Miracle-dz/UniFAR-FTUs huggingface dataset:
The Narrowband Internet of Things (NB-IoT) Resources Management Performance State of Art, Challenges, and Opportunities

The Narrowband Internet of Things (NB-IoT) has been introduced in the 3rd Generation Partnership Project (3GPP) Rel-13 with the aim to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. With the exponentially increasing number of connected wireless devices in the order of 100 billion, it has become crucial that researchers develop efficient resource management techniques to meet the 5th Generation (5G) quality of service (QoS) requirements. Recently, several research challenges including the low modulation data rates, energy-expensive channel coding techniques, and the fast-growing number of connected devices have been identified as some of the main issues encountered in the design and deployment of NB-IoT systems. In addition, several techniques have emerged in the literature to resolve some of these challenges of NB-IoT systems. However, the research activities towards the enhancement of the NB-IoT resource management are yet to continue in the next half-decade before, high data rates, energy-efficient, and scalable NB-IoT specifications can be released for the standardisation and commercialisation. Considering the limited number of existing surveys of such technical enhancement approaches in a broader perspective (i.e. energy efficiency, data rate performance and scalability) and also the non-existence, to the best of our knowledge, of a comparative survey, this paper seeks to elaborate, describe and compare the performances of such resources management approaches. Of the multiple NB-IoT resources, the focus of this paper is on the data rate, energy efficiency, and scalability enhancement schemes that have been proposed for the last three years. The contribution of the paper lies in the analysis, synthesis, comparison and summarised alignments of some of the major existing schemes towards identifying challenges faced by the NB-IoT development. Finally, this work seeks to identify research challenges, open questions, and opportunities that could arise from the existing techniques analysed.
Development of System Integrity Protection Scheme for Real-time Protection of an Overloading Feeder

Power system security is one of the major challenges to utilities. Some of the power system loads can be critical that require uninterrupted supply all the time. Supply security can be at risk specially when there are project works that require outages of substation equipment. Operator actions are not always adequate to maintain system security. Therefore, utilities develop System Integrity Protection Schemes (SIPS), which could help operators to automatically monitor the risks and take appropriate actions relatively faster. This paper presents development of SIPS for real-time overload protection of a critical feeder in a large utility transmission system. The scheme is developed considering various factors such as the measurement quality, time behaviour, controlled switching, and operator inputs. The effectiveness of the scheme is tested in various overload scenarios of the critical branch. The results show that the scheme can effectively alleviate the feeder overload by controlled switching of surrounding feeders and re-configuring the power flow on the network.
Image classification using machine learning

Flower Species Recognition is a very hard and challenging mission to identify different types of flowers as they are very similar. The idea of automated flower recognition is bewildering as the flowers are not rigid objects and their images can be affected by many external influences . The existing system is based on classifying flowers into different categories which has very less accuracy. Now the proposed system use machine learning algorithm to fully automate and increase the accuracy of flower classification. Machine learning model will be used to extract image features and predicts the class and scientific name of the flower. The identification of flower name from an input image is based on RGB histogram data. The researchers found that the proposed system is able to classify flower images with an accuracy of 80.67%. The training set consists of 17 different types of flower species. In the proposed model we use feature extraction algorithm to extract features.
A metabolomic study of red and processed meat intake and acylcarnitine concentrations in human urine and blood.

