Miracle-dz/UniFAR-FTUs huggingface dataset:
OPTIMIZED PRACTICAL BYZANTINE FAULT TOLERANCE (O-PBFT) ALGORITHM USING GROUPING METHOD FOR CONSORTIUM BLOCKCHAIN

Blockchain is a distributed ledger that records every transaction that has ever occurred in a system. A consensus algorithm is required in blockchain to ensure that all transactions function properly. At present, the consensus algorithm that is commonly used in consortium blockchain is Practical Byzantine Fault Tolerance (PBFT) algorithm. PBFT requires all nodes in the network to participate in the consensus process. However, PBFT still has some problems such as communication overhead, low throughput, and high latency due to increasing the number of nodes in the network. To overcome these disadvantages, an optimized PBFT (O-PBFT) algorithm is proposed. The O-PBFT algorithm used grouping method in a mid-stage (prepare stage) to reduce communication complexity and assign random Byzantine nodes to improve consensus efficiency. The consistency protocol in O-PBFT were modify from the original PBFT so that O-PBFT can reach consensus with less communication in a stable network. Experimental results show that O-PBFT algorithm reduces number of communication times between nodes, increases transaction throughput, and improves consensus efficiency compared to the original PBFT algorithm. Experimental results prove that O-PBFT algorithm can be used when many nodes are involved.
A geochemical investigation of trace elements in well RN-17 at Reykjanes geothermal system, SW-Iceland

Here we present the results of an investigation of well RN-17 at Reykjanes geothermal system (Reykjanes Peninsula, SW-Iceland). We show that the adoption of micro-spot secondary ion mass spectrometry (SIMS) together with bulk-rock techniques, such as X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS), and thermal ionisation mass spectrometry (TIMS) carried out on 10 basaltic drill-cuttings over a depth of 3000 m gives us insights into the geochemical characteristics of the well. Whole rock and mineral phases, i.e., plagioclase, clinopyroxene, epidote and amphibole, indicate that the rocks pertaining to the cuttings were affected by hydrothermal alteration that has been able to modify significantly the Sr isotope ratios and the concentration of K, Rb, Cs, Ba and B. Seawater/rock ratio, evaluated for 87Sr/86Sr, achieved its maximum for the shallow cutting RN-17-650, decreasing although not systematically with depth, with the minimum value for the deep cutting RN-17-2800. Moreover, the occurrence of Na-rich plagioclase, epidote and amphibole down to the well bottom are evidence of the persistence of fluid/rock interaction at depth.
Impact of resolution on simulation of closed mesoscale cellular convection identified by dynamically guided watershed segmentation

Organized mesoscale cellular convection (MCC) is a common feature of marine stratocumulus clouds that forms in response to interactions among dynamic, microphysical, and radiative processes at the mesoscale. Cloud resolving models begin to resolve some of these processes, but using high resolutions is extremely costly. To understand the impact of limited resolution on MCC, we use the Weather Research and Forecasting model with chemistry and fully coupled cloud‐aerosol interactions to simulate MCC over the southeast Pacific during the Variability of the American Monsoon Systems Ocean‐Cloud‐Atmosphere‐Land Study Regional Experiment (VOCALS‐REx). A suite of experiments with 3 and 9 km grid spacing indicates that the simulations with finer grid spacing have smaller liquid water paths and cloud fractions, while cloud tops are higher. The observed diurnal cycle is reasonably well simulated. To isolate organized MCC characteristics, we develop a new automated method that uses the watershed segmentation combining the detection of cloud boundaries with coincident vertical velocities. This ensures that the detected cloud fields are dynamically consistent for closed MCC, a common feature within the VOCALS‐REx region. We demonstrate that the 3 km simulation is able to reproduce the scaling between horizontal cell size and boundary layer depth seen in satellite observations for the conditions of 900–1400 m deep boundary layers. However, the 9 km simulation is unable to resolve smaller circulations corresponding to shallower boundary layers, instead producing MCC with an invariant horizontal scale for all simulated boundary layers depths. The results demonstrate that the grid spacing needed for proper simulation of the MCC structure in marine stratocumulus regions depends on the boundary layer depth.
ASPECT-BASED SENTIMENT ANALYSIS ON CHATGPT IN TWITTER USING BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BERT)

ChatGPT, a chatbot developed by OpenAI, is currently being widely discussed, especially on social media platforms like Twitter. Many users have provided positive feedback regarding this chatbot. However, some have provided negative feedback. To understand the sentiment of users regarding ChatGPT, this research conducted aspect-based sentiment analysis using the Bidirectional Encoder Representations from Transformers (BERT) model. The aspects analyzed include general aspects, functions, performance, and potential of ChatGPT. The data used in this research was obtained from the social media platform Twitter. The BERT model used is IndoLEM/IndoBERT-base-uncased, which has been pre-trained on 220 million Indonesian language words from various sources. From the conducted tests, the model was able to achieve an f1-score of 84% for the general aspect, 89% for the functional aspect, 98% for the performance aspect, and 98% for the potential aspect.
PERFORMANCE ANALYSIS OF PLASTIC INJECTION MOLDING USING PARTICLE SWARM BASED MODIFIED SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM MULTI OBJECTIVE OPTIMIZATION MODEL

