature effects of transcranial photobiomodulation (t-PBM). Thirty adult subjects with major depressive disorder were randomized to three t-PBM sessions with different doses (low: 50 mW/cm2, medium: 300 mW/cm2, high: 850 mW/cm2) and a sham treatment. The low and medium doses were administered in continuous wave mode, while the high dose was administered in pulsed wave mode. A 3T MRI scanner was used to perform proton magnetic resonance spectroscopy (1H-MRS). A voxel with a volume of 30 × 30 × 15 mm3 was placed on the left prefrontal region. Brain temperature (°C) was derived by analyzing 1H-MRS spectrum chemical shift differences between the water (~ 4.7 ppm) and N-acetyl aspartate (NAA) (~ 2.01 ppm) peaks. After quality control of the data, the following group numbers were available for both pre- and post-temperature estimations: sham (n = 10), low (n = 11), medium (n = 10), and high (n = 8). We did not detect significant temperature differences for any t-PBM-active or sham groups post-irradiation (p-value range = 0.105 and 0.781). We also tested for potential differences in the pre-post variability of brain temperature in each group. As for t-PBM active groups, the lowest fluctuation (variance) was observed for the medium dose (σ2 = 0.29), followed by the low dose (σ2 = 0.47), and the highest fluctuation was for the high dose (σ2 = 0.67). t-PBM sham condition showed the overall lowest fluctuation (σ2 = 0.11). Our 1H-MRS thermometry results showed no significant brain temperature elevations during t-PBM administration.

A new strategy for reducing pressure fluctuation of Francis turbine by bionic modification of local components

Long term operation of hydroelectric units in low load conditions can induce large-scale blade vortices, swirling vortex ropes in the draft tube, and low-frequency high amplitude pressure fluctuation. These phenomena will cause adverse consequences such as excessive vibration of the unit and blade breakage of the runner. Taking inspiration from the protuberances of the leading edge of a humped whale flipper, the present study firstly proposes bionic modifications of guide vanes and draft tube to suppress high-amplitude pressure fluctuations for Francis turbine. Numerical simulations of transient flow characteristics of the prototype unit (PU), the bionic guide vane unit (BG), and the bionic draft tube unit (BDG) under two low load conditions are conducted. Results indicated that the bionic draft tube has a good effect on suppressing pressure fluctuations in the vaneless area and draft tube. Under the Q/QBEF = 0.41 working condition, BDG causes the main frequency amplitude of pressure fluctuation at the center point of the draft tube inlet to change from 8000.2 Pa to 390.9 Pa, a decrease of 95.11 %. Under the Q/QBEF = 0.57 working condition, BDG causes the maximum decrease rate of the main frequency amplitude in the draft tube to be 60.5 %. The reducing effect in BDG of monitoring points in the guide vane area has reached over 43 %. Under low load conditions, the vortices near the wall in the draft tube of BDG are intercepted by bionic structures, reducing the vortex scale and helping to prevent the generation of large swirling vortex ropes. The bionic guide vanes have a significant control effect on pressure pulsation in the the guide vane and vaneless regions, although perform poorly in the draft tube.

Using bionic tubercles to control swirling flow instabilities of a hydraulic turbine during the load rejection process

With the development of renewable energy sources, operations of hydroelectric units under off-designed conditions and frequently transition processes have gradually become normalized, which is accompanied by threats from swirling vortex ropes, high amplitude pressure fluctuations, and strong hydraulic vibrations. This study introduces bionic tubercles into the Francis turbine's draft tube and guide vane design to suppress pressure fluctuation and stabilize swirling vortex rope during the load rejection process. Results reveal that the bionic Francis turbine (BFT) reaches its maximum speed of 686.4 rpm at 3.804s, marking a 6.08 % reduction from the prototype Francis turbine's maximum speed during load rejection. Moreover, it significantly diminishes the principal frequency amplitude of low-frequency pressure fluctuations caused by swirling flow instability from the draft tube cone, achieving a reduction by one to two magnitude orders. It is interesting that the scale of the circumferential vortex in the vaneless region and blade vortices within the runner channels is verified to decrease. More importantly, it is attributed that the bionic tubercles effectively disrupt and disintegrate the walled circumferential vortex structures in the draft tube.

