Terms Of Weight Research Articles

Perbandingan Metode Pembobotan TF-RF Dan TF-ABS Pada Kategorisasi Berita Di BDI Denpasar

BDI Denpasar is a government agency tasked with carrying out training and education for human resources of animation, crafts and art. BDI Denpasar in managing news classes in the Kabar Insan Oke service still uses conventional methods. Therefore an automatic news classification module is needed. This study was made to compare the performance level of news classification at BDI Denpasar using K-NN classification with the TF-RF and TF-ABS term weighting methods. Methods that have a high level of performance will be implemented in the news classification module. This research was carried out by collecting news documents, text preprocessing, term weighting, classification, model validation and testing. The K-NN classification uses the n_neighbhor (k), namely k=3, k=5, k=7 and k=9 using a dataset of 324 documents containing 7 classes taken from BDI Denpasar website. Based on the results of the tests performed, TF-RF method obtained a higher performance at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%. TF-ABS method with the highest performance value is found at k=9 which obtains 70% accuracy, 63% precision and 70% recall. So the method that will be implemented in the news classification module is TF-RF at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%.

BIOINSPIRED UNDERWATER ROBOT WITH SHAPE MEMORY MET-AL-BASED ACTUATORS

A bio-inspired robot was implemented in a fish whose main objective is the analysis of the interaction of the pectoral and caudal fins during the two-dimensional displacement, the pectoral and caudal fins were implemented for rotation and displacement, its pectoral fin has a dimension of 30mm high and 33mm wide, It consists of two parts, its base with cavities to house the actuator and its cover to hold and compact the fin, the caudal fin is created from a mold with cavities to house the actuator with a rigid fin at the end to have a greater movement, its dimension is 91. 69mm long, 30mm wide at the beginning and 40mm wide at the end, the robot as a whole has a dimension of 249.37mm long and 92.5mm wide with a weight of 254.5g. The actuators based on nitinol shape memory metals were used, for which tests were conducted with 2 wire gauges with diameters of 0. 5mm and 1mm, the 0.5mm cable has an activation of 60±10°C and the 1mm of 40±10°C, for which it was first analyzed the force developed by each of the actuators at different temperatures obtaining the following results, the 0. 5mm when subjected to force tests with a configuration of 60°C activation temperature with a voltage of 1.25 V at 2 A manages to lift an intermediate weight in terms of our tests of 20g reaches its final set point but with a not favorable time and having small deformations in its initial engraving, the 1mm material was subjected to the same tests with a configuration of 40°C activation temperature with a voltage of 0. 54 V at 2.25 A having favorable results in terms of reaching its final set point in the desired time and without having any final deformation, for which already established the tests it is analyzed that the two arrive to lift corresponding but the material of 0. 5mm material consumes more energy with a longer time than the 1mm material, so for the displacement of the robot the 1mm material was chosen because it has the necessary force to fulfill its objective, besides not consuming energy in excess. Once analyzed it was possible to achieve the two-dimensional displacement with pectoral and flow fins, with an oscillatory movement of the rigid flow fin, and turns with the pectoral fins, with an activation of 40°C at 0.54V with 2.25A and with the 1mm actuators a speed of 3.33 m/s was obtained.

Enhancing the Accuracy of Water-Level Forecasting with a New Parameter-Inversion Model for Estimating Bed Roughness in Hydrodynamic Models

The accurate and efficient estimation of bed roughness using limited historical observational data is well-established. This paper presents a new parameter-inversion model for estimating bed roughness in hydrodynamic models that constrains the roughness distribution between river sections. The impact of various factors on the accuracy of inversed roughness was analyzed through a numerical experiment with the number of measurement stations, observed data amount, initial bed roughness, observational noise, and the weight of the regularization term. The results indicate that increasing the number of measurement stations and the amount of observed data significantly improves the robustness of the model, with an optimal parameter setting of 3 stations and 30 observed data. The initial roughness had little impact on the model, and the model showed good noise resistance capacity, with the error significantly reduced by controlling the smoothness level of inversed roughness using a small weight of the regularization term (i.e., 100). An experiment conducted on a real river using the calibrated model parameters shows a forecasted water level RMSE of 0.041 m, 31% less than that from the Federal Emergency Management Agency. The proposed model provides a new approach to estimating bed roughness parameters in hydrodynamic models and can help in improving the accuracy of water-level forecasting.

503 Stimulation of the Paraventricular Nucleus Activates Brown Adipose Tissue via Local GABA Release: A Novel Target for the Treatment of Obesity

INTRODUCTION: Sympathetic activation of brown adipose tissue (BAT) has shown significant promise in preclinical studies to elicit long term weight loss and reversal of type 2 diabetes however it is inhibited in obese individuals. METHODS: Rats were anesthetized and monitored for body temperature, BAT temperature, expired CO2, mean arterial pressure, and heart rate. Postganglionic BAT sympathetic nerve activity (SNA) was recorded from the interscapular BAT pad. Stimulating electrode and micropipette were placed steroeotactically in the PVN. Stimulation pulses at an intensity of 100 µA at 100 Hz for 5 minutes were delivered. Changes in BAT SNA and physiology were compared with and without stereotactically injected 500 µM bicuculline, a GABAA receptor antagonist. RESULTS: Stimulation of the PVN increased BAT SNA compared to no stimulation (411.7 ± 75.8% baseline vs 98.18 ± 1.4% baseline n = 5; P < 0.05) along with BAT temperature (35.8 ± 0.8°C vs 34.2 ± 0.7°C, n = 5, P < 0.001), expired CO2 (4.7 ± 0.6% vs 4.2 ± 0.5%, n = 5, P < 0.05) indicating increased metabolic rate. There was no change in blood pressure, body temperature, or paw temperature suggesting no change in sympathetic tone. Heart rate increased (476 ± 21 bpm vs 429 ± 23 bpm, n = 5, P < 0.01) consistent with increased metabolic demand. Microinjection of 500 µM bicuculline into the PVN prevented stimulated activation of BAT SNA. CONCLUSIONS: Stimulation of the PVN increases BAT activation and basal metabolic activity in the absence of cold-stimulus with minimal off-target effects on systemic sympathetic tone and may be a viable therapeutic target for stimulation of BAT activity to treat obesity and type 2 diabetes.

A Lorenz-like Chaotic OTA-C Circuit and Memristive Synchronization

In this paper, a new set of lorenz-like hyper-chaotic equation set is obtained using the anti-control procedure. The chaoticity of the system is verified by MATLAB simulations using mathematical analysis methods. A new OTA-C circuit is designed for the new equation set. In the difference term addition technique, synchronizing the OTA-C circuit with a memristor rather than a resistor is proposed. Circuit design and synchronization are performed in PSpice simulation. The fact that the transresistance of the OTA element can be easily adjusted with a bias current provides the parameters that will make the proposed dynamic circuit a chaotic oscillator. The advantage of the proposed synchronization method is that the memristor automatically reaches to the value that will provide the required weight of the differential term required for synchronization, rather than the computational methods used to determine the weight.

Sentiment Analysis and Twitter Social Media Visualization Regarding the Omnibus Law Draft

This study will classify Twitter users' positive and negative opinions about the omnibus method using a frequency-inverse document frequency algorithm and a multi-layer perceptron method. The sentiment analysis process involves several stages, including B. Collecting and preprocessing data, calculating term weights using inverse term frequencies and document frequencies, and classifying data using multi-layer perceptrons. Additionally, the study visually represents Twitter's sentiment analysis results on the omnibus method. These visualizations include word cloud, top accounts, tweet frequency, hashtags, and sentiment. Three scenarios were considered to perform the classification experiments. Scenario 1 used 700 training data, scenario 2 used 800, and Scenario 3 used 900 training data. The findings show that the Term Frequency Inverse Document Frequency algorithm and the multi-layer perceptron method are adequate for sentiment analysis, with Scenario 3 yielding the highest accuracy rate of 88%.

TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis

In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.

Betaine and nano-emulsified vegetable oil supplementation for improving carcass and meat quality characteristics of broiler chickens under heat stress conditions.

This research aimed to examine the effects of water-added betaine (BET) and/or nano-emulsified vegetable oil (MAGO) on carcass and meat quality characteristics of broilers raised under thermoneutral (TN) and heat stress (HS) conditions. On day 21, 640 birds (Ross 308) were randomly assigned to one of two thermal conditions (thermoneutral 22 ± 1°C and heat stress 32 ± 1°C) each containing four treatment groups: Control, BET, MAGO, and a mixture of both (BETMAGO) in a 2 × 4 factorial arrangement (eight groups). Each group has eight replicates, with ten birds each. The birds' carcass and meat quality characteristics were evaluated at 35 days. The dressing percentage, breast, leg, wing, heart, initial pH, color change, cooking loss (CL), water-holding capacity (WHC), shear force (SF), and texture profile with exception of springiness significantly affected by the treatments. The results showed that HS had negative effects on carcass weight and relative weights of the breast, spleen, and heart. Moreover, HS increased dressing percentage, wing, initial pH, final core temperature, initial lightness, WHC, and hardness. Significant differences in interactions between treatments and temperature were observed in the spleen, WHC, and SF. Water supplemented with BET effectively improved carcass dressing percentage, breast weight, and meat quality in terms of water-holding capacity and tenderness under HS conditions. More studies on the use of BET and/or MAGO at different levels were recommended.

Current and emerging medications for the management of obesity in adults.

Current and emerging medications for the management of obesity in adults.

The Clothes Maketh the Cult

The Clothes Maketh the Cult

Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery.

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

Challenging inhibitory control with high- and low-calorie food: A behavioural and TMS study.

Most people are often tempted by their impulses to "indulge" in high-calorie food, even if this behaviour is not consistent with their goal to control weight in the long term and might not be healthy. The outcome of this conflict is strongly dependent on inhibitory control. It has already been reported that individuals with weaker inhibitory control consume more high-calorie food, are more often unsuccessful dieters, overweight or obese compared to people with more effective inhibitory control. In the present study, we aimed at investigating inhibitory control in the context of human eating behaviour. A sample of 20 healthy normal-weight adults performed a 50% probability visual affective Go/NoGo task involving food (high- and low-calorie) and non-food images as stimuli. Single-pulse transcranial magnetic stimulation (TMS) was administered over the right primary motor cortex (M1) either 300 ms after image presentation to measure corticospinal excitability during the different stimulus categories or 300 ms after the appearance of a fixation point, as a control stimulation condition. The experimental session consisted of a food target and a non-food target block. Behavioural outcomes showed a natural implicit inclination towards high-calorie food in that participants were faster and more accurate compared to the other categories. This advantage was selectively deleted by TMS, which slowed down reaction times. MEPs did not differ according to the stimulus category, but, as expected, were bigger for Go compared to NoGo trials. Participants judged high-calorie food also as more appetising than low-calorie food images. Overall, our results point to a differential modulation when targeting inhibitory control, in favour of the more palatable food category (high-calorie). Present data suggest that the activity of the motor system is modulated by food nutritional value, being more engaged by appetising food. Future work should explore to what extent these processes are affected in patients with eating disorders and should aim to better characterise the related dynamics of cortical connectivity within the motor network.

Supervised Contrastive Learning with Term Weighting for Improving Chinese Text Classification

With the rapid growth of information retrieval technology, Chinese text classification, which is the basis of information content security, has become a widely discussed topic. In view of the huge difference compared with English, Chinese text task is more complex in semantic information representations. However, most existing Chinese text classification approaches typically regard feature representation and feature selection as the key points, but fail to take into account the learning strategy that adapts to the task. Besides, these approaches compress the Chinese word into a representation vector, without considering the distribution of the term among the categories of interest. In order to improve the effect of Chinese text classification, a unified method, called Supervised Contrastive Learning with Term Weighting (SCL-TW), is proposed in this paper. Supervised contrastive learning makes full use of a large amount of unlabeled data to improve model stability. In SCL-TW, we calculate the score of term weighting to optimize the process of data augmentation of Chinese text. Subsequently, the transformed features are fed into a temporal convolution network to conduct feature representation. Experimental verifications are conducted on two Chinese benchmark datasets. The results demonstrate that SCL-TW outperforms other advanced Chinese text classification approaches by an amazing margin.

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application

Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (μ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However, λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (μ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predicting μ-DL from λ-MLAA and μ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose 18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies. Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe 18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh 18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data. Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures. Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.

Collagenase and Tyrosinase Inhibitory Activities and Stability of Facial Cream Formulation Containing Cashew Leaf Extract

The cashew tree (Anacardium occidentale L.) is a tropical plant found widely in many Southeast Asian countries, including Thailand, and contains bioactive phenolic compounds with antioxidant activity. The natural antioxidants such as collagenase and tyrosinase inhibitors found in medicinal plants are promising agents in cosmetic products. This study evaluated the inhibitory activities of the collagenase and tyrosinase from cashew leaf extracts by developing and evaluating the stability of facial cream formulations. The ethanol (DEN), ethyl acetate (DEA) and distilled water (DDW) crude extracts of cashew leaves were investigated for their bioactive compound efficacy. The DDW extract had the highest yield (24.97%). All the extracts were investigated for their antioxidant activities. The DEN extract showed the highest DPPH radical-scavenging ability, ferric-reducing power and flavonoid compounds, which were 152.04 ± 2.40 mg gallic acid/g extract, 37.90 ± 1.07 mg gallic acid/g dry weight and 7.63 ± 0.07 mg quercetin/g dry weight, respectively. The DDW extract exhibited the highest potent activity, which was 111.00 ± 0.78 mg gallic acid/g dry weight in terms of phenolic content, while the DEN extract showed the highest tyrosinase inhibition at 0.100 mg/mL (46.97 ± 3.34%) and collagenase activity at 40 µg/mL. The results suggested that the ethanolic extracts from cashew leaves showed promise for use in skincare product development. Cosmeceutical formulations for skincare were prepared. The formula mixed with DEN extract and added to whitening and anti-aging skincare cream demonstrated good stability and physical properties.

Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters

We consider prediction based on a main model. When the main model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a Model Averaging Prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some common covariate effect. We show that when the main model is misspecified, MAP yields the optimal weights in terms of prediction. Further, if the main model is correctly specified, then MAP will automatically exclude all incorrect helper models asymptotically. Simulation studies are conducted to demonstrate the superior performance of MAP. We further implement MAP to analyze a dataset related to the probability of credit card default.

The inhibitive performance of 2,3-pyrazine dicarboxylic acid and synergistic impact of KI during acid corrosion of 70/30 and 90/10 copper–nickel alloys

Pyrazine derivatives are efficient environmentally–safe chemicals for protecting metallic materials against corrosion during industrial acid cleaning. However, they have been mostly investigated for steel–based materials, which limits their versatility. Therefore, assessing their efficiency for protecting other important industrial alloys, like the Cu–Ni alloys employed in industrial heat exchangers, is also necessary. Herein, we investigated 2,3-pyrazine dicarboxylic acid (PDA), singly and mixed with potassium iodide (KI), as a promising inhibitor against the corrosion of 90Cu–10Ni and 70Cu–30Ni alloys in 2% HCl. Based on short–term weight loss, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) measurements, an optimum PDA concentration (1500 ppm) impacted average of 39% and 55% on 90Cu–10Ni and 70Cu–30Ni, respectively. These values, however, depreciated slightly after 72 h. Through cooperative synergism, the addition of 5 mM KI significantly boosted PDA efficiency to 73% (90Cu–10Ni)/92% (70Cu–30Ni) at room temperature, and 51% (90Cu–10Ni)/63% (70Cu–30Ni) at 60 °C. PDA is a mixed–type corrosion inhibitor with slightly more anodic impact on 70Cu–30Ni but cathodic effect on 90Cu–10Ni. This mechanism translates into more efficient surface microstructural protection for 70Cu–30Ni than 90Cu–10Ni, based on SEM characterization.

Comparison of Term Weighting Methods in Sentiment Analysis of the New State Capital of Indonesia with the SVM Method

The relocation of the State Capital to “Nusantara”, which was inaugurated with the enactment of UU No. 3 of 2022, is a significant project that has sparked polemics among Indonesian citizens. Many people expressed their opinions and thoughts regarding the relocation of the State Capital on Twitter. This tendency of public opinion needs to be identified with sentiment analysis. In sentiment analysis, term weighting is an essential component to obtain optimal accuracy. Various people are trying to modify the existing term weighting to increase the performance and accuracy of the model. One of them is icf-based or tf-bin.icf, which combines inverse category frequency (ICF) and relevance frequency (RF). This study compares the tf-idf, tf-rf, and tf-bin.icf term weighting with the SVM classification method on the new State Capital of Indonesia topic. The tf-idf weighting results are still the best compared to the tf-bin.icf and tf-rf term weights, with an accuracy score of 88.0% a 1,3% difference with tf-bin.icf term weighting.

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Model predictive control (MPC) approaches are widely used in robotics, because they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of the parameters of the cost function in order to obtain good performance. For instance, when a legged robot has to react to disturbances from the environment (e.g., to recover after a push) or track a specific goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work, we propose a novel optimization-based reference generator which exploits a linear inverted pendulum (LIP) model to compute reference trajectories for the center of mass while taking into account the possible underactuation of a gait (e.g., in a trot). The obtained trajectories are used as references for the cost function of the nonlinear MPC presented in our previous work. We also present a formulation that ensures guarantees on the response time to reach a goal without the need to tune the weights of the cost terms. In addition, footholds are corrected by using the optimized reference to drive the robot toward the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data. In this research, we proposed a modification term weighting scheme: RTF-C-IEF and compare the proposed model with four extraction methods: TF, TF-IDF, TF-IHF, and TF-IEF. The experiment was conducted on two new datasets for COVID-19 patients. The first dataset was collected from government hospitals in Iraq with 3053 clinical records, and the second dataset with 1446 clinical reports, was collected from several different websites. Based on the experimental results using several popular classifiers applied to the datasets of Covid-19, we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments. Further, the modified RTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments. Thus, the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.