Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.

Despite advances in algorithms and computational platforms, de-novo genome assembly remains a challenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454, PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This comparative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods.

The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities, and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.

Integrated ultra-low-loss waveguides are highly desired for integrated photonics to enable applications that require long delay lines, high-Q resonators, narrow filters, etc. Here we present an ultra-low-loss silicon waveguide on 500 nm thick SOI platform. Meter-scale delay lines, million-Q resonators and tens of picometer bandwidth grating filters are experimentally demonstrated. We design a low-loss low-reflection taper to seamlessly integrate the ultra-low-loss waveguide with standard heterogeneous Si/III-V integrated photonics platform to allow realization of high-performance photonic devices such as ultra-low-noise lasers and optical gyroscopes.

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.

This paper presents the systematic development and performance characterization of a non-proprietary 3D-printable ultra-high-performance fiber-reinforced concrete (UHPFRC) for digital construction. Several fresh and hardened properties of the developed 3D-printable UHPFRC matrix (without fiber) and composite (with 2% volume fraction of steel fibers) were evaluated and compared to that of conventionally mold-cast UHPFRC. Additionally, the effects of testing direction on the compressive and flexural strengths of the printed UHPFRC were investigated. The fresh properties of the UHPFRC developed in this study satisfied the criteria for extrudability, buildability, and shape-retention-ability, which are relevant for ensuring printability. The printed UHPFRC exhibited superior flexural performance to the mold-cast UHPFRC due to alignment of the short fibers in the printing direction. The high compressive and flexural strengths, along with the deflection-hardening behavior, of the developed UHPFRC can enable the production of thin 3D-printed components with significant reduction or complete elimination of conventional steel bars.

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

Because this issue journal is dedicated to Gelatin here we present a few applications of gelatin in the field of optics. It is understood that optics is the science that studies the production, propagation, interaction and detection of light. Regarding the detection there are some materials sensitive to light (photosensitive) that are used like photomultipliers, CCD’s, crystals, two dimension (2D) materials and more. Among the 2D materials the most popular through several centuries has been gelatin based photographic emulsion that records spatial distributions of light. More recently (1970) films made of Gelatin with Dichromate (DCG) and dyes have been used. We describe some characteristics and applications of these two photosensitive materials. Also we describe examples where gelatin is used as Relative Humidity (RH) sensor and in the fabrication of optical elements based on gelatin. This article is intended to researchers outside the optics community.

Nonsteroidal anti-inflammatory drugs (NSAIDs), which block the activity of cyclooxygenase (COX) isoenzymes and inhibit the synthesis of prostaglandin, have been used for pain relief. We have developed a method to separate a mixture of three NSAIDs, such as aspirin, paracetamol, and naproxen, using reverse-phase high-performance liquid chromatography (RP-HPLC). An isocratic mobile phase consisting of acidic water and acetonitrile was selected to run at a low flow rate, such as 0.8 mL/min. The mixture of three NSAIDs was injected at a low volume into a C18 column that was 150 mm in length and characterized using a UV detector at 230 nm. We identified three peaks in the chromatogram indicating the three compounds. The elution time of the peaks was less than 10 minutes. To identify multiple peaks on the isocratic flow using a short column, further studies are required regarding the proposed method to generate microfluidic devices for nanoLC.

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

In small-scale hydropower scheme, the most important component is electro-mechanical equipment. Since cost contribution of this component is high because hydrokinetic projects require negligible civil works. Turbine and alternator contribute major fraction of the hydrokinetic projects. Thus, there is a requirement to estimates the electromechanical equipment cost for the hydrokinetic hydropower scheme. The present paper investigates design parameters of the hydrokinetic turbines and intends to develop cost correlation which depends on most critical parameters of hydropower sites such as velocity and power capacity. In this present work, three zero head turbines are considered including straight blade Darrieus, two Stage Savonius, and Gorlov Helical. The size and cost of major components have been calculated based on material, manufacturing, research and design, and assembly costs. Based on cost and site parameters, cost correlation has been developed. The obtained cost has been validated with available zero head turbines in the market and installed projects. A techno-economic analysis has been carried out to select economical hydrokinetic turbine for river and canal application.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density.

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.

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.

We report a very frugal microscope building method from easily realizable parts. We show a highly portable field ready compound type microscope with bright field, dark field and projection microscopy capabilities. It has good resolution and magnification for multiple aspects of education and diagnosis. We demonstrate that such a system can be built from simple lenses in laser pointers and camera modules with no specific 3d printed parts or costly lenses. We show all the parts of the system like stage, coarse and fine focussing system, microscope body, even slides can be built from commonly available materials like soda bottle and DVD disks. We list alternate, safer and easily available staining methods and chemicals. The microscope is developed with a target that it can be self-developed even in rural areas of the world with only resources at one’s disposal with very little education. This features a stable focussing and panning system for comfortable viewing through eyes and also easy imaging with a smartphone. We show its utility for microorganism analysis and potential for clinical diagnosis.

The prevalence of internet gaming disorders (IGD) is considerably high among youth, especially with social isolation imposed by the ongoing COVID-19 pandemic. IGD adversely affects mental health, quality of life, and academic performance. The Internet Gaming Disorder Scale (IGDS9-SF) is designed to detect IGD according to DSM-IV diagnostic criteria. However, inconsistent results are reported on its capacity to diagnose IGD evenly across different cultures. To ensure the suitability of the IGDS9-SF as a global measure of IGD, this study examined the psychometric properties of the IGDS9-SF in a sample of Sri Lankan university students (N = 322, mean age =17.2 ± 0.6, range = 16-18 years, 56.5% males) and evaluated its measurement invariance across samples from Sri Lanka, Turkey, Australia, and the USA. Among Sri Lankan students, a unidimensional structure expressed good fit, invariance across different groups (e.g., gender, ethnicity, and income), adequate criterion validity (strong correlation with motives of internet gaming, daily gaming duration, and sleep quality), and good reliability (alpha = 0.81). Males and online multiplayers expressed higher IGD levels, greater time spent gaming, and more endorsement of gaming motives (e.g., Social and Coping) than females and offline players. Across countries, the IGDS9-SF was invariant at the configural, metric, and scalar levels, albeit strict invariance was not maintained. The lowest and highest IGD levels were reported among Turkish and American respondents, respectively. In conclusion, the IGDS9-SF can be reliably used to measure IGD among Sri Lankan youth. Because the scale holds scalar invariance across countries, its scores can be used to compare IGD levels in the studied countries.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems in which systematic architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope up with current Information and Communication Technologies (ICT). The distributed generation in smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review on architecture for smart grids. An in depth analysis of layered and agent based architectures is presented and compared under various domains.

The aim of this study was to examine the oxidation of human serum albumin (HSA) caused by oxidative stress after an exhaustive exercise such as ultra-marathon race. Thus, blood samples from 12 adult runners who underwent a 103 km mountain ultra-marathon race were collected pre- and 24, 48 and 72 h post race. HSA was partially purified using affinity chromatography and then was subjected to Western blot analysis for disulfide dimers determination, indicating oxidation. The results were correlated with those from a previous study in which the same samples were analyzed using different oxidative stress markers and a good correlation with protein carbonyls (PC) at all time points was observed. Moreover there was a significant correlation with static oxidation-reduction potential (sORP) at 24 h, and a negative correlation with capacity oxidation-reduction potential (cORP) at 24 and 48 h. In addition, an individual analysis of albumin dimers exhibited great inter-individual differences. This inter-individual variability in the oxidation of HSA may suggest different interventions (e.g. through diet) in order to confront the effects on athletes’ organism after a strenuous exercise. In conclusion, this study supported the importance of the assessment of albumin dimers as a predictive marker for exercise-induced oxidative stress.