Monitoring and warning for ammonia nitrogen pollution of urban river based on neural network algorithms.

Ammonia nitrogen (AN) pollution frequently occurs in urban rivers with the continuous acceleration of industrialization. Monitoring AN pollution levels and tracing its complex sources often require large-scale testing, which are time-consuming and costly. Due to the lack of reliable data samples, there were few studies investigating the feasibility of water quality prediction of AN concentration with a high fluctuation and non-stationary change through data-driven models. In this study, four deep-learning models based on neural network algorithms including artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were employed to predict AN concentration through some easily monitored indicators such as pH, dissolved oxygen, and conductivity, in a real AN-polluted river. The results showed that the GRU model achieved optimal prediction performance with a mean absolute error (MAE) of 0.349 and coefficient of determination (R2) of 0.792. Furthermore, it was found that data preprocessing by the VMD technique improved the prediction accuracy of the GRU model, resulting in an R2 value of 0.822. The prediction model effectively detected and warned against abnormal AN pollution (> 2mg/L), with a Recall rate of 93.6% and Precision rate of 72.4%. This data-driven method enables reliable monitoring of AN concentration with high-frequency fluctuations and has potential applications for urban river pollution management.

Influence of menstrual cycle and oral contraceptive phases on strength performance, neuromuscular fatigue, and perceived exertion.

The primary aim of the study was to assess differences in strength performance, neuromuscular fatigue, and perceived exertion across phases of the menstrual cycle [MC; early follicular (eFP), late follicular (lFP), and mid-luteal phase (mLP)] and oral contraceptives [OCs; active pill phase (aPP) and nonactive pill phase (nPP)]. The secondary aim was to analyze the influence of fluctuating serum 17β-estradiol and progesterone concentrations on these parameters in naturally menstruating women. Thirty-four women (21 with a natural MC and 13 using OCs) completed three or two experimental sessions, respectively. Mean propulsive velocity (MPVmean) and total number of repetitions (REPtotal) were assessed during a power [3 × 8 at 60% 1RM (one-repetition maximum)] and hypertrophy squat loading (3 sets to failure at 70% 1RM), respectively. Changes in bench press and squat MPV at 60% 1RM in response to the loadings were used as surrogates for nonlocal and local fatigue, respectively. Total blood lactate accumulation (BLAA) and markers of perceived exertion were assessed in each session. No significant differences between any of the MC or OC phases were observed for MPVmean, REPtotal, nonlocal and local fatigue, and markers of perceived exertion (all P > 0.050). A higher intraindividual 17β-estradiol concentration was significantly associated with a lower MPVmean (P = 0.019). BLAA was significantly higher in the lFP than in the mLP (P = 0.019) and negatively associated with the intraindividual progesterone concentration (P = 0.005). Although 17β-estradiol may negatively influence the MPV, it appears that fluctuations of both sex hormones across the MC and OC phases are not prominent enough to induce significant or practically relevant changes in the assessed parameters.NEW & NOTEWORTHY Although a high intraindividual 17β-estradiol concentration was associated with a lower movement velocity, markers of strength performance and surrogates for nonlocal and local fatigue remained unaffected by MC and OC phases. Blood lactate accumulation was significantly reduced in the mLP. Furthermore, our findings suggest that the impact of the MC phases varies greatly among individuals. Individuals with high fluctuations in sex hormone concentrations may experience relevant changes in the assessed parameters.