BACKGROUND Acylcarnitines (ACs) play a major role in fatty acid metabolism and are potential markers of metabolic dysfunction with higher blood concentrations reported in obese and diabetic individuals. Diet, and in particular red and processed meat intake, has been shown to influence AC concentrations but data on the effect of meat consumption on AC concentrations is limited. OBJECTIVES To investigate the effect of red and processed meat intake on AC concentrations in plasma and urine using a randomized controlled trial with replication in an observational cohort. METHODS In the randomized crossover trial, 12 volunteers successively consumed 2 different diets containing either pork or tofu for 3 d each. A panel of 44 ACs including several oxidized ACs was analyzed by LC-MS in plasma and urine samples collected after the 3-d period. ACs that were associated with pork intake were then measured in urine (n = 474) and serum samples (n = 451) from the European Prospective Investigation into Cancer and nutrition (EPIC) study and tested for associations with habitual red and processed meat intake derived from dietary questionnaires. RESULTS In urine samples from the intervention study, pork intake was positively associated with concentrations of 18 short- and medium-chain ACs. Eleven of these were also positively associated with habitual red and processed meat intake in the EPIC cross-sectional study. In blood, C18:0 was positively associated with red meat intake in both the intervention study (q = 0.004, Student's t-test) and the cross-sectional study (q = 0.033, linear regression). CONCLUSIONS AC concentrations in urine and blood were associated with red meat intake in both a highly controlled intervention study and in subjects of a cross-sectional study. Our data on the role of meat intake on this important pathway of fatty acid and energy metabolism may help understanding the role of red meat consumption in the etiology of some chronic diseases. This trial was registered at Clinicaltrials.gov as NCT03354130.
CUE: CpG impUtation Ensemble for DNA Methylation Levels Across the Human Methylation450 (HM450) and EPIC (HM850) BeadChip Platforms

DNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analyzed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study (n=127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository (n=144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root mean square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbors, logistic regression, penalized functional regression, random forest and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.
Maximum volumetric production of β-galactosidase by Kluyveromyces fragilis in fed-batch culture with automatic control

The production of β-galactosidase by Kluyveromyces fragilis was studied in different culture systems, with dissolved oxygen concentration control and using defined media. An operating strategy of fed-batch culture with automatic control of substrate addition regulated by dissolved oxygen concentration, consisting of the replacement of variable volumes of broth by fresh medium (once the fed-batch culture has finished), was designed. The volumetric enzyme productivity (Qp, 13 600 UI dm−3 h−1) obtained was 38% higher than that reached in continuous culture of K fragilis with dissolved oxygen concentration control and far higher than that obtained by batch culture of K fragilis under the same aeration conditions. © 2002 Society of Chemical Industry
Pollen Morphological Studies in the Burmanniaceae

Abstract The pollen morphology of the Burmanniaceae is very varied, including the following shape classes; ovoid, planoconvex, biconvex, spheroidal, ovoid reniform and tetrahedral tetrads. Two types of apertures, colpate and porate, are common as well as a probably atreme type. The sexine patterns are not very varied. All the species investigated have pollen grains which are pertectate. The morphological features and their taxonomical relevance are discussed.
A Sub-Range Error Characterization based Selection Methodology for Approximate Arithmetic Units

Error characterization is a fundamental step in selection of a suitable approximate arithmetic unit (AU) for a given application. For error characterization and comparison of complex AUs, researchers rely on simulations, which are typically done over the full range of input operands of an AU while assuming uniform distribution. In this paper, we demonstrate that the inputs to AUs in many practical applications are neither uniform nor spread over the full range and the error behavior of approximate AUs may vary across different sub-ranges of inputs (segments of the full range). We present a novel design methodology for selecting suitable approximate AUs for an application, which incorporates the sub-range error behavior of AUs, statistical analysis of application input, and a selection algorithm. We demonstrate the efficacy of the proposed methodology using two practical applications (JPEG compression and Gaussian filtering) with several approximate multipliers from the literature. The results show that the multiplier suggested by our proposed methodology leads to an increase in application-level quality (increase in PSNR of up to 9dB for JPEG and 7dB for Gaussian filtering) when compared to the multiplier from the traditional full-range based error characterization.
Physiological effects of Shinrin-yoku (taking in the atmosphere of the forest) in a mixed forest in Shinano Town, Japan

Abstract The purpose of this study was to examine the physiological effects of Shinrin-yoku (taking in the atmosphere of the forest). The subjects were 12 male university students. On the first day of the experiments, six subjects went to the forest area, and the other six went to a city area as a control. On the second day, subjects went to the opposite areas as a cross-check. In the afternoon, they were seated on chairs watching the landscapes of their given area for 15 min. Heart rate variability (HRV), salivary cortisol and pulse rate were measured as physiological indices in the morning and in the evening at the place of accommodation, before and after watching the landscapes in the field areas. The high-frequency power of HRV of subjects in the forest area was significantly higher than that of subjects in the city area. The pulse rate of subjects in the forest area was significantly lower than that of subjects in the city area. The salivary cortisol concentration of the subjects in the forest area was significantly lower than that of subjects in the city area. The results of physiological measurements show that Shinrin-yoku was an effective form of relaxation.
Sensei: How many samples to tell evolution in single-cell studies?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github. io/sensei/table_beta. html.
VeREFINE: Integrating Object Pose Verification With Physics-Guided Iterative Refinement

