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.

In this era of technology, programming has become more significant than ever before. Python and C++ are both widely used programming languages. Python, the most popular programming language in today’s world, is a high-level object-oriented language whereas C++, the language behind most operating systems, is a low-level object-oriented language. In this paper, we present a comparative study of Python and C++. This paper discusses the introduction to these languages, their memory management techniques, and the reasons behind their program execution speed. Furthermore, we analyzed the execution time and memory used by multiple algorithms in both the languages with best, average, and worst cases. They are also compared with respect to the benefits and issues related to them. Results indicate that C++ is faster than Python in execution speed but Python serves as a better language for beginners due to its simplicity. Moreover, for the best results, the language should be selected according to the type of project.

Long-haul COVID-19 illness first gained widespread recognition among social support groups and later in scientific and medical communities. This illness is mysterious as it affects COVID-19 survivors at all levels of disease severity, even younger adults and children. While the precise definition may be lacking, the defining symptoms are fatigue, dyspnea, and headache that last for months after hospital discharge. The less typical symptoms may include cognitive impairments, chest and joint pains, myalgia, smell and taste dysfunctions, cough, mood changes, and gastrointestinal and cardiac issues. Presently, there is limited literature discussing the possible pathophysiology, risk factors, and treatments in long-haul COVID-19, which the current review aims to address. In brief, long-haul COVID-19 may be driven by long-term lung and brain damage and unresolved inflammation from multiple sources. The associated risk factors may include female sex, more than five early symptoms, early dyspnea, and specific biomarkers like D-dimer. While only rehabilitation training has been useful for long-haul COVID-19, therapeutics repurposed from mast cell activation syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, and pulmonary fibrosis also hold potential. In sum, this review hopes to provide the current understanding of what is known about long-haul COVID-19.

In the wake of the Covid-19 crisis, a need has arisen to prevent and treat two related conditions, Covid vaccine injury and long Covid, both of which have a significant vascular component. Therefore, the management of these conditions require the development of strategies to prevent or dissolve blood clots and restore circulatory health. This review summarizes the evidence on strategies that can be applied to treat both long and vaccine injuries based on similar mechanisms of action.

Background The World Health Organization has recently recognized Long COVID, calling the international medical community to strengthen research and comprehensive care of patients with this condition. However, if Long COVID pertains to children as well is not yet clear. Methods An anonymous, online survey was developed by an organization of parents of children suffering from persisting symptoms since initial infection. Parents were asked to report signs and symptoms, physical activity and mental health issues. Only children with symptoms persisting for more than four weeks were included. Results 510 children were included (56.3% females) infected between January 2020 and January 2021. At their initial COVID-19 infection, 22 (4.3%) children were hospitalized. Overall, children had persisting COVID-19 for a mean of 8.2 months (SD 3.9). Most frequent symptoms were: Tiredness and weakness (444 patients, 87.1% of sample), Fatigue (410, 80.4%), Headache (401, 78.6%), Abdominal pain (387, 75.9%), Muscle and joint pain (309, 60.6%), Post-exertional malaise (274, 53.7%), rash (267, 52.4%). 484 (94.9%) children had had at least four symptoms. 129 (25.3%) children have suffered constant COVID-19 infection symptoms, 252 (49.4%) have had periods of apparent recovery and then symptoms returning, and 97 (19.0%) had a prolonged period of wellness followed by symptoms. Only 51 (10.0%) children have returned to previous levels of physical activity. Parents reported a significant prevalence of Neuropsychiatric symptoms. Conclusions Our study provides further evidence on Long COVID in children. Symptoms like fatigue, headache, muscle and joint pain, rashes and heart palpitations, and mental health issues like lack of concentration and short memory problems, were particularly frequent and confirm previous observations, suggesting that they may characterize this condition. A better comprehension of Long COVID is urgently needed..

RNA polymerase II (RNAPII) frequently transcribes non-protein coding DNA sequences in eukaryotic genomes into long non-coding RNA (lncRNA). Here, we focus on the impact of the act of lncRNA transcription on nearby functional DNA units. Distinct molecular mechanisms linked to the position of lncRNA relative to the coding gene illustrate how non-coding transcription controls gene expression. We review the biological significance of the act of lncRNA transcription on DNA processing, highlighting common themes, such as mediating cellular responses to environmental changes. This review presents the background in chromatin signaling to appreciate examples in different organisms where we can interpret functions of non-coding DNA through the act of RNAPII transcription.

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.

Using recent developments in econometrics and computational statistics we consider the estimation of the instantaneous rate of asset return process when the underlying Data Generating Mechanism (DGM) is an Ornstein-Uhlenbeck process, driven by fractional noise, and sampled at fixed intervals of length h. To address the problem we adopt throughout the paper an exact discretization approach. This enable us to exploit the fact that a flow sampling scheme arises naturally when observing the DGM. For, while the instantaneous rate of return process is unobservable at points in time, its time integral over successive observations is observable since it equals the increment of log-prices. Exact discretization delivers an ARIMA(1,1,1) model for log-prices with a fractional driving noise. Building on the resulting exact discretization formulae and covariance function, a new Markov Chain Monte Carlo (MCMC) scheme is proposed and we examine the properties of both the time and frequency domain likelihoods / posteriors through Monte Carlo. For the exact discrete model we adopt a general sampling interval of length h. This allow us to determine the optimal choice of h independent of the sample size. An empirical application using high frequency stock price data is presented showing the relevance of aggregation over time issues in modelling asset prices.

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.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology and the role of interventions to upregulate autophagy, including compounds like spermidine, resveratrol, rapamycin, and metformin. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention for spike protein related diseases; with broad implications for human health. This review presents the therapeutic possibility of using autophagy to treat spike protein related diseases, and details the interventions to deploy this therapeutic modality.

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology. Additionally, we discuss factors that affect fasting, such as duration, type (dry vs. water), and the role of specific compounds like spermidine, resveratrol, rapamycin, and metformin. Furthermore, we analyse the interactions between fasting and other practices such as exercise, and highlight important considerations regarding participant characteristics, including pregnancy, breastfeeding, medication interactions, and metabolic disorders. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention with broad implications for human health.

This article focuses on obtaining the analytical solutions for parabolic Volterra integro- differential equations in d-dimensional with different types frictional memory kernel. Based on theories of Laplace transform, Fourier transform, the properties of Fox-H function and convolution theorem, analytical solutions of the equations in the infinite domain are derived under three frictional memory kernel functions respectively. The analytical solutions are expressed by infinite series, the generalized multi-parameter Mittag-Leffler function, Fox-H function and convolution form of Fourier transform. In addition, the graphical representations of the analytical solution under different parameters are given for one-dimensional parabolic Volterra integro-differential equation with power-law memory kernel. It can be seen that the solution curves subject to Gaussian decay at any given moment.

Asthma is a chronic inflammatory airway disease induced by many environmental factors. The inhalation of allergens and pollutants promote the reactive oxygen species (ROS) production leading to airway inflammation, hyper-responsiveness and remodeling in allergic asthma. The effects of asthma medications on ROS production are unclear. The present study investigated the anti-ROS effects of current asthma medications including inhaled corticosteroid (ICS; budesonide and fluticasone), leukotriene receptor antagonist (LTRA; montelukast), long acting β2 agonists (LABAs; salmeterol and formoterol) and a new extra-LABA (indacaterol). The human monocyte cell line THP-1 cells were pre-treated with different concentrations of the asthma medications at different time-points after hydrogen peroxide (H₂O₂) stimulation. H₂O₂ production was measured with DCFH-DA by flow cytometry. Montelukast, fluticasone and salmeterol suppressed H₂O₂-induced ROS production. Indacaterol enhanced H₂O₂-induced ROS production. Budesonide and formoterol alone had no anti-ROS effects, but the combination of these two drugs significantly suppressed H₂O₂-induced ROS production. Different asthma medications have different anti-ROS effects on monocytes. The combination therapy with LABA and ICS seemed not be the only choice for asthma control. Montelukast may be also a good supplemental treatment for the poorly-controlled asthma because of its powerful anti-ROS effects. Our findings provide a novel therapeutic view in asthma.

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

Background: Clinical trails have revealed that patients with depression generally accompanied with learning and memory impairment, which critically impact on individual’s health and development. Danggui-Shaoyao-San (DSS), a famous Chinese complex prescription, significantly overcame depression and relieved cognitive disorder based on previous research and publication. However, its effectsand potential mechanism against chronic restraint stress (CRS) remained unknown. Methods: CRS animal model was established and mice were divided to six groups while they were oral administrated with Danggui-Shaoyao-San at doses of 1.25, 2.5 and 5 g/kg for 14 days. Emotional and cognitive performances were detected by behavior tests, meanwhile neural plasticity and its molecular mechanism were examined by Dil staining, western blot and immunofluorescence. Results: DSS treatment dose-dependently improved locomotion ability in open filed test, overcame depression behavior in forced swimming test and elevated plus maze test, enhanced learning and memory ability in Morris water maze test. CRS decreased number of total spines and mushroom spines, while DSS treatment dose-dependently restored these by Dil staining. Expression of BDNF and GluR1 were significantly down-regulated in CRS group, which were significantly normalized by DSS. Conclusions: DSS treatment dose-dependently reversed CRS-induced cognitive impairments by inducing structural remodeling of neurons.

