Spatially heterogeneous dynamics in a thermosensitive soft suspension before and after the glass transition

The microscopic dynamics and aging of a soft thermosensitive suspension was investigated by looking at the thermal fluctuations of tracers in the suspension. Below and above the glass transition, the dense microgel particles suspension was found to develop an heterogeneous dynamics, featured by a non Gaussian Probability Distribution Function (PDF) of the probes' displacements, with an exponential tail. We show that non Gaussian shapes are a characteristic of the ensemble-averaged PDF, while local PDF remain Gaussian. This shows that the scenario behind the non Gaussian van Hove functions is a spatially heterogeneous dynamics, characterized by a spatial distribution of locally homogeneous dynamical environments through the sample, on the considered time scales. We characterize these statistical distributions of dynamical environments, in the liquid, supercooled, and glass states, and show that it can explain the observed exponential tail of the van Hove functions observed in the concentrated states. The intensity of spatial heterogeneities was found to amplify with increasing volume fraction. In the aging regime, it tends to increase as the glass gets more arrested.Comment: 19 pages, 10 figures, Soft Matter accepte

Helical Packings and Phase Transformations of Soft Spheres in Cylinders

The phase behavior of helical packings of thermoresponsive microspheres inside glass capillaries is studied as a function of volume fraction. Stable packings with long-range orientational order appear to evolve abruptly to disordered states as particle volume fraction is reduced, consistent with recent hard sphere simulations. We quantify this transition using correlations and susceptibilities of the orientational order parameter psi_6. The emergence of coexisting metastable packings, as well as coexisting ordered and disordered states, is also observed. These findings support the notion of phase transition-like behavior in quasi-1D systems.Comment: 5 pages, with additional 4 pages of supplemental material, accepted to Physical Review E: Rapid Communication

Length-weight relationships and relative condition factor of Parapenaeopsis sculptilis (Heller, 1862) from the coastal waters of Perak, Peninsular Malaysia

Length-weight relationship (LWR) parameters and relative condition factor (K n) of marine shrimp, Parapenaeopsis sculptilis (Heller, 1862) were estimated using length-weight data collected between February 2012 and January 2013 from the coastal waters of Terong, Perak, Peninsular Malaysia. The estimated length-weight relationship of P. sculptilis for both sexes was W = 0.00027TL2.80. Meanwhile, the estimated relative growth coefficient (b) was 2.80 for both sexes, indicating a negative allometric growth pattern of P. sculptilis in the investigated area. Relative condition factor (Kn) values ranged from 0.99 to 1.064 (1.013±0.005, mean ±SD). Kn value changes in various months: the highest peak was in March-April, indicating the spawning period and the trough and small peaks indicating the cycle gonadal development

Population dynamics of sergestid shrimps Acetes japonicus in the estuary of Tanjung Dawai, Kedah, Malaysia

Population parameters of male and female A. japonicus were studied using the monthly length frequency data to evaluate the mortality rates and its exploitation level. The sex ratio (male: Female) was found at 1: 0.94. Asymptotic length (L∞) was 25.20 mm and 28.88 mm for male and female, respectively. Growth co-efficient (K) for males and females was estimated at 1.80 and 1.30 year-1, respectively. Total mortality (Z) was calculated at 5.98 and 4.44 year-1 for male and female of A. japonicus respectively. Natural mortality (M) was 2.82 and 2.19 year-1 for the male and female shrimps. The fishing mortality (F) was 3.16 year-1 for male and 2.25 year-1 for female. Exploitation level (E) for male and female of A. japonicus was calculated at 0.53 and 0.51. The exploitation level was slightly over (E>0.50) the optimum level of exploitation (p = 0.50). The stock of A. japonicus was found to be slightly over exploited in Tanjung Dawai estuarine waters

Direct entropy determination and application to artificial spin ice

From thermodynamic origins, the concept of entropy has expanded to a range of statistical measures of uncertainty, which may still be thermodynamically significant. However, laboratory measurements of entropy continue to rely on direct measurements of heat. New technologies that can map out myriads of microscopic degrees of freedom suggest direct determination of configurational entropy by counting in systems where it is thermodynamically inaccessible, such as granular and colloidal materials, proteins and lithographically fabricated nanometre-scale arrays. Here, we demonstrate a conditional-probability technique to calculate entropy densities of translation-invariant states on lattices using limited configuration data on small clusters, and apply it to arrays of interacting nanometre-scale magnetic islands (artificial spin ice). Models for statistically disordered systems can be assessed by applying the method to relative entropy densities. For artificial spin ice, this analysis shows that nearest-neighbour correlations drive longer-range ones.Comment: 10 page

Association between coronavirus cases and seasonal climatic variables in Mediterranean European Region, evidence by panel data regression

The coronavirus pandemic is one of the most fast-spreading diseases in the history, and the transmission of this virus has crossed rapidly over the whole world. In this study, we intend to detect the effect of temperature, precipitation, and wind speed on the Coronavirus infected cases throughout climate seasons for the whole year of epidemic starting from February 20, 2020 to February 19, 2021 with considering data patterns of each season separately; winter, spring, summer, autumn, in Mediterranean European regions, whereas those are located at the similar temperature zone in southern Europe. We apply the panel data approach by considering the developed robust estimation of clustered standard error which leads to achieving high forecasting accuracy. The main finding supports that temperature and wind speed have significant influence in reducing the Coronavirus cases at the beginning of this epidemic particularly in the first-winter, spring, and early summer, but they have very weak effects in the autumn and second-winter. Therefore, it is important to take into account the changes throughout seasons, and to consider other indirect factors which influence the virus transmission. This finding could lead to significant contributions to policymakers in European Union and European Commission Environment to limit the Coronavirus transmissions. As the Mediterranean region becomes more crowded for tourism purposes particularly in the summer season

Turkish Stock Market from Pandemic to Russian Invasion, Evidence from Developed Machine Learning Algorithm

In recent time, the two significant events; Coronavirus epidemic and Russian invasion are effecting all over the world in various aspects; healthily, economically, environmentally, and socially, etc. The first event has brought uncertainties to the economic situation in most countries based on the epidemic transmission. In addition to that, on 24th February 2022 the Russian invasion of Ukraine affected negatively almost all stock markets all over the world, but the effects are heterogeneous across countries according to their economic-political relationship or neighbourhood, etc. Due to that, the stock market price in Turkey has been affected dramatically over that period. This empirical study is the first attempts to explore the impact of Coronavirus epidemic and Russian invasion on the stock market index XU100 in Turkey by applying the developed statistical method namely elastic-net regression based on empirical mode decomposition which can precisely tackle the nonstationary and nonlinearity data. Then we performed the robustness check by applying a nonlinear techniques Markov switching regression. The data are collected from the beginning of the epidemic in Turkey from March 11, 2020 until May 31, 2022. The finding reveals that there is significant effect of the Coronavirus spreading on the Turkish stock market index, particularly during the first wave. Then after the Russian Invasion the XU100 index is effected more negatively. As the credit default swap and TL reference interest rate have a negative impact but the foreigner exchange rate has a positive significant impact on the XU100 index, and it varies according to the period of short term and long term. Moreover, the results obtained by using the robustness check shows a robust and consistent finding. In conclusion, understanding the impact of Coronavirus pandemic and Russian invasion on the Turkish stock market can provide important implications for investors, financial sectors, and policymakers

Enhancing Model Selection by Obtaining Optimal Tuning Parameters in Elastic-Net Quantile Regression, Application to Crude Oil Prices

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning

Fuzzy logic power management for a PV/Wind microgrid with backup and storage systems

This work introduces a power management scheme based on the fuzzy logic controller (FLC) to manage the power flows in a small and local distributed generation system. The stand-alone microgrid (MG) includes wind and PV generators as main power sources. The backup system includes a battery storage system (BSS) and a diesel generator (DG) combined with a supercapacitor (SC). The different energy sources are interconnected through the DC bus. The MG is modeled using MATLAB/Simulink Sim_Power System™. The SC is used to compensate for the shortage of power during the start-up of the DG and to compensate for the limits on the charging/discharging current of the BSS. The power balance of the system is the chief objective of the proposed management scheme. Some performance indexes are evaluated: the frequency-deviation, the stability of the DC bus voltage, and the AC voltage total harmonic distortion. The performance of the planned scheme is assessed by two 24-hours simulation sets. Simulation results confirm the effectiveness of FLC-based management. Moreover, the effectiveness of the FLC approach is compared with the deterministic approach. FLC approach has saved 18.7% from the daily load over the deterministic approach. The study shows that the quality of the power signal in the case of FLC is better than the deterministic approach

Diagnosis of neuronal ceroid lipofuscinosis type 2 (CLN2 disease): Expert recommendations for early detection and laboratory diagnosis

Neuronal ceroid lipofuscinoses (NCLs) are a heterogeneous group of lysosomal storage disorders. NCLs include the rare autosomal recessive neurodegenerative disorder neuronal ceroid lipofuscinosis type 2 (CLN2) disease, caused by mutations in the tripeptidyl peptidase 1 (TPP1)/CLN2 gene and the resulting TPP1 enzyme deficiency. CLN2 disease most commonly presents with seizures and/or ataxia in the late-infantile period (ages 2-4), often in combination with a history of language delay, followed by progressive childhood dementia, motor and visual deterioration, and early death. Atypical phenotypes are characterized by later onset and, in some instances, longer life expectancies. Early diagnosis is important to optimize clinical care and improve outcomes; however, currently, delays in diagnosis are common due to low disease awareness, nonspecific clinical presentation, and limited access to diagnostic testing in some regions. In May 2015, international experts met to recommend best laboratory practices for early diagnosis of CLN2 disease. When clinical signs suggest an NCL, TPP1 enzyme activity should be among the first tests performed (together with the palmitoyl-protein thioesterase enzyme activity assay to rule out CLN1 disease). However, reaching an initial suspicion of an NCL or CLN2 disease can be challenging; thus, use of an epilepsy gene panel for investigation of unexplained seizures in the late-infantile/childhood ages is encouraged. To confirm clinical suspicion of CLN2 disease, the recommended gold standard for laboratory diagnosis is demonstration of deficient TPP1 enzyme activity (in leukocytes, fibroblasts, or dried blood spots) and the identification of causative mutations in each allele of the TPP1/CLN2 gene. When it is not possible to perform both analyses, either demonstration of a) deficient TPP1 enzyme activity in leukocytes or fibroblasts, or b) detection of two pathogenic mutations in trans is diagnostic for CLN2 disease