Accurate and robust object pose estimation for robotics applications requires verification and refinement steps. In this work, we propose to integrate hypotheses verification with object pose refinement guided by physics simulation. This allows the physical plausibility of individual object pose estimates and the stability of the estimated scene to be considered in a unified optimization. The proposed method is able to adapt to scenes of multiple objects and efficiently focuses on refining the most promising object poses in multi-hypotheses scenarios. We call this integrated approach VeREFINE and evaluate it on three datasets with varying scene complexity. The generality of the approach is shown by using three state-of-the-art pose estimators and three baseline refiners. Results show improvements over all baselines and on all datasets. Furthermore, our approach is applied in real-world grasping experiments and outperforms competing methods in terms of grasp success rate. Code is publicly available at github.com/dornik/verefine.
Hydraulic conductance of maize hybrids differing in transpiration response to vapor pressure deficit

Limited transpiration rate (TR) under high vapor pressure deficit (VPD) conditions has been proposed as a desirable trait for crop yield improvement. The limited-TR trait has been identified in several single-cross maize hybrids, and among these hybrids, a range in the VPD breakpoint for limited TR was identified. It was hypothesized that the variation in the VPD breakpoint was due to differences in hydraulic conductance in their roots or leaves, or both. Therefore, the objective of this study was to compare relative hydraulic conductance in the roots and leaves across the maize hybrids expressing the VPD breakpoint. It was found that the VPD of the breakpoint was correlated with each of three indices of hydraulic conductance. That is, low VPD breakpoint was associated with low hydraulic conductance in both leaves and roots indicating a common, underlying limiting mechanism in these two tissues. It was hypothesized that expression of similar aquaporin populations influencing hydraulic flow across membranes in the roots and leaves may account for the consistency in results across the indices of hydraulic conductance.
Pan‐European δ13C values of air and organic matter from forest ecosystems

We present carbon stable isotope, δ13C, results from air and organic matter samples collected during 98 individual field campaigns across a network of Carboeuroflux forest sites in 2001 (14 sites) and 2002 (16 sites). Using these data, we tested the hypothesis that δ13C values derived from large‐scale atmospheric measurements and models, which are routinely used to partition carbon fluxes between land and ocean, and potentially between respiration and photosynthesis on land, are consistent with directly measured ecosystem‐scale δ13C values. In this framework, we also tested the potential of δ13C in canopy air and plant organic matter to record regional‐scale ecophysiological patterns.
Application of Long short-term Memory Neural Networks in Dynamic State Estimation of Generators Subjected to Ageing in Complex Power Systems

In this paper, Long short-term memory(LSTM) neural networks based techniques for estimating dynamic states of generators in highly complex power systems is presented. It is proven that time-series prediction techniques can be used for dynamic state estimation. The most benefit that proposed method offers, is its independency from the mathematical model of the generators. The results proves superiority of the proposed technique over particle filter and unscented Kalman filter when parameters of the generators alter. The proposed scheme sustain its accuracy and precision even in the presence of unobservable variances in generator parameters. Parameter alterations in generators usually happen due to ageing of the equipment and environment impacts, and so on.
A Robotic Assistant using Speech Recognition to Create Appointments and Provide Reminders

Voice recognition services are very common nowadays. These services offer a lot of opportunities to shift the challenges of input and output between both humans and computers to a more communicative realm. An event reminder system based on speech recognition with autonomous features can help to create and catalogue appointments and events of interest in a natural and efficient way.The goal of this project is to aid users in creating digital reminders through the use of robots based on speech recognition. We propose a hardware-software architecture for a robot based on an Arduino Uno and a smartphone running the Android platform.The first prototype of this small robot assitant is able to receive voice commands and move forward, backward, to the right, and to the left as well as move its head forward and backward. In addition, it is capable of converting voice input into stored events and then providing aural reminders according to the date and time provided.
The Influence of Time of Mating on Fertility