In the case of long-stator linear drive, unlike rotative drives for which speed or position sensors are a single unit attached to the shaft, these sensors extend along the guideway. The position signals transmitted from maglev vehicle can’t meet the need of the real-time propulsion control in the on-ground inverter power substations. In this paper the design of the propulsion inverter control system with a position estimator for driving long-stator synchronous motor in high speed maglev train is proposed. The experiments have been carried out at the 150m long guideway in O-song test track. To investigate the performance of the position estimator, the propulsion control system with and without the position estimator are compared. The result confirms that the proposed strategy can meet the dynamic property need of propulsion inverter control system for driving long-stator linear synchronous motors

The present study aims to develop an efficient numerical method for computing the diffraction and radiation of water waves with horizontal long cylindrical structures, such as floating breakwaters. A higher-order scheme is used to discretize geometry of the structure as well as the relevant physical quantities. As the kernel of this method, Wehausen’s free-surface Green function is calculated by a newly-developed Gauss-Kronrod adaptive quadrature algorithm after elimination of its Cauchy-type singularities. To improve computational efficiency, a Chebyshev approximation approach is applied to a fast calculation of the Green function that needs evaluation thousands of times. In addition, OpenMP parallel technique is used to the formation of influence coefficient matrix, which significantly reduces CPU time. Finally, computations are performed on wave exciting forces and hydrodynamic coefficients for the long cylindrical structures, either floating or submerged. Comparison with other numerical and analytical methods demonstrates good performance of the present method.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

Heavy caffeine consumption is associated with adverse health effects. The effects of moderate and high doses of caffeine mixed with drinking water on the motor coordination, learning and memory and the social behavior in mice were studied in mice. Animals were divided into 3 groups: control group, moderate dose group (Ac MD) and high dose group (Ac HD). The animals were tested after 7 days of caffeine administration. Rota rod test for motor coordination showed that the mice of the moderate dose group could stay more time on the rotating rod before they fall than the control group and the high dose group. Water maze test for learning and memory showed better performance of mice receiving moderate dose of caffeine compared to the other groups. Animals that were administered moderate as well as high doses of caffeine showed no sociability and no preference for social novelty in the three-chamber test used to test the social behavior. In elevated plus maze, control animals showed no anxiety- like behavior while mice administered with caffeine were both showing anxiety-like behaviors. We concluded that acute administration of moderate dose of caffeine to mice could enhance their spatial memory and motor coordination. High doses however caused defects in memory and learning. The social behavior as the level of anxiety and sociability was affected negatively by moderate as well as high dose caffeine administration.

In this paper, we study a one-dimensional Bresse-Cattaneo system with infinite memories and time-dependent delay term (the coefficient of which is not necessarily positive) in the internal feedbacks. First, it is proved that the system is well-posed by means of the Hille-Yosida theorem under suitable assumptions on the relaxation functions. Then, without any restriction on the speeds of wave propagations, we establish the exponential or general decay result by introducing suitable energy and Lyapunov functionals.

Understanding brain function is one of the most important problems in human history. At present, there is no concrete theory for how the brain works. Here, a theory is presented that provides a detailed mechanistic biological account of the brain's capacities, including motor control, functional states, language, and thinking. Brain function is managed by a well-defined response (R) process that is generally similar to the process underlying the immune system. The R process is strongly reflected in the brain's anatomy, physiology, and external interactions. Different R process stages are supported by distinct excitatory networks located in different cortical layers, hippocampal fields, and bagal ganglia paths, by distinct coordination networks comprised of GABAergic interneurons, and by distinct molecular agents. The roles of norepinephrine, serotonin, dopamine and acetylcholine is to promote the alert, planning, goal-setting and execution R process modes, respectively. Opioids and oxytocin promote termination by success, failure, fight or run, while glucocorticoids and cannabinoids suppress acute responses to protect cells. The R process has two instances occurring at different time scales. The millisecond-scale Quax process implements the execution of hierarchical sequences of movements and thoughts, in which the selection of the next action is determined via interaction between top-down predictions and sensory inputs. The slower Need process controls the satisfaction of internal and external needs. The theory differs from the existing standard accounts in many of the major topics (e.g., the basal ganglia, dopamine, language), and shows how cognition results from biological processes.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

The purposes of this study were to identify clinician’s characteristics associated with higher prescription rates of long-acting injectable (LAI) antipsychotics, as well as the information sources influencing medical decision-making about treatment of schizophrenia. We surveyed 202 psychiatrists during 6 regional French conferences (Bordeaux, Lyon, Marseille, Nice, Paris, Strasbourg). Data on the characteristics of practice, prescription rates of antipsychotic and information sources about their clinical decisions were collected. Most of psychiatrists used second-generation antipsychotic (SGA), and preferentially an oral formulation, in the treatment of schizophrenia. SGA LAI was prescribed to 30.4% of schizophrenic patients. The duration and the type of practice did not influence the class or formulation of antipsychotics used. The clinicians following the higher percentage of schizophrenic patients were associated with the higher use of LAI antipsychotics and the lower use of oral SGA. Personal experience, government regulatory approval and guidelines for the treatment of schizophrenia were the 3 main contributing factors guiding the clinical decision-making of clinicians about treatment of schizophrenia. The more clinicians follow schizophrenic patients, the more they use LAI antipsychotic. The development of specialised programmes with top specialists should lead to better use of LAI in the treatment of schizophrenia.

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.

Loss of function of the hippocampus or frontal cortex is associated with reduced performance on memory tasks in which subjects are incidentally exposed to cues at specific places in the environment and are subsequently asked to recollect the location at which the cue was experienced. Here, we examined the roles of the rodent hippocampus and frontal cortex in cue-directed attention during encoding of memory for the location of a single incidentally experienced cue. During a spatial sensory preconditioning task, rats explored an elevated platform while an auditory cue was incidentally presented at one corner. The opposite corner acted as an unpaired control location. The rats demonstrated recollection of location by avoiding the paired corner after the auditory cue was in turn paired with shock. Damage to either the dorsal hippocampus or the frontal cortex impaired this memory ability. However, we also found that hippocampal lesions enhanced attention directed towards the cue during the encoding phase while frontal cortical lesions reduced cue-directed attention. These results suggest that the deficit in spatial sensory preconditioning caused by frontal cortical damage may be mediated by inattention to the location of cues during the latent encoding phase, while deficits following hippocampal damage must be related to other mechanisms such as generation of neural plasticity.

Long memory plays an important role in many fields by determining the behaviour and predictability of systems; for instance, climate, hydrology, finance, networks and DNA sequencing. In particular, it is important to test if a process is exhibiting long memory since that impacts the accuracy and confidence with which one may predict future events on the basis of a small amount of historical data. A major force in the development and study of long memory was the late Benoit B. Mandelbrot. Here we discuss the original motivation of the development of long memory and Mandelbrot's influence on this fascinating field. We will also elucidate the sometimes contrasting approaches to long memory in different scientific communities

In this work, we consider a one-dimensional laminated beam in the case of non-equal wave speeds with only one infinite memory on the effective rotation angle. In this case, we establish the general decay result for the energy of solution without any kind of internal or boundary control. The main result is obtained by applying the method used in Guesmia et al. (Electron. J. Differential Equations 193: 1-45, 2012) and the second-order energy.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Introduction: Despite more than one year passed since the first cases of SARS-CoV-2 were reported, there is still no consensus on the definition and clinical management of post-acute-COVID-19. The condition has heterogeneously been named as Chronic COVID syndrome, Post COVID-19 Syndrome, post-acute sequela of SARS-CoV-2 (PASC), and the more familiar long COVID. Method: In order to capture all relevant published studies, we undertook a multi-step search with no language restriction. The following four-step search strategy was utilized: First, a preliminary (limited) search was conducted on January 20, 2021, in Google Scholar and PubMed to identify the appropriate keywords. Then, on January 30, 2021, we adopted a search strategy of electronic databases from Cochrane Library, PsycINFO, PubMed, Embase, Scopus, and Web of sciences, using those keywords. Then, after duplicate removal, we screened all titles, abstracts, and full texts. This resulted in 66 eligible studies. Subsequently, after a forward and backward search of their references and citations an additional 54 publications were found, resulting in a total of 120 publications that formed the basis of the present analysis. The titles, abstracts, and full-texts of non-English articles were translated using Google Translate for further evaluation. We conducted our scoping review based on the PRISMA-ScR Checklist.Results: We found only one randomized clinical trial in our search. Of the 67 original studies, 22 were cohort and 28 were cross-sectional studies totaling 74.6% of the original studies. Of the total of 120 publications, 59 (49.1%) focused on signs and symptoms, 28 (23.3%) were focused on management, and 13 (10.8%) focused on pathophysiology. Ten (9%) publications focused on imaging studies. Ninety-one percent of the original investigations came from high and upper-middle-income countries, highlighting the scarcity of reports originating from low-income and lower-middle-income countries.Conclusion: The predominant symptoms among those with the so-called “Long COVID” were: fatigue, breathlessness, arthralgia, sleep difficulties, and chest pain. Recent reports also point to the risk of long-term sequela with cutaneous, respiratory, cardiovascular, musculoskeletal, mental health, neurologic, and renal involvement in those who survive the acute phase of the illness. The ambiguity and controversies in its definition have impaired proper recognition and management of those requiring additional support following the resolution of the acute phase of this infection. This has resulted in long-standing distress for the patients and their families. Our findings highlight the need for a multidisciplinary approach, support, and rehabilitation for these patients in terms of long-term mental and physical health.

This paper describes an $\sim {\cal O}(n)$ pre-compute technique to speed up the Karatsuba algorithm for multiplying two numbers.

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.

Exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access network. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this work, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.

Observation of the ambient aerosol surface area concentrations is important to understand the aerosol toxicity because an increased surface area may be able to act as an enhanced reaction interface for certain reactions between aerosol particles and biological cells, as well as an augmented carrier surface for co-pollutants. In this study, the concentration of aerosol surface area was measured from April 2015 to March 2016 in Fukuoka, Japan. We investigated the monthly and diurnal variations in the correlations between the aerosol surface area and black carbon (BC) and sulfate concentrations. Throughout the year, aerosol surface area concentration was strongly correlated with the concentrations of BC, which has relatively large surface area since BC particles are usually submicron agglomerates consisting of much smaller (tens of nanometers) sized primary soot particles. The slopes of the regression between the aerosol surface area and BC concentrations was highest in August and September 2015. This appears to have been the results of an increase in the proportion of the airmass that originated on the main islands of Japan. This may enhance the introduction of the BC from the main islands of Japan that is relatively fresh (or “uncoated”), thereby maintaining its larger surface area.

The article addresses the function of (post)colonial nostalgia in a context of multidirectional memory (Rothberg 2009) in contemporary Europe. How can different cultural memories of the Second Word War be put into respectful dialogue with each other? The text is based on a contrapuntal reading (Said 1994) of British and Egyptian popular narratives, using a qualitative content analysis of 10 British tv documentary films about the North Africa Campaign, and data from qualitative interviews collected during ethnographic fieldwork in Alexandria and Cairo, Egypt, during visits 2013--2015. The study highlights considerable differences between the British and Egyptian narratives, but also significant similarities regarding the use and function of nostalgia. In addition, the Egyptian narrative expresses a profound cosmopolitan nostalgia and a longing for what is regarded as Egypt’s lost, modern Golden Age, identified as the decades before the nation’s fundamental change from western-oriented monarchy to Nasser’s Arab nationalist military state. The common elements between the two national narratives indicate a possibly fruitful way to open up for a shared popular memory culture about the war years, including postcolonial aspects.

Here, we have discovered a X-ray excited long afterglow phosphor β-NaYF₄: Tb³⁺. After the irradiation of X-ray, the green emission can persist for more than 240 h. After 36 h, the afterglow intensity arrived at 0.69 mcd•m^-2, which can clearly be observed by naked eyes. Even after 84 h, the afterglow emission brightness still reached 0.087 mcd•m^-2. Also, combined with the results of thermoluminescence and photoluminescence, the super long afterglow emission of β-NaYF₄: Tb³⁺ can be ascribed to the tunneling model associated with F centers. More importantly, the super long green afterglow emission of β-NaYF₄: Tb³⁺ has been successfully used as in vivo light source to activate g-C₃N₄ for photodynamic therapy（PDT）and bacteria destruction. Furthermore, super long persistent luminescence of β-NaYF₄: Tb³⁺ could be repeatedly charged by X-ray for many circulations, which indicates that the phosphors have high photo stability under repeated cycles of alternating X-ray irradiation.

In computer networks, loss of data packets is inevitable, in particular, because of the buffer memory overflow of at least one of the nodes located on the path from the source to the receiver, including the latter. Such losses associated with overflows are hereinafter referred to as congestion of network nodes. There are many ways to prevent and eliminate overloads; these methods, in the majority, are based on the management of data flows. A special place is taken by the maintenance of packages, taking into account their priorities. The article considers a number of original technical solutions to improve the quality of control and reduce the required amount of buffer memory of network nodes. The ideas of these solutions are quite simple for their implementation in the development of appropriate software and hardware for telecommunication devices.

Cervical cancer is known as one of the most important cancers in women worldwide. Chemotherapy is a standard treatment for advanced/recurrent cervical cancer in which the prognosis of the disease is really poor and the 1-year survival chance in these patients is maximally 20%. However, resistance to anticancer drugs is a major problem in treating cancer. Cervical cancer stem cells are considered as a fundamental cause of chemo and radio-resistance and also relapse after primary successful treatment. Signaling pathways include a wide range of molecular mechanisms contribute to drug resistance. Recently, microRNAs (miRNAs) are announced as a group of molecular biomarkers involving in response to chemotherapy in cancer patients. As the miRNAs, there are some long non-coding RNAs (LncRNAs) which their aberrant expression is considered as a biomarker for monitoring chemoresistance. In this review, we summarized current reports about the involvement of signaling pathways during chemoresistance in cervical cancer. Then, genes that have been demonstrated their involvement during drug resistance in cervical cancer were tabulated. Further, miRNAs that have been reported as biomarkers during treatment are listed. By bioinformatic analysis, we predictedmiR-335-5p and miR-16-5p as the most potential biomarkers for monitoring resistance to chemotherapy. Finally, long non-coding RNAs that have been introduced in recent studies as novel biomarkers during the response to chemotherapy were mentioned.

The “memory of water” experiments suggested the existence of molecular-like effects without molecules. Although no convincing evidence of modifications of water – specific of biologically-active molecules – has been reported up to now, consistent changes of biological systems were nevertheless recorded. We propose an alternate explanation based on classical conditioning of the experimenter. Using a probabilistic model, we describe not only the biological system, but also the experimenter engaged in an elementary dose-response experiment. We assume that during conventional experiments involving genuine biologically-active molecules, the experimenter is involuntarily conditioned to expect a pattern, namely a relationship between descriptions (or “labels”) of experimental conditions and corresponding biological system states. The model predicts that the conditioned experimenter could continue to record the learned pattern even in the absence of the initial cause, namely the biologically-active molecules. The phenomenon is self-sustained because the observation of the expected pattern reinforces the initial conditioning. A necessary requirement is the use of a system submitted to random fluctuations with autocorrelated successive states (no forced return to the initial position). The relationship recorded by the conditioned experimenter is, however, not causal in this model because blind experiments with an “outside” supervisor lead to a loss of correlations (i.e., system states randomly associated to “labels”). In conclusion, this psychophysical model allows explaining the results of “memory of water” experiments without referring to water or another local cause. It could be extended to other scientific fields in biology, medicine and psychology when suspecting an experimenter effect.

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

The rapid growth rate of the elderly population is a serious current issue in most countries, affecting them economically through needed medical treatment and healthcare planning. The priority concern is how to reduce the number of elderly people requiring long-term healthcare and raise the number who are able to live independently. This study executed a behavior assessment of elderly person’s self-reported use of electric scooters and analyzed their degree of acceptance of these assisted living tools, partly through a related factor analysis of our survey instrument. We used this questionnaire survey as our research method, applying SPSS22 software for factor analysis that revealed five survey facets.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a frequent, debilitating and still enigmatic disease. There is a broad overlap in the symptomatology of ME/CFS and the Post-COVID Syndrome (PCS). A fraction of the PCS patients develops the full clinical picture of ME/CFS. New observations in microvessels and blood from patients suffering from PCS have appeared and include microclots and malformed pathological blood cells. Capillary blood flow is impaired not only by pathological blood components but also by prothrombotic changes in the vascular wall, endothelial dysfunction, and expression of adhesion molecules in the capillaries. These disturbances can finally cause a low capillary flow and even capillary stasis. A low cardiac stroke volume due to hypovolemia and the inability of the capacitance vessels to adequately constrict to deliver the necessary cardiac preload generate an unfavorable low precapillary perfusion pressure. Furthermore, a predominance of vasoconstrictor over vasodilator influences exists, in which sympathetic hyperactivity and endothelial dysfunction play a strong role, causing constriction of resistance vessels and of precapillary sphincters which leads to a fall in capillary pressure behind the sphincters. The interaction of these two precapillary cardiovascular mechanisms causing a low capillary perfusion pressure is hemodynamically highly unfavorable in the presence of a primary capillary stasis already caused by the pathological blood components and their interaction with the capillary wall, to severely impair organ perfusion. The detrimental coincidence of the microcirculatory with the precapillary cardiovascular disturbances may constitute the key disturbance of the Post-COVID-19 syndrome and finally lead to ME/CFS in pre-disposed patients because the interaction causes a particular kind of perfusion disturbance - capillary ischemia-reperfusion - which has a high potential of causing mitochondrial dysfunction by inducing sodium- and calcium-overload in skeletal muscles. The latter in turns worsens the vascular situation by the generation of reactive oxygen species to close a vicious cycle from which the patient can hardly escape.