Distributed robust [formula omitted]asso-[formula omitted] based on [formula omitted]ash optimization for smart grid: Guaranteed robustness and stability

The integration of variable renewable energy supplies into smart grid energy management poses several obstacles to system operation. An efficient solution for resource management is essential to ensuring reliable operation. This research presents distributed robust Lasso-model predictive control (D − RLMPC) as a way to handle energy problems in a multi-layer and multi-time frame optimization method. The D − RLMPC is a hierarchical system that integrates a centralized supervisory management (SM) layer for long-term optimization with a distributed coordination management (CM) layer for short-term adaptation to high power fluctuations. The higher layer, known as the SM, is responsible for providing the grid operator with specific operating plans and offering guidance to the bottom layer, known as the CM. The CM is responsible for coordinating the interaction between the centralized optimization goals and the physical power system layer. Furthermore, a distributed extended Kalman filter (DEKF) is used to ascertain the inter-dependencies among subsystems. Next, an iterative approach based on Nash optimization is proposed to get the globally optimum solution of the whole system in a partly distributed manner. The simulation results demonstrate the effectiveness of the proposed control approach, which combines the advantages of centralized and distributed control to provide a comprehensive solution for the grid operating issue. To verify and assess the effectiveness of the suggested approach, the acquired outcomes are compared to those of the centralized robust, distributed robust, and distributedMPC approaches. The simulation findings confirm the practicality of using the suggested system to manage future smart grid assets.

Anisotropic Heating and Cooling within Interplanetary Coronal Mass Ejection Sheath Plasma

This study presents the first comprehensive investigation of the relationship between heating and cooling, temperature anisotropy, turbulence level, and collisional age within interplanetary coronal mass ejection (ICME) sheaths, which are highly compressed, heated, and turbulent. Using Wind spacecraft data, we analyze 333 ICME sheaths observed at 1 au from 1995 to 2015. The proton temperature within the ICME sheaths has a log-normal probability distribution. Irrespective of instability growth rates, plasma unstable to proton-cyclotron (PC) and firehose instabilities appear to be statistically hotter, at least by a factor of 5 to 10, compared to stable plasma. We also observe relatively enhanced magnetic fluctuations and low collisional age, especially in regimes unstable to PC and firehose instabilities at low proton betas β p ≤ 2. In the case of high beta β p ≥ 2, we observe high magnetic fluctuations close to the instabilities and less collisional age to the plasma unstable to firehose instability rather than near the mirror mode and PC threshold. Our findings suggest that heating processes dominate over cooling processes in producing proton temperature anisotropy in the ICME sheath region. Moreover, collisional age and magnetic fluctuations are critical in maintaining anisotropic and isotropic conditions.

Shedding light on cancer immunology at the molecular level: A quantum biochemistry study of representative PD-1/PD-L1 conformations

BackgroundProgrammed death 1 (PD-1) binding to PD-L1 is a potent mechanism used by immunogenic tumors to evade the immune system and the immune checkpoint PD-1PD-L1 has emerged as a promising target in the search for new drugs to improve cancer treatment. The crystallographic structure of humanPD-1humanPD-L1 shed light on the molecular characterization of this system and allowed computational studies to be carried out to characterize structural behaviors. MethodsThis study demonstrated the importance of analyzing the flexibility of protein systems through molecular dynamics simulations (MDS) and its impacts on the interaction energy obtained through quantum biochemistry. ResultsThe computational results obtained provide a description of the flexibility and energetic profile of the PD-1PD-L1 contact surface using representative conformations from MDS. Variations of up to 50 % in the total interaction energy values were detected depending on the scrutinized conformation, which can be mainly attributed to the flexibility of the CC' loop, FG loop and ASP85-GLN91 of PD-1 and the MET58-LYS62 segment of PD-L1. Quantum biochemistry revealed the three hot spots in PD-L1: ARG113L-ARG125L > ILE54L-VAL76L > ALA18L-ASP26L; and two energetic hot spots in PD-1: ALA125-ARG139 > VAL63-GLN88. Nonetheless, VAL63-GLN88 and GLY124-ARG139 exhibit significant variation in interaction energy between different conformations, while ARG113L-ARG125L is the only hot spot with high energetic fluctuation on the PD-L1 surface. ConclusionThis is the first application of MDS coupled to dimensionality reduction and density functional theory (DFT) demonstrating new structural and energetic features that might be useful in discovering/designing more potent PD-1PD-L1 inhibitors.