Plastic Injection Molding (PIM) is one among the trending technology which is used in maximum of the plastics based industrial applications. The functionalities of PIM are material insertion, melting, injection, molding closing, mold ejection and cooling. Several variables are involved in the process of molding such as melt and mold temperature, melting and injection time, packing pressure and cooling time. In order to achieve high quality, effective performance in terms of maximizing the weld line temperature and reduction of clamping force. To attain this it is very essential to optimize the variable selection during PIM process. For that purpose, in this research Multi Objective Optimization (MOO) is proposed in plastic injection molding which is the combination of Particle Swarm Optimization (PSO) and a Modified Sequential Quadratic Programming Algorithm (MSQPA) as well as finally using the Ant–Lion Optimization algorithm our proposed PSO-MSQPA is compared. The simulation of this process is performed using the software such as CATIA V5 and MATLAB. Through the numerical and simulation evaluation of the proposed PSO-MSQPA approach, the variables are effectively optimized during the process of injection molding that leads to improve the convergence and reduces the bit error rate and that leads to improve the quality by reducing the clamping force of the PIM process.
Edinburgh Research Explorer Fission yeast mto2p regulates microtubule nucleation by the centrosomin-related protein mto1p

From an insertional mutagenesis screen, we isolated a novel gene, mto2 (cid:1) , involved in microtubule organization in ﬁssion yeast. mto2 (cid:2) strains are viable but exhibit defects in interphase microtubule nucleation and in formation of the postanaphase microtubule array at the end of mitosis. The mto2 (cid:2) defects represent a subset of the defects displayed by cells deleted for mto1 (cid:1) (also known as mod20 (cid:1) and mbo1 (cid:1) ), a centrosomin-related protein required to recruit the (cid:3) -tubulin complex to cytoplasmic microtubule-organizing centers (MTOCs). We show that mto2p colocalizes with mto1p at MTOCs throughout the cell cycle and that mto1p and mto2p coimmunoprecipitate from cytoplasmic extracts. In vitro studies suggest that mto2p binds directly to mto1p. In mto2 (cid:2) mutants, although some aspects of mto1p localization are perturbed, mto1p can still localize to spindle pole bodies and the cell division site and to “satellite” particles on interphase microtubules. In mto1 (cid:2) mutants, localization of mto2p to all of these MTOCs is strongly reduced or absent. We also ﬁnd that in mto2 (cid:2) mutants, cytoplasmic forms of the (cid:3) -tubulin complex are mislocalized, and the (cid:3) -tubulin complex no longer coimmunoprecipitates with mto1p from cell extracts. These experiments establish mto2p as a major regulator of mto1p-mediated microtubule nucleation by the (cid:3) -tubulin complex.
A DETAILED ANALYSIS ON MEDICAL IMAGE PROCESSING TECHNIQUES USED FOR BRAIN TUMOR DETECTION AND CLASSIFICATION

Detection and classification of tumor portions from the brain images are the challenging and demanding tasks in the field of medical imaging applications. Because, the earlier prediction of tumor is highly essential for the patients to provide proper treatment at the time. So, an automated tumor detection system can be more useful for the medical experts to identify the growth and structure of tumor region. For this purpose, there are different types of medical image processing techniques are developed in the existing works. The main aim of this work is to present the comprehensive survey for analyzing the techniques used for detecting the brain abnormalities. Also, it objects to investigate the operating characteristics, working nature and performance of various image processing techniques. Typically, the preprocessing techniques are mainly used to filter the noisy contents for improving the quality of images with increased contrast. Specifically, the feature extraction models are used for extracting the high level feature attributes and patterns from the filtered image. Then, the optimal numbers of features are selected with the help of optimization techniques by estimating the objective function based on the best fitness value. Here, the importance of using segmentation approaches is to partition the image into collection of pixels, which are helpful for locating the tumor, affected regions. Finally, the classifiers are used for predicting the output label as normal or tumor-affected by training the samples based on the optimal features. For experimental validation, there are different measures have been used to evaluate the performance results of these techniques.
Fool Me Twice: Entailment from Wikipedia Gamiﬁcation . North American Association for Computational Linguistics

Accessible Abstract: Democracy and the free press depends on being able to recognize when facts online are true or not. For machine learning to help this critical problem, it needs good data identifying which statements are backed up by trusted sources and which are not. This research creates a game people can play online to craft diﬃcult claims that can train computers to spot disinformation online
Amphibian skin extracts as source of bioactive multi-target agents against different pathways of Alzheimer’s disease