Energy consumption of processors and memories is quickly becoming a limiting factor in the deployment of large computing systems. For this reason it is important to understand the energy performance of these processors and to study strategies allowing to use them in the most efficient way. In this work we focus on computing and energy performance of the Knights Landing Xeon Phi, the latest Intel many-core architecture processor for HPC applications. We consider the 64-core Xeon Phi 7230, and profile its performance and energy efficiency using both its on-chip MCDRAM and the off-chip DDR4 memory as the main storage for application data. As a benchmark application we use a Lattice Boltzmann code heavily optimized for this architecture, and implemented using several different arrangements of the application data in memory (data-layouts, in short). We also assess the dependence of energy consumption on data-layouts, memory configurations (DDR4 or MCDRAM), and number of threads per core. We finally consider possible trade-offs between computing performance and energy efficiency, tuning the clock frequency of the processor using the Dynamic Voltage and Frequency Scaling (DVFS) technique.

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.

Abstract: COVID-19 Long Haulers, an estimated 3% to 12% of people infected globally with coronavirus having latter devasting symptoms 12 weeks after the initial infection is on the rise. We conducted a collaborative study with the long covid patient organization in Greece in order to estimate the prevalence, symptoms and problems that adult long haulers experience and then propose a management plan for these patients. Symptoms were obtained from 208 patients using unstructured qualitative free text entries in an anonymized online questionnaire. The majority of respondents (68.8%) were not hospitalized and had been diagnosed more than six months ago with lingering symptoms (66,8%). Eighteen different symptoms (fatigue, tachycardia, shortness of breath, parosmia etc) were mentioned in both hospitalized and community patients. Interestingly, patients with initial mild symptoms suffer from the same persistent symptoms as those who were hospitalized. Awareness of long covid sequelae seems to be low even among medical doctors. Treatment options incorporating targeted rehabilitation programs are either not available or still excluded from the management plan of long covid patients. Since long COVID is a multi-systemic entity, we propose a holistic interventional approach using a multidisciplinary medical team in order to securely and effectively diagnose and treat these specific patients. Academic and medical community must collaborate with long covid patients’ organizations so as to provide personalized medicine.

Ag+ pollution is great of harm to the human body and the biology. Therefore, it is an urgent need to develop inexpensive and accurate detection methods. Herein, lignin-derived structural memory carbon nanodots (CSM-dots) with outstanding fluorescence property were fabricated via a green method, which reserve functional and structural units of the precursor molecules. The CSM-dots could specifically bind Ag+, accompanied with a remarkable fluorescence quenching response. This “turn-off” fluorescence behavior was used for Ag+ determination in a linear range of 5-290 μM with the detection limit as low as 500 nM. Furthermore, the finding showed that this sensing nano-platform was successfully used for Ag+ determination in real samples and intracellular imaging, showing great potential in biological and environmental monitoring applications.

Understanding the complex relationship between sleep and memory is a major challenge in neuroscience. Many studies on memory consolidation in humans suggest that sleep triggers offline memory processes, resulting in less forgetting of declarative memory and performance stabilization in non-declarative memory. However, issues related to non-optimal experimental designs, task characteristics and measurements, and inappropriate data analysis practices can significantly affect the interpretation of the effect of sleep on memory. In this article, we discuss these issues and suggest constructive solutions to address them. We believe that implementing these solutions in future sleep and memory research will significantly advance this field by improving the understanding of the specific role of sleep in memory consolidation.

The effects of tyrosine on plasma response and cognition in aging are unknown. We assessed the dose-dependent response to tyrosine administration in older adults in both plasma tyrosine concentrations and working memory performance. In this double blind randomized cross-over trial 17 older adults (aged 60-75 years) received a single administration of 100, 150 or 200 mg/kg body weight of tyrosine. For comparison, 17 young adults (aged 18-35 years) received a dose of 150 mg/kg body weight of tyrosine. Tyrosine plasma concentrations were determined before and 90, 120, 150, 180, 210 and 240 minutes after tyrosine intake. Working memory was assessed using the N-back task at 90 minutes after tyrosine administration. Older adults showed a dose-dependent increase in plasma tyrosine concentrations (p<.001), and the plasma response was higher than for young adults with the same dose (p<.001). Load-dependent working memory performance decreased with higher doses of tyrosine (p=.048), especially in older adults with greater dose-dependent plasma tyrosine responses (p=.035). Our results show an age-related increase in plasma tyrosine response, which was associated with a dose-dependent decline in cognitive functioning in older adults.

COVID-19 patients can recover with a median SARS-CoV-2 clearance of 20 days post initial symptoms (PIS). However, we observed some COVID-19 patients with existing SARS-CoV-2 for more than 50 days PIS. This study aimed to investigate the cause of viral clearance delay and the infectivity in these patients. Demographic data and clinical characteristics of 22 long-term COVID-19 patients were collected. SARS-CoV-2 nucleic acid, peripheral lymphocyte count, and functionality were assessed. SARS-CoV-2-specific and neutralization antibodies were detected, followed by virus isolation and genome sequencing. The median age of the studied cohort was 59.83±12.94 years. All patients were clinically cured after long-term SARS-CoV-2 infection ranging from 53 to 112 days PIS. Peripheral lymphocytes counts were normal. Interferon gamma (IFN-ƴ)-generated CD4+ and CD8+ cells were normal as 24.68±9.60% and 66.41±14.87%. However, the number of IFN-ƴ-generated NK cells diminished (58.03±11.78%). All patients presented detectable IgG, which positively correlated with mild neutralizing activity (ID50=157.2, P=0.05). SARS-CoV-2 was not isolated, and a cytopathic effect was lacking. Only three synonymous variants were identified in spike protein coding regions. In conclusion, decreased IFN-γ production by NK cells and low neutralizing antibodies might favor SARS-CoV-2 long-term existence. Further, low viral load and weak viral pathogenicity was observed in COVID-19 patients with long-term SARS-CoV-2 infection.

Counterfeiting electronic components is a serious problem for the security and reliability of any electronic systems. Unfortunately, the number of counterfeit components has increased considerably after the introduction of horizontal semiconductor supply chain. In this paper, we propose a new method for detecting recycled Flash memory, a major target of the counterfeiters due to its ubiquitous usage. Proposed method is based on measurement of change in Flash array characteristics (such as erase time, program time, fail bit count, etc.) with its usage. We find that erase time is the best metric to distinguish a used Flash chip from a fresh one for the following reasons: (1) erase time shows minimal variation among different fresh memory blocks/chip and (2) erase time increases significantly with usage. We verify our method for a wide range of commercial off the shelf Flash chips from several vendors, technology nodes, storage density and storage type (single-bit per cell and multi-bit per cell). The minimum detectable chip usage varies from 0.05% to 3.0% of its total lifetime depending on the exact details of the chip.

In recent years, music education in Ibero-America has been losing ground within the school environment in favor of the development of curricular systems that benefit academic results in standardized tests. Despite this, several studies in the field of cognitive neurosciences have found evidence of great relevance and in which it can be observed how music education can favor cognitive development and performance in practically all stages of human development, with important results in language tasks, attention, and executive functions such as planning, inhibition, cognitive flexibility and working memory.

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%.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

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.

Great Britain’s stocks of coal, natural gas, and petroleum have seen major changes to the levels of stored energy over the years 2005 to 2019, a reduction of 200 TWh (35%) from 570 TWh to 370 TWh. The transformation of its electrical system over this timeframe saw a reduction in coal generation, leading to a corresponding reduction of the levels of stockpiled coal of 85 TWh (68%), partially offset by an increase in the stocks of biomass for electrical generation. The reduction in natural gas storage of 24 TWh (44%) was primarily due to the closure of Britain’s only long-term seasonal natural gas storage facility in January 2018. This was partially offset by the construction of medium-term natural gas storage facilities and the use of LNG storage in the years preceding its closure. For stocks of crude oil and oil products the reduction was 35 TWh (21%), linked to the overall reduction in demand.

The shape memory alloys belong to the smart materials thanks to their thermomechanical proprieties' reply to thermal or to mechanical loading. These materials can change shape, stiffness, displacement, natural frequency, and many mechanical characteristics in response to stress or to heat such as conduction, convection or radiation. However, heating by convection or conduction are the most useful and studied methods unlike radiation. Therefore, this paper aims to study the radiation effect on the shape memory alloy behavior

Physical Unclonable Functions (PUFs) based on the retention times of the cells of a Dynamic Random Access Memory (DRAM) can be utilised for the implementation of cost-efficient and lightweight cryptographic protocols. However, as recent work has demonstrated, the times needed in order to generate their responses may prohibit their widespread usage. In order to address this issue, the Row Hammer PUF has been proposed by Schaller et al. [1], which leverages the row hammer effect in DRAM modules to reduce the retention times of their cells and, therefore, significantly speed up the generation times for the responses of PUFs based on these retention times. In this work, we extend the work of Schaller et al. by presenting a run-time accessible implementation of this PUF and further reducing the time required for the generation of its responses. Additionally, we also provide a more thorough investigation of the effects of temperature variations on the the Row Hammer PUF and briefly discuss potential statistical relationships between the cells used to implement it. As our results prove, the Row Hammer PUF could potentially provide an adequate level of security for Commercial Off-The-Shelf (COTS) devices, if its dependency on temperature is mitigated, and, may therefore, be commercially adopted in the near future.

This paper reports a rational design of temperature-responsive nanofiber meshes with shape-memory effect. The meshes were fabricated by electrospinning a poly(ε-caprolactone) (PCL)-based polyurethane with different contents of soft and hard segments. The effects of PCL diol/hexamethylene diisocyanate (HDI)/1,4-butanediol (BD) molar ratio in terms of the contents of soft and hard segments on the shape-memory properties were investigated. Although the mechanical property improved with increasing hard segment ratio, optimal shape-memory properties were obtained with a PCL/HDI/BD molar ratio of 1:4:3. At a microscopic level, the original nanofibrous structure was easily deformed into a temporary shape, and recovered its original structure when the sample was reheated. A higher recovery rate (>89%) was achieved even when the mesh was deformed up to 400%. Finally, the nanofiber meshes were used to control the alignment of human mesenchymal stem cells (hMSCs). The hMSCs aligned well along the fiber orientation. The proposed nanofibrous meshes with the shape-memory effect have the potential to serve as in vitro platforms for the investigation of cell functions as well as implantable scaffolds for wound-healing applications.

In manual maintenance inspections of large-scaled photovoltaic (PV) or rooftop PV systems, several days are required to survey the entire PV field. To improve reliability and shorten the amount of time involved, this study proposes an electrical examination based method for locating multiple faults in the PV array. The maximum power point tracking (MPPT) algorithm is used to estimate the maximum power of each PV panel; this is then compared with metering the output power of PV array. Power degradation indexes are parameterized to quantify the degradation between maximum power and metered output power. Bidirectional hetero-associative memory (BHAM) networks are then used to locate multiple faults within the entire PV field. For a rooftop PV system with two strings, as seen in Figure 1, experimental results demonstrate that the proposed model has computational efficiency for real-time applications and that its algorithm is easily implemented in a mobile intelligent vehicle.

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.

Purpose: To analyze the five-year results of Trabectome ab interno trabeculectomy of a single glaucoma center. Method: In this retrospective interventional single-center case series, data of 93 patients undergoing ab interno trabeculotomy between September 2010, and December 2012 were included. Kaplan-Meier analysis was performed using success criteria defined as postoperative intraocular pressure (IOP) ≤21 mm Hg, or >20% reduction from preoperative IOP, and no need for further glaucoma surgery. Risk factors for failure were identified using Cox proportional hazards ratio (HR). Results: The retention rate for five years follow-up was 66%. The cumulative probability of success at 1, 2, 3, 4 and 5 years was 82.6%, 76.7%, 73.9%, 72.3%, and 67.5%. Risk factors for failure were lower baseline IOP (HR=0.27, P=0.001), younger age (HR=0.25, P=0.02), and higher central corneal thickness (HR=0.18, P= 0.01). Pseudoexfoliation was associated with a higher success rate (HR= 0.39, P=0.02). IOP was decreased significantly from 20.0±5.6 mmHg at baseline to 15.6±4.6 mmHg at 5-year follow-up (P=0.001). The baseline number of glaucoma medications was 1.8±1.2, which decreased to 1.0±1.2 medications at 5 years. Conclusion: Trabectome surgery was associated with a good long-term efficacy and safety profile in this single-center case series with a high retention rate. A higher baseline IOP, older age, thinner cornea, and pseudoexfoliation glaucoma were associated with a higher success rate.

Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.

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.

Understanding brain function is one of the most important open problems in science today. At present, there is no concrete theory for how the brain works. Here, a theory is presented that provides a detailed mechanistic biological account of the brain’s capacities. Brain function is managed by a response (R) process that is structurally similar to the immune response, and shows anatomical and molecular specificity. Different R process stages utilize different cortical layers, hippocampus fields, basal ganglia paths, GABAergic interneurons, cerebellum paths, and molecular agents such as dopamine, serotonin and opioids. We show how the R process supports hierarchical action sequences, language and thought. The theory is supported by a large body of experimental evidence in many modalities, and accounts for virtually all of the major facts known about the brain at the system level.

We study the possibility of determining the octant of the neutrino mixing angle 23, that is, whether 23 > 45 or 23 < 45, in long baseline neutrino experiments. Here we numerically derived the sensitivity limits within which these experiments can determine, by measuring the probability of the  ! e transitions, the octant of 23 with a 5 certainty. The interference of the CP violation angle  with these limits, as well as the effects of the baseline length and the run-time ratio of neutrino and antineutrino modes of the beam have been analyzed.

Post-viral fatigue syndrome (PVFS) encompasses a wide range of complex neuroimmune disorders of unknown cause characterized by disabling post-exertional fatigue, myalgia and joint pain, cognitive impairments, unrefreshing sleep, autonomic dysfunction, and neuropsychiatric symptoms. It includes myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and more recently post-COVID-19 condition (Long COVID). To date, there are no definitive clinical case criteria and no FDA-approved pharmacological therapies for PVFS. Given the current lack of effective treatments, there is a need to develop novel therapeutic strategies for these disorders. Mitochondria, the cellular organelles responsible for tissue energy production, have recently garnered attention in research into PVFS due to their crucial role in cellular bioenergetic metabolism in these conditions. Accumulating literature has identified a link between mitochondrial dysfunction and low-grade systemic inflammation in ME/CFS, FM, and Long COVID. To address this issue, this article aimed to critically review the evidence relating to mitochondrial dysfunction in the pathogenesis of these disorders; in particular, to evaluate the effectiveness of coenzyme Q10 supplementation on chronic fatigue and pain symptoms as a novel therapeutic strategy for the treatment of PVFS.

Long non-coding RNAs (lncRNAs) have been found to be involved in many biological processes, including the regulation of cell differentiation, but a complete characterization of lncRNA is still lacking. Additionally, there is evidence that lncRNAs interact with ribosomes, raising questions about their functions in cells. Here, we used a developmentally staged protocol to induce cardiogenic commitment of hESCs and then investigated the differential association of lncRNAs with polysomes. Our results identified lncRNAs in both the ribosome-free and polysome-bound fractions during cardiogenesis and showed a very well-defined temporal lncRNA association with polysomes. Clustering of lncRNAs was performed according to the gene expression patterns during the five timepoints analyzed. In addition, differential lncRNA recruitment to polysomes was observed when comparing the differentially expressed lncRNAs in the ribosome-free and polysome-bound fractions or when calculating the polysome-bound vs ribosome-free ratio. The association of lncRNAs with polysomes could represent an additional cytoplasmic role of lncRNAs, e.g., in translational regulation of mRNA expression.

In The Work of the Dead: A Cultural History of Mortal Remains, Thomas Laqueur argues that the work of the dead is carried out through the living and through those who remember, honour, and mourn the dead. Further, he maintains that the brutal or careless disposal of the corpse “is an attack of extreme violence”. To treat the dead body as if it does not matter or as if it were ordinary organic matter would be to deny its humanity. From Laqueur’s point of view it is inferred that the dead are believed to have rights and dignities that are upheld through rituals, practices, and beliefs of the living. Drawing on dark tourism scholarship and cultural memory theory, this paper examines the display of human bones at Sedlec Ossuary, Czech Republic and the tourist culture that has built up around the site. Primarily, my writing calls into question the commoditization of burial places as a conceivable violation of the human rights of the dead. My research is driven by a number of questions: What is it that draws tourists to burial grounds and how do heritage sites negotiate visitor experiences? What are the ethical boundaries when a final resting place with bodies on display is also marketed as a tourist site? Do the dead have human rights and how are the living responsible for preserving those rights?

We investigate the stationary and dynamic properties of the celebrated Nosé-Hoover dynamics of many-body interacting Hamiltonian systems, with an emphasis on the effect of inter-particle interactions. To this end, we consider a model system with both short- and long-range interactions. The Nosé-Hoover dynamics aims to generate the canonical equilibrium distribution of a system at a desired temperature by employing a set of time-reversible, deterministic equations of motion. A signature of canonical equilibrium is a single-particle momentum distribution that is Gaussian. We find that the equilibrium properties of the system within the Nosé-Hoover dynamics coincides with that within the canonical ensemble. Moreover, starting from out-of-equilibrium initial conditions, the average kinetic energy of the system relaxes to its target value over a size-independent timescale. However, quite surprisingly, our results indicate that under the same conditions and with only long-range interactions present in the system, the momentum distribution relaxes to its Gaussian form in equilibrium over a scale that diverges with the system size. On adding short-range interactions, the relaxation is found to occur over a timescale that has a much weaker dependence on system size. This system-size dependence of the timescale vanishes when only short-range interactions are present in the system. An implication of such an ultra-slow relaxation when only long-range interactions are present in the system is that macroscopic observables other than the average kinetic energy when estimated in the Nosé-Hoover dynamics may take an unusually long time to relax to its canonical equilibrium value. Our work underlines the crucial role that interactions play in deciding the equivalence between Nosé-Hoover and canonical equilibrium.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

Clupeiformes, such as sardines and herrings, represent an important share of worldwide fisheries. Among those, the European sardine (Sardina pilchardus, Walbaum 1792) exhibits significant commercial relevance. While the last decade showed a steady and sharp decline in capture levels, recent advances in culture husbandry represent promising research avenues. Yet, the complete absence of genomic resources from sardine imposes a severe bottleneck to understand its physiological and ecological requirements. We generated 69 Gbp of paired-end reads using Illumina HiSeq X Ten and assembled a draft genome assembly with an N50 scaffold length of 25579 bp and BUSCO completeness of 82.1% (Actinopterygii). The estimated size of the genome ranges between 655 and 850 Mb. Additionally, we generated a relatively high-level liver transcriptome. To deliver a proof of principle of the value of this dataset, we established the presence and function of enzymes (elovl2, elovl5 and fads2) that have pivotal roles in the biosynthesis of long chain polyunsaturated fatty acids, essential nutrients particularly abundant in oily fish such as sardines. Our study provides the first omics dataset from a valuable economic marine teleost species, the European sardine, an essential resource for their effective conservation, management and sustainable exploitation.

Formalin-Fixed Paraffin Embedded (FFPE) tissues are a valuable resource in studying different markers and mechanistic molecules (protein, DNA and RNA) in order to understand the etiology of different cancers as well as many other diseases. Degradation and modification of RNA is the major challenge in utilizing FFPE tissue samples in medical research. Recently, non-protein coding transcripts long non-coding RNAs (lncRNAs), have gained significant attention due to their important biological actions and potential involvement in cancer. There is no validated method except qRTPCR or RNAseq to evaluate and study lncRNA expression. We have standardized and are reporting a sensitive Z probe based in situ hybridization method to identify, localize and quantitate lncRNA in FFPE tissues. This assay is sensitive to single transcript and localizes lncRNA in individual cells within tumor. We have characterized a tumor suppressor lncRNA-NRON (non coding repressor of NFAT), which is scarcely expressed, a moderately expressed oncogeneic lncRNA UCA1 (urothelial cancer associated 1), and a highly studied and expressed lncRNA MALAT1 (metastasis associated lung adenocarcinoma transcript1) in different cancers. High MALAT1 staining was found in colorectal, breast and pancreatic cancer. MALAT1 expression increased with the progression of the stage in colorectal cancer and invasiveness in breast cancer.

Background: Alzheimer’s disease affecting about 24 million people world-wide. The socio-economic burden on world-economies costing more than 172 billion US $ annually for the US alone. Objectives: To prepare aqueous extract of T. foenum graecum seeds (FSE) to explore the possible treatment for cognitive deficit in experimental animals. Materials and methods: FSE was subjected to preliminary phytochemical evaluation and antioxidant effect using free radical scavenging method (DPPH). All the animal behavior was video recorded with no human intervention during observation and animal groupings were blinded to avoid investigator bias. Different doses of FSE (5%, 10% and 20%), control, standard (Piracetam, 200 mg/kg, IP.) were given for male albino mice a period of 15 days followed by cognitive assessment in elevated plus maze and novel objection recognition tests. Ttransfer latencies and time exploring novel and familiar objects were recorded in respective tests. Retention of this learned-task was examined again 24 h later and inflexion ratio (IR) and discriminative index (DI) were calculated respectively. Next in the second set of experiment same groups and treatments were continued but scopolamine was administered to all the groups except normal control one hour after the last dose and examined similarly. Results: FSE showed potential antioxidant effect and a dose dependent increase in transfer latency and improved DI indicating a nootropic effect. FSE at 20% showed significant reversal of scopolamine induced dementia in the second set of experiment. Conclusion: FSE improved memory as well as reversed the chemically induced memory deficits in experimental mice.

Montiaceae comprise a clade of at least 270 species plus about 20 accepted subspecific taxa, primarily of western America and Australia. The present paper is the first of a two-part work that seeks to evaluate evolutionary theory via metadata analysis of Montiaceae. In particular, it uses metadata analysis to evaluate the theory in theory-laden methods that have been applied in evolutionary analyses of Montiaceae. This part focuses on phylogeny and phylogeography. The second part focuses on phenotypic and ecological diversification. An emergent theme in this paper is the degree to which historical idiosyncrasy during Montiaceae evolution misleads quantitative methods of evolutionary reconstruction and phylogeographic interpretation. This suggests that idiosyncraticity itself is a fundamental property of evolution. The second part of this work elaborates this notion as the Principle of Evolutionary Idiosyncraticity. The present part describes idiosyncraticity in molecular phylogenetic and phylogeographic data and uses this notion to refine ideas on Montiaceae evolution. Phylogenetic metadata conflicts and conflicting phylogeographic interpretations are discussed. I conclude that, owing to PEI, quantitative methods of evolutionary analysis cannot be globally accurate, though they are useful heuristically. In contrast, classical narrative analysis is robust in the face of PEI.

Mercury is a toxic pollutant emitted from both natural sources and through human activities. A global interest in atmospheric mercury has risen ever since the discovery of the Minamata disease in 1956. Properties of gaseous elemental mercury enable long range transport which can cause pollution even in pristine environments. Total gaseous mercury (TGM) was measured from winter 2016 to spring 2017 over the Baltic Sea. A Tekran 2357A mercury analyser was installed aboard the research and icebreaking vessel Oden for the purpose of continuous measurements of gaseous mercury in ambient air. Measurements were performed during a campaign along the Swedish east coast and in the Bothnian Bay near Lulea during the icebreaking season. Data was evaluated from Gothenburg using a plotting software and back trajectories for air masses were calculated. The TGM average of 1.365 ± 0.054 ng/m³ during winter and 1.288 ± 0.140 ng/m³ during spring was calculated as well as a total average of 1.362 ± 0.158 ng/m³. Back trajectories showed a possible correlation of anthropogenic sources elevating the mercury background level in some areas. There were also indications of depleted air, i.e., air with lower concentrations than average, being transported from the Arctic to northern Sweden resulting in a drop in TGM levels.

1) Background: We analyzed, by PET-SCAN, how growth hormone (GH) could act on the brain of an older woman, not GH-deficient, which was beginning to show some cognitive deficiencies and presented an ApoE genotype 4/3; 2) Methods: After performing a first psychometric study (TAVEC verbal learning test), the metabolic activity of brain structures related to knowledge, memory, and behavior was analyzed using 18-F Fluorodeoxyglucose PET-SCAN. The patient was then treated with GH (0.4 mg/day) for three weeks and on the last day under this treatment, a new PET-SCAN was performed. One month after beginning treatment with GH, a new TAVEC test was performed; 3) Results: GH administration normalized the cognitive deficits observed in the first psychometric test and increased significantly (P < 0.025) the metabolic activity in practically all brain cortical areas, specifically in the left hippocampus and left amygdala, although not in the left parahippocampus; and 4) Conclusions: This is the first study in which the positive effects of GH on cerebral metabolism have been visualized in a human patient. Our data confirm the positive effects of this hormone on cognition, memory and behavior in patients affected by mild cognitive impairments.

The Ni-Co-Cr-W-Mo system is critical for the design of nickel-based super-alloys. This system stabilizes different topologically close packed (TCP) phases in many of the commercially super-alloys with high W and Mo contents. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermodynamic calculations are applied to investigate the thermodynamics of the precipitates in two different W content Ni-Co-Cr-W-Mo super-alloys. Computational thermodynamics verified experimental observation of the f u phase formation as a function of temperature and alloy chemistry, but the kinetics for the precipitation of M6C phase do not agree with the experimental findings. The major precipitates of alloy 1 at temperatures 700 and 750 °C during long time exposure are M23C6, γ′ phase, and MC, and alloy 2 are M23C6, γ′ phase, MC, M6C and u phase. The W addition is found to promote the precipitation of M6C and u phase during exposure. The M6C has higher W and lower Ni content than that of u phase, meanwhile, M6C is an unstable phase would transform into M12C after 5000 h exposure at 750 °C. A great quantity of needle-like u phases precipitated after exposure at 750 °C for 5000h, which have no effect on the impact property of alloy 2.

Recently, several reports have emerged describing the long-term consequences of COVID-19 that may affect multiple systems, suggesting its chronicity. As further research is needed, we conducted a longitudinal observational study to report the prevalence and associated risk factors of long-term health consequences of COVID-19 by symptom clusters in patients discharged from the Temporary COVID-19 Hospital (TCH) in Mexico City. Self-reported clinical symptom data were collected via telephone calls over 90 days post-discharge. Among 4670 patients discharged from the TCH, we identified 45 symptoms across eight symptom clusters (neurological; mood disorders; systemic; respiratory; musculoskeletal; ear, nose, and throat; dermatological; and gastrointestinal). We observed that the neurological, dermatological, and mood disorder symptom clusters persisted in >30% of patients at 90 days post-discharge. Although most symptoms decreased in frequency between day 30 and 90, alopecia and the dermatological symptom cluster significantly increased (p<0·00001). Women were more prone than men to develop long-term symptoms and invasive mechanical ventilation also increased the frequency of symptoms at 30-days post-discharge. Overall, we observed that symptoms often persisted regardless of disease severity. We hope these findings will help promote public health strategies that ensure equity in the access to solutions focused on the long-term consequences of COVID-19.

Shape memory alloys (SMAs) are increasingly used in the fields of aviation, automotive and biomedicine due to their unique properties. Nickel-Titanium (NiTi) alloy materials, which are one of the shape memory alloys, are among the most frequently used alloy materials. The shape memory and super elastic effects of NiTi alloys, high ductility and deformation hardening make it difficult to shape burr. An additional problem is the formation of a white layer during machining. In this study, surface milling operations were performed in dry cutting conditions with uncoated cutting tools with different nose radii. The processing parameters were determined based on the experience gained as a result of the preliminary tests. Tungsten carbide cutting tools with different nose radii (0.4mm and 0.8mm) were used for the milling operations. Milling was carried out at three different cutting speeds (20, 35, 50 m/min), feed rates (0.03, 0.07, 0.14 mm/tooth), and a constant axial cutting depth (0.7 mm). As a result of our experimental studies, the best tool life was found to be in 0.8 mm nose radius cutting tools at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.264 mm). The minimum average surface roughness was found after milling with 0.8 mm nose radius cutting tool at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.346 μm). It has been determined that increasing the cutting tool nose radius reduces both the flank wear over the cutting tool and the average surface roughness.

In higher eukaryotes, enhancers determine the activation of developmental gene transcription in specific cell types and stages of embryogenesis. Enhancers transform the signals produced by various transcription factors within a given cell, activating the transcription of the targeted genes. Often, developmental genes can be associated with dozens of enhancers, some of which are located at large distances from the promoters that they regulate. Currently, the mechanisms that underly the specific distance interactions between enhancers and promoters remain unknown. This review describes the properties and activities of enhancers and discusses the mechanisms of distance interactions and potential proteins involved in this process.

With the rise of long-read sequencers and long-range technologies, delivering high-quality plant genome assemblies is no longer reserved to large consortia. Indeed, sequencing techniques but also computer algorithms have reached a point where the reconstruction of assemblies at the chromosome-scale is now feasible at the laboratory scale. Current technologies, and especially long-range technologies, are numerous and selecting the most promising one for the genome of interest is crucial to obtain optimal results. In this study, we resequenced the genome of the yellow sarson, Brassica rapa cv. Z1, using the Oxford Nanopore PromethION sequencer and assembled the sequenced data using current assemblers. To reconstruct complete chromosomes, we used and compared three long-range techniques, optical mapping, Omni-C and Pore-C sequencing libraries commercialized by Bionano Genomics, Dovetail Genomics and Oxford Nanopore Technologies respectively, or a combination of the three, in order to evaluate the capability of each technology.

A comprehensive model for designing robust all-in-fiber microresonator-based optical sensing setups is illustrated. The investigated all-in-fiber setups allow light to selectively excite high-Q whispering gallery modes (WGMs) into optical microresonators, thanks to a pair of identical long period gratings (LPGs) written in the same optical fiber. Microspheres and microbubbles are used as microresonators and evanescently side-coupled to a thick fiber taper, with a waist diameter of about 18 µm, in-between the two LPGs. The model is validated by comparing the simulated results with the experimental data. A good agreement between the simulated and experimental results is obtained. As an application example, the sensing of the concentration of an aqueous glycerol solution is demonstrated. The model is general and by exploiting the refractive index and/or absorption characteristics at suitable wavelengths, the sensing of other substances or pollutants can be also predicted.

Background: This study investigated the prevalence of worsening problems using Post Stroke Checklist (PSC) at 3, 6, and 12 months post-stroke and their associations with health-related quality of life. Methods: In stroke patients admitted between June 2014 and December 2015, PSC and EuroQol-5Dthree level (EQ-5D-3L) were assessed at post-stroke 3 (n=181), 6 (n=175), and 12months (n=89). The prevalence of worsening problems and its association withEQ-5D-3L at post-stroke 3 and 6months were analyzed. Results: An average of 0.59 (range 0–12), 1.47 (range 0–12), and 1.00 (range 0–10) worsening problems per patient was identified at 3, 6, and 12months after stroke, respectively. The most frequently and continuously identified worsening problems were mood disturbances (reported by 8.8%, 16.0% and13.5% of patients at 3, 6, and 12 months post-stroke, respectively). Worsening mobility was significantly associated with worse EQ-5D index at post-stroke 3 months (β,-0.583; 95% CI, -1.045 to -0.120). The worsening of mobility and communication was significantly associated with worse EQ-5D index at post-stroke 6 months (mobility: β,-0.170; 95% CI, -0.305 to -0.034, communication: β,-0.164; 95% CI, -0.309 to -0.020). Conclusions: PSC may be useful for the detection of various subjective worsening problems during serial clinical follow-up after stroke. Appropriate rehabilitation and management strategy to solve the identified problems could improve the quality of life in stroke survivors.

There are a large number of wildlife and insect species that are in trouble on this planet, and most believe that monarch butterflies in eastern North America are too, because of the well-publicized declines of their winter colonies in central Mexico in the last 25 years. A small number of studies over the last decade have cast doubt on this claim by showing declines are not evident at other stages of the annual cycle. To determine how extensive this pattern is, I conducted an exhaustive review of peer-reviewed and grey literature on (eastern) monarch population censuses and studies, conducted across all seasons, and extracted data from these sources to evaluate how monarch abundance has or has not changed over time. I identified 20 collections of data that included butterfly club reports, compilations of citizen-science observations, migration roost censuses, long-term studies of isotopic signatures, and even museum records. These datasets range in duration from 15 years to over 100 years, and I endeavored to also update each with information from the most current years. I also re-examined the winter colony data after incorporating historical records of colony measurements dating back to 1976. This represents the most complete and up-to-date synthesis of information regarding this population. When I examined the long-term trajectory within each dataset a distinct pattern emerged. Modest declines are evident within the winter colonies (over the full 45 year dataset), and, within three censuses conducted during the spring recolonization. Meanwhile, 16 completely separate monitoring studies conducted during the summer and fall (and from varying locations) revealed either no trend at all or in fact an increase in abundance. While each of these long-term studies has inherent limitations, the fact that all 16 sources of data show the same pattern is undeniable. Moreover, this evidence is consistent with recently-conducted genetic work that shows a lack of decline. Collectively, these results indicate that despite diminishing winter colonies and spring migrations, monarchs in eastern North America are capable of rebounding fully each year, implying that milkweed is not limiting within their collective range. Moreover, there is no indication from these data that the summer population was ever truly diminished by changing agricultural practices in the Midwest that reduced milkweed in crop fields within that region. It is possible that the larger population is not as dependent on Midwestern agricultural milkweed as once thought, and/or that monarchs are adapting to increasingly human-altered landscapes. These results are timely and should bear on the upcoming USFWS decision on whether the monarch requires federal protection in the United States. Importantly, they argue that despite losses of many insects globally, the eastern North American monarch population is not in the same situation.

In modern Commercial Off-The-Shelf (COTS) multicore systems, cores can produce several simultaneous memory requests. The processing of such requests over the memory controller negatively impacts the interference delay triggered by running parallel tasks on the platform. In this paper, we propose a software-based testing approach for analyzing memory interference delay, when cores are exposed to extensive read/write requests that access in parallel their Cache Coherent Interconnect. The hardware targeted in this work is the well-known LayerScape QorIQ LS2085A, which can be approached as a potential successor to the Freescale QorIQ P4080. The test analysis was conducted based on a bare-metal operating system that we developed to guarantee a deterministic execution environment at all time points. Our testing was accomplished using a set of carefully designed synthetic benchmarks as well as TACLeBench benchmarks.

In this study, 6 residents of a long-term care facility were asked to try on Virtual Reality glasses and report their first experiences with Virtual Reality. The results show that Virtual Reality is of great interest to elderly residents of in-patient long-term care facilities. The wearing period was longer than expected and no symptoms of cyber sickness occurred. For the residents it was exciting to explore the virtual environments. Austrian destinations, nature scenes in the mountains and forests but also trips to the zoo, the museum, in churches or even densely populated areas like shopping streets or train stations would be places for the residents, they would like to explore virtually. Far-off destinations such as Rio de Janeiro or the Caribbean are more of an exception. Biographically relevant places such as the parental home or the location of their wedding were not named. Concerning the usability, an adjustment of the VR glasses is necessary for a longer-term use in any case.

A graphene-containing LDH was prepared by re-hydration of the oxides produced by the calcination of an organic LDH. While the memory effect is a widely recognized effect on oxides produced by inorganic LDHs, it is unprecedented from the calcination/re-hydration of organic ones. Different temperatures (400, 600 and 1100 °C) were tested, on the basis of thermogravimetric data. Water instead of a carbonate solution was used for the re-hydration, with CO₂ available from water itself and/or air to induce a slower process with an easier and better intercalation of the carbonaceous species within the layers. The samples were characterized by X-ray Powder Diffraction (XRPD), IR and Raman spectroscopy and scanning electron microscopy (SEM). XRPD indicate the presence of carbonate LDH mixed with a layered phase with a larger d-spacing. IR confirmed that the prevailing anion is carbonate, coming from the water used for the re-hydration and/or air. Raman data indicated the presence of low-ordered graphenic species moieties and SEM the absence of separated graphene of graphitic sheets, suggesting an intimate mixing of the carbonaceous phase with reconstructed LDH. Organic LDHs gave better memory effect after calcination at 400 °C. Conversely, the graphenic species are observed after rehydration of the sample calcined at 600 °C with a reduced memory effect, demonstrating the interference of the carbonaceous phase with LDH reconstruction and the bonding with LDH layers to form a graphene-LDH nanocomposite.

Energy-efficient microprocessors are essential for a wide range of applications. While near-threshold computing is a promising technique to improve energy efficiency, optimal supply demands from logic core and on-chip memory are conflicting. In this paper, we perform static reliability analysis of 6T SRAM and discover the variance among different sizing configuration and asymmetric minimum voltage requirements between read and write operations. We leverage this asymmetric property in near-threshold processors equipped with voltage boosting capability by proposing an opportunistic dual-supply switching scheme with a write aggregation buffer. Our results show that proposed technique improves energy efficiency by more than 21.45% with approximate 10.19% performance speed-up.

This paper deals with the analysis of trends in road accidents on major highways in Brazil. Using updated time series techniques, our results indicate that a low degree of long memory was detected in the series with shocks having transitory effects over time. We further find that the number of accidents taking place in Brazil has been reducing over time, though in the presence of negative shocks, the recovery is not going to be immediate due to the long memory nature of the data. Despite the absence of relevant investment relating to infrastructure expansion, it is worth mentioning the consolidation of a nationwide tolled road system in Brazil involving concessions to private administrators, alongside more severe traffic laws that can impose limitations on driving licences.

The developmental phase of adolescence is characterized by a multitude of neurocognitive and psychosocial changes and is therefore considered one of the most critical developmental periods of life. Experimentation on the use of substances often begins in adolescence and so does the addiction process. Most research in human subjects shows that chronic cannabis abuse is the cause of the impairment of some cognitive functions, affecting the performance on divided attention, verbal memory and working memory. In this study, we wanted to investigate how the abuse of cannabis (chronic, occasional and absence use) can influence global cognitive functioning, also through executive functions. From the statistical analyzes of our study, it emerges that the group of subjects who use chronic cannabis (group 1) has a significant drop in working memory tasks compared to the group that does not use it (group 3). In addition, the goal of future studies by our group is to verify the permanent alteration of cognitive processes affected through revaluations with calendar follow-up (controlled).

Objectives: A wealth of literature has established risk factors for social isolation among older people, however much of this research has focused on community-dwelling populations. Relatively little is known about how risk of social isolation is experienced among those living in long-term care (LTC) homes. We conducted a scoping review to identify possible risk factors for social isolation among older adults living in LTC homes. Methods: A systematic search of five online databases retrieved 1535 unique articles. Eight studies met the inclusion criteria. Results: Thematic analyses revealed that possible risk factors exist at three levels: individual (e.g., communication barriers), systems (e.g., location of LTC facility), and structural factors (e.g., discrimination). Discussion: Our review identified several risk factors for social isolation that have been previously documented in literature, in addition to several risks that may be unique to those living in LTC homes. Results highlight several scholarly and practical implications.

Nervous systems and brains’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signaling originated in cells’ communication with the outside world and with each other, in order to cooperate toward adaptive construction and repair of multicellular bodies. Here we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering, cognitive science, and even machine learning and artificial intelligence. One of the predictions of this view is that regeneration and regulative development are able to restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states - in this case, pattern memories. Based on this idea, we propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution, and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology, and biomedicine of information processing in vivo.

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.

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.

The classical Rotational Isomeric State (RIS) model, originally proposed by Flory, has been used to rationalize a wide range of physicochemical properties of neutral polymers. However, many weak polyelectrolytes of interest are able to regulate their charge depending on the conformational state of the bonds. Recently, it has been shown that the RIS model can be coupled with the Site Binding (SB) model, for which the ionizable sites can adopt two states: protonated or deprotonated. The resulting combined scheme, the SBRIS model, allows to analyse ionization and conformational equilibria on the same foot. In the present work this approach is extended to include pH-dependent electrostatic Long Range (LR) interactions, ubiquitous in weak polyelectrolytes at moderate and low ionic strengths. With this aim the original LR interactions are taken into account by defining effective Short Range (SR) and pH-dependent parameters, such as effective microscopic protonation constants and rotational bond energies. The new parameters are systematically calculated using variational methods. The machinery of statistical mechanics for SR interactions, including the powerful and fast transfer matrix methods, can then be applied. The resulting technique, to which we will refer as Local Effective Interaction Parameters (LEIP) method, is illustrated with a minimal model of a flexible linear polyelectrolyte containing only one type of rotating bonds. LEIP reproduces very well the pH dependence of the degree of protonation and bond probabilities obtained by semi-grand canonical Monte Carlo simulations, where LR interactions are taken explicitly into account. The reduction in the computational time in several orders of magnitude suggests that the LEIP technique could be useful in a range of areas involving linear weak polyelectrolytes, allowing direct fitting of the relevant physical parameters to the experimental quantities.

In this study, we report a new epoxy acrylate based shape memory polymer(SMP) fabricated by Liquid crystal display (LCD) Stereolithographic 3D printing. The printed 3D object has a high resolution and high transparency in visible light region. The uniaxial tensile tests showed enhanced tensile toughness and tunable mechanical properties. The fix-recovery and cycle tests indicated high shape recovery properties including high shape recovery rate and excellent cycling stability. In addition, a smart electrical valve actuator was fabricated that can be used in fast heat or electricity responsive electrical circuits. LCD 3D printing provides a low-cost and high efficient way to fabricate fast responsive SMP, which can be used in wide applications in various fields on aerospace engineering, biomedical devices, soft robots and electronic devices.

Background: The Apolipoprotein E4 (ApoE4) genotype is strongly associated with Alzheimer’s disease (AD), although the presence of the ApoE4 allele alone is not sufficient to explain AD. The pathophysiology of amnestic mild cognitive impairment (aMCI) remains unclear. This study aims to examine associations between peripheral blood biomarkers coupled with ApoE4 and episodic and semantic memory. Methods: The CERAD battery was completed and various biomarkers were assayed in 60 subjects with aMCI, 60 with AD and 62 healthy controls. Results: Deficits in semantic and episodic memory were significantly predicted by anion gap and bicarbonate, albumin and glucose coupled with Apo E4. Furthermore, these peripheral biomarkers interacted with ApoE to predict greater memory impairments. Conclusions: Peripheral blood biomarkers may interact with pathways related to ApoE4 to predict greater semantic and episodic memory impairments, thus contributing to the pathophysiology of aMCI and AD. Our data suggest that the transition from aMCI to AD could at least in some cases be associated with significant interactions between ApoE4 and those peripheral blood biomarkers.