Abstract Malmstrom (1943) observed that when pullets with a membranous egg in the uterus were inseminated with 0.05 c.c. of semen, the eggs subsequently produced in a 10-day period were more fertile than were the eggs laid by a group of individuals inseminated at other stages of egg formation. Fertility was least (32.6 percent) when the insemination was made with a hard shell egg in the uterus and greatest (70.0 percent) when a membranous egg was in the shell gland. If, at the time of insemination, no egg was in the process of formation in the oviduct, a fertility of 64.3 percent was recorded. With an egg in the isthmus, 55.2 percent of the eggs laid following insemination were fertile. Where there was a possibility of an egg being in one of the more anterior divisions of the oviduct at time of insemination, fertility was low (48.4 percent). It is well .
Experiments with a Socratic Intelligent Tutoring System for Source Code Understanding

Computer Science (CS) education is critical in today’s world, and introductory programming courses are considered extremely difﬁcult and frustrating, often considered a major stumbling block for students willing to pursue computer programming related careers. In this paper, we describe the de-sign of Socratic Tutor, an Intelligent Tutoring System that can help novice programmers to better understand programming concepts. The system was inspired by the Socratic method of teaching in which the main goal is to ask a set of guiding questions about key concepts and major steps or segments of complete code examples. To evaluate the Socratic Tutor, we conducted a pilot study with 34 computer science students and the results are promising in terms of learning gains.
Machine learning approaches for the identification of new driver-like genes using microarray expression profiles

Cancer is the second most important cause of deaths worldwide. Because of this, research efforts have generated vast amounts of data, such as gene expression profiles and cell lines to use in knowledge discovery research. By using the expression profiles from these cell lines, it is possible to identify novel driver-like candidates through the development of machine learning models. In this study, we focused on constructing a robust classifier capable of identifying new driver genes from a prediction set composed of non-coding genes. The training set was constructed using 300 known pan-cancer driver genes previously reported in the literature, and ~2700 non-driver genes. For our work we used two machine learning algorithms: random forests and support vector machines. During the construction of each model, optimization or fine-tuning was performed, which included feature selection using a random forest-based method, balancing of the classes due to the training set being imbalanced, and normalization in order to reduce the effects of extreme values, and to make samples comparable. Our results indicate that the highest performing model was the random forest, with an AUC-ROC of 0,9696. When applied in the prediction set, it identified 525 potential non-coding driver-like genes, with potential association to cancer. We expect that a next step would be to functionally annotate these candidates and look for co-expression between the diversity of cancer that data exposes.
Systematic Review and Evaluation of Mathematical Attack Models of Human Inhalational Anthrax for Supporting Public Health Decision Making and Response

Abstract Background: Anthrax is a potential biological weapon and can be used in an air-borne or mail attack, such as in the attack in the United States in 2001. Planning for such an event requires the best available science. Since large-scale experiments are not feasible, mathematical modelling is a crucial tool to inform planning. The aim of this study is to systematically review and evaluate the approaches to mathematical modelling of inhalational anthrax attack to support public health decision making and response. Methods: A systematic review of inhalational anthrax attack models was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The models were reviewed based on a set of defined criteria, including the inclusion of atmospheric dispersion component and capacity for real-time decision support. Results: Of 13 mathematical modelling studies of human inhalational anthrax attacks, there were six studies that took atmospheric dispersion of anthrax spores into account. Further, only two modelling studies had potential utility for real-time decision support, and only one model was validated using real data. Conclusion: The limited modelling studies available use widely varying methods, assumptions, and data. Estimation of attack size using different models may be quite different, and is likely to be under-estimated by models which do not consider weather conditions. Validation with available data is crucial and may improve models. Further, there is a need for both complex models that can provide accurate atmospheric dispersion modelling, as well as for simpler modelling tools that provide real-time decision support for epidemic response.