Abstract Natural products represent a rich source of bioactive compounds that have been historically used to obtain substances with great medicinal potential. The skin of anuran amphibians is a rich source of compounds with a wide range of biological activity. Alzheimer's disease (AD) is a complex disease associated with all kind of different pathways, making their simultaneous modulation necessary. Nowadays anti-AD treatments are focused on enzymatic inhibitors. Here in we report the activity of skin extracts from nine species from Argentina, belonging to three anuran families, as inhibitors of AChE, BChE and MAO-B enzymes. The extracts also showed antioxidant activities, acting as multi-target on four important pathways of AD. Graphical Abstract
Enhanced Network Lifetime in Wireless Sensor Networks Through Hierarchical Clustering and Routing

: Wireless Sensor Networks (WSNs) have gained an emerging importance in different application domains especially in event tracking and monitoring. The sensor nodes in WSNs are observed to have shorter lifetime due to the continuous sensing and processing operations that result in quicker energy depletion. Based on the research works carried out, it is evident that clustering in an optimal solution to reduce the energy consumption and to enhance network lifetime. It is proposed to introduce a novel clustering technique called as Energy Efficient Hierarchical Clustering (E 2 HC) to optimize the network lifetime while consuming minimal energy.
Investigation of Food Additive Potential of the Rosa damascena Mill. (Isparta Rose): Vinegar with Probiotic Addition

Rosa damascena Mill. , named also 'Isparta rose', is grown in the province of Isparta in Turkey. It is an aromatic plant having sharp and intensive scent and rich in bioactive compounds. In the literature, it has been reported that Isparta rose has a potential as natural food additives. However, the application to be natural food additive has not been study detailed. Therefore, rose vinegar was produced by the traditional method in present study and analyzed reported. Moreover, by adding probiotic strains to these vinegars produced, quality characteristics of the vinegars such as physicochemical, microbiological and some bioactive compounds were determined during storage time. The presence of main aroma compound associated with a rose honey-like odor and volatile aroma compounds such as dodecene, tetradecanol, linalool as well as phenyl ethyl alcohol are determined in all the vinegars. The aroma components have increased the pleasant taste and acceptability of vinegars in terms of sensory properties. On the other hand, it is determined that various organic acids (93.43 mg/100 mL oxalic acid, 53.57 mg/100 mL propionic acid, 10.11 mg/100 mL fumaric acid) and aroma components produced by Lactiplantibacillus plantarum strain are signi�cantly affected the lactic and acetic acid bacteria growth. Additionally, the fact that the strain has completed the storage period with 83.96% viability, it shows that the has including the required number of viable microorganisms for produced vinegar to be called a probiotic during this time. All the results showed that it has potential that vinegar with culture additives will also create an alternative to functional probiotic drinks.
Chemical weathering in a region of active orogeny: Pescadero Creek Watershed, California

Base flow chemical signals were used to determine the weathering reactions that control the groundwater chemistry of a geologically heterogeneous, mountainous watershed. Major ion signatures result from cyclic salt and formation water inputs and from the weathering of easily oxidized or highly soluble minerals, such as pyrite, calcite, and dolomite. If montmorillonite is the dominant secondary mineral product, then the bulk of silicate weathering probably involves volcanic rocks. Spatial rates of base flow chemical denudation range from 0.0004 to 0.02 mm yr−1, and temporal rates range from 0.0006 to 0.6 mm yr−1. The mean chemical denudation rate for the watershed is 0.03 mm yr−1, which is comparable to some of the world's most rapidly weathering large drainage basins. Because highly soluble or easily oxidized minerals contribute the bulk of the chemical signal to basin waters, spatial and temporal rates of chemical denudation are constrained largely by recharge and discharge rather than local variations in lithology.
Emissions and climate forcing from global and Arctic fishing vessels

Fishing vessels were recently found to be the largest source of black carbon ship emissions in the Arctic, suggesting that the fishing sector should be a focus for future studies. Here we developed a global and Arctic emissions inventory for fishing vessel emissions of short‐lived and long‐lived climate forcers based on data from a wide range of vessel sizes, fuel sulfur contents, engine types, and operational characteristics. We found that previous work generally underestimated emissions of short‐lived climate forcers due to a failure to account for small fishing vessels as well as variability in emission factors. In particular, global black carbon emissions were underestimated by an order of magnitude. Furthermore, our order of magnitude estimate of the net climate effect from these fishing vessel emissions suggests that short‐lived climate forcing may be particularly important in regions where fuel has a low sulfur content. These results have implications for proposed maritime policies and provide a foundation for future climate simulations to forecast climate change impacts in the Arctic.
A tissue-hybrid right ventricular pulsatile circulatory simulator with a functional tricuspid regurgitation

