Increased sugar intake is implicated in Type-2 diabetes and fatty liver disease. Mechanisms by which glucose and fructose components promote these conditions are unclear. We hypothesize that alterations in intestinal metabolite and microbiota profiles specific to each monosaccharide are involved. Two groups of six adult C57BL/6 mice were fed for 10-weeks with a diet where either glucose or fructose was the sole carbohydrate component (G and F, respectively). A third group was fed with normal chow (N). Fecal metabolites were profiled every 2-weeks by ¹H NMR and microbial composition was analysed by real-time PCR (qPCR). Glucose tolerance was also periodically assessed. N, G and F mice had similar weight gains and glucose tolerance. Multivariate analysis of NMR profiles indicated that F mice were separated from both N and G, with decreased butyrate and glutamate and increased fructose, succinate, taurine, tyrosine and xylose. Compared to N and G, F mice showed a shift in microbe populations from gram-positive Lactobacillus spp. to gram-negative Enterobacteria species. Substitution of normal chow carbohydrate mixture by either pure glucose or fructose for 10 weeks did not alter adiposity or glucose tolerance. However, F G and N mice generated distinctive fecal metabolite signatures with incomplete fructose absorption as a dominant feature of F mice.

Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.

Presynaptic Ca2+-influx through voltage-gated Ca2+-channels (VGCC) is a key signal for synaptic vesicle release. Synaptic neurexins can partially determine the strength of transmission by regulating VGCCs. However, it is unknown whether neurexins modulate Ca2+-influx via all VGCC subtypes similarly. Here, we performed live cell imaging of synaptic boutons from primary hippocampal neurons with a Ca2+-indicator. We used the expression of inactive and active Cre recombinase to compare control to conditional knockout neurons lacking either all or selected neurexin variants. We found that reduced total presynaptic Ca2+-transients caused by the deletion of all neurexins were primarily due to the reduced contribution of P/Q-type VGCCs. The deletion of neurexin1α alone also reduced the total presynaptic Ca2+-influx but increased Ca2+-influx via N-type VGCCs. Moreover, we tested whether the decrease of Ca2+-influx induced by activation of cannabinoid receptor 1 (CB1-receptor) is modulated by neurexins. Unlike earlier observations emphasizing a role for β-neurexins, we found that the decrease of presynaptic Ca2+-transients induced by CB1-receptor activation depended more strongly on the presence of α-neurexins in hippocampal neurons. Together, our results suggest that neurexins have unique roles in the modulation of presynaptic Ca2+-influx through VGCC subtypes and that different neurexin variants may affect specific VGCCs.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

The vagina is an essential component of the female reproductive system and is responsible for providing female sexual satisfaction. Vaginal smooth muscle contraction plays a crucial role in various physiological processes, including sexual arousal, childbirth, and urinary continence. In pathophysiological conditions such as pelvic floor disorders, aberrations in vaginal smooth muscle function can lead to urinary incontinence and pelvic organ prolapse. A set of cellular and sub-cellular physiological mechanisms regulates the contractile properties of the vaginal smooth muscle cells. Calcium influx is a crucial determinant of smooth muscle contraction, facilitated through voltage-dependent calcium channels and calcium release from intracellular stores. Comprehensive reviews on smooth muscle biophysics are relatively scarce within the scientific literature, likely due to the complexity and specialized nature of the topic. The objective of this review is to provide a comprehensive description of alterations in the cellular physiology of vaginal smooth muscle contraction. The benefit associated with this particular approach is that conducting a comprehensive examination of the cellular mechanisms underlying contractile activation will enable the creation of more targeted therapeutic agents to control vaginal contraction disorders.

This study unveils verapamil's compelling cytoprotective and proliferative effects on pancreatic β-cells amidst diabetic stressors, spotlighting its unforeseen role in augmenting cholecystokinin (CCK) expression. Through rigorous investigations employing MIN6 β-cells and zebrafish models under type 1 and type 2 diabetic conditions, we demonstrate verapamil's capacity to significantly boost β-cell proliferation, enhance glucose-stimulated insulin secretion, and fortify cellular resil-ience. A pivotal revelation of our research is verapamil's induction of CCK, a peptide hormone known for its role in nutrient digestion and insulin secretion, which signifies a novel pathway through which verapamil exerts its therapeutic effects. Furthermore, our mechanistic insights reveal that verapamil orchestrates a broad spectrum of gene and protein expressions pivotal for β-cell survival and adaptation to immune-metabolic challenges. In vivo validation in a zebrafish larvae model confirms verapamil's efficacy in fostering β-cell recovery post-metronidazole inflic-tion. Collectively, our findings advocate for verapamil's reevaluation as a multifaceted agent in diabetes therapy, highlighting its novel function in CCK upregulation alongside enhancing β-cell proliferation, glucose sensing, and oxidative respiration. This research enriches the therapeutic landscape, proposing verapamil not only as a cytoprotector but also as a promoter of β-cell re-generation, thereby offering fresh avenues for diabetes management strategies aimed at pre-serving and augmenting β-cell functionality.

We study electrohydrodynamic (EHD) linear (in)stability of microfluidic channel flows, i.e. the stability of interface between two-shearing viscous (perfect) dielectrics exposed to an electric field in large aspect ratio microchannels. We then apply our results to particular microfluidic systems known as electroosmotic (EO) pumps. Our results are detailed analytical expressions for the growth rate of two-dimensional EHD modes in Couette-Poiseuille flows in the limit of small Reynolds number ($R$); the expansions to both zeroth- and first-order-$R$ are considered. The growth rates are complicated functions of viscosity-, height-, density- and dielectric-constant ratio, as well as of wavenumbers and voltages, and to our knowledge have not been presented before in literature. To make the results more useful, e.g., for voltage-control EO pump operations, we also derive equations for the impending voltages of the neutral stability curves that divide stable from unstable regions in voltage-wavenumber stability diagrams. The voltage equations and the stability diagrams are given for all wavenumbers. We finally outline the flow regimes in which our first-order-$R$ voltage corrections could potentially be experimentally measured. Our work gives the insight into the coupling mechanism between electric field and shear flow in parallel-planes channel flows, correcting an erroneous attempt from literature. Our general analysis enables us also to refine the renowned instability mechanism due to viscosity stratification, i.e., the case of pure shear instability, when the first-order-$R$ voltage correction equals zero.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

To study the influence of electrothermal effect of ESD protection device on the reliability of inte-grated circuit,a theoretical model of ESD protection device thermal resistance is constructed in the paper based on the Conservation of Energy Principle. By analyzing the temperature relativity between electricity parameter and thermology parameter of ESD protection device characterized by self-heating thermal resistance, the quantitative relationship expression of physical parameter and geometric parameter is obtained, which can be used to improve ESD protection device elec-tro-thermal effect. The simulation testing and the real tape-out testing of ESD protection device self-heating thermal resistance based on ggNMOS are shown and compared. It is concluded that self-heating thermal resistance is an effective index for the protection device electrothermal char-acterization. Based on the established model, the self-heating thermal resistance could be decreased to improve the homogeneity of the heat transference. The uneven distribution of device temperature can be reduced, and the overall reliability of chips can be improved.

Recent advances in synthetic membranes allow their use in fields as diverse as food and agriculture, biotechnology, industrial wastewater treatment, and potable water production. Among the newly developed technologies, nanofiltration for liquids and more particularly for desalination of seawater or saline aquifers are actively pursued. However, current solid-state membranes performance is limited, which calls for the development of novel formulations for membranes offering both high permeability (ion and water flux) and ion differentiation (selectivity), usually considered antagonist features. We report the development of hybrid nanoporous membranes made of solid-state polymeric carbonate track-etched (PCTE) thin films in which biological ion channels such as Gramicidin A (GA), alpha-hemolysin (α-HL), and outer-membrane protein F (OmpF) aquaporin are confined and graphene oxide (GO) conjugated cobalt ion selective binding peptide motifs (Copep) are adsorbed, referring as bioinspired and biomimetic approaches, respectively. These solid-state nanoporous membranes are attracting widespread attention since they offer scalability, mechanical robustness, selectivity, controlled pore dimension and shape, for water sustainability. The ion permeability, ion diffusion coefficient and selective ion recognition are evaluated via nanofiltration, cell diffusion kinetics, and UV-Visible absorption spectroscopy to gain insights into the role of key performance parameters including effective ion confinement in track-etched pores, rich surface chemistry, and peptide-induced channel for selective ion transport. These findings provide new avenues for artificial ion channels by synergistic combination of eco-friendly material design such as GO enabled by bio-molecular recognition chemistry.

During the response of plants to adverse stresses, aquaporin (AQP) plays a prominent role in membrane water transport based on received upstream signals. In addition, they have various physical parts for dealing with environmental stresses. Due to the importance of the AQP gene family, studies have been conducted investigating the function and regulatory system of these genes. However, many of their molecular aspects are still unknown. This study aims to carry out a genomic-wide investigation of the AQP gene family in durum wheat using bioinformatics tools and to investigate the expression patterns of some members in response to salt stress. Our results showed that there are 80 TtAQP genes in durum wheat, which are classified into four main groups based on phylogenetic analysis. Many duplications were observed between the members of the TtAQP gene family, and high diversity in response to post-translational modifications was observed between TtAQP family members. The expression pattern of TtAQP genes disclosed that these genes are primarily upregulated in response to salt stress. Besides, qPCR data revealed that TtAQPs are more induced in delayed responses to salinity stress. Overall, our findings illustrate that TtAQP members are diverse in terms of their structure, regulatory systems and expression levels.

Spider toxin SNX-482 is a cysteine-rich peptide that interferes with calcium channel activity by binding to voltage-sensing domains of CaV2.3 subtype. Two general binding mechanisms are present in nature: direct binding from the aqueous phase or through lateral diffusion from the membrane, the so-called reduction in dimensionality mechanism. In this work, via coarse-grained and atomistic molecular dynamics simulations, we have systematically studied the spontaneous partitioning of SNX-482 with membranes of different anionic compositions and explored via diffusional analysis both binding mechanisms. Our simulations revealed a conserved protein patch that inserts within the membrane, a preference for binding towards partially negatively charged membranes, and that electrostatics drives membrane binding. Finally, diffusivity calculations showed that the toxin diffusion along the membrane plane is an order of magnitude slower than the aqueous phase suggesting that the critical factor in determin-ing the SNX-482-CaV2.3 binding mechanism is the affinity between the membrane and SNX-482

Alzheimer's disease (AD) is the most common cause of dementia and affects millions of people around the world. Neuronal death in AD is initiated by the toxic action of oligomeric amyloid-β (Aβ) peptides. The formation of membrane channels by Aβ is a primary molecular action and does not require any other proteins. Channels are formed by short amyloid fragments faster and more frequently than by full-length peptides. The channel formation is dependent on an electrostatic interaction between a positively charged peptide and a negatively charged membrane. Negative membranes can be found in several locations of a cell – the inner leaflet of plasma membrane, mitochondria, and lysosomes, which all are well-known cellular targets in AD. Considering that the amyloid enters a cell by endocytosis and is exposed to lysosomal enzymes, we propose the amyloid degradation toxicity hypothesis. Endopeptidases degrade the endocytosed peptide. Produced fragments form membrane channels, which can transfer various ions (including protons) and even relatively large compounds. The neutralization of lysosomal content inactivates enzymes, which fails the whole system of recycling cellular content, including autophagy. The permeabilization of lysosomes could also lead to cell death through necrotic and apoptotic mechanisms. We discuss several mechanisms that describe how amyloid degradation products reach plasma and mitochondrial membranes, and form membrane channels. The pathogenesis of AD is discussed at various levels in a context of how the primary molecular mechanism of membrane channel formation could progress into the disease state. The discussion starts at the molecular level and extends to why the development of a disease takes years and is closely associated with aging. The proposed hypothesis offers an interpretation to several clinical observations such as the involvement of iron metabolism and an inverse association between developing Alzheimer's disease and cancer. Predictions about potential biomarkers and effectiveness of future treatments are discussed.

Many clustering approaches succeed in pattern segmentation in many applications. This unsupervised segmentation should be effective to reduce an expert labelling time: i.e, they must be able to detect the number of patterns and identify them in a sequence or map with the right cuts. Several direct and hierarchical clustering approaches are compared for this task. A divisive spectral clustering architecture with a no-cut criteria is also proposed. This new algorithm achieves promise segmentation of spatial UCI databases and marine time series compared to other approaches.

Market channel choice makes important contributions to the incomes and other livelihood attributes among smallholder farmers in developing countries. Often considered from a number of perspectives, the dominant view articulated suggests an advantageous integration into formal market channels. This position is questioned as it has implications for smallholder farmers’ food security and rural incomes. Using a mixed methods approach, the study collected primary data from 174 smallholder farmers and applied both a descriptive and multinomial logit regression model to analyze factors contributing to cocoyam production and market channel choices among respondents in the study area. Findings indicate that financial returns and available markets were key factors in cocoyam production, while amount received was a driver of market channel choice among 89% of respondents who sold directly at farm-gate. Farmers’ age (p=0.044), household size (p=0.043), distance to market (p=0.021), additional income (p=0.017) and amount received (p=0.014) were significant variables (p<.05) in the determinants of market channel choice. The study recommended improving market information provision and strengthening farmer associations which will enable smallholder farmers in rural communities to make informed choices with respect to produce price, access other markets and consolidate their collective market bargaining position.

Lung cancer remains the leading cancer killer worldwide. Early diagnosis can effectively increase the patient cure rate but existing diagnostic methods limit early lung cancer diagnosis. Therefore, development of a simple but efficient lung cancer screening method is important to improvement of both the diagnosis rate and the survival rate of lung cancer patients. In this study, ten photosensitive materials with high sensitivity and high specificity were screened accurately to construct a microarray sensor that can rapidly identify six types of lung cancer biomarkers in exhaled breath. Results from hierarchical cluster analysis (HCA), principal component analysis (PCA) and difference maps showed that the classification of the analytes agreed with structure similarity laws. The detection results from parallel experiments and structurally similar analytes, in turn, cluster into a group; the fingerprints of the different analytes have specific response regions. The well-screened sensor chip fabrication workload and cost were both reduced by approximately two thirds, while the microfluidic device sensitivity and stability increased by approximately 1.3 times their corresponding values before optimization. The dual-channel device also offers real-time contrast detection and synchronous parallel detection functions and has potential application prospects for use in extensive screening of high-risk populations for lung cancer.

The atomic structures of Zn and Na borophosphate glasses were studied by X-ray and neutron diffraction. Peaks assigned to the B−O, P−O, and O−O distances confirm that only BO4 units co-exist with the PO4 tetrahedra. The Zn−O and Na−O coordination numbers are found to be a little larger than four. The narrowest peaks of the Zn−O first-neighbor distances exist for the glasses along a line connecting the Zn(PO3)2 and BPO4 compositions (50 mol% P2O5) which is explained by networks of ZnO4, BO4, and PO4 tetrahedra with twofold coordinated oxygens. Calculated amounts of the available oxygen support this interpretation. Broadened peaks occur for glasses with lower P2O5 contents, consistent with the presence of threefold coordinated oxygens. The two distinct P−O peak components of the Zn and Na borophosphate glasses differ in their relative abundances which is interpreted by Na+ cations that coordinate oxygens in some P−O−B bridges, something not seen for the Zn2+ ions.

The fundamental theory of nuclear interactions, Quantum Chromodynamics (QCD), operates in terms of quarks and gluons at higher resolution. At low resolution the relevant degrees of freedom are nucleons. Two-nucleon Short-Range Correlations (SRC) help to interconnect these two descriptions. SRCs are temporary fluctuations of strongly interacting close pairs of nucleons. The distance between the two nucleons is comparable to their radii and their relative momenta are larger than the fermi sea level. According to the electron scattering experiments held in the last decade, SRCs have far-reaching impacts on many-body systems, the nucleon-nucleon interactions, and nuclear substructure. The modern experiments with ion beams and cryogenic liquid hydrogen target make it possible to study properties of the nuclear fragments after quasi-elastic knockout of a single nucleon or an SRC pair. The first SRC experiment at BM@N (2018) with a carbon beam has shown that detection of an intact 11B nucleus after interaction selects out the quasi-elastic knockout reaction with minimal contribution of initial- and final-state interactions. Also, 25 events of SRC-breakups showed agreement in SRC properties as knows from electron beam experiments. The analysis of the second measurement of SRC at BM@N held in 2022 with an improved setup is currently ongoing. The SRC project at JINR moved to a new experimental area in 2023, where the next measurement is being planned in terms of experimental setup and physics goals.

In semi-arid regions, climate change has affected crop growing season length and sowing time, potentially causing low yield of the rainfed staple crop pearl millet (Pennisetum glaucum L.) and food insecurity among smallholder farmers. In this study, we used 1994–2023 rainfall data from Namibia's semi-arid North-Central Region (NCR), receiving November–April summer rainfall, to analyze rainfall patterns and trends and their implications on the growing season to propose climate adaptation options for the region. The results revealed high annual and monthly rainfall variabilities, with nonsignificant negative trends for November–February rainfalls, implying a shortening growing season. Furthermore, we determined the effects of sowing date on grain yields of the early-maturing Okashana-2 and local landrace Kantana pearl millet varieties and the optimal sowing window for the region, using data from a two-year split-plot field experiment conducted at the University of Namibia-Ogongo Campus, NCR, during the rainy season. Cubic polynomial regression models were applied to grain yield data sets to predict grain production for any sowing date between January and March. Both varieties produced the highest grain yields under January sowings, with Kantana exhibiting a higher yield potential than Okashana-2. Kantana, sown by 14 January, had a yield advantage of up to 36.0% over Okashana-2, but its yield gradually reduced with delays in sowing. Okashana-2 exhibited higher yield stability across January sowings, surpassing Kantana’s yields by up to 9.4% following the 14 January sowing. We determined the pearl millet optimal sowing window for the NCR from 1–7 and 1–21 January for Kantana and Okashana-2, respectively. These results suggest that co-cultivation of early and late pearl millet varieties and growing early-maturing varieties under delayed seasons could stabilize grain production in northern Namibia and enhance farmers' climate adaptation. Semi-arid agro-region policymakers could utilize this information to adjust local seed systems and extension strategies.

The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.

This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.

Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article we will leverage a computational neuron-astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we will demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage.

Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.

Processing of the RNA polymerase I pre-rRNA transcript into the mature 18S, 5.8S, and 25S rRNAs requires removing the “spacer” sequences. The canonical pathway for the removal of the ITS1 spacer, located between 18S and 5.8S rRNAs in the primary transcript, involves cleavages at the 3’ end of 18S rRNA and at two sites inside ITS1. The process generates a long and a short 5.8S rRNA that differ in the number of ITS1 nucleotides retained at the 5.8S 5’ end. Here we document a novel pathway that generates the long 5.8S for ITS1 while bypassing cleavage within ITS1. It entails a single endonuclease cut at the 3’-end of 18S rRNA followed by exonuclease Xrn1 degradation of ITS1. Mutations in RNase MRP increase the accumulation of long relative to short 5.8S rRNA; traditionally this is attributed to a decreased rate of RNase MRP cleavage at its target in ITS1, called A3. In contrast, we report here that the MRP induced switch between long and short 5.8S rRNA formation occurs even when the A3 site is deleted. Based on this and our published data, we propose that the switch may depend on RNase MRP processing RNA molecules other than pre-rRNA.

In order to reconstruct the origin and pathways of variola virus (VARV) dispersion, we analyzed 47 VARV isolates available in public databases and their SNPs. The mean substitution rate of the whole genomes was 9.41x10-6 (95%HPD:8.5-11.3x10-6) substitutions/site/year. The time of the tree root was estimated to be a mean 68 years (95%HPD:60.5–75.9). The phylogeographical analysis showed that the Far East and India were the most probable locations of the tree root and of the inner nodes, respectively, whereas for the outer nodes it corresponded to the sampling locations. The Bayesian Skyline plot showed that the effective number of infections started to grow exponentially in 1915-1920, peaked in the 1940s, and then decreased to zero. Our results suggests that the VARV major strains circulating between 1940s-1970s probably shared a common ancestor originated in the Far East; subsequently moved to India, which became the center of its dispersion to eastern and southern Africa, and then to central Africa and the Middle East, probably following the movements of people between south-eastern Asia and the other places with a common colonial history. These findings may help to explain the controversial reconstructions of the history of VARV obtained using long- and short- term calibrations.

The zebrafish Danio rerio is suitable to study gametes as a model organism. There were &gt; 70% of zebrafish spermatozoa activated, because they were contaminated with urine or excrement. The movement of spermatozoon in water was propagated along the flagellum at 16 s after sperm activation, then damped from the end of the flagellum for 35 s and fully disappear at 61 s after activation. For artificial fertilization, milt must be added to an extender, which stops the movement of sperm and keeps the sperm motionless until fertilization. E400 was shown to be the most suitable extender as it allows to store sperm for fertilization for 6 to 12 h at 0-2oC. Sperm motility decreased only to 36% at 12 h post stripping (HPS) for E400 extender and to 19% for Kurokura extender. To achieve an optimal level of fertilization and hatching, a test tube with a well-defined amount of 6,000,000 spermatozoa in E400 extender per 100 eggs and 100 µl of activation solution has proved to be more successful than using a Petri dish. The highest fertilization and hatching rates reached 80% and 40-60%, respectively, with milt stored for 1.5 h in E400 extender at 0-2oC.

Short stature has been associated with increased various disease and all-cause death, but no reliable data exist the association between height and end-stage renal disease (ESRD) in diabetic patients. We investigated the relationship between short stature, development of ESRD, and mortality in type 2 diabetes. This study analyzed clinical data using the National Health Insurance Database in Korea. Height was stratified by five groups according to age and sex. Risk of ESRD and all-cause mortality was analyzed with Cox proportional hazards models. During a 6.9-year follow-up period, 220,457 subjects (8.4%) died and 28,704 subjects (1.1%) started dialysis. Short stature significantly increased the incidence of ESRD and all-cause mortality in the overall cohort analysis. In multivariable analysis, hazard ratios (HR) for development of ESRD comparing the highest versus lowest quartiles of adult height were 0.86 (95% confidence interval (CI), 0.83–0.89). All-cause mortality also decreased with highest height compared to patients with lowest height after fully adjusting for confounding variables (HR 0.79, 95% CI, 0.78–0.81). Adult height had an inverse relationship with newly diagnosed ESRD and all-cause in both males and females. Short stature is strongly associated with an increased risk of ESRD and all-cause mortality in type 2 diabetes.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding this link has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, in-silico model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Malaria parasites increase their host erythrocyte’s permeability to obtain essential nutrients from plasma and facilitate intracellular growth. In the human P. falciparum pathogen, this increase is mediated by the plasmodial surface anion channel (PSAC) and has been linked to CLAG3, as encoded by one member of the conserved clag multigene family. Whether paralogs encoded by other clag genes also contribute to PSAC formation or serve unrelated roles remains unknown. Here, we generated transfectant lines carrying epitope-tagged versions of each CLAG. Each paralog colocalized with CLAG3, with concordant trafficking via merozoite rhoptries to the host erythrocyte membrane of newly-invaded erythrocytes. Each also exists within infected cells in at least two forms: an alkaline-extractable soluble form and a form integral to the host membrane. Like CLAG3, CLAG2 has a variant region cleaved by extracellular protease but CLAG8 and CLAG9 are protease-resistant. Paralog knockout lines, generated through CRISPR/Cas9 transfection, exhibited uncompromised growth in PGIM, a modified medium with more physiological nutrient levels; this finding is in marked contrast to a recently reported CLAG3 knockout parasite. CLAG2 and CLAG8 knockout lines exhibited compensatory increases in transcription of remaining clags and associated rhoph genes, yielding increased PSAC-mediated uptake to specific solutes. We also report distinct transport properties of these knockout lines, suggesting partially overlapping roles of CLAG paralogs in PSAC activity. We propose that the nutrient channel pore is formed by a higher order assembly of CLAG proteins at the host membrane.

When the epipelic algae group growing on the concrete inclined side wall, which is typical of urban rivers and open diversion channels, enters the water flow, it will seriously affect the func-tion of the rivers or the channels. It is urgent to predict the transport capacity of epipelic algae group with water flow. However, the current research on the transport of pollutants is mostly based on mathematical models, and the study of the transport of flake epipelic algae group is extremely rare. A generalized hydrodynamic experimental model of a flume with sidewall epi-pelic algae group was established. Through a series of hydraulic experiments, the physical movement form and transport capacity of the suspended epipelic algae group on the side wall of the channel were studied qualitatively and quantitatively by taking the thickness, sheet size and flow rate of the epipelic algae group as the influencing factors. The key influencing factors of the suspension of epipelic algae are clarified, so as to quantitatively analyze the critical conditions of its suspension. Through flow scheduling, the probability of epipelic algae gathering some-where is increased. It provides a theoretical basis for realizing the long-distance migration of epipelic algae and optimizing the mathematical model.

Predictions of mean values of statistical variables of large scale turbulent flow by the widely used basic k-ϵ model and a new fundamentally based model are verified against published results of Direct Numerical Simulations DNS of the Navier-Stokes equations. The verification concerns turbulent channel flow at shear Reynolds numbers of 950, 2000 and 104. The basic k-ϵ model is largely based on empirical formulations accompanied by calibration constants. This contrasts with the new model where descriptions of leading statistical quantities are based on the general principles of statistical turbulence at large Reynolds number. Predicted values of major output variables such as turbulent viscosity, diffusivity of passive admixture, temperature, and fluid velocities compare well with DNS in case of the new model. Significant differences are seen in case of the basic k-ϵ model.

In Active Flow Control (AFC) applications, to activate the boundary layer the use of pulsating flow has notorious energy advantages versus constant blowing/suction jet injections. For a given AFC application, five parameters, jet location and width, inclination angle, frequency of injection and momentum coefficient need to be tuned. Two main devices are nowadays capable of injecting pulsating flow with the sufficient momentum coefficient to delay the boundary layer separation, these are the Zero Net Mass Flow actuators (ZNMFA) and the Fluidic Oscillators (FO). In the present research, a novel FO configuration is analyzed for the first time at relatively high Reynolds numbers, fluid is considered as incompressible. After obtaining the typical linear correlation between the incoming Reynolds number and the outlet flow oscillating frequency, the effect of outlet width and mixing chamber wedge inclination angle dimensional modifications is addressed, modifications of the outlet width are observed to create large variations on the FO performance. The origin of the self-sustained oscillations is also analyzed in the present manuscript and greatly helps in clarifying the forces acting on the jet inside the mixing chamber. In fact we can conclude saying that the current FO configuration is pressure driven although the mass flow forces appear to be a much more relevant than in previous FO configurations studied.

Blood testing is a crucial application in the field of clinical studies for disease diagnosis and screening, biomarkers discovery, organ function assessment, and personalized medication. Therefore, it is of utmost importance to collect precise data in a short time. In this study, we utilized Raman spectroscopy to analyze blood samples and extracted comprehensive biological information, including the primary components and compositions present in the blood. We conducted short-wavelength (532 nm green light) Raman scattering spectroscopy on blood samples, plasma, and serum, and subsequently analyzed the biological characteristics detected in each sample type. Our results indicated that whole blood had high hemoglobin content, which suggests that hemoglobin is a major component of blood. The characteristic Raman peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562–1649 cm−1. Analysis of plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm−1. Additionally, in the field of medical blood testing, the novel 3D Silicon micro-channel device technology holds immense potential. It can serve as a substrate and helps to detect various diseases and biomarkers, providing real-time data to help medical professionals and patients better understand their health conditions. Changes in biological data collected in this manner could potentially be used to diagnose clinical diseases.

The construction of large-scale water diversion projects alleviated effectively the current situa-tion of uneven distribution of water resources in China. However, due to the siltation of very fine sediment and organic matter on the side wall of the open channel, and the slow velocity of the side wall flow field, it is easy to produce epipelic algae, which affects water quality. For the prototype observation cannot be used to predict the series of flow in real time, and the mathe-matical model calculation is affected by parameter limitations, these two methods often cannot truly reflect the hydrodynamic characteristics of open channel with epipelic algae. Therefore, by referring to the design parameters of the water diversion project channel, this paper takes the epipelic algae growing on the side wall of the open channel as the research object, and uses the scale of 1:30 to carry out the generalized flume experiment. Through the analysis of the physical characteristics of the prototype sample, and the simulation of the cohesive force between the oblique side wall and the epipelic algae, the multi-group and multi-series hydrodynamic tests are carried out. The flow velocity distribution law and the development and change of the tur-bulent vortex on the side wall are analyzed. It has important scientific guiding significance and practical value for water quantity regulation water regulation and water quality safety protec-tion of long-distance projects.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of the fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surfaces (RIS), one of the potential technologies for the sixth generation (6G), has received a lot of attention. Since it is easier to deploy, consumes less power, and is inexpensive. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This paper conducts a comprehensive investigation of the existing channel estimation methods of RIS, analysis and comparison of channel model building and CSI acquisition schemes in different frequency bands, in addition to a comprehensive description of generic channel estimation methods, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook in the future development of RIS channel estimation.

Due to the numerous heat sources in electronic equipment, the periodic nature of stream-wise flow occurs in a cooling channel so often, necessitating the development of an effective technique to improve the heat-cooling convection in such a situation. This paper investigates thermal convection enhancement in a heated-block duct through the periodic boundary conditions using the element-by-element (EBE) treatment in a semi-implicit projection finite element method (FEM) through preconditioned conjugate gradient (PCG) solver. This paper used the time-mean Nusselt number ratio, friction factor, and thermal performance to study how installing rectangular cylinders in the channel works with periodic boundary conditions varying Reynolds numbers (100, 175, and 250). The outcomes show that the rectangular cylinders installed stream-wise above an upstream block promote thermal convection in the heated-block duct. However, increasing the number of rectangular cylinders can raise friction factor. As a result, the case for periodic boundary conditions with a rectangular cylinder above every two blocks has the best thermal performance.

The membrane potential or resting potential of the neuron has been the subject of many studies. Although this theory explains the generation and maintenance of the membrane potential by direct or even facilitated diffusion, there are too many contradictions to doubt that these forces are sufficient or even at work in a process whose initial conditions are of rare complexity. The aim of this article is to show that already in the past, a competing theory has been developed whose hypothesis seems more scientifically sound. To confirm this last theory, Hirohisa Tamagawa carried out an experiment of great simplicity which makes it possible to invalidate the current theory and to question the teaching and the knowledge in Biology and Biophysics.

The generation and maintenance of membrane potential is a fundamental part of Membrane Pump Theory. One of the key points of this hypothesis is based on a natural or facilitated molecular diffusion through several types of ion channels and pumps like the Na/K ATPase. Following the principles of chemistry, electrostatics and geometry, it becomes clear that ion channels cannot function in this way. The ions channels cannot by their location have both a filter function and be ion concentrators, and the Na/K pump by its position in the membrane and by the proposed assumptions is not able to perform its regulatory function. The current model must absolutely be revised according to the current state of our knowledge and allow an advance in the understanding of the phenomena opening new research perspectives.

Our study provides guidelines on how to build long-term customer relationship in the non-contract mechanism context. More specifically, the findings show that special, social, and core benefits influence calculative commitment, and operational and special benefits influence affective commitment. This study also supports that calculative and affective commitment play a crucial role in understanding multi-channel agencies’ loyalty. In sum, this study revealed that calculative and affective commitment can be considered as partial or full mediators in the relationship between RBs (relational benefits) and loyalty. This study not only contributed to the existing SET (social exchange theory) and RBs paradigm but also provided practical implications for food distribution management.

Two desirable functions of healthy soil are nutrient cycling and pest suppression. We review recent literature on the contributions of soil microarthropods to soil health through their intersecting roles in decomposition and nutrient cycling and direct and indirect suppression of plant pests. Microarthropods can impact soil and plant health directly by feeding on pest organisms or serving as alternate prey for larger predatory arthropods, and indirectly, by mediating the ability of crop plants to resist or tolerate insect pests and diseases through interactions with the decomposition food web in support of plant nutrition. Soil fauna, including microarthropods, are key regulators of decomposition at local scales but their role at larger scales is unresolved. Future research priorities include the incorporation of multi-channel omnivory into food web modeling and understanding the vulnerability of our soil carbon to increased global temperatures.

Background: We investigated ion channels at the skin, including peripheral nerve endings, which serve as output machines and molecular integrators of many pruritic inputs mainly received by multiple G protein-coupled receptors (GPCRs). Methods: Based on the level of chronic kidney disease–associated pruritus (CKD-aP), subjects were divided into two groups: non-CKD-aP (no or slight pruritus; n=12) and CKD-aP (mild, moderate, or severe pruritus; n=11). Skin samples were obtained from the forearm or elbow during operations on arteriovenous fistulas. We measured ion channels expressed at the skin, including peripheral nerve endings by RT-PCR: Nav1.8, Kv1.4, Cav2.2, Cav3.2, BKCa, Anoctamin1, TRPV1, TRPA1, and ASIC. Results: Expression of Cav3.2, BKCa, and anoctamin1 was significantly elevated in patients with CKD-aP. On the other hand, expression of TRPV1 was significantly reduced in these patients. We observed no significant difference in the levels of Cav2.2 or ASIC between subjects with and without CKD-aP. TRPA1, Nav1.8，and Kv1.4 were not expressed. Conclusions: It was concluded that this greater difference in expression of ion channels at the skin tissue including specific for cutaneous peripheral nerve endings in CKD patients with CKD-aP may increase generator potential related to itching.

Hypertension is a common and growing medical problem that leads to enormous cardiovascular and kidney disease worldwide. While many drugs exist to treat hypertension, there is large individual variation in how a given individual responds to different agents, which contributes to dismal rates of hypertension control. While demographic factors predict which drugs may work better in certain individuals, a great degree of this variation has a genetic basis. In recent years, genome wide association studies have begun to identify specific gene variants that predict drug response to particular agents. This review identifies the major genetic variants influencing antihypertensive response that have emerged from this growing body of work. For novel genetic variants without a previously known biologic basis in blood pressure, it is crucial to validate initial findings in subsequent studies. This information may eventually lead to a more personalized approach to hypertension management that will improve blood pressure control and patient outcomes. The integration of this large amount of data and its real world application will be highly challenging, but strategies to accomplish this are discussed.

The controls of depositional environments on reservoir quality have been evaluated in terms of porosity and permeability of the Gabo Field, Niger Delta, Nigeria. Data used in this research include Well logs, Core data and photos, and grain size analysis for Wells 51 and 52 in the study area. Standard methods as applicable in petrophysical and sedimentological analysis has been adopted. Thirteen reservoir units have been identified in wells 51 and 52 which had 5 reservoirs cored each. The lithofacies units of the identified reservoirs across the study area, comprise pebbly sands, coarse -, medium -, fine- and very fine-grained sands, sandy mud, silty sands and heteroliths. The heteroliths – very fine-grained silty muds are highly bioturbated. Ophiomorpha and skolithos are the major trace fossils with sedimentary structures (ripple lamination, wavy lenticular and planar beds, cross bedded sands, coarsening and fining upward). The facies associations interpreted for the study area are Channel and Coastal barrier systems and the environment of deposition as distributary channel, upper and lower shoreface. The sedimentary processes that deposited facies ranged from high energy regimes, reworking by waves to low energy with periodic influx of silts and muds. The average porosity and permeability for reservoirs in Well 51 is 16.7% and 1317 Md, reservoirs in Well 52 is 28.2% and 2330Md whereas porosity range for the study area is 2% - 32% and permeability is 1.2 – 10600 Md. The reservoir quality reservoir of the sand units in Well 51 (7, 9 and 13) and Well 52 (5, 7, 9, 11 and 13) is excellent - good, this is because of the dynamics environments of deposition (upper shoreface and distributary channel) as well as the mechanisms that play out during deposition such as bioturbation, sorting, sedimentary structures formed. Whereas the poor quality across the reservoirs especially the lower shoreface and prodelta facies is as result of lack bioturbation, connectivity, multiplicity of burrows that may have been plugged by clay and intercalation of shale and sand (heteroliths). This research has shown that environments of deposition have direct influence the reservoir quality in terms of porosity and permeability.

There have been a great number of investigations about the influence of weak magnetic fields on biological systems, such as isolated cells and whole organisms. This is also a subject of considerable medical concern since old epidemiologic observations have indicated a possible tumorigenic effect of these fields. Their mechanism of action, however, is not firmly established. A large number of biological effects of electromagnetic fields have been attributed either to the production of reactive oxygen species (ROS) or to the entrance Ca²⁺ in the cell. A new biochemical pathway is proposed that covers these two possibilities: the primary effect of the magnetic field would be by the mechanism of radical pairs resulting in the production of ROS; these could activate the ion channels TRPM2 producing cellular inflow of Ca²⁺, which would induce the calcium dependent effects. Thus, a large number of biological effects observed up to the present could be explained.

The purpose of this work is to optimize angular extrusion for sever plastic deformation of tubular specimens. A finite element model (FEM) was built in Deform-3D^® for three extrusion dies and analyzed. The dies were ECAP (135°), TCAP with external groove of 90° (denoted as TCAP-e) and TCAP with internal groove of 90° (denoted as TCAP-i). The analysis for process parameters (such as coefficient of friction-μ, die angles-ѱ and φ, temperature - T, and radius ratio-R) showed that TCAP-i was the optimal die in processing Al6063 tubes based on strain and load distribution of the model. The TCAP-i die model was further optimized for different parameters namely die angles, coefficient of friction, deformation ratio and temperature. The results showed that at constant process temperature of 25 °C, the optimal TCAP-i has the following parameters: φ₂=80⁰, Ѱ₁=30⁰, Ѱ₂=80⁰, Ѱ₃=30⁰, μ =0.4 and R =1.5.

In airborne sensor networks (ASNs), the media access control (MAC) protocol is facing with serious unfairness problem due to the traditional protection mechanism of air-to-air communications among aircrafts. Actually by using the binary exponential back-off algorithm at high traffic loads to minimize collisions among users, the latest successful node can always benefit from this kind of MAC to obtain channel resources. Moreover, when taking the existence of the hidden nodes in ASNs into account, the inaccurate traffic load information will further aggravate the system’s unfairness. In this paper, a neighbor-channel-aware (NCA) protocol is proposed to improve the fairness of MAC protocol in ASNs. In the proposal, the NCA frame is firstly added and exchanged between neighbor nodes periodically, which helps to resolve the inaccurate traffic load information, so as to avoid reducing the probability of successful message transmission. Then a traffic-loading based back-off algorithm is involved to make the neighbor nodes cooperatively adjust the inter-frame space (IFS) interval to further reduce the unfairness. The simulation results show that, the proposed MAC protocol can guarantee the satisfied fairness, simultaneously avoiding heavy network overloads to protect key messages’ successful transmissions in ASNs.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz. From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds). The estimation of the parameters characterizing the probability density functions was performed using different methods; the statistical models were evaluated by the coefficient of determination and the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the mean square error, the mean absolute error, and the mean absolute percentage error. It was found that Weibull with the energy pattern factor method and Gamma using the method of moments were the probability density functions that best describe the statistical behavior of the wind speed and, therefore, better estimate the energy generated, so it is concluded that the proposed methodology will allow having greater confidence, both in the estimation of the wind speed and in the small wind energy available for its use.

The intense motion of a ship can greatly impacts the comfort of crew members and the safety of the vessel. Therefore, accurately estimating and predicting ship attitudes has become an important issue. This paper introduces the latest development in functional deep learning model called DeepOnet. It takes wave height as input and ship motion as output, using a cause-to-result prediction approach. The modeling data used in this study is sourced from publicly available experimental data from the Iowa Institute of Hydraulic Research. Firstly, parameters system tuning was conducted for the neural network's hyperparameters to determine the appropriate combination of parameters. Secondly, the DeepOnet model for wave height and multi-degree-of-freedom motion was established, and the influence of increasing time steps on prediction accuracy was examined. Finally, a comparison was made between the DeepOnet model and the classical time series model LSTM. It was found that the DeepOnet model had a 10-fold improvement in accuracy for roll and heave attitudes. Moreover, as the forecast duration increased, the advantage of DeepOnet showed a trend of strengthening. As a functional prediction model, DeepOnet provides a new promising tool for very short-term prediction of ship motion.

The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. It explores the analytic forms of state probabilities and their maximization based on entropy functionals, subject to normalization and mean value constraints. The credibility of KLDF is justified through consistency axioms, and the application of Rényi's and Tsallis's formalisms on a stable M/G/1 queue is examined, resulting in novel state probabilities and insights into information theory of Queue Learning.

Understanding the implications of organic acids in animal diets on physiological markers is critical for developing viable strategies for improving overall health and performance. The aim of this study was to evaluate the influence of dietary organic acids inclusion on rabbits’ growth performance and physiological status by determining blood and intestine development indicators. Forty weaned rabbits aged 28-d randomly assigned into 2 groups and fed with standard compound diet (SCD) and SCD + 0.2% organic acids mixture (butyric, propionic, sorbic; OAM). Growth performance was evaluated between 28-77 d of age, physiological status at 77 d of age. OAM’s rabbits final body weight, ADG, DFI, growth rate indicators were higher, but FCR lower compared to SCD (P &lt; 0.05). OAM increased total protein and albumin levels in blood plasma; reduced pH, increased DM, achieved greater viscosity in caecum’s content (P &lt; 0.05). Compared to SCD better gut development observed in OAM, as total intestinal weight was significantly higher (P &lt; 0.05). Acetic and propionic acids concentrations were increased, but butyric reduced in OAM caecum’s content (P &lt; 0.05). OAM had significant effect on the most caecum histomorphometric parameters: increased villus height, crypt depth and their ratio (P &lt; 0.05). According to our results OAM can improve rabbits’ growth performance, nutrient intake, and feed digestibility by significantly improving physiological status.

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers, which include the fluid-structure interaction. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids, and then introduced as a variable of the stiffness matrix during the structural analysis. The materials used were non-linearly modeled using an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor can be expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC) where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

Salmonella enterica includes enteric pathogens of zoonotic potential, possessing one of the largest pools of temperate phages in their genomes. One such class of phages previously called Podoviridae (Now genera placed directly under class Caudoviricetes temporarily) is the largest group of temperate phages that lysogenize clinically and economically important Salmonella enterica serovars. These phages are capable of generalized transduction and are well known for carrying foreign DNA (mobile genetic elements and resistance genes) to new bacterial species that play a major role in the evolution, pathogenicity, and host adaptability of Salmonella serovars. Here we report a novel species of podo viruses; Salmonella phage BIS08P22 (BIS08) that infects Salmonella enterica serovar Typhimurium strain SE-BS17 (Acc. NO: MZ503545). Phage BIS08 was viable only at biological pH and temperature (pH7 and 37 °C) and features 41,574 -base pair (bp) linear ds DNA genome with 47% GC content. It encodes 73 putative Open Reading Frames (ORFs), has a mosaic arrangement, and shares only 16 core genes with its closest homologs. BIS08 genome possesses only 52 % homology (genome-wide Nucleotide homology by VIRIDIC analysis) with members of the genus Lederbergvirus which is not sufficient to place it in the same genus. This phage has a unique terminase enzyme (DNA packaging motor) with no nucleotide and protein homology with any known member of the Lederbergvirus genus. We carried out a detailed phylogenetic and genome analysis of this novel phage and proposed its placement in a new genus. Our study also suggests a revision in the classification of the genus Lederbergvirus. We also performed in silico structural analysis of the BIS08 unique terminase enzyme comparing it to its close homologs.

Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.

Liquid biopsy is a rapidly emerging field which involves the minimal/non-invasive assessment of signature somatic mutations through analysis of circulating tumor DNA (ctDNA) shed by tumor cells in bodily fluids. The broad, unmet need for liquid biopsy lung cancer detection is the lack of multiplex platform that can detect a mutation panel of lung cancer genes using minimum amount of sample, especially for ultra-short ctDNA (usctDNA). Here we developed a non-PCR and non-NGS-based single droplet based multiplexing microsensor technology “Electric Field-Induced Released and Measurement (EFIRM) Liquid Biopsy” (m-eLB) for lung can-cer-associated usctDNA. The m-eLB provides multiplexable assessment of usctDNA within a single droplet of biofluid in only one well of micro-electrodes, as each electrode coated with dif-ferent probes for the ctDNA. In this m-eLB prototype, it demonstrates accuracy for 3 tyrosine ki-nase inhibitor related EGFR target sequences in synthetic nucleotides. The accuracy for the multi-plexing assay has an AUC of 0.98 for L858R, Ex19del, AUC of 0.94 for Ex19 deletion and AUC of 0.93 for T790M. By combining the 3 EGFR assay together, the AUC was 0.97 for the multiplexing assay.

The aim of the study was to find the effect of polyethylene (PE) coatings on the short-circuit current of silicon photovoltaic cells covered with glass, in order to improve the short-circuit current of the cells. Various combinations of PE films (thicknesses ranging from 9 to 23 µm, number of layers ranging from 2 to 6) with glasses (greenhouse, float, optiwhite and acrylic glass) were investigated. The best current gain of 4.05% was achieved for the coating combining a 1.5 mm-thick acrylic glass with 2 × 12 µm-thick PE films. This effect can be related to the formation of an array of micro-wrinkles and micrometer-sized air bubbles with a diameter of 50 to 600 µm in the films, which serve as micro-lenses and enhance light trapping.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Recent preclinical studies have shown the potential benefits of short-term calorie reduction (SCR) on cancer treatment. In this integrative review, we aimed to identify and synthesize current evidence regarding the feasibility, process, and effects of SCR in cancer patients receiving chemotherapy. PubMed, Cumulative Index to Nursing and Allied Health Literature, Ovid Medline, PsychINFO, and Embase were searched for original research articles using various combinations of Medical Subject Heading terms. Among the 311 articles identified, seven studies met the inclusion criteria. The majority of the reviewed studies was small randomized controlled trials or cohort study with fair quality. The results suggest that SCR is safe and feasible. SCR is typically arranged around the chemotherapy with the duration ranging from 24 to 96 hours. Most studies examined the protective effects of SCR on normal cells during chemotherapy. The evidence supports that SCR had the potential to enhance both physical and psychological wellbeing of patients during chemotherapy. SCR is a cost-effective intervention with great potential. Future well-controlled studies with sufficient sample sizes are needed to examine the full and long-term effects of SCR and its mechanism of action.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional ECG. Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

Background: Previous papers have developed a statistical mechanics of neocortical interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated Annealing (ASA) has been developed to perform fits to such nonlinear stochastic systems. An N-dimensional path-integral algorithm for quantum systems, qPATHINT, has been developed from classical PATHINT. Both fold short-time propagators (distributions or wave functions) over long times. Previous papers applied qPATHINT to two systems, in neocortical interactions and financial options. Objective: In this paper the quantum path-integral for Calcium ions is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Using fits of this SMNI model to EEG data, including these effects, will help determine if this is a reasonable approach. Method: Methods of mathematical-physics for optimization and for path integrals in classical and quantum spaces are used for this project. Studies using supercomputer resources tested various dimensions for their scaling limits. In this paper the quantum path-integral is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Results: The mathematical-physics and computer parts of the study are successful, in that there is modest improvement of cost/objective functions used to fit EEG data using these models. Conclusions: This project points to directions for more detailed calculations using more EEG data and qPATHINT at each time slice to propagate quantum calcium waves, synchronized with PATHINT propagation of classical SMNI.

Agavins consumption has lead to accelerate body weight loss in mice. We investigated the changes on cecal microbiota and short chain fatty acids (SCFA) associated to body weight loss in overweight mice. Firstly, mice were fed with standard (ST5) or high fat (HF5) diet for 5 weeks. Secondly, overweight mice were shifted to standard diet alone (HF-ST10) or supplemented with agavins (HF-ST+A10) or oligofructose (HF-ST+O10), five more weeks. Cecal contents were collected before and after supplementation to determine microbiota and SCFA concentrations. At the end of first phase, HF5 mice showed a significant increase of body weight, which was associated with reduction of cecal microbiota diversity (PD whole tree; non-parametric t-test, P < 0.05), increased Firmicutes/Bacteroidetes ratio and reduced SCFA concentrations (t-test, P < 0.05). After diet shifted, HF-ST10 normalized its microbiota, increase its diversity and SCFA levels, whereas agavins (HF-ST+A10) or oligofructose (HF-ST+O10) led to partial microbiota restoration, with normalization of the Firmicutes/Bacteroides ratio as well as higher SCFA levels (P < 0.1). Moreover, agavins noticeably enriched Klebsiella and Citrobacter (LDA > 3.0); this enrichment has not been reported previously under a prebiotic treatment. In conclusion, agavins or oligofructose modulated cecal microbiota composition, reduced extent of diversity and increased SCFA. Furthermore, identification of bacteria enriched by agavins, opens opportunities to explore new probiotics.

Abstract: Congenital hyperinsulinism (CHI) is a rare disorder of glucose metabolism and is the most common cause of severe and persistent hypoglycemia (hyperinsulinemic hypoglycemia, HH) in the neonatal period and childhood. Most cases are caused by mutations in the ABCC8 and KCNJ11 genes that encode the ATP-sensitive potassium channel (KATP). We present the correlation between genetic heterogeneity and the variable phenotype in patients with early-onset hyperinsulinemic hypoglycemia caused by ABCC8 gene mutations. In the first patients, which presented persistent severe hypoglycemia since the first day of life, molecular genetic testing revealed the presence of a homozygous mutation in the ABCC8 gene [deletion in the ABCC8 gene c.(2390+1_2391-1)_(3329+1_3330- 1)del] correlated with a diffuse form of hyperinsulinism (the parents being healthy heterozygous carriers). In the second patient, the onset was on the third day of life with severe hypoglycemia, and genetic testing identified a heterozygous mutation in the ABCC8 gene c.1792C&gt;T (p.Arg598*) inherited on the paternal line, which led to the diagnosis of focal form of hyperinsulinism. To locate the focal lesions, (18)F-DOPA (3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine) PET/CT was recommended (an investigation that cannot be carried out in the country), but the parents refused to carry out the investigation abroad. In this case, early surgical treatment could have been curative. In addition, the second child also presented secondary adrenal insufficiency requiring replacement therapy. At the same time, she developed early recurrent seizures that required antiepileptic treatment. We emphasize the importance and of molecular genetic testing for diagnosis, management and genetic counseling in patients with HH.

Originally developed as a chemotherapeutic agent, miltefosine (hexadecylphosphocholine) is an inhibitor of phosphatidylcholine synthesis with proven antiparasitic effects. It is the only oral drug approved for the treatment of Leishmaniasis and American Trypanosomiasis (Chagas Disease). Although its precise mechanisms are not yet fully understood, miltefosine exhibits broad-spectrum anti-parasitic effects primarily by disrupting the intracellular Ca2+ homeostasis of the parasites while sparing the human hosts. In addition to its inhibitory effects on phosphatidylcholine synthesis and cytochrome c oxidase, miltefosine has been found to affect the unique giant mitochondria and the acidocalcisomes of parasites. Both of these crucial organelles are involved in Ca2+ regulation. Furthermore, miltefosine has the ability to activate a specific parasite Ca2+ channel that responds to sphingosine, that is different to its L-type VGCC human ortholog. Here, we aimed to provide an overview of recent advancements of the anti-parasitic mechanisms of miltefosine. We also explored its multiple molecular targets and investigated how its pleiotropic effects translate into a rational therapeutic approach for patients afflicted by Leishmaniasis and American Trypanosomiasis. Notably, miltefosine's therapeutic effect extends beyond its impact on the parasite to also affect positively the host's immune system. These findings enhance our understanding on its multi targeted mechanism of action. Overall, this review sheds light on the intricate molecular actions of miltefosine highlighting its potential as a promising therapeutic option against these debilitating parasitic diseases.

Hypertension is a frequent risk factor for cardiovascular diseases and the prevalence rate is continuously rising. Multiple pathophysiological mechanisms are responsible for hypertension, necessitating a combination of drugs from different classes for best management. Combination therapy is five times more effective in decreasing blood pressure compared to escalating the dose of a single agent. As per the ACCOMPLISH trial analysis, coupling a renin-angiotensin system inhibitor with a calcium channel blocker is more effective than the combination of a renin-angiotensin system inhibitor with diuretics in reducing major cardiovascular events, cardiovascular-related mortality, and the progression of chronic kidney disease. The renin-angiotensin system can be suppressed by inhibiting the angiotensin-converting enzyme or angiotensin type 1 receptor (AT1R), resulting, in decreased vascular smooth muscle contraction, heart performance, and reduced aldosterone synthesis. Calcium channel blockers work by inhibiting L-type calcium channels, reducing heart performance via negative inotropic, chronotropic, and bathmotropic effects, along with reducing vascular smooth muscle contraction. Diuretics exert their antihypertensive impact by changing body fluid volume and electrolytes. Initially, hypertension treatment commences with a single medication but often, different classes of two or more antihypertensive medications are necessary for achieving the target blood pressure goal. Sometimes, for Resistant hypertension four different classes of antihypertensive medications are necessary to reach the target blood pressure goal.

Environmental responsibility and economic benefits have promoted the development of closed-loop supply chain (CLSC), and shortages and channels are considered to be two important issues in CLSC. This paper explores ordering and channel decisions in CLSC with new and differentiated remanufactured products, and considers the price and sales effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses, and establishes an integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. Numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. Wherein, the upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, decisions of integrated channel are better than those of decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out of stock costs. According to the research in this article, some management insights are also provided.

A mathematical model that comprehensively captures the real behavior of riverbed deformation, encompassing all pertinent effects, is developed. The underwater slope reformation process, with the generatrix aligned along the flow velocity in the model, is considered. A numerical model is introduced to calculate the flow involving a deformable bottom, and the model's validation is established through rigorous analysis of experimental findings. The research firmly confirms the suitability of the proposed mathematical and numerical model for describing deformations in uneven and unsteady river flows, including the movement of dredging slots and channel quarries. The model's minimal equation count and reliance on empirical constants demonstrate the model's efficiency. The model's predictions align strongly with experimental data, although optimal values of empirical coefficients vary slightly across different experiments. Hence, there is a call for further investigation to derive more universally applicable closure relationships for the model. The importance of validating the model with reliable field data and its potential extension to accommodate hydraulically diverse soils is emphasized. Such an extension is feasible due to the concentration transfer equation, enabling independent calculations for particle fractions of varying sizes as long as the total particle concentration in the stream remains within reasonable limits. This dedicated research contributes significantly to understanding riverbed deformations and advancing accurate modeling and management of riverine environments.

The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical information theory alone does not provide a unique way to decompose information in this manner and additional assumptions have to be made. Inspired by Kolchinsky's recent proposal for measures of intersection information, we introduce three new measures based on well-known partial orders between communication channels and study some of their properties.

This paper is our attempt on the basis of physical theory to bring more clarification on the question ``What is life?'' formulated in the well-known book of Schr\"odinger in 1944. According to Schr\"odinger, the main distinguishing feature of biosystem's functioning is the ability to preserve its order structure or, in the mathematical terms, to prevent increasing of entropy. Since any biosystem is fundamentally open, it is natural to use open system's theory. However, Schr\"odinger's analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schr\"odinger should also appeal to the ambiguous notion of negative entropy. We suggest to apply the quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of the classical entropy. We consider a complex biosystem $S$ composed of many subsystems, say proteins, or cells, or neural networks in the brain, i.e., $S=(S_i).$ We study the following problem: if the composed system $S$ can preserve the ``global order'' in the situation of increase of local disorder and if $S$ can preserve its entropy while some of $S_i$ increase their entropies We show that within quantum information theory the answer is positive. The significant role plays entanglement of the subsystems states. In the absence of entanglement, increasing of local disorder generates disorder increasing in the compound system $S$ (as in the classical regime).

Vertical-channel organic dual-gate/base transistors would be highly interesting since vertical organic transistors are the fastest organic transistors demonstrated today. However, incorporating a dual gate/base structure into an ultra-short channel vertical architecture represents a substantial challenge. Here, we successfully realize a new device concept of vertical organic permeable dual-base transistors (OPDBTs), where either of both base electrodes can be used to change the on-currents and tune the threshold voltages. The optimized devices yield a high on-current density of 1.54 A cm^-2 and a large current gain of 9.2×10⁵, corresponding to a high transmission value of 99.998%. The detailed operation mechanisms are investigated by calibrated TCAD simulations with the Poole-Frenkel mobility model, Gaussian density of states and tunneling model. Finally, high-performance logic circuits, e.g. inverter, NAND/AND computation functions are demonstrated with one single OPDBT operating at supply voltages of < 2.0 V. We believe that OPDBTs offer a compact and technologically simple hardware platform with excellent application potential for vertical-channel organic transistors in complex logic circuits.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

Acute ischemic strokes are the third leading cause of death and the leading cause of neurological disability worldwide. The oxygen and glucose deprivation associated with ischemic strokes not only leads to neuronal cell death, but also increases the inflammatory response and decreases functional output of the brain. The only intervention approved by US Federal Drug and Food Administration for treatment of ischemic strokes is tissue plasminogen activator (tPA), however, such treatment can only be given within 4.5 hours of the onset of stroke-like symptoms. This narrow time-range limits its application, and it also might induce detrimental rather than beneficial effects to stroke patients by treatment of the tPA. In order to reduce the infarct volume of an acute ischemic stroke while increasing the time period for treatment, emerging therapies reveal great potential by targeting inflammation, growth factors, ion channels, and neurotransmitter receptors with monoclonal antibody (MAB). With successfully application in the treatment of cancer patient by MAB, in this review, we will focus on recent advances on stroke therapy by using MAB on the treatment of stroke by targeting inflammation, growth factors, ion channels, and neurotransmitter receptors. Therefore, developing specific MAB targeting the signaling pathway of stroke will contribute to stroke therapy.

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

In this work modeling and analysis of an integrated opto-fluidic sensor, with a focus on achievement of single mode optical confinement and continuous flow of micro particles in the microfluidic channel for Lab-on-a Chip (LOC) sensing application is presented. This sensor consists of integrated optical waveguides, microfluidic channel among other integrated optical components. A continuous flow of micro particles in a narrow fluidic channel is achieved by maintaining the two sealed chambers at different temperatures and by maintaining a constant pressure of 1Pa at the centroid of narrow fluidic channel geometry. The analysis of silicon on insulator (SOI) integrated optical waveguide at an infrared wavelength of 1550nm for single mode sensing operation is presented. The optical loss is found to be 0.0005719dB/cm with an effective index of 2.2963. The model presented in this work can be effectively used to detect the nature of micro particles and continuous monitoring of pathological parameters for sensing applications.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

Fungi belong to the species with the highest biodiversity and high potential for use in biotechnology and industry. Therefore, their long-term quality conservation is very important and it is necessary to continuously develop new methods and improve existing methods. Currently, cryopreservation appears to be the best available technique for the preservation of fungi. This study summarises the latest trends and advances in fungal cryopreservation over the last 7 years, i.e. findings from scientific publications from 2017 to 2023 dealing with cryopreservation of both basidiomycetes and non-basidiomycete fungi. The work analyzed the studied organisms and the cryopreservation method used, focused on the use of new procedures and modifications of existing methods; summarized the conditions for successful cryopreservation, such as cryopreservation time and temperature, as well as the substrate and cryoprotectant used.

Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely used in natural language processing, speech recognition and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM) and dynamic polynomial models. The obtained results are satisfactory, which suggests a good possibility of application.

This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country's administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.

Urban rail transit offers advantages like high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou's Shantang Street station. Shantang Street's high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case. Wavelet denoising and LSTM modeling were combined to predict short-term flows, comparing the results to standalone LSTM and SVR approaches. This study illustrates that the adopted algorithms exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance.

Background: The validation of measuring instruments in health is a requirement to be able to use them safely and reliably. The Short Physical Performance Battery (SPPB) tool is an instrument widely used in the clinic and validated in numerous countries and languages and for different populations. The objective of this research was to determine the psychometric properties of SPPB for a sample composed of institutionalized Spanish older adults. Methods: Multicenter study of the psychometric properties of the Short Physical Performance Battery tool with a convenience sample of 194 institutionalized older adults. Reliability (internal consistency), validity (construct validity and convergent validity) tests were performed. Results: The results show a very good internal consistency, construct validity and convergent validity. In addition, the factorial structure of the SPPB is provided, which reflects that it is a unidimensional scale. Conclusions: In conclusion, the Short Physical Performance Battery is a valid and reliable tool for use with institutionalized older adults. Its use is recommended as part of the Comprehensive Geriatric Assessment for the evaluation of the physical or functional sphere. Authors should include one of the following statements about the registration status of the manuscript here: This study was prospectively registered and approved by the Ethics Committee of the University of Burgos (IR 11/2018).

The magnetic properties of RCo3B2 compounds, with heavy rare earths, are investigated by using magnetic measurements and electronic structure calculations. The cobalt sublattice magnetizations are very small and antiparallel oriented to rare earths moments. Contributions of cobalt moments to the Curie constants were also shown. The cobalt magnetic behavior has been analyzed in spin fluctuations model. The presence of short-range order above the Curie temperatures in RCo3B2 compounds with R=Tb, Dy, and Ho has been attributed to reminiscent R5d-Co3d interactions.

Popped amaranth is a nutritious food consumed since pre-Hispanic times, currently its consumption has been associated with recovery of malnourished children. However, there is no information on the impact that popped amaranth consumption has on gut microbiota composition. A non-randomized pilot trial was conducted to evaluate the changes on composition, structure, and function of gut microbiota of stunted children who received four grams of popped amaranth daily for three months. Stool and serum were collected at the beginning and at the end of the trial. Short-chain-fatty acids were quantified and gut bacterial composition was analyzed by 16S rRNA gene sequencing. Biome-try and hematology results showed that children have no other pathology more than to be low-height-for age. Increase of total SCFAs levels in feces was ob-served as well as a decrease in relative abundance of Alistipes putredinis, Bac-teroides coprocola, and Bacteroides stercoris, bacteria related with inflammation and colitis. On the contrary, an increase in relative abundance of Akkermansia muciniphila and Streptococcus thermophiles, bacteria associated with health and longevity, was observed. Results have proven that popped amaranth, a nutri-tious food, help to combat children malnutrition through gut microbiota modu-lation.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.

Working memory (WM) is a brain system for short-term storage and manipulation of information and plays an important role in complex cognitive tasks. In the synaptic theory of WM memorized elements are stored in the form of short-term potentiated connections in a sample population of neurons. In this paper, we show that such populations can be formed due to the mechanisms of spike-timing-dependent plasticity (STDP) – the phase dependence associated with the ratio of the pulse times of the interacting neurons. We propose a WM model considering two types of plasticity: short-term plasticity and STDP. We have shown formation of neuronal clusters encoding items in the WM model, that can be formed by external stimulation of a group of neurons due to the mechanisms of STDP and hold and reactivated by short-term plasticity mechanisms. The dynamic formation of neuronal clusters instead of pre-formed clusters gives additional flexibility to the model.

This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

Dietary factors play an important role in shaping the gut microbiome which, in turn, regulates the molecular events in colonic mucosa. The composition and resulting metabolism of the gut microbiome have been implicated in the development of colorectal cancer (CRC). Diets low in dietary fibers and phytomolecules as well as other lifestyle-related factors may predispose to CRC. Emerging evidence demonstrates that the predominance of microbes, such as Fusobacterium nucleatum, can predispose the colonic mucosa to malignant transformation. Dietary and lifestyle modifications have been demonstrated to restrict the growth of potentially harmful opportunistic organisms. In this study, we aim to present evidence regarding the relationship of dietary factors to the gut microbiome and development of CRC.

We find that the overnight returns of Korean exchange-traded index funds (ETFs) are significantly positive, whereas the subsequent intraday returns are negative. These intraday return reversals are caused by relatively higher opening prices than the closing prices. In the Korean ETF market, where institutional investors are dominant participants, the return reversals are not explained by the attention hypothesis as in Berkman et al. [1]. Hence, we investigate whether the disagreement hypothesis can explain return reversals. Under the disagreement situations between positive and negative traders at the open, positive traders can have a positive influence on the ETF prices by increasing their investments. However, negative traders, who give up investments due to limited short selling opportunities in the ETF market, have no effects on the prices. Comparing ETF markets with KOSPI 200 Futures where there are no restrictions on short selling, we find that short selling constraints are significant factors for the return reversals. This implies that disagreement among the investors can cause return reversals even in the markets without noise traders. Using unique Korean market data, we conclude that return reversals cannot be completely explained by the attention hypothesis, and that disagreement among investors is also a significant factor for the return reversals. This study contributes to the existing literature by showing that the attention hypothesis does not explain return reversals in the ETF market completely, and suggesting the disagreement hypothesis as an alternative.

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for monsoon region. We develop a deep neural network composed of convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the ECMWF-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including 1) quantile mapping, 2) support vector machine, and 3) convolutional neural network. To test the robustness of the model and its applicability in practical forecast, we apply the trained network for precipitation prediction forced by retrospective forecasts from ECMWF model. Compared to ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead time from 1 day up to 2 weeks. This superiority decreases along forecast lead time, as GCM’s skill in predicting atmospheric dynamics being diminished by the chaotic effect. At last, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is just slightly worse than the observed precipitation forced simulation (NSE=0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.

Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, a new technique to improve the robustness of speech/music classifier for 3GPP enhanced voice service (EVS) using long short-term memory (LSTM) is proposed. For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. Experiments show that LSTM-based speech/music classification produces better results than conventional EVS under a variety of conditions and types of speech/music data.

It is required to manage sustainable Short-Rotation Coppices (SRCs) as an important role on carbon sink and bioenergy output, because most of SRCs were established in reclaimed land in South Korea. However, during the last three years, growth pattern of the SRCs was remarkably changed with soil condition. This study aimed to identify the sustainability of SRCs on carbon storage, biomass and fuel pellet production, monitoring the neighboring vegetation of SRCs by land-use exchange, physiological change of poplar on seasonal trend, and to evaluate whether poplar is suitable for making wood pellets. The calculated biomass yield per area of poplar grown was 103.07 Mg per total area (55.6 ha), and volumes of carbon dioxide absorption was estimated to be 330 Mg CO2. Wood pellet quality based on the criteria scored third grade, indicating that poplar is suitable for manufacturing fuel pellets. Moreover, monitoring of the flora distribution in SRCs revealed changes in species composition. As halophyte was increased during drought, soil organic matter, net growth and total chlorophyll of poplar were significantly decreased. These findings indicated that photosynthesis and growth pattern of SRCs may be negatively affected by microclimate and will provide valuable information for effective management of SRCs.

The only cases where exact distributions of estimates are known is for samples from exponential families, and then only for special functions of the parameters. So statistical inference was traditionally based on the asymptotic normality of estimates. To improve on this we need the {\it Edgeworth expansion} for the distribution of the standardized estimate. This is an expansion in $n^{-1/2}$ about the normal distribution, where $n$ is typically the sample size. The 1st few terms of this expansion were originally given for the special case of a sample mean. In earlier work we derived it for {\it any } standard estimate, hugely expanding its application. We call an estimate $\hat{w}$ of an unknown vector $w\in R^p$, a {\it standard estimate}, if $E\ \hat{w}\rightarrow w$ as $n\rightarrow \infty$, and for $r\geq 1$ the $r$th order cumulants of $\hat{w}$ have magnitude $n^{1-r}$ and can be expanded in $n^{-1}.$ Here we give another huge extension. We give the expansion of the distribution of {\it any smooth function} of $\hat{w}$, say $t(\hat{w})\in R^q,$ giving its distribution to $n^{-5/2}$. We do this by showing that $t(\hat{w})$, is a standard estimate of $t(w)$. This provides far more accurate approximations for the distribution of $t(\hat{w})$ than its asymptotic normality. %NOT USED: The building blocks of the Edgeworth expansions are the {\it cumulant coefficients } of the estimate. These cumulant coefficients are also needed for bias reduction, Bayes estimates and confidence regions. We give {\it chain rules} for the cumulant coefficients of $t(\hat{w})$ in terms of those of $\hat{w}$ and the derivatives of $t(w)$, up to those needed for 5th order Edgeworth expansions for $t(\hat{w})$ and its {\it tilted expansion}, useful for the tail of the distribution.

Malaria, leishmaniasis, and African trypanosomiasis are protozoan diseases that constitute a major global health problem, especially in developing countries; however the development of drug resistance coupled with the toxicity of current treatments have hindered their management. The implication of certain enzymes (dihydrofolate reductase, DHFR) or proteins (potassium channels) in the pathogenesis of these protozoan diseases is undeniable. In this study, a series of three DHFR inhibitors (6-5 fused heterocyclic derivatives X, Y and Z) and one K+ channel blocker (E4031) were screened for their inhibitory effect on Leishmania donovani, Plasmodium falciparum, and Trypanosoma brucei. Resazurin assay was used to assess antitrypanosomal and antileishmanial activities of test compounds, whereas the antimalarial activity was appraised through SYBR Green I test. Moreover, cytotoxicity of test compounds was evaluated on Vero, Raw 264.7 and HepG-2 cells using the resazurin-based test, whilst their pharmacokinetic properties were predicted using the online tool pkCSM. As a result, compound Y revealed a selective (selectivity index range: 2.69 - &gt;61.4; Vero, Raw 264.7 and HepG-2 cells) and broad-spectrum antiprotozoal activity against L. donovani promastigotes (IC50: 12.4 µM) and amastigotes (IC50: 4.28 µM), P. falciparum (IC50: 0.028 µM), and T. brucei brucei (IC50: 0.81 µM). In addition, compound X inhibited the growth P. falciparum (IC50: 0.0052 µM) and T. brucei brucei (IC50: 6.49 µM). In silico screening of the active antiprotozoal compounds revealed positive drug-likeness scores as none of Lipinski's rule of five’s criteria was violated by these compounds. However, in-depth pharmacokinetic and mechanistic studies are warranted to support discovery of novel antiprotozoal agents against malaria, leishmaniasis, and African trypanosomiasis by repurposing K+ channel blocker and DHFR inhibitors.