The monitoring of sick newborns is a challenging task that health care providers in Neonatal Intensive Care Units (NICU) must contend with each day. Conventionally, newborns are monitored via probes that are affixed to their skin and attached to processing monitors (Fig.1). However, an alternative exists in contactless imaging to record such physiological signals (Physio-Markers), surface changes and internal structures which can be used independently of, or in conjunction with conventional monitors. Advantages of contactless monitoring methods include: i) quick data generation; ii) lack of contact with skin, which reduces skin breakdown and decreases risk of infection; and iii) minimizing the number of probes and monitors affixed to the skin, which allows greater body surface-area for other care. This paper is an attempt to build a foundation for and to provide a vision of the potential neonatal clinical applications of technologies that use non-contact modalities such as Visible Light Imaging (VLI), Near InfraRed Spectrum (NIRS), and Thermal Imaging (TI) using InfraRed Spectrum (IRS).

Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

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.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

An ultra-low anti-reflection optical coating on both surfaces of a plastic cover slip was studied for use in confocal image measurement. The optical reflectance at a wavelength of 632.8 nm was less than 0.1% when the coated sample was placed in a liquid having a refractive index of 1.34 close to the aqueous solution of the biomaterial. The high- and low-index coating films, Substance-2 (PrTiO3) and silicon dioxide (SiO2), were measured by an ellipsometer to determine their optical refraction indices and extinction coefficients. Theoretically, when the two layer thicknesses are designed using the optical admittance diagram of the cover slip to approach the equivalent index of 1.34, a reflectance of 1.6×10-5% in the liquid could be obtained. Experimentally, the reflectance of the sample deposited on the two faces of the cover slip was 4.223±0.145% as measured in the air; and 0.050±0.002% as measured by a He-Ne laser in the liquid.

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.

Probiotics have been used to ameliorate gastrointestinal symptoms since ancient times. Over the past 40 years, probiotics have been shown to exert major effects on the immune system, both in vivo and in vitro. This interaction is clearly linked to gut microbes, their polysaccharide antigens, and key metabolites produced by these bacteria. At least four metabolic pathways have been implicated in mechanistic studies of probiotics, based on carefully studied animal models. Microbial-immune system crosstalk has been linked to short chain fatty acid production and signaling, tryptophan metabolism and the activation of aryl hydrocarbon receptors, nucleoside signaling in the gut, and activation of the intestinal histamine-2 receptor. Several randomized controlled trials have now shown that microbial modification by probiotics may improve gastrointestinal symptoms and multi-organ inflammation in rheumatoid arthritis, ulcerative colitis, and multiple sclerosis. Future work will need to carefully assess safety issues, selection of optimal strains and combinations, and attempts to prolong the duration of colonization of beneficial microbes.

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.

The capacity and the mechanism of the adsorption of aqueous barium (Ba), cobalt (Co), strontium (Sr) and zinc (Zn) by Ecuadorian (NatAllo) and synthetic (SynAllo-1 and SynAllo-2) allophanes were studied as a function of contact time, pH and metal ion concentration using kinetic and equilibrium experiments. The mineralogy, nano-structure and chemical composition of the allophanes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and specific surface area analyses. The evolution of adsorption fitted to a pseudo-first-order reaction kinetics, where equilibrium between aqueous metal ions and allophane was reached within < 10 min. The metal ion removal efficiencies varied from 0.7 to 99.7 % at pH 4.0 to 8.5. At equilibrium, the adsorption behavior is better described by the Langmuir model than by the Dubinin-Radushkevich model, yielding sorption capacities of 10.6, 17.2 and 38.6 mg/g for Ba^(2+), 12.4, 19.3 and 29.0 mg/g for HCoO_2^-, 7.2, 15.9 and 34.4 mg/g for Sr^(2+) and 20.9, 26.9 and 36.9 mg/g for Zn^(2+), respectively, by NatAllo, SynAllo-2 and SynAllo-1. The uptake mechanism is based on a physical adsorption process. Allophane holds great potential to remove aqueous metal ions and could be used instead of zeolites, montmorillonite, carbonates and phosphates for wastewater treatment.

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.

More people around the world now have obesity than suffer from starvation thanks to our modern food system. Agriculture was transformed over the 20^th century by a variety of technological advancements that relied heavily on fossil fuels. In the United States of America, government policies and economic incentives led to surplus production of cheap inputs to processed food industries that produced a wide variety of heavily marketed, convenient, rewarding, timesaving, and relatively inexpensive ultra-processed foods. The energy available in the United States food supply increased much more than population needs, albeit with large inequities in food distribution and nutrition security. While most of the rise in per capita food availability during the late 20^th and early 21^st centuries in the United States resulted in increased food waste, a variety of mechanisms have been proposed by which changes in the increasingly ultra-processed food environment resulted in excess energy intake disproportionately in people genetically susceptible to obesity. As populations continue to grow, substantial investments in coordinated nutrition and agricultural research are needed to transform our current food system to one that relies less on fossil fuels, preserves biodiversity, ensures environmental health, and provides equitable access to affordable, safe, and nutritious food that reduces the prevalence of chronic diseases like obesity.

Conventional plant breeding has contributed enormously towards feeding the world and has played crucial roles in the development of modern society. The conventional method creates variation by transferring genes between or within the species. In general, these methods are more expensive and takes more time, to overcome these limitations, new technology is required. Genome editing is a powerful tool for biotechnology applications, with the capacity to alter the function of any gene. With the availability of gene information for the majority of the traits, genome editing emerged as a potential to create a new variation with the introduction of any transgene. The important genome editing tools used nowadays are ZFNs, TALEN, Pentatricopeptide repeats protein, adenine base editor, RNA interference, and CRISPR/Cas9. These tools have opened a new era for crop improvement. Due to the complex genetic architecture of most traits, it is challenging to edit genes controlling them. To overcome these challenges, genome editing provides a broader perspective. Among the above-mentioned tools, CRISPR/Cas9 is the most powerful tool for gene editing. These technologies are being used to create abiotic and biotic resistance crop varieties.

The VLSI industry is facing parasitic effects that trouble development in the nanoscale domain. However, instead of replacing the traditional MOSFET design, it would be more advantageous to apply different doping profiles and discerning which deal with specific parasitic effects the best. With a review of Gaussian doping, Uniform doping, and Delta doping profiles and analysis of the FET technology characteristics that use these doping profiles, a comparison can be made among them for integrated circuit design engineers. These doping profiles are compared based on how well they perform against non-ideal and ideal environments. Also, both digital and analog performance are measured to ensure the uniqueness of each doping profile that is present. After getting a list of benefits from each doping profile, it is derived to determine which doping profile works best against a host of parasitic effects and what type of application do these doping profiles have

Power quality is a major concern in electrical power systems. The power quality disturbances such as sags, swells, harmonic distortion and other interruptions have impact on the electrical devices and machines and in severe cases can cause serious damages. Therefore it is required to recognize and compensate all types of disturbances at an earliest to ensure normal and efficient operation of the power system. To solve these problems, many types of power devices are used. At the present time, one of those devices, Dynamic Voltage Restorer (DVR) is the most efficient and effective device used in power distribution system. In this paper, design and modeling of a new structure of multifunctional DVR for voltage correction is presented. The performance of the device under different conditions such as voltage swell, voltage sag due to symmetrical and unsymmetrical short circuit, starting of motors, and voltage distortion are described. Simulation result shows the superior capability of proposed DVR to improve power quality under different operating conditions. The proposed new DVR controller is able to detect the voltage disturbances and control the converter to inject appropriate voltages independently for each phase and compensate to load voltage through three single- phase transformers.

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

Three-phase AC electric arc furnace (EAF) is a typical non-linear load, causing many power quality problems. Most of the researches on the voltage problems of EAF mainly focus on the voltage fluctuation, and less on the transient voltage problems caused by EAF short circuit and open circuit. In this paper, the relationship between voltage and current of EAF is obtained by combining hyperbolic function and exponential function, then the white noise and chaotic circuit are added to establish the EAF model which is suitable for the study of voltage fluctuation and transient voltage. This paper analyzes the causes of the transient voltage problem of the EAF, calculates the short-circuit current, reactive impact and the influence on voltage at the point of common coupling (PCC) in the three-phase short-circuit of the EAF, and compares the calculation results with the simulation results to prove the accuracy of this model. The results show that the reactive impact of three-phase short circuit is about twice as much as that of normal operation of EAF, resulting in about 30% voltage sag at the PCC, which is very unfavorable to the power grid. This paper provides reference for transient power quality evaluation and dynamic reactive power compensation of EAF.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

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 deep Cretaceous tight sandstone in Kuqa Depression of Tarim foreland basin is an ultra-low porosity and ultra-deep gas-bearing reservoir, which is characterized by small pores, fine throats, and poor connectivity. The wireline logging responses are so complex, and especially, it is difficult to identify fluid types from resistivity logs. Based on acoustic, density, and neutron logs response differences in gas and water layers, effective fluid sensitivity factors are constructed for gas layer identification. From conventional logs, acoustic-neutron porosity difference, density-neutron porosity difference, and triple-porosity ratio are all sensitive parameters to the gas layer. From the NMR logging response mechanism, the density and NMR porosity difference, and T2 geometric mean of the movable fluid are also two sensitive parameters to the gas layer. Based on these parameters, a series of fluid typing charts are constructed and their adaptabilities are analyzed and compared. By contrast, NMR log interpretation is better, and triple-porosity comprehensive method from conventional logs is also effective when NMR logging is not available. Finally, the comprehensive fluid typing strategy by combining some methods for ultra-low porosity tight sandstone is summarized and optimized. This study is another alternative for fluid identification using non-electrical logs.

One of the key challenges of stock trading is the stock prices follow a random walk process, which is a special case of a stochastic process, and are highly sensitive to new information. A random walk process is difficult to predict in the short-term. Many linear process models that are being used to predict financial time series are structural models that provide an important decision boundary, albeit not adequately considering the correlation or causal effect of market sentiment on stock prices. This research seeks to increase the predictive capability of linear process models using the SPDR S\&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto-Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence (MACD), focusing on forecast performance. The profitability of various algorithmic trading strategies is compared based on a combination of these two indicators. This research finds that linear process models that incorporate the VIX Index do not improve the performance of algorithmic trading strategies.

A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest-neighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

Purpose: To compare the efficacy and safety of graft-free short tunnel small flap (STSF) technique with that of scleral patch graft (SPG) in Ahmed glaucoma valve (AGV) implantation. Design: Randomized clinical trial. Participants: Eighty-eyes of eighty patients with medically uncontrolled glaucoma including 41 in STSF and 39 eyes in SPG. Methods: Patients were enrolled and assigned randomly to STSF or SPG. Main Outcome Measures: tube exposure, Intraocular pressure (IOP), number of glaucoma medications, best corrected visual acuity (BCVA), surgical complications, and success rate ( defined as intraocular pressure (IOP) >5 mmHg, ≤21 mmHg, and IOP reduction ≥20% from baseline at two consecutive visits after three months, no reoperation for glaucoma). Results: only one case in SPG developed tube exposure at 1-year follow-up. The cumulative probability of success during the first year of follow-up was 70% in the STSF and 65% in SPG (P = 0.36). IOP decreased significantly from 29.6 ± 8.6 mmHg at baseline to 16.4 ± 3.6 mmHg at the final follow-up in STSF (p = 0.001). The corresponding numbers for SPG were 30.9 ± 11.2 and 15.8 ± 4.7, respectively (p = 0.001). The final IOP was comparable between both groups (p = 0.65). Mean ± standard deviation of the number of glaucoma medications was 1.8 ± 0.9 in STSF and 1.6 ± 0.9 in SPG at final follow-up (P = 0.32). Postoperative complications developed in 8 patients (19%) in STSF and 9 patients (23%) in SPG (P = 0.81). Conclusions: STSF and SPG techniques had comparable complication rate at one-year follow-up. Both techniques were comparable in terms of success rate, postoperative IOP, and glaucoma medications.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

Research background: Relationship between inflation rate, unemployment rate, interest rate and real gross domestic product per capita in Nigeria. However, there seems to be a short-run or long-run relationship among the macroeconomic variables.Purpose: This study investigated the impact of the inflation rate, unemployment rate and interest rate on real gross domestic product per capita (RGDPPC) (proxy for economic growth) and proffered recommendations towards enhancing economic growth and to reduce the distasteful effects of inflation rate, unemployment rate and interest rate in Nigeria in this present time economic challenges.Research methodology: This study applied a linear dynamic model Autoregressive Distributed Lag (ARDL) modeling technique to analyze the short-run dynamics and long-run relationship of the economic growth in Nigeria over the sample period between 1984 and 2017 using annual secondary data extracted from World Bank Development Indicators Report (last updated January 2019).Results: The empirical results showed that there was long-run relationship between inflation rate, unemployment rate and interest rate on real gross domestic product per capita (proxy for economic growth) in Nigeria. The result further revealed that only unemployment rate had a significant positive impact on real gross domestic product per capita in the long-run and inflation rate had a significant negative impact on real gross domestic product per capita in the short-run.Novelty: Therefore, the study concluded that unemployment rate and inflation rate proved to have significant impacts on economic growth in the long-run and short-run respectively. Formulation of policies to reduce unemployment through the adoption of labour concentrated technique of production, entrepreneurship development and policy to keep the inflation rate at single digit.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

Background: Extremely rare progressive diseases like Sedaghatian-type Spondylometaphyseal Dysplasia (SSMD) can be neonatally lethal and therefore go undiagnosed or are difficult to treat. Recent sequencing efforts have linked this disease to mutations in GPX4, with consequences in the resulting enzyme, glutathione peroxidase 4. This offers potential diagnostic and therapeutic avenues for those suffering from this disease, though the steps toward these treatments is often convoluted, expensive, and time-consuming. Main body: The CureGPX4 organization was developed to promote awareness of GPX4-related diseases like SSMD, as well as support research that could lead to essential therapeutics for patients. We provide an overview of the 21 published SSMD cases and have compiled additional sequencing data for four previously unpublished individuals to illustrate the genetic component of SSMD, and the role of sequencing data in diagnosis. We outline in detail the steps CureGPX4 has taken to reach milestones of team creation, disease understanding, drug repurposing, and design of future studies. Conclusion: The primary aim of this review is to provide a roadmap for therapy development for rare, ultra-rare, and difficult to diagnose diseases, as well as increase awareness of the genetic component of SSMD. This work will offer a better understanding of GPx4-related diseases, and help guide researchers, clinicians, and patients interested in other rare diseases find a path towards treatments.

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.

Objective: To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design: Systematic umbrella review of existing meta-analyses. Data Sources: MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manually searching reference lists from 2009 to June 2023. Inclusion Criteria: Systematic reviews and meta-analyses of cohort, case-control, and/or cross-sectional study designs that evaluated the associations between exposure to ultra-processed foods and adverse health outcomes in humans across the lifespan. Results: The search identified 45 unique pooled analyses, including 13 dose-response and 32 non-dose-response associations (n=9,888,373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health outcome domains spanning cardiometabolic, gastrointestinal, and respiratory conditions, cancer, mental health, and mortality. Based on a pre-specified evidence classification criteria, convincing evidence (Class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease-related mortality (RR: 1.50; 95%CIs: 1.37 to 1.63), type two diabetes (dose-response RR: 1.12; 95%CIs: 1.11 to 1.13), and depressive outcomes (HR: 1.21; 95%CIs: 1.16 to 1.28), as well as higher risks of prevalent anxiety outcomes (OR: 1.48; 95%CIs: 1.37 to 1.59) and combined common mental disorder outcomes (OR: 1.53; 95%CIs: 1.43 to 1.63). Highly suggestive (Class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all-cause mortality (RR: 1.21; 95%CIs: 1.15 to 1.27), heart disease-related mortality (dose-response HR: 1.66; 95%CIs: 1.51 to 1.84), and type two diabetes (OR: 1.40; 95%CIs: 1.23 to 1.59), together with higher risks of prevalent obesity (OR: 1.55; 95%CIs: 1.36 to 1.77), adverse sleep-related outcomes (OR: 1.41; 95%CIs: 1.24 to 1.60), and wheezing (RR: 1.40; 95%CIs: 1.27 to 1.55). Out of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (Class III-IV) and 13 were graded as no evidence (Class V). Using the GRADE framework, 22 pooled analyses were rated as "Low" quality, with 19 rated as "Very Low” and four rated as "Moderate" quality. Conclusions: Higher exposure to ultra-processed food was associated with a greater risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to evaluate the effectiveness of using population-based measures to target and reduce dietary exposure to ultra-processed foods for improved human health. Systematic Review Registration: PROSPERO CRD42023412732.

Although phylogenetic analysis shows coronaviruses (CoV) share similar genome sequences, CoVs encode different number of proteins (5 to 14), which has implication on viral pathogenicity and infection. Here, we aimed to identify (in-silico) the similarities between different members of coronavirus family. The analysis included 50 coronavirus proteomes, including SARS-CoV-2 (COVID-19), to find the variation of the number of protein functional motifs and domain in each coronavirus. For this role, we used the experimentally validated domain (motif) that known to be crucial for viral infection. Although human CoVs are classified within one genus, we found variations among them. SARS-CoV-1, SARS-CoV-2 and MERS-CoV encode different type of domains, which has implications on the molecular interactions triggered by each virus within human cells. Secondly, we used functional motifs to reconstruct the potential molecular pathways or interactions triggered by SARS-CoV-2 proteins within human cell.

Proper species identification from ancient DNA samples is a difficult task that sheds light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies [1](Santiago-Rodriguez et al. 2016) has called into question the identification of Leishmania in South America. Here, the metagenomic data filed in MG-RAST (Metagenomics RAST server) were used for a further attempt to identify members of the Trypanosomatidae family infecting these ancient remains. For this purpose, we used two metagenomic analysis tools. In the first step, data were analysed using the ultrafast metagenomic sequence classifier, based on exact alignment of k-mers (Kraken). In the second step, we used Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. We then compared the output results. These approaches highlight some interesting findings on potential infections by human pathogenic trypanosomatids in these three pre-Columbian mummies.

An innovative wireless sensor network (WSN) based on Ultra-Wide Band (UWB) technology for 3D accurate superficial monitoring of ground deformations, as landslides and subsidence, is proposed. The system has been designed and developed as part of an European Life+ project, called Wi-GIM (Wireless Sensor Network for Ground Instability Monitoring). The details of the architecture, the localization via wireless technology and data processing protocols are described. The flexibility and accuracy achieved by the UWB two-way ranging technique is analysed and compared with the traditional systems, such as robotic total stations (RTSs), Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR), highlighting the pros and cons of the UWB solution to detect the surface movements. An extensive field trial campaign allows the validation of the system and the analysis of its sensitivity to different factors (e.g., sensor nodes inter-visibility, effects of the temperature, etc.). The Wi-GIM system represents a promising solution for landslide monitoring and it can be adopted in conjunction with traditional systems or as an alternative in areas where the available resources are inadequate. The versatility, easy/fast deployment and cost-effectiveness, together with the good accuracy, make the Wi-GIM system a possible solution for municipalities that cannot afford expensive/complex systems to monitor potential landslides in their territory.

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16\% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

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.

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

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.

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

The aim of the studies was to test the hydraulic pressure losses along the length, local pressure losses, absolute and relative roughness values of the pipeline walls made of modern polymer material. The article presents the results of the experimental studies of hydraulic resistance of pipelines from modern polymer materials, the method of theoretical determination of resistance coefficients in the VALTEC ISO 21003 metal polymer pipeline, the classification and method of determining the values of local hydraulic resistance coefficients of T-Junctions are given, the dependence for determining the Darcy λ hydraulic friction coefficient associated with the value of the equivalent hydraulic roughness coefficient of the inner surface of pipes and the Reynolds number. The experimental data of the hydraulic coefficient of friction Darcy λ was compared with the known theoretical relationships of A.D. Altschul, Colbrooke for smooth pipes, Colbrooke and White, an alternative to the Colbrooke-White, Offengenden equation for low-shear pipes and pipes from polymer materials with Reynolds numbers Re>10^4. Dependence of hydraulic coefficient of friction on relative equivalent roughness of pipeline walls and Reynolds number, specific energy loss per one linear meter on total flow rate in system of hydraulically short pipeline is obtained. The empirical dependence of values of coefficients of local resistance ζ=f(Q2/Q3) from a ratio of the divided expenses of Q2/Q3 of the equal-pass T-Junction α=90 the pipeline to a branch up, on pass in the direct T-Junction and division of streams at various internal pipeline diameters of VALTEC.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

UHPC is a cement-based composite that is used in new construction and/or renovation of existing structures to increase their service life. It is a unique composite material that may be used as an alternative to concrete in harsh conditions. Following decades of research and development, a wide range of commercial UHPC compositions are now accessible across the world to fulfill the growing number of applications and demand for high-quality construction materials. Although UHPC has significant advantages over conventional concrete, its use is limited because of rigid design restrictions and excessive pricing. As a consequence, a detailed analysis of UHPC's durability qualities is necessary to provide critical information for material testing requirements and methods, as well as to widen its practical applications. The goal of this study is to learn more about UHPC and to encourage more research and use of UHPC.

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.

Detection of early stage corrosion on slender steel members is crucial for preventing buckling failures of steel structures. An active photoacoustic fiber optic sensors (FOS) system has been reported for early stage steel corrosion detection of steel plates and rebars using surface ultrasonic waves. The objective of this paper is to investigate the surface corrosion/rust detection problem on steel rods using numerically simulated surface ultrasonic waves. The finite element method (FEM) is applied in simulating the propagation of ultrasonic waves on steel rod models. Transmission mode of damage detection is adopted, in which one source (transmitter) and one sensor (receiver) are considered. In this research, radial displacements at the receiver were simulated and analyzed by short-time Fourier transform (STFT) for detecting, locating, and quantifying a surface rust located between the transmitter and the receiver. From our time domain and frequency domain analyses, it is found that the presence, location, and dimensions (length, width, and depth) of surface rust can be estimated by ultrasonic waves propagating through the surface rust.

Probiotics make up a large and growing segment of the commercial market of dietary supplements and are touted as offering a variety of human health benefits. Some of the purported positive impacts of probiotics include, but are not limited to, stabilization of the gut microbiota, prevention of gastrointestinal disorders and modulation of the host immune system. Current research suggests that the immunomodulatory effects of probiotics are strain specific and vary in mode of action. Here, we examined the immunomodulatory properties of Bacillus subtilis strain DE111 in a healthy human population. In a randomized, double blind, placebo-controlled four-week intervention, we examined peripheral blood mononuclear cells (PBMCs) at basal levels pre- and post-treatment as well as in response to stimulation with bacterial lipopolysaccharide (LPS). We observed an anti-inflammatory effect of B. subtilis, manifested as a decrease in immune cell populations within the basal state along with an increase in anti-inflammatory immune cells in response to LPS stimulation. Overall gastrointestinal health, microbiota, and circulating and fecal markers of inflammation and gut barrier function were largely unaffected by DE111 treatment. These data suggest that the novel probiotic B. subtilis DE111 may have clinical applications in modulating immune homeostasis via anti-inflammatory mechanisms.

Ultra Low Temperature Process (ULTP) involve the material cooling in temperatures close to the liquid nitrogen (-196 °C), which is different from the cold-treatment (CT) made in temperatures close to -80 °C. ULTP treatments could raise the tool steel wear resistance through microstructural change that occurs on the material, enhancing, that way, the tools and dies lifetime. To investigate the impact on the wear resistance of tool steel AISI D2, micro abrasive wear tests were carried out and an analysis based on the Archard’s law was considered, evaluating specimen mass loss by laser interferometry. Micro hardness tests, X-ray diffractometry, scanning and optical microscopy and quantitative evaluation of carbides with image analysis were carried out aiming material characterization. Micro-scale abrasion tests shown a wear coefficient k about 1.73E-7 e 2.61E-7 mm3/N.mm to the specimens that received the ULTP phase and 3.12E-7 mm3/N.mm to the conventional thermal treatment, representing a wear resistance increase of 16.3 – 44.5% to cryogenically treated specimens. The results demonstrated a micro hardness improvement, ranging from 0.9 - 4.7% for the cryogenically treated specimens, when compared to the bulk material. This effect is related, mainly, to the retained austenite transformation in martensite and to the increase in the amount of fine secondary carbides dispersed in the martensitic matrixes of cryogenically treated specimens with ULTP. The best wear resistance improvements, on micro-scale, were achieved when the ULTP step is performed immediately after tempering.

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.

It was demonstrated during the past decade that ultra-short intense laser pulse tightly focused deep inside a transparent dielectric generates the energy density in excess of several MJ/cm$^3$. Such energy concentration with extremely high heating and quenching rates leads to unusual solid-plasma-solid transformation paths overcoming kinetic barriers to formation of previously unknown high-pressure material phases, which are preserved in the surrounding pristine crystal. These results were obtained with the pulse of Gaussian shape in space and in time. Recently it was shown that the Bessel-shaped pulse could transform much larger amount of a material and allegedly create even higher energy density than that was achieved with the Gaussian (GB) pulses. Here we present a succinct review of previous results and discuss the possible routes for achieving higher energy density employing the Bessel beams (BB) and take advantage of its unique properties.

The analysis of particulate matter (PM) in dilute solutions is an important target for environmental, geochemical and biochemical researches. Here we show how the microdrop technology may allow to control, through the evaporation of small droplets, the deposition of insoluble materials dispersed in a solution on a well-defined area with a specific spatial pattern. Using this technology the superficial density of the deposited solute can be accurately controlled. In particular, it becomes possible to deposit an extremely reduced amount of insoluble material -in the order of few μg- on a confined area, thus allowing a relatively high superficial density to be reached within a limited time. In this work we quantitatively compare the microdrop technique for the preparation of particulate matter samples with the classical filtering technique. After having been optimized, the microdrop technique allows to obtain a more homogeneous deposition and limit sample consumption of a factor ~25. This method is potentially suitable for many novel applications in different scientific fields.

Objective: Lipids are secreted into milk as bilayer-coated structures: milk lipid globules (MLGs). Adipophilin (ADRP) and perilipin 3 (TIP47) are associated with MLGs in human breast milk; however, the role of these proteins in milk lipid secretion is not fully understood. The aim of the study was to investigate levels of ADRP, TIP47 and total lipid content in human breast milk, their mutual correlations and dynamics during lactation. Research Methods & Procedures: Milk samples from 22 healthy lactating women (Caucasian, Central European) were collected at five time points during lactation (1–3, 12–14, 29–30, 88–90 and 178–180 days postpartum). Mass spectrometry-based method was used for quantification of ADRP and TIP47 in the samples. The gravimetric method was used to determine milk total lipid content. Results: We observed distinctive trends in ADRP, TIP47 levels and lipid content in human breast milk during the first 6 months of lactation. We also found a significant association between lipid content and ADRP, lipid content and TIP47, and ADRP and TIP47 concentrations in breast milk at all sampling points. Moreover, we derived an equation for estimating the mean lipid content of milk based on ADRP concentration in human breast milk. Conclusions: A mass spectrometry-based method was developed for quantifying ADRP and TIP47 in human breast milk. Strong mutual correlations were found between ADRP, TIP47 and total lipid content in human breast milk.

The search for new materials that could improve the energy density of Li-ion batteries (LIB) is one of today’s most challenging issues. Recently, cation-disordered lithium-excess metal oxides have emerged as a promising new class of cathode materials for LIB, due to their high reversible capacities and nice structural stability. However, a full structural model of the Li-transition metal (TM) sharing sublattice and the origin of short range ordering (SRO) of cation ions requires further investigation. In this work, we put forward a Monte Carlo strategy of building a cation-disordered rocksalt material supercell model. The cation ions of Li_1.0Ti_0.5Ni_0.5O₂ (LTNO) are placed at the FCC sublattice sites with the constraint of Pauling’s electroneutrality rule, instead of a random way. This constraint causes the Li-Ti and Ni-Ni clustering (the cation short range ordering). Based on this model, we discussed the relationship between the short range ordering, the local distorting, the theoretic capacity and the order-disorder strengths. A unified understanding of these factors in cation-disordered materials may enable a better design of disordered-electrode materials with high capacity and high energy density.

This study analyses the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost (SRMC) curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation capability and the water shadow price. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves.

The increased consumption of high fat-containing foods has been linked to the prevalence of obesity and abnormal metabolic syndromes. Rhizopus oligosporus, a fungus in the family Mucoraceae, is widely used as a starter for homemade tempeh. Although R. oligosporus can prevent the growth of other microorganisms, it grows well with lactic acid bacteria (LAB). Lactobacillus plantarum can produce β-glucosidase, which catalyzes the hydrolysis of glucoside isoflavones into aglycones (with greater bioavailability). Therefore, the development of a soybean-based functional food by the co-inoculation of R. oligosporus and L. plantarum is a promising approach to increase the bioactivity of tempeh. In this study, the ameliorative effect of L. plantarum in soy tempeh on abnormal carbohydrate metabolism in high-fat diet (HFD)-induced hyperglycemic rats was evaluated. The co-incubation of L. plantarum with R. oligosporus during soy tempeh fermentation reduced the homeostatic model assessment of insulin resistance, HbA1c, serum glucose, total cholesterol, triglyceride, free fatty acid, insulin, and low-density lipoprotein contents and significantly increased the high-density lipoprotein content in HFD rats. It also increased the LAB counts as well as the bile acid, cholesterol, triglyceride, and short-chain fatty acid contents in the feces of HFD rats. Our results suggested that the modulation of serum glucose and lipid levels by LAB occurs via alterations in the internal microbiota, leading to the inhibition of cholesterol synthesis and promotion of lipolysis. Tempeh, produced with both L. plantarum and R. oligosporus, may be a beneficial dietary supplement for individuals with abnormal carbohydrate metabolism.

Much evidence has accumulated over the past decade in favor of a significant association between dysbiosis, neuroinflammation and neurodegeneration. Presently, the pathogenetic mechanisms triggered by molecules produced by the altered microbiota, also responsible for the onset and evolution of Alzheimer Disease will be described. Our attention will be focused on the role of astrocytes and microglia. Numerous studies have progressively demonstrated how these glial cells are important to ensure an adequate environment for neuronal activity in healthy conditions. Furthermore, it is becoming evident how both cell types can mediate the onset of neuroinflammation and lead to neurodegeneration when subjected to pathological stimuli. Based on this information, the role of major microbiota products in shifting the activation profiles of astrocytes and microglia from a healthy to a diseased state will be discussed focussing on Alzheimer Disease pathogenesis.

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

Under the big unified framework of quantum gravitational field and quantum repulsive field, the earlier in this series of work has comprehensively expanded the general relativity and loop quantum ring gravitation theory, set up the quantum repulsive field equation corresponding to the quantum gravitational field equation, and then established the quantum hedge-unified field equation describing the interaction of quantum gravitational field and quantum repulsive field; On this basis, the quantum repulsive universe equation corresponding to the quantum gravitational universe equation has be established, thus a set of new equations describing the expansive universe model under the condition of fully considering the hedge effect of quantum gravitational field and quantum repulsion field, which can be called the expansive universe equation of quantum hedge-unified field theory, has be established. Now, let's go deep into the arche-unified physics behind the creation of the universe. Although all kinds of inflationary universe models can solve cosmological problems to a certain extent, there is no natural and reasonable analysis and explanation for all cosmological and physical consequences. For example, it is impossible to predict a large tensor-scale ratio and a relatively large spectral index run at the same time. At present, string theory, membrane theory, loop quantum gravity theory, super-gravitational holography principle and M theory, which are the theoretical basis of cosmology, are basically established by simplification in the case of breaking away from the extreme background conditions of the universe. While cosmology, which is deficient in nature, cannot provide strong support for particle physics to further advance to a wider field and a deeper level. Due to the lack of new ideas, the construction of existing theoretical models is difficult. Therefore, the existing research can only be further promoted by new astronomical observation. Unlike existing studies, we concentrate on the emergence of the big rip-rebound clusters of quantum chaotic-reticulate distribution between the outwards-push of the universe as a whole (related to pseudo-vacuum energy, Higgs field, dark energy, etc.) and the inwards-pull of the universe in local scale (related to dark matter, quantum gravity and initial gravity separated from high-dimensional supergravity, and the initial strong force separated from the grand unified force subsequently). On the one hand, we find a great impetus to make the inflation of the universe into firstly-imposed suppression, then-imposed acceleration and finally-imposed deceleration, which really explains why the universe expands at a critical rate and has the same temperature in different regions, on the other hand, we find a fundamental unit of matter and its emerging clusters evolving in the big bang, inflation and expansion of the universe accompanied by the great rip-rebound clusters of quantum chaos-reticulate distribution, which can be reduced to an arche-pulsator of neither punctiform particle nor linear string. From these two aspects, it can been seen that the evolution of the early universe is much more complicated than people originally thought. The complexity of the super inflation of the universe is mainly reflected in the big rip-rebound cluster emerging behind the gravitational disturbance (the pressure and tensor disturbance related to dark matter), the repulsive disturbance (the pressure and scalar disturbance related to dark energy), the cosmological disturbance (related to the interaction between high-dimensional supergravity and high-dimensional superrepulsion). This is a system of nonlinear stochastic differential dynamics, or a quantum statistical physical process of nonlinear nonequilibrium state. In the new research paradigms established in this series, the arche-conjugation between the high-dimensional supergravity and the high-dimensional superrepulsion, as well as between the quantum gravity and the quantum repulsion, is higher than the supersymmetry, and then becomes the core concept of the new theory. In this series, cluster-inflating configuration, spin network space and cosmic paradigm vectors are proposed, so the model of the super-inflated universe with quantum chaos-reticulate distribution emerged under the great impetus. The new model reveals that the unified inflation of the universe as a whole and the local inflation of the universe emerge together in the great impetus of the big rip-rebound cluster of quantum chaos-reticulate distribution. As a spin network model variable, the spatiotemporal mode variable is a vector composed of a series of variables, which variables include: dimension, curvature, range, expansion speed, dynamics, quantization, chaotic-reticulate distribution (inhomogeneity, concentration, fluctuation), synergy, and so on. Between particle physics and cosmology, this series of papers proposes the cosmic quantum evolution mode, the physical-sphere distributing configuration and the interaction situation variables, organizes the concepts, variables and models of these three levels, and establishes a systematic super-synergy paradigm. In this paradigm, the general Langevan equation and the general Fokker-Planck equation are established. For the quantum cosmic-sphere, a series of trend parameters are introduced to establish the dynamics of quantum gravitational mode and its master equation, and then to establish the operator distribution function and its motion equation of the action mode. For the Tachyon of upheaval in the rip-rebound-inflating of the universe, the hypothesis on the bifurcate-chaos wave of quantum is given, the quantum theory of the early photons in upheaval is set up, and the analysis on the particles of the ultrahigh energy cosmic ray from the γ ray burst is made. Finally, using Wigner-Ville distribution as a nonlinear time-frequency distribution, we establish the ultra-synergistic field equation of quantum-classic unifying action-sphere.

This pilot study of Streptococcus mutans ATCC 35668 grown in media with and without polyols (erythritol) measured the resultant metabolites, including the short chain fatty acids by using head space analysis. Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing no polyols or either erythritol or xylitol and Streptococcus mutans (ATCC 35668) was grown aerobically. After 48 hours of growth the supernatant were harvested and centrifuged to pellet bacteria. Supernatants were removed from bacterial pellets then submitted for Short Chain Fatty Acid (SCFA) analysis with an Agilent Technologies (Santa Clara, CA 95051) system configured from three components, a 5973 mass selective detector, a 6890N gas chromatographer, and a 7697A headspace sampler. Streptococcus mutans growing in Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose but containing no polyols produced the following short chain fatty acids: methyl isovalerate, acetic acid, propionic acid, butanoic acid, pentanoic acid, ethyl butaric acid, 4-methylvaleric acid, hexanoic acid. When the Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing erythritol was used as media for this Streptococcus mutans strain, the following were produced: ethanol, acetoin, and acetic acid. Our results would suggest that constituents of the media may affect the bacterial metabolite production.

Statistics of languages are calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G – the semantic index G_C, proportional to the number of characters per word, and the syntactic index G_F, proportional to the reciprocal of the number of words per sentence −, G_F is dominant because G_C is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y=mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is, in author’s opinion, that between the short−term memory capacity, described by Miller’s “7∓2 law”, and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval is spread in the same of Miller’s law, and the time interval is spread in the same range of short−term memory response times. The connection between the word interval (and time interval) and short−term memory appears, at least empirically, justified and natural, and should further investigated. Technical and scientific writings (papers, essays etc.) ask more to their readers. A preliminary investigation of these texts shows clear differences: words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as Greek or Latin, could bring us a flavor of the short term−memory features of these ancient readers.

Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.

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.

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.

Dementia patients express a set of problematic and deteriorating symptoms, along self-care dependency. Overtime, the mental health of family caregivers of persons with dementia may suffer, putting them at a high risk for psychopathology, which may be associated with endangered wellbeing of demented people. This cross-sectional study examined the psychometric properties of the Depression Anxiety Stress Scale 8-items (DASS-8), DASS-12, DASS-21 in a convenient sample of 571 caregivers from northern Italy and southern Switzerland (Mean age = 53, SD = 12, range = 24–89 years). A bifactor structure of the three measures had the best fit; some items of the DASS-12/DASS-21 failed to load on their domain-specific factors. The three-factor structure was invariant across various groups (e.g., gender, education, etc.), expressed adequate reliability and convergent validity, and had strong positive correlation with the 3-item UCLA Loneliness Scale (UCLALS3). Dementia type had no effect on distress scores, which were higher among females, adult children caregivers, those caring for dependent patients, and those who received help with care. For 54.9 and 38.8% of the latter, care was provided by relatives and health professionals, respectively. Since the DASS-8 expresses adequate psychometrics comparable with the DASS-21, it may be used as a brief measure of distress in this population.

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.

The modern diet, which consists of food produced with high level of industrial processing, is associated with an increased risk of the development of lifestyle diseases. Current nutritional science is, however, focused on chemical composition of food, and not on the type and degree of processing used during the food production. Here, we take a fresh perspective on the relationship between the extent and type of food processing, and evaluate its impact on consumer health. We argue that the preference for consumption of minimally processed foods, and restriction of ultra-processed foods should be an essential part of a healthy lifestyle, disease prevention, and even treatment. We also present a simple, user-friendly consumer guide, which is intended to be used as a practical, ready-to-go identifier of ultra-processed foods.

We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.

Glycosyl inositol phospho ceramides (GIPCs) are the major sphingolipids on earth as they account for a considerable fraction of the total lipids in plants and fungi which in turn represent a large portion of the biomass on earth. Despite their obvious importance, GIPC analysis remains challenging due to the lack of commercial standards and automated annotation software. In this work, we introduce a novel GIPC glycolipidomics workflow based on reversed-phase ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry. For the first time, automated GIPC assignment was performed using the open-source software Lipid Data Analyzer based on platform-independent decision rules. Four different plant samples (salad, spinach, raspberry, strawberry) were analyzed and revealed 64 GIPCs based on accurate mass, characteristic MS2 fragments and matching retention times. Relative quantification using lactosyl ceramide for internal standardization revealed GIPC t18:1/h24:0 as the most abundant species in all plants. Depending on the plant sample, GIPCs contained mainly amine, N-acetylamine or hydroxyl residues. Most GIPCs revealed a Hex-HexA-IPC core and contained a ceramide part with a trihydroxylated t18:0 or t18:1 long chain base and hydroxylated fatty acid chains ranging from 16 to 26 carbon atoms in length (h16:0 – h26:0). Interestingly, six GIPCs containing t18:2 were observed in raspberry, which was not reported so far. The presented workflow supports the characterization of different plant samples by automatic GIPC assignment potentially leading to the identification of new GIPCs. For the first time, automated high‑throughput profiling of these complex glycolipids is possible by liquid chromatography-high-resolution mass spectrometry and subsequent automated glycolipid annotation based on decision rules.

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

For the numerical simulation of components made of short fiber-reinforced composites the correct prediction of the deformation including the elastic and plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches the information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields. In this study numerical simulations of tensile test specimens are conducted utilizing a finite deformation elastic-ideal plastic material model. A selection of the material parameters covering the elastic and plastic domain are represented by cross-correlated second-order Gaussian random fields to incorporate the probabilistic nature of the material parameters. To validate the modeling approach tensile tests until failure are carried out experimentally, that confirm the assumption of spatially distributed material behavior in both the elastic and plastic domain. Since the correlation lengths of the random fields cannot be determined by pure analytic treatments, additionally numerical simulations are performed for different values of the correlation length. The numerical simulations endorse the influence of the correlation length on the overall behavior. For a correlation length of 5mm a good conformity with the experimental results is obtained. Therefore, it is concluded, that the presented modeling approach is suitable to predict the elastic and plastic deformation of a set of tensile test specimens made of short fiber-reinforced composite sufficiently.

The purpose of this study was to conduct a meta-analysis that shows the influence of fiber on ultimate compressive strength and tensile strength of ultra-high performance concrete. The internet scholarly search engines and ScienceDirect article references were used to illustrate the papers concerning the experimental investigations of mechanical properties of ultra-high strength concrete with and without fiber with clearly, completely and comparative raw data. The normal concrete test results were dismissed from this search. Seven trials were identified based on the adopted inclusion and exclusion criteria above. The meta-analysis based on standardized mean difference was carried out on the basis of a fixed-effects model for the major outcomes of the ultimate compressive and tensile properties of ultra-high performance concrete. A total of 888 test specimens were enrolled in these seven trials. The combined analysis yielded a sign of a significant improvement in ultimate compressive strength and tensile strength of ultra-high strength concrete with fiber addition of 2% by concrete volume. The summary effect size of ultimate compressive strength was 2.34 while a more improvement in term of tensile strength with effect size of 2.64. By addition fiber of 2% provides a significant benefit in mechanical properties of ultra-high performance concrete.

UHPC is a type of cement-based composite used in new construction and/or rehabilitation of existing buildings to extend service life. It is a novel composite material that can serve as an alternative to concrete construction in hostile climates. Following decades of study and production, a diverse variety of commercial UHPC compositions are now available globally to meet the rising number of applicants and demand for high-quality building materials. Although UHPC offers major benefits over normal concrete, its utilization is restricted due to restrictive design rules and exorbitant costs. As a result, a thorough examination of the durability properties of UHPC is required to give important information for material testing requirements and processes, as well as to broaden its practical uses. This report is aimed at increasing basic understanding of UHPC and supporting more UHPC research and applications.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

Leucokinins (LKs) constitute a family of neuropeptides identified in numerous insects and many other invertebrates. The LKs act on G-protein coupled receptors that display only distant relations to other known receptors. In adult Drosophila, 26 neurons/neurosecretory cells of three main types express LK. The four brain interneurons are of two types, and these are implicated in several important functions in the fly’s behavior and physiology, including feeding, sleep-metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. The 22 neurosecretory cells (ABLKs) of the abdominal neuromeres coexpress LK and a diuretic hormone (DH44), and together these regulate water and ion homeostasis and associated stress, as well as food intake. In Drosophila larvae, LK neurons modulate locomotion, escape responses, and aspects of ecdysis behavior. A set of lateral neurosecretory cells, ALKs, in the brain express LK in larvae, but inconsistently so in adults. These ALKs coexpress three other neuropeptides and regulate water and ion homeostasis, feeding and drinking, but the specific role of LK is not yet known. This review summarizes Drosophila data on embryonic lineages of LK neurons, functional roles of individual LK neuron types, interactions with other peptidergic systems, and orchestrating functions of LK.

This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

Ultra-wideband wavelength division multiplexed networks enable operators to use more effectively the bandwidth offered by a single fiber pair and thus make significant savings, both in operational and capital expenditures. The main objective of this study is to minimize optical node resources, such as transponders, multiplexers and wavelength selective switches, needed to provide and maintain high quality of network services, in ultra-wideband wavelength division multiplexed networks, at low cost. A model based on integer programming is proposed, which includes a detailed description of optical network nodal resources. The developed optimization tools are used to study the ultra-wideband wavelength division multiplexed network performance when compared with the traditional C-band wavelength division multiplexed networks. The analysis is carried out for realistic networks of different dimensions and traffic demand sets.

As a part of the proposed article, the authors presented comprehensive data analysis for movement data that comes from a positioning system based on ultra-wide band (UWB) technology. For purpose of this article, a test was carried out during which the car equipped with cruise control overcame the given path at a speed from 10 km/h to 60 km/h. The obtained motion models (information about position) have been filtered through a series of filters - from fundamentals filters with a variable window (median, moving average, Savitzky-Golay filter), through more complex ones like the Wiener or Kalman filter. As a result, the authors proposed a form of data analysis and filtration depending on the speed of the moving object. In addition, the maximum accuracy that can be obtained for a given traffic model was also determined. The whole research proves that it is possible to use a system based on UWB technology in positioning objects for urban applications - smart city, in industry 4.0 applications as well as for positioning autonomous vehicles in urban applications, such as well as on highways to maintain cohesion of convoys vehicles.

Extensive limb lengthening (ELL) was completed in 75 patients: 66 achondroplasia and 9 hypochondroplasia. The average lengthening was 27cm for achondroplasia (12-40cm) and 17cm for hypochondroplasia (range 10-25cm). There were 48 females and 27 males. Lengthening was done either by 2-segment (14 patients; both tibias and/or both femurs) or by serial 4-segment lengthenings (64 patients; both femurs and tibias same time). Most patients also had bilateral humeral lengthening. Lengthenings were either juvenile-onset (31), adolescent-onset (38) or adult-onset (6). The average age at final follow-up was 26 years old (range 17-43 years). There were few permanent sequelae of complications. The most serious was one paraparesis. All patients returned to activities of normal living and only one was made worse by the surgery (paraparesis). This is the first study to show that ELL can lead to increase of height into the normal height range. Previous studies showed mean increases of height of up to 20cm, while this study consistently showed an average increase of 30 cm (range 15-40cm) for juvenile-onset and increase of 26cm (range 15-30cm) for adolescent-onset. This results in lower normal height at skeletal maturity for males and females. The adult-onset had a mean increase of 16.8 (range 12-22cm). This long-term follow-up study shows ELL can be done safely even with large lengthenings and that 4-segment lengthening may offer advantages over 2-segment lengthening. While the majority of cases were performed using external fixation, implantable limb lengthening promises to be an excellent alternative and perhaps an improvement.

Background: In this secondary analysis of the Supporting the Modification of Lifestyle In Lowered Emotional States (SMILES) randomised controlled trial, we investigated if the beneficial effects of a dietary intervention on clinical depression were driven, in part, by reducing the consumption of foods classified as ultra-processed.Methods: The SMILES trial enrolled 67 adults with major depressive disorder, randomly assigning them to either a 12-week modified Mediterranean dietary intervention or a social support control. Our analysis included 44 participants with non-missing dietary data and at least one valid Montgomery–Åsberg Depression Rating Scale (MADRS) assessment at 12 weeks of follow-up. The Nova food classification system was used to estimate the proportion of ultra-processed foods in the overall diet (percentage of grams) based on data from seven-day food diaries. We fitted linear regression models under blinded conditions to determine whether ultra-processed food intake-change from baseline to 12 weeks moderated the dietary intervention effects on depressive symptoms. We estimated mean differences in depressive symptoms along with ninety-five per cent confidence intervals (95%CIs).Results: For participants in the dietary intervention, there was an additional 2.5-point improvement in MADRS scores for each 10% reduction in the dietary share of ultra-processed foods compared to participants in the control group (between-group mean differences in depressive symptoms: −2.46, 95%CIs −4.71 to −0.20, p=0.039, η _p ² of 0.10).Conclusions: These preliminary findings suggest that the therapeutic benefit of a dietary intervention for depression may stem, at least in part, from reductions in the dietary share of ultra-processed foods.

The increasing use of energy resources recovered from the subsurface environments and the resulting carbon imbalance in the environment has motivated the need to develop thermodynamically downhill pathways to convert and store CO₂ as water insoluble calcium or magnesium carbonates. While previous studies extensively explored aqueous routes to produce calcium and magnesium carbonates from CO₂, there has been a limited scientific understanding of the phase evolution and textural changes during the direct gas-solid conversion routes to produce calcium carbonate from calcium hydroxide, which is one of the abundant constituents of alkaline industrial residues. With increasing interest in developing integrated pathways for capturing, converting and storing CO₂ from dilute flue gases, understanding the compositions of the product phases as they evolve is essential for evaluating the efficacy of a given processing route. Therefore, in this study, we investigate the phase evolution and the corresponding textural changes as calcium hydroxide is converted to calcium carbonate under the continuous flow of CO₂ at an ambient pressure of 1 atm and on heating continuously from 30 °C to 500 °C using in-operando wide angle X-ray scattering (WAXS), small angle X-ray scattering (SAXS), and ultra-small angle X-ray scattering (USAXS) measurements.

This paper describes the packaging models that are fundamental for the design of ultra-high-performance concrete (UHPC), and their evolution. They are divided into two large groups: continuous and discrete models. The latter are those that provide the best answer in obtaining an adequate simulation of the packing of the particles up to nanometric size. This includes the interaction among the particles by means of loosening and wall coefficients, allowing a simulation of the virtual and real compactness of such particles. In addition, a relationship between virtual and real compactness is obtained, through the compaction index, which may simulate the energy of compaction that the particles undergo in their placement in the mold. The use of last-generation additives allows such models to be implemented with water-cement (w/c) ratios close to 0.18. However, the premise of maximum packing as a basic pillar for the production of UHPC should not be the only one. The cement hydration process affected by nanoadditions and the ensuing effectiveness in the properties in both fresh and hardened state according to the respective percentages in the mixture should also be studied. An adequate ratio and proportion of these additions may lead to an obtaining of better results even with lower levels of compactness.