We hypothesize that the development of a pulsatile circulatory simulator with physiological morphology of tricuspid valve (TV) and right ventricle is essential to evaluate the performance after tricuspid valvuloplasty. This study aimed to develop a tissue-hybrid pulsatile circulatory simulator with clinically relevant tricuspid regurgitation (TR) to evaluate the effects of tricuspid annuloplasty. The tissue-silicone hybrid model consisted of a porcine TV sutured to a silicone right ventricular model with enlarged annulus, which can be pulsated while maintaining the relative position between the TV annulus and papillary muscle. The annulus of porcine TV was enlarged by degrading collagen �bers (pre-and post-annular perimeter 115.7 ± 5.5 vs. 133.1 ± 8.2 mm; p = 0.036). Severe TR models were successfully produced (pre-and post-regurgitation 0.64 ± 0.22 vs. 3.1 ± 0.59 L/min p = 0.0002). After the TV annuloplasty, regurgitation was reduced to a clinically mild grade (pre-and post-regurgitation 3.1 ± 0.59 vs 1.2 ± 0.27 L/min p = 0.013). The regurgitant jet in TR models was remarkably reduced after the annuloplasty. The novel tissue-hybrid system was useful to simulate severe TR and quantify the effects of the annuloplasty. The simulator would be useful to evaluate e�cacy of emerging medical treatments and optimize them.
Improved Skill for the Anomaly Correlation of Geopotential Height at 500 Hpa

This paper addresses the anomaly correlations of the 500 hPa geopotential heights from a suite of global multimodels and from a model-weighted ensemble mean called the superensemble. This procedure follows a number of current studies on weather and seasonal climate forecasting that are being pursued. This study includes a slightly different procedure from that used in our experimental forecasts for other variables. Here we construct a superensemble for the ∇ 2 of the geopotential based on the daily forecasts of the geopotential fields at the 500 hPa level. The geopotential of the superensemble is recovered from solution of the Poisson equation. This procedure appears to improve the skill for those scales where the variance of the geopotential is large and contributes to a marked improvement in the skill of the anomaly correlation. We note especially large improvements over the Southern Hemisphere. Consistent day-6 forecast skill above 0.80 is achieved on a day-today basis. The superensemble skills are higher than those of the best model and the ensemble mean. For days 1 through 6, the percent improvement in anomaly correlations of the superensemble over the best model are 0. day-5 and day-6, the superensemble realizes major improvement of anomaly correlation skills. The collective regional strengths of the member models, which reflected in the proposed superensemble, provide a useful consensus product that may be useful for future operational guidance.
Abortion Legalization and Life-Cycle Fertility

The early-1970s abortion legalization led to a significant drop in fertility. We investigate whether this decline represented a delay in births or a permanent reduction in fertility. We combine Census and Vital Statistics data to compare the lifetime fertility of women born in early-legalizing states, whose peak childbearing years occurred in the early 1970s, to that of women from other states and cohorts. We find that much of the reduction was permanent, in that women did not compensate by having more children later, and that it largely reflects an increased share of women remaining childless throughout their fertile years.
DESIGN AND IMPLEMENTATION OF A 3-TASK CONSTRUCT FOR MANAGING REDUNDANT FILES

The operating system ensures efficient memory and disk space management which contributes a lot to computing speed. However, not all users have the technical expertise to make use of operating system tools for disk space management. This paper therefore proposes and implements a 3-task user friendly construct for managing redundant files which inadvertently occupy so much space in the computer disk and slow down the system. Redundant files such as duplicate files, temporary files and dormant files which are often taken for granted are detected. The construct employs the concept of hashing by making use of python hashlib.SHA-1 to detect duplicate files while dormant files are identified using file attributes such as date of creation and access date. The deduplication construct earmarks redundant files for deletion or compression in order to free disk space. Algorithms for implementation of the constructs are presented. The construct was implemented using python programming language. In order to test the efficacy of the construct in detecting file duplicates, the New York Times Comments for March, 2018 dataset was extracted from www.kaggle. com and used for that purpose. The simulation recorded an efficiency of 99%. The Construct is designed to provide alternative user friendly disk management support tools for computer users other than that provided by Operating systems.
RECOMMENDATION SYSTEM FOR ONLINE JOB VACANCY USING MACHINE LEARNING MODELS

The Recommendation is used to exploit the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Name Entity Recognition (NER) provides a straightforward way to identifies one item from a set of other items that have similar attributes of the related objects. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying NER models to hybrid recommendation systems in job vacancy environment. This paper is proposed a way to adapt the name entity recognition approaches to construct a real hybrid job recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems the approach of accuracy, precision, recall and F-measure is using in this recommendation system in a principled way